Robots Among Us

ROBOTS AMONG US

Editors Janez Prašnikar, Tjaša Redek, Matjaž Koman

Published by Časnik Finance, d. o. o.

Ljubljana, November 2017

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PPKP_2017_book.indbKP_2017_book.indb 1 221/11/20171/11/2017 06:5206:52 CIP - Kataložni zapis o publikaciji Narodna in univerzitetna knjižnica, Ljubljana

330.341.1:338.45:007.52(082)

ROBOTS among us / editors Janez Prašnikar, Tjaša Redek, Matjaž Koman. - 1st printing. - Ljubljana : Časnik Finance, 2017

ISBN 978-961-6541-57-2 1. Prašnikar, Janez, 1950- 292760576

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PPKP_2017_book.indbKP_2017_book.indb 2 221/11/20171/11/2017 06:5206:52 Authors

Faculty of Economics, Students of International Full Time Master University of Ljubljana Programme in Business Administration - IMB, Faculty of Economics, University of Ljubljana Andreja Cirman Barbara Čater Petra Ajdovec Samo Knafelj Tomaž Čater Katja Avsenik Nina Kovač Matej Černe Teresa Baeta Da Silva Robert Kovačič Batista Polona Domadenik Aleksander Bobič Anej Peter Lah Ada Guštin Žiga Borišek Rok Lavrič Marko Jakšič Domen Boštjančič Fabijan Leskovec Matjaž Koman Blaž Božič Anastasia Liakhavets Mitja Kovač Esta Carciu Tadej Ocvirk Denis Marinšek Enya Caserman Simon Pangeršič Janez Prašnikar Katarina Čop Klemen Pavačič Tjaša Redek Matjaž Dolenc Andrei Putukh Jakob Döller Jan Ratej Institute for Economic Špela Drnovšek Dmitrii Sazonov research (IER) Peter Emri Nikola Sionov Miha Dominko Eva Erjavec Tjaša Skubic Ana Rita Fernandes Urban Smolar Lara Flegar Nejc Šaranović Kristian Groznik Rok Štemberger Domen Gulič Matjaž Vidmar Dimitrije Ivanović Aida Zukić Nina Jagodic Tamara Žarković Katarina Kern Pirnat

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PPKP_2017_book.indbKP_2017_book.indb 4 221/11/20171/11/2017 06:5206:52 Contents

PREFACE ...... 7

I. GENERICS OF INDUSTRY 4.0

Andreja Cirman, Ada Guštin, Žiga Borišek, Katarina Čop, Nejc Šaranović: HOW TECHNOLOGY SHAPES HISTORY: LONG TECHNOLOGICAL DEVELOPMENT WAVES AND THEIR ECONOMIC CONSEQUENCES ...... 11

Denis Marinšek, Esta Carciu, Teresa Baeta Da Silva, Aida Zukić: THE IMPACT OF INDUSTRY 4.0 AND ROBOTIZATION ON THE SECTORAL STRUCTURE ...... 27

II. ROBOTIZATION

Ada Guštin, Matjaž Koman, Aleksander Bobić, Peter Emri, Katarina Kern Pirnat: GLOBAL TRENDS IN ROBOTIZATION ...... 45

Mitja Kovač, Janez Prašnikar, Tjaša Redek, Jakob Döller, Lara Flegar, Tamara Žarković: ROBOTIZATION IN DENMARK, AUSTRIA AND SLOVENIA ...... 59

Matej Černe, Petra Ajdovec, Robert Kovačič Batista, Matjaž Vidmar: CORPORATE STRATEGY AND INDUSTRY 4.0 ...... 79

Matjaž Koman, Tjaša Redek, Samo Knafelj, Nina Kovač, Jan Ratej: TPV GROUP ...... 93

Tomaž Čater, Miha Dominko, Domen Gulič, Simon Pangeršič, Rok Štemberger: DOMEL ...... 111

Barbara Čater, Marko Jakšič, Kristian Groznik, Rok Lavrič, Tjaša Skubic: YASKAWA ...... 125

Ljubica Knežević Cvelbar, Enya Caserman, Eva Erjavec, Ana Rita Fernandes: SLOW ADAPTORS: ROBOTIZATION IN THE HOSPITALITY INDUSTRY ...... 139

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PPKP_2017_book.indbKP_2017_book.indb 5 221/11/20171/11/2017 06:5206:52 III. INDUSTRY 4.0 AND CHANGES IN THE LABOR MARKET

Polona Domadenik, Špela Drnovšek, Anej Peter Lah, Urban Smolar: LABOR MARKET POLARIZATION: WELL-PAID HIGH-TECH JOBS VS. LOW-PAID SERVICE JOBS? ...... 153

Marko Pahor, Nada Zupan, Katja Avsenik, Domen Boštjančič, Nina Jagodic: HUMAN CAPITAL FOR THE FUTURE ...... 167

IV. BROADER SOCIAL ISSUES ON INDUSTRY 4.0

Miha Dominko, Matjaž Koman, Fabijan Leskovec, Anastasia Liakhavets, Andrei Petukh, Dimitrii Sazonov: SOCIAL CHALLENGES RELATED TO INDUSTRY 4.0 ...... 185

Polona Domadenik, Blaž Božič, Dimitrije Ivanović, Nikola Sionov: PERCEPTIONS OF ROBOTS AMONG THE GENERAL PUBLIC ...... 201

V. POLICY PROPOSALS

Matjaž Koman, Janez Prašnikar, Tjaša Redek, Matjaž Dolenc, Tadej Ocvirk, Klemen Pavačič: POLICY RESPONSES TO THE CHALLENGES OF THE FOURTH INDUSTRIAL REVOLUTION ...... 217

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PPKP_2017_book.indbKP_2017_book.indb 6 221/11/20171/11/2017 06:5206:52 PREFACE

The book titled Robots among us is the result of an entire year’s work of a select research team (Andreja Cirman, Barbara Čater, Tomaž Čater, Matej Černe, Polona Domadenik, Miha Dominko, Ada Guštin, Marko Jakšič, Matjaž Koman, Mitja Kovač, Denis Marinšek, Janez Prašnikar, Tjaša Redek), with the help of students from the XXIV generation of the International Master in Business and Administration Programme (IMB) at the Faculty of Economics in Ljubljana. The book consists of five parts. The first part of the book deals with the generics of Industry 4.0. The second part discusses robotization and presents cases of using robots in companies. The third part introduces labor market changes, the fourth part broader social issues, and the fifth part policy proposals. Students from the XXIV IMB generation invested much hard work and knowledge, and their contributions were of great help with the studies presented in the book. The work could not have been finished without the expert work and great dedication of our already mentioned colleagues. Many thanks to Tanja Povhe for proofreading the work, Ciril Hrovatin for technical editing and graphic design, and Laura Pompe Sterle for the cover design. Nina Kovač and Anej Peter Lah provided us with invaluable assistance. Many thanks also to colleagues from the Newspaper Finance for handling the final execution of the book.

Ljubljana, November 2017

Editors

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PPKP_2017_book.indbKP_2017_book.indb 8 221/11/20171/11/2017 06:5206:52 I. GENERICS OF INDUSTRY 4.0

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PPKP_2017_book.indbKP_2017_book.indb 1010 221/11/20171/11/2017 06:5206:52 Andreja Cirman, Ada Guštin, Žiga Borišek, Katarina Čop, Nejc Šaranović

HOW TECHNOLOGY SHAPES HISTORY: LONG TECHNOLOGICAL DEVELOPMENT WAVES AND THEIR ECONOMIC CONSEQUENCES

Introduction

The technological changes have been one of the most important factors determining economic and social changes throughout the world, thus having a great impact on our lives. In order to observe these changes, several theories have emerged but in this chapter, readers will be introduced to perhaps one of the most impactful of them, the Kondratieff wave theory. With the emergence of the First Industrial Revolution, the late 18th century marks the beginning of the industrial phase in the global economy and consequently indicates a faster pace of economic evolution. Major technological breakthroughs that highly affected manufacturing, productivity, sectoral development and even social changes, predominantly emerged in cyclical patterns but cyclicality has changed its pattern throughout the history, which translated into increasing frequency of disruption.

The purpose of this chapter is to define economic cyclicality and to show that technological development coincided with cyclicality and helped fostering the evolution of economic and social changes. Moreover, Industry 4.0 will be placed into this context. In the first section of the chapter, a brief historical overview of the first five Kondratieff waves is presented, including their technological innovations, the affected key industries and their economic and social impacts. The second section focuses on the sixth Kondratieff wave and the coinciding Industry 4.0, explaining in detail the major technologies that are expected to affect our lives in the future, while the last section focuses on the major socioeconomic changes of the new technologies. The conclusion briefly summarizes the main findings of the chapter.

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PPKP_2017_book.indbKP_2017_book.indb 1111 221/11/20171/11/2017 06:5206:52 1 Kondratieff cycles: Technologically-determined socio-economic development

Several revolutionary innovations that played a key role in the growth of modern economy can be identified, according to the developmental cycle theory. These cycles are called Kondratieff waves, after the Russian economist N. Kondratieff. In his research, he observed price level statistics of wholesale commodity prices and other data for England, France and the U.S, and identified the first three long waves of technological development. Schumpeter, Mensch, Duijn, Barnett, and others, later on followed his work. Joseph Schumpeter strengthened his theory by suggesting that the cause of Kondratieff waves is in fact innovations. The significant technological changes are the primary force enabling Kondratieff waves and further leading to the growth of the whole economy (Narkus, 2012 and Schumpeter, 1939).

Kondratieff waves also coincide with the industrial revolutions that started in the 18th century. The First Industrial Revolution, which began in 1784, was based on water and steam power, and mechanization, the Second, which started in 1870, on electric power and mass production and the Third, which began in 1969, on electronics, IT and automated production. Currently, we are experiencing the Fourth Industrial Revolution or Industry 4.0, which is based on cyber- physical systems and fusion of different technologies (Schwab, 2016). The four industrial revolutions are characterized by the predominance of certain energy resources and technological innovations that have a major effect on the economy and public transport (Prisecaru, 2017). Table 1 presents the main technologies that marked each wave and the respective industries the innovations affected, as well as the linkage between Kondratieff waves and the industrial revolutions and where the two coincide.

1.1 First Kondratieff wave – end of 1780’s to 1840’s

The first Kondratieff wave was caused by the invention of a steam engine. New machines and new production methods significantly improved the textile industry, which accordingly became the leading industry at that time. In addition, infrastructure of water routes for coal transportation was developed (Narkus, 2012 and Wilde, 2017).

As a consequence, modern factories replaced the slow textile manufactories. This period also marked the emergence of mass production and as a result unem-

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PPKP_2017_book.indbKP_2017_book.indb 1212 221/11/20171/11/2017 06:5206:52 Table 1: Four centuries of innovations, the linkage between Kondratieff waves and industrial revolutions

KONDRATIEFF WAVES INDUSTRIAL REVOLUTIONS Leading Industrial K-Wave Macro Revolution Initial Additional No. Time A New Mode Sector Invention No. Year Information Steam, water, 1780s Factory 1st The textile (consumer Steam engine 1.0 1784 mechanical -1840s industry production industry) equipment Mining industry 1840s 2nd Railway lines, and primary Railway, steel -1890s coal, steel heavy Division industry and of labor, transport 2.0 1870 electricity, Electricity, Secondary mass production 1890s chemical heavy 3rd industry industry and Electrification, -1940s and heavy mechanic chemicals engineering engineering Automobile 1940s manufacturing, Electronics, 4th manmade General Automobiles, 3.0 1969 IT, automated -1980s materials, services petrochemicals production electronics Information 1980s Microelectronics, Highly- 5th personal qualified technology, -2020s communication computers services technology Cyber-physical MBNRIC- 4.0 ? systems 2020/30s technologies Medical 6th (med-bio-nano- human -2050/60s robo-info- services cognitive) Source: Grinin and Grinin, 2013 and WEF, 2015.

ployment dropped and consumption increased. The developing manufacturing caused also migration from rural to urban area with people seeking opportunity to improve the quality of their lives (Narkus, 2012). The main colonial conquests were already ceased by the time the first Kondratieff wave ended. European countries were consequently developing their industrial sectors with the supply of raw materials from the periphery. Among them, England was considered as the world leader at the time, which also initiated the Anglo-Spanish War (1789- 1793) that took place due to intention of England to maintain its economic position (Grinin et al., 2016).

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PPKP_2017_book.indbKP_2017_book.indb 1313 221/11/20171/11/2017 06:5206:52 1.2 Second Kondratieff wave – 1840’s to 1890’s

The first significant breakthroughs of the second Kondratieff wave were the invention of locomotive and the construction of the first public steam railway in Great Britain in 1825. The industries that benefited the most were the mining industry, primary heavy industry and transport, due to construction of the practical implementations, such as new rail lines, bridges, tunnels and other. The changes were most evident in the U.S., England, Germany and France (Narkus, 2012).

Innovations further strengthened mass production and meanwhile, division of labor was becoming the norm observed in all leading industries. Migration made spreading technical knowledge from Great Britain, the leading country in the industry, to other countries possible. Many new jobs were created in the steel industry, with the construction of the railroads themselves and with increasing interest in metal related sciences. In the 19th century, railroads started to be built in other countries as well. This brought a transformation of transport, significantly lowered transportation costs and increased the trade of goods (Narkus, 2012). The decision-making power was held in the hands of the most influencing European leaders, whose worldwide reach was extensive, particularly because of the colonial conquests. Moreover, an active opening of agricultural lands in the American West took place, due to the impact railroads had on American society and culture (Grinin et al., 2016).

1.3 Third Kondratieff wave – 1890’s to 1940’s

The third wave started with the wide use of electricity and is dated in 1896, which caused a transition from waterpower and steam engines to electric motors. The biggest impact was on the secondary industries, with the chemical industry identified as the leading industry in this period. The wave was most apparent in England, the US and France (Narkus, 2012).

Electrification fundamentally changed the manufacturing systems around the world, resulting in the rapid growth of labor productivity, lower levels of labor and material needed for production and lower pollution, as the connectedness to the electrical networks accelerated. It can be said that the final stages of the transition from Europe to the U.S. as the world leading economy started to take place in this period, with the crucial factor for the U.S. being its economic expansion. The peripheral and semi peripheral countries on the other side started their activation, thus gaining more power compared to the core countries. A resource inflow can

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PPKP_2017_book.indbKP_2017_book.indb 1414 221/11/20171/11/2017 06:5206:52 be observed in Russia, Japan and some other semi peripheral countries, where investors saw opportunities of introducing new technologies (Grinin et al., 2016).

1.4 Fourth Kondratieff wave – 1940’s to 1980’s

The basic innovation that triggered the fourth Kondratieff wave was the automobile with an internal combustion engine. However, a second wave of innovations was powered by the third industrial revolution, which brought further development in electronics sector, paved the ground for IT development and introduced automated production facilities to further enhance productivity. The leading industries of the period were the automotive and petrochemical industry however, all of the key innovations of the period were made in Europe.

During this period, enormous political and social tensions rose, alongside structural crises of adjustment, which ultimately led to the World War II. Increasing demand and affordability of automobiles because of mass production resulted in boosting the petrochemical industry sector. Mass streets transit was followed by air transit upswing during this period (Grinin et al., 2014). The Japanese, Spanish, Ger- man and Italian economies and Eastern Block grew severely due to concentration and redistribution of the capitals and technologies in initial phase of the period. A process of deindustrialization in developed countries (decline in the share of industry in GDP) took off, and the share of services employment rapidly grew. Developing economies on the other hand benefited from the transfer of industrial technologies and off shoring, mainly from the developed countries (Grinin et al., 2016).

1.5 Fifth Kondratieff wave - 1980’s to 2020’s

Information technology is the basic innovation of the fifth Kondratieff wave. Consequently, with the introduction of IT the world gradually turned into a global village with the technological, economic, and social changes being predominantly shaped in the developed countries. In the majority of industries, the most performance-enhancing product was digital computer. IT and related industries contributed directly or indirectly (by impacting other sectors, such as education, consulting services, media advertising, etc.) to more than 70 percent of U.S. growth during the 1990’s (Nefiodow and Nefiodow, 2014a).

With development of infrastructure (e.g. telecommunications network), technological advancement became beneficial for the whole society. Due to

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PPKP_2017_book.indbKP_2017_book.indb 1515 221/11/20171/11/2017 06:5206:52 the innovation in IT, production and management processes in companies became more efficient (Nefiodow and Nefiodow, 2014b). During this period, the industrial society changed to the information society and economic growth was primarily driven by the dynamics of IT manufacturing, IT services and IT related sectors (Grinin et al., 2014). Despite the main technological breakthroughs occurring in developed countries, peripheral countries actually experienced the fastest economic growth, which was fostered by development of underdeveloped regions on behalf of major construction projects and massive urbanization of rural population (Grinin et al., 2016).

Despite providing an innovative approach of explaining how technology shapes history, the N. Kondratieff and J. Schumpeter’s long wave theory was also subject to criticism. Most of the critics deplored the absence of explaining the origin and the dynamics of the long cycles as well as criticizing Kondratieff’s sources, methods and conclusions. Another thing various authors could not agree upon is the timeline of the waves; a literature review shows discrepancy among authors of up to 20 years for start/end of a particular wave (Garvy, 1943). The British economist A. Maddison (1982) argues that the long cycle theory lacks empirical evidence, that the wave-like movement in economic activity has not been proven, and that the major innovations and events are, in contrast, subject to change.

2 From the sixth Kondratieff wave to the Fourth Industrial Revolution

The sixth Kondratieff wave and the coinciding Fourth Industrial Revolu- tion are going to be significantly different from the previous three revolutions, which liberated humankind from animal power, introduced mass production and brought digitalization to billions of people. The Fourth Industrial Revolution is about to bring technologies that are capable of connecting digital, biological and physical worlds, influence all industries, and even challenge ideas about humanity. The leading macro sector in this period will be the medical human services, where the breakthroughs in medical technologies will enable many other technologies to combine into a single complex of MBNRIC-technologies (med-bio-nano-robo-info-cognitive technologies) (Grinin et al, 2014).

Industry 4.0 is characterized by the emergence of multiple revolutionizing technologies that have influenced various industries and can be grouped into three major interconnected technological drivers: digital, physical, and biological technologies (Li et al., 2017).

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PPKP_2017_book.indbKP_2017_book.indb 1616 221/11/20171/11/2017 06:5206:52 2.1 Digital technological drivers

Digital technological drivers can be considered a base for Industry 4.0, where the four most important elements are: the internet of things, artificial intelligence, robotics and machine learning, big data and cloud computing, and digital platforms (Li et al., 2017). The basic idea of the Internet of Things (IoT) is to establish a network of physical items that are embedded with various possibilities of electronics, sensors and similar. This would allow these objects to communicate with each other, collect and exchange data. The IoT technology, such as wired and wireless sensors and actuator networks, can identify, monitor, locate and track its subjects (Sarma and Girão, 2009). The network of physical devices and the data collected in real time is valuable to businesses and can help optimize manufacturing processes. Best market examples of IoT include home automation for automation and control of home devices, industrial automation for gathering data and different wearables such as watches for fitness and health monitoring (Rahul, 2017), where automation or automatic control can be defined as technology or system by which a process is performed without human assistance.

The artificial intelligence (AI) and robotics have developed significantly due to the progress made in technology, such as network technology, the expansion of IT storage capacity and the advances of calculation speed (Li et al., 2017). Robots are machines that can automatically carry out complicated jobs that prove hard for humans to perform and collaborate together with them as well. In order to achieve high-level application, the intelligence of machines or AI has gained more focus. One of the niches within the AI is the Machine learning (ML). The ML enables computers to make reliable decisions when exposed to new data by using algorithms that are programmed to learn from existing data. Due to this ability, insights and useful information can be found much easier and faster (Li et al., 2017).

Today, data sets have become really large, complex and impossible to analyze without adequate software. Due to computer storage capacity improvements, sensors development, and progress of machine learning, advanced analytics is capable of extracting value from data. The term big data (BD) is used for large amount of useful data. A more precise definition of BD is: “BD comes from a variety of resources and contains a great volume of data in a variety of formats, its data streams are rapid and must be handled timely, which implies its velocity, and BD has to be cleaned to ensure the veracity”. (Fernández et al., 2014). Cloud computing, which has developed in the recent years, relies

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PPKP_2017_book.indbKP_2017_book.indb 1717 221/11/20171/11/2017 06:5206:52 on shared resources, may it be for data storage or application, rather than local servers. The use of cloud technology makes it possible for conducting big data analysis without high hardware requirements that would be needed otherwise (Purcell, 2014). Another technology, technology-enabled platforms, is enabling the on-demand or sharing economy globally possible. The digital platforms act as informational brokers and match supply and demand of diverse goods while allowing both sides to interact and provide feedback as well. In addition, it also reduces transaction and friction costs significantly (Li et al., 2017).

In relation to digital technology, it is necessary to mention Virtual Reality (VR) and Augmented Reality (AR), where VR uses software to generate realistic images, sound and other sensations in a virtual environment and AR integrates digital information with user’s real environment. Another example of a useful application of digital technology is Digital Clone or Simulation. A virtual manufacturing environment and a digital clone of production line is used, in which the operations are tested and optimized. Lastly, Cybersecurity is another field in which extensive progress was made recently. Cybersecurity refers to security management of information technology and operation technology in organizations (Jain and Mondal, 2017).

2.2 Physical and biological technological drivers

Among key drivers of the Fourth Industrial Revolution, physical technology is recognized to have the most direct impact on people’s daily lives. Examples of such technology, not yet widely used, are 3D printing and autonomous vehicles. The latter will only be available following legislator development, as the existing institutional frame will represent an impediment to wider adoption. However, the potential of self-driving vehicles is huge, especially in the field of car accidents prevention and providing mobility to those who are not capable or not allowed to drive on their own (elderly, disabled, negligent drivers, etc.). In addition, a wide introduction of self-driving vehicles will enable fuel savings, as the driving is expected to be more economical, cause lower emissions from exhaust systems and increase road capacity with fostering models of sharing economy even more. Consequently, lower amount of more efficiently used vehicles is expected to create eco-friendlier environment (Fagnant and Kockelman, 2015).

Another example, 3D printing, or in other words additive manufacturing, is a technology that creates a physical object into a three-dimensional shape by printing layer upon layer from a digital 3D drawing or model. 3D offers a com-

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PPKP_2017_book.indbKP_2017_book.indb 1818 221/11/20171/11/2017 06:5206:52 plete design freedom, which is how it opens door to an even more rapid process of innovation. It is not very costly to use 3D printing technology. There are no specific equipment requirements for manufacturing, which is why it is possible to avoid extra costs and achieve greater production effectiveness using 3D. An interesting example of what 3D printing offers is food printing. Trials related to food printing have already been done but this technology is not yet ready to replace food as we know it today (3D Printing Industry, 2014).

Genetic technology and neurotechnologies are the most important breakthroughs of biotechnological development in Industry 4.0. Due to the increasing computing power, more demanding tasks (e.g. using computer models for molecular and genetic studies) that used to take hours to be carried out, only take minutes or even seconds. Similar is true for cost savings. Furthermore, genetic engineering has helped to increase crop yields, which is why the percent of active population employed in the agricultural sector is lower compared to the past, despite the global demographic explosion (Li et al., 2017).

2.3 Major industrial changes following the introduction of Industry 4.0 technologies

Cyber-physical systems provide a much greater connectivity in organizations, companies and factories. With the help of the new technological advancements, such as big data and IoT, the manufacturing process can be monitored in real-time, which allows for higher processes optimization. In addition, the network allows for a decentralized decision-making in the company. Besides increasing efficiency, these technologies are also revolutionizing the design process, mass production and even changing the product lifecycles (Reaney, 2016).

Moreover, supply chains are expected to be redesigned in the following developmental wave due to smart manufacturing. Smart manufacturing will redesign product supply chains by integrating the local and the global more strategically. Suppliers are chosen nearer to home, but the demand is served globally. In sustainable manufacturing resources are re-manufactured, components are re-used, and bio, waste or natural products are used as feedstock. In a process where production waste is re-used, and alternative energy created by production re-stored, characteristics of circular economy become recogniz- able (WEF, 2016). In addition, sustainable management will provide one of the main orientations to stimulate growth opportunities in value chain. Increas-

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PPKP_2017_book.indbKP_2017_book.indb 1919 221/11/20171/11/2017 06:5206:52 ing resource and more efficient use of energy is where greater contribution of nanotechnology and biotechnology is expected (Allianz, 2010).

The ongoing trend towards automation has been increasing the demand for industrial robots, where the acceleration can be observed since 2010. The need for continuous quality improvements requires more sophisticated robot systems. On the other hand, robots can improve the quality of work by taking over jobs that prove dangerous or not possible for humans to perform. The main drivers of the growth are the electrical/electronics industry, metal industry and rubber and plastics industry. The most important industry for the industrial robots, the automotive industry, has increased their investments around the world as well. From the companies’ perspective, not just the big manufacturing companies, but the small and medium sized companies as well, will increasingly use the industrial robots. In the future years, the compact and easy-to-use collaborative robots are expected to drive the market, where China will remain the biggest robot market and its main driver of growth. Increasing robot installations can be expected in other Asian markets as well (World Robotics Report, 2016).

3 Major socioeconomic changes and movements of embracing new technologies

The impact of new technologies does not affect only various industries but causes social changes as well. In the upcoming decade, we are likely to observe a trend towards a rebalance of world power. During the fourth Kondratieff wave downswing and the fifth wave upswing, we could observe a change in the trend to a convergence between the core and periphery countries, which is expected only to accelerate during the current fifth wave. The main contributing factors to this process are the exponential technological progress, together with the faster diffusion of technologies from the developed countries to the periphery and also the deindustrialization of the Western countries. Mentioning the global leader, it seems impossible to replace the U.S. as the world leading economy in the foreseeable future (Grinin et al, 2016).

A change of relative power between governments and citizens is another field of change. The use of digital communication, cryptography and public sensor networks has granted huge powers to citizens, while global social media allow new movements to unite, organize, and innovate alongside government to influence and even co-create public policy. However, an example of negative use of such technologies is demonstrated by oppressive governments, which are desperate to

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PPKP_2017_book.indbKP_2017_book.indb 2020 221/11/20171/11/2017 06:5206:52 solidify their own power and to manipulate, spy upon, and censor their citizens, often in the name of national security. Thus, they are abusing, or to put it differently, effectively leveraging information communication technologies. Another example of use of technology to foster civic participation and co-creation of public opinion is global citizenship. Global citizens fight against oppressive uses of technology, often with technology. Proliferation of ICT helps them not only to participate in global discussions that affect us but also to amplify the voices of those who have been marginalized or are altogether missing from such conversations. A critical concern is that the technological shifts will cause greater inequalities or security risks. However, the current stream of events proves that security of even the most well protected institutions can be breached and that the inequality, especially in developed countries, is rising (Davis, 2015).

The forecast is that the influence of the universal regulation mechanisms will grow more important with the development of the sixth Kondratieff, with innovations and technology that comes with it (Grinin and Grinin, 2013). Due to the rapid technological development, regulatory and taxation problem are on the rise. The largest countries, such as the U.S. and China, are the main origin of powerful multinational corporations. It is also in their best interest to headquarter large multinationals due to their desire to oversee a multinational corporation’s taxable income. The desire for multinationals gives the latter even more lobbying power and consequently, they are capable of reaching very favorable agreements with governments as opposed to smaller companies. The future of decision-making in determining global taxation and regulatory policies guidelines only lays in the hand of the largest. But the relationship between them (e.g. China, the U.S., Russia) remains one of the crucial questions that has yet to be answered (Lye, 2017).

It is also important how the rise of advanced computing and robotics will affect our lives. Some authors believe that employment stagnation and weak employment growth after recessions, especially among the lower and middle-skill workers who are replaced by machinery, are due to technological development (Graetz and Michaels, 2017). Human jobs that involve intensive routine tasks and are more exposed to automation are more susceptible to be replaced by industry robots (Schwab, 2016). There are positive associations regarding the adoption of robots with increases in labor productivity, employment, output and product prices, relieving people from doing mundane chores, etc. (Wang et al., 2016).

Another concern is the natural resourc es that are facing rapid depletion all around the world. An example of such resource is phosphate. The world’s

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PPKP_2017_book.indbKP_2017_book.indb 2121 221/11/20171/11/2017 06:5206:52 known phosphate deposits are anticipated to be exhausted by the end of the century. The largest phosphate deposits are found in North Africa (Morocco), the United States, and China. Although phosphorus is used for other purposes, its use in agricultural fertilizers may be one of the most critical for the future of civilization (Magdoff, 2013). Industry 4.0 has shown examples of both, positive and negative consequences. Innovation in the direction of reducing the pressure on resources and saving lives by substituting dangerous labor can be observed on the positive side. The negative consequences of new technologies are the ever-increasing control of nature (e.g. artificial islands, or mountain tunnels) and the consequent amplification of the natural hazards, deforestation, as well as increased exploitation and environmental degradation due to the easy access to fossil fuels (World Robotics Report, 2016).

Conclusion

To summarize, in the last three centuries, four industrial revolutions took place. Each of them is characterized by a predominance of certain energy resources and technological innovations that have had a major effect on the economy. Technological innovations also contributed significantly to the cyclicality of the capitalist economies and have helped fostering global economic and social changes throughout the history, which is why we used Kondratieff’s long technological development wave theory to describe an ongoing phenomenon.

Industry 4.0 has already started to influence various aspect of the way we do business with the development of digital, physical, and biological technologies. New levels of efficiency are achievable with the smart manufacturing, which presents a real opportunity for advanced economies to pursue more distributed and sustainable socio-economic growth (Allianz, 2010). During the current fifth Kondratieff wave, IT related sectors can be considered the main drivers of economic growth, where the leading sector in the forthcoming sixth wave is expected to be the medical human services. Another challenge that the technological progress brings are the social changes. A change in the world power configuration can already be observed, the trend of robotization is posing new questions regarding the change of labor market and the sustainability concerns are gaining more attention.

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PPKP_2017_book.indbKP_2017_book.indb 2222 221/11/20171/11/2017 06:5206:52 References 3D Printing Industry. 2014. “The free beginner’s guide to 3D printing.” URL: https://3dprintingindustry.com/3d-printing-basics-free-beginners-guide.

Allianz Global Investors. 2010. “The sixth Kondratieff – long waves of prosperity. Analysis & Trends.” URL: https://www.allianz.com/v_1339501901000/media/press/document/other/ kondratieff_en.pdf.

Davis, N. 2015. “5 ways of understanding the Fourth Industrial Revolution.” World Economic Forum. URL: https://www.weforum.org/agenda/2015/11/5-ways-of-understanding-the- fourth-industrial-revolution/.

De Propris, L. 2016. “How the fourth industrial revolution is powering the rise of smart manufacturing.” World Economic Forum. URL: https://www.weforum.org/agenda/2016/06/ how-the-fourth-industrial-revolution-is-powering-the-rise-of-smart-manufacturing.

Fagnant, D. J., and Kockelman, K. 2015. “Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations.” Policy and Practice 77: 167-181. URL: http://www.sciencedirect.com/science/article/pii/S0965856415000804.

Fernández, A., del Río, S., and López, V. 2014. “Big data with cloud computing: an insight on the computing environment, MapReduce, and programming frameworks.” Wiley Interdis- ciplinary Reviews 4(5): 380-409. URL: http://onlinelibrary.wiley.com/doi/10.1002/widm.1134.

Garvy, G. 1943. “Kondratieff’s Theory of Long Cycles.” The Review of Economic Statistics 25(4): 203-220. URL: http://www.jstor.org/stable/1927337?origin=crossref&seq=1#pa ge_scan_tab_contents.

Graetz, G., and Michaels, G. 2017. “Is Modern Technology Responsible for Jobless Recover- ies?” CEP. URL: http://eprints.lse.ac.uk/69043/.

Grinin, E. L., and Grinin, L. A. 2013. “The Sixth Kondratieff Wave and the Cybernetic Revolu- tion.” Volgograd: Uchitel Publishing House.

Grinin, L. E., Devezas, T. C., and Korotayev, A. 2014. “Kondratieff Waves. Juglar – Kuznets – Kondratieff.” Volgograd: Uchitel Publishing House.

Grinin, E. L., Korotayev, A., and Tausch, A. 2016. “Economic Cycles, Crises, and the Global Periphery.” Switzerland: Springer International Publishing.

Jain, P., and Mondal, T. 2017. “HfS Blueprint Guide: Industry 4.0 Services.” URL: https://www. accenture.com/ca-en/_acnmedia/PDF-52/Accenture-Industry-4-Excerpt-for-Accenture- Report.pdf.

Kondratieff, N. D., and Stolper, W. F. 1935. “The Long Waves in Economic Life.” The Review of Economics and Statistics 17(6): 105-115. URL: http://threecrises.org/wp-content/uploads/2015/01/Long-Waves-in-Economic-Life.pdf.

Li, G., Hou, Y., and Wu, A. 2017. “Fourth Industrial Revolution: Technological Drivers, Im- pacts and Coping Methods.” Chinese Geographical Science 27(4): 626-637. URL: https:// link-springer-com.nukweb.nuk.uni-lj.si/content/pdf/10.1007%2Fs11769-017-0890-x.pdf. — 23 —

PPKP_2017_book.indbKP_2017_book.indb 2323 221/11/20171/11/2017 06:5206:52 Lye, D. 2017. “The Fourth Industrial Revolution and Challenges for Government.” General Electric Reports. URL: http://www.ge.com/reports/fourth-industrial-revolution-challenges- government/. Maddison, A. 1982. “Phases of Capitalist Development.” Oxford: Oxford University Press. Magdoff, F. 2013. “Global Resource Depletion: Is Population the Problem?” Monthly Review 64(8). URL: https://monthlyreview.org/2013/01/01/global-resource-depletion/. Narkus, S. 2012. “Kondratieff, N. and Schumpeter, Joseph A. long-waves theory. Analysis of long-cycles theory.” Universitet I Oslo. URL: https://www.duo.uio.no/bitstream/handle/10852/38107/Sarunas-Narkus.pdf?sequence=1. Nefiodow, L., and Nefiodow, S. 2014a. “The Sixth Kondratieff. The new Long Wave of the World Economy.” Rhein-Sieg-Verlag: Sankt Augustin. Nefiodow, L., and Nefiodow, S. 2014b. “The Sixth Kondratieff. The Growth Engine of the 21st Century.” In Grinin, L. E., Devezas, T. C. and Korotayev, A.: Kondratieff Waves. Juglar – Kuznets – Kondratieff, Uchitel Publishing House, 2014, 326-353. URL: https://www.researchgate.net/file.PostFileLoader.html?id=590adacc404854cb3f5d379c&assetKey=AS %3A490197482774531%401493883595573. Prisecaru, P. 2017. “The Challenges of the Industry 4.0.” Global Economic Observer 5(1): 66-72. URL: http://www.globeco.ro/wp-content/uploads/vol/split/vol_5_no_1/geo_2017_ vol5_no1_art_008.pdf. Purcell, B. M. 2014. “Big data using cloud computing.” Journal of Technology Research 5(8): 1-8. URL: https://www.google.si/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja &uact=8&ved=0ahUKEwiLwcCWq7fWAhXPfFAKHc4hDWgQFggrMAA&url=https%3A%2 F%2Fwww.researchgate.net%2Fpublication%2F256888844_Big_data_using_cloud_co mputing&usg=AFQjCNHtDE4Fy6Zwl0HAvgaxTde2Fvw61w. Rahul, M. 2017. “IoT applications spanning across industries.” URL: https://www.ibm.com/ blogs/internet-of-things/iot-applications-industries/. Reaney, M. 2016. “Inside Industry 4.0: What’s Driving The Fourth Industrial Revolution?” URL: https://channels.theinnovationenterprise.com/articles/inside-industry-4-0-what-s- driving-the-fourth-industrial-revolution. Sarma, A. C., and Girão, J. 2009. “Identities in the Future Internet of Things.” Wireless Personal Communications 49(3): 353-363. URL: https://link.springer.com/article/10.1007/s11277-009-9697-0. Schwab, K. 2016. “The Fourth Industrial Revolution: what it means, how to respond.” World Economic Forum. URL: https://www.weforum.org/agenda/2016/01/the-fourth-industrial- revolution-what-it-means-and-how-to-respond/. Wang, S., Wan, J., Zhang, D., Li, D., and Zhang, C. 2016. “Towards smart factory for industry 4.0: a self- organized multi-agent system with big data based feedback and coordination.” Computer Net- works 101: 158-168. URL: http://www.sciencedirect.com/science/article/pii/S1389128615005046. Wilde, R. 2017. “Textiles During the Industrial Revolution.” Thought Co. URL: https://www. thoughtco.com/textiles-during-the-industrial-revolution-1221644. World Robotics Report. 2016. “Executive Summary World Robotics 2016 Industrial Robots.” URL: https://ifr.org/img/uploads/Executive_Summary_WR_Industrial_Robots_20161.pdf.

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PPKP_2017_book.indbKP_2017_book.indb 2626 221/11/20171/11/2017 06:5206:52 Denis Marinšek, Esta Carciu, Teresa Baeta Da Silva, Aida Zukić

THE IMPACT OF INDUSTRY 4.0 AND ROBOTIZATION ON THE SECTORAL STRUCTURE

Introduction

Industry 4.0 is transforming the manufacturing and services, operations, supply chains in immeasurable ways, via robotics and Artificial Intelligence (AI). Industry 4.0 is representing a disruptive technological paradigm and is changing all future fields of applications – redesigning of manufacturing, production and service (Sharma, 2017). Businesses are keeping up with new models and business relationships that are transforming the existing value chains beyond recognition, representing one of the most important challenges for them.

In the past, every industrial revolution changed the economic landscape. New industries and new profitable sectors emerged, while other sectors stagnated or gradually even disappeared. With the First Industrial Revolution, the textile industry prospered, later mining and metals, and transport. The purpose of this chapter is to provide an understanding of how the nature and the sectoral structure of manufacturing and services are being and might be also in the future affected by Industry 4.0.

The chapter comprises of four sections. The first section provides an overview of Industry 4.0, as well as the technologies used. In the second section, an extensive study of the impact of Industry 4.0 on the structure of manufacturing is provided, shedding light on the emerging sectors as well as those that could face decline due to the technologically related structural change. The impact of Industry 4.0 on the service sector structure is examined in detail in section three. The last section concludes this chapter.

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PPKP_2017_book.indbKP_2017_book.indb 2727 221/11/20171/11/2017 06:5206:52 1 Technologies shaping the face of Industry 4.0

The First Industrial Revolution began at the end of the 18th century with the introduction of steam power and hydropower causing the shift in society from agricultural to industrial. Due to the use of mechanical energy, production processes significantly accelerated in comparison with the tasks previously performed manually. Furthermore, transport and logistics improved significantly due to the increased use of steamboats and railway transport. Shortly after, the Second Industrial Revolution started, followed by the introduction of mass production (introduction of a production line) with the help of electrical energy (Wolter et al., 2015). The use of assembly lines in production facilitated faster product assembly, reduced the required physical input from workers, reduced the costs, and increased productivity. Subsequently, the change from analogue and mechanical systems to digital ones led to the Third Industrial Revolution, impacting the development of computer, information and communication technology (ICT) (Columbus, 2016).

The society changed from industrial to informational because of the increased use of computers. All of that was followed by the introduction of electronics and information technologies, allowing further automation of production. The impact of these industrial revolutions can be seen through the employment share by sector in the US from 1850 to 2015 (Lund, 2017) (Figure 1).

The economic transformation led to major shifts also in the labor market. The agricultural sector suffered the largest decline in employment (even though due to population growth and productivity growth in agriculture, the food production increased). There was also a significant decline in the share of household workers (but increased activation of women in the labor market) and manufacturing workers. On the other hand, employment in the retail and wholesale sector increased the most, followed by the healthcare and education sectors (Figure 1).

According to Forschungsunion Wirtschaft und Wissenschaft (German Science-Industry Research Union, 2013), Industry 4.0 essentially refers to the technical integration of cyber-physical system into production and logistics as well as applying the internet of things and services (merging the internet with the object or service) in industrial processes – including the resulting impacts on the value chain, business models, as well as downstream services and work organization. The Fourth Industrial Revolution can be perceived as a continu- ation or rigorous implementation of the ideas and technologies from the Third Industrial Revolution. Industry 4.0 stands for interactive networking between

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PPKP_2017_book.indbKP_2017_book.indb 2828 221/11/20171/11/2017 06:5206:52 Figure 1. Employment share change by sector in the US 1850-2015 in percent

-40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15

Retail and wholesale 10.7% Construction 1.8% -2.5% Transportation -39.0% Agriculture -5.2% Manufacturing -6.1% Household workers -1.9% Mining Professional services 5.0% Utilities 0.7% Business and repair services 7.0% Telecommunications 0.3% Healthcare 9.6% Entertainment 2.0% Education 8.6% Government 4.5% Financial services 4.6%

Source: Lund, 2017.

analogue production and the digital world. This transformation includes elements such as big data, autonomously operating systems, cloud computing, social media, mobile and self-learning systems (Wolter et al., 2015).

According to Jain and Mondal (2017), Industry 4.0 is shaped by 13 major technologies. These are: Manufacturing Data Analytics (a systematic analysis of data to optimize manufacturing operations); Robots (machines that can automatically carry out several complicated actions and collaborate with humans); Manufac- turing Automation (implementation of control systems to work with minimal/no human intervention); Digital Clone or Simulation (a digital clone that optimizes operations with tests); Three-Dimensional (3D) Printing (additive manufacturing is used for prototypes, spares, actual parts, and ultimately entire products); Manufacturing IoT (a network in which physical devices communicate, it also exploits sensor data collected); Plant Cybersecurity (security management of information technology (IT) and operation technology (OT) in manufacturing and plant operations); Manufacturing on Cloud (implementation of manufacturing and

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PPKP_2017_book.indbKP_2017_book.indb 2929 221/11/20171/11/2017 06:5206:52 production systems on SaaS- and IaaS-based cloud platforms); Virtual Reality (VR) in Manufacturing (software for multi-projected environments used for many applications, including plant construction, plant maintenance, and operator training); Augmented Reality (AR) in Manufacturing (integration of digital information of the user’s environment in real time); Artificial Intelligence (AI) in Manu- facturing (intelligent behavior of machines); Visual Analytics in Manufacturing (the science and technology of analyzing visual information to aid reasoning and decision making); and Small Batch Manufacturing (implementing solutions that enable manufacturers to cost-effectively manufacture in small quantities) (Jain and Mondal, 2017; Oesterreich and Teuteberg, 2016).

2 The impact of Industry 4.0 on manufacturing

Industry 4.0 will make manufacturing more efficient and productive, as it will directly improve yield on the product side, and will extract higher value from data for usage-based design and mass customization. This, in turn, will open the way to new markets (Rossi, 2016).

Industry 4.0 will accelerate the structural change towards more services, as the percentage of employees in the service sector is increasing while the agricultural and manufacturing industries are declining, though perhaps to a different degree. Industry 4.0 is as relevant to industrial companies as to service companies (Wolter et al., 2015). Industry 4.0 accelerates the structural change in services, with (at least) 11 percent of jobs in the selected sectors also changing. This leads to some significant structural changes in all sectors of the national economy. The blurred distinction between manufactured goods and services, along with the complex technology and economic transformation, has increased the level of quality and service in demand, as well as differentiation and innovation in the market.

2.1 Impact on productivity and investment

The strongest potential impact of Industry 4.0 is expected to be observed in the productivity of manufacturing, increased speed, reliability and quality. Productivity is namely expected to lower conversion costs (costs of labor and overheads) by 15 to 25 percent. When material costs are factored in, productivity gains of five to eight percent are expected, although the impact will be sector- specific. Industrial-component manufacturers stand to accomplish some of the

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PPKP_2017_book.indbKP_2017_book.indb 3030 221/11/20171/11/2017 06:5206:52 Table 1. “Winner” and “Loser” sectors in Industry 4.0 “Winner” Sectors “Loser” Sectors 1. IT and Electronics 1. Retail 2. Automotive 2. Telecommunications and media 3. Transportation and logistics 3. Finance (including insurance) 4. Healthcare Source: Collins et al., 2016.

most significant productivity improvements (20 to 30 percent), while automotive companies can expect increases of 10 to 20 percent. Food and beverage industries are expected to record productivity increases between 20 and 30 percent (more than ever, the manufactures in this industry are maximizing flexibility and standardization), similar is expected also for machinery and mechanical engineering. In other industries, productivity increases between 10 to 15 percent are expected (Gerbert et al., 2015; Geissbauer et al., 2014; HPS, 2017).

The productivity increases will depend also on the level of the employment of AI and digital technologies. Sectors that will employ faster will be the winners of tomorrow’s market place. As can be seen in Table 1, two manufacturing sectors (electronics and automotive) will be the “winners”, while no manufacturing sector will be among the “losers”.

Companies are already investing intensely into the integration of new technologies (Geissbauer et al., 2016). PwC’s 2015 survey among more than 2,000 companies from 26 countries in the industrial sector revealed that one-third of companies had already achieved advanced levels of integration and digitiza- tion, and 72 percent are expected to reach that point by 2020. Currently, the highest level of digitalization and integration in operations, supply chain and related activities is achieved in electronics (45 percent) and the automotive (41 percent) sector. By 2020, sectors with the highest level of digitalization and integration will be electronics, aerospace and defense, industrial manufacturing, and chemicals (see Table 2 for details).

On average, companies expect to reduce operational costs by 3.6 percent per annum while increasing efficiency by 4.1 p.a. Aerospace, defense and security expect cost reduction by 3.7 percent until 2020, chemicals 3.9 percent; engineering and construction 3.4 percent; forest products, paper, and packaging 4.2 percent; metals 3.2 percent; industrial manufacturing 3.6 percent; transportation and logistics 3.2 percent; electronics 3.7 percent and automotive 3.9 percent (PwC, 2016). The survey indicates that 92 percent of Industry 4.0 suppliers and 74 percent of

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PPKP_2017_book.indbKP_2017_book.indb 3131 221/11/20171/11/2017 06:5206:52 Table 2. Adoption of Industry 4.0 by sector 2015 Sector 2020 45% Electronics 77% 32% Aerospace and Defense 76% 35% Industrial Manufacturing 76% 32% Chemicals 75% 38% Forest Products, Paper, and Packaging 72% 28% Transportation and Logistics 71% 30% Engineering and Construction 69% 41% Automotive 65% 31% Metals 62% Source: Geissbauer et al., 2016.

manufacturers expect Industry 4.0 to have an impact on their business model (Wee et al., 2015). The successful implementation of lean manufacturing (automation and computer-integrated technologies to improve productivity) has been reported not only in manufacturing sectors but also in services. Mainly in the developed countries where manufacturing is predominant, the expression “servitization of manufacturing” emerged (The Manufacturer, 2016). The key is to be proactive and apply a three-step process to successfully implement new business models and face the changing competitive landscape. Technology suppliers, as well as manufacturers, generally view Industry 4.0 as an opportunity (Wee et al., 2015; Geissbauer et al., 2014).

To implement the new technologies related to Industry 4.0, the European industry is forecasted to invest €140 billion annually in Industry 4.0 solutions until 2020. The investment growth will be the highest in manufacturing and engineering (3.5 percent per annum), automotive industry (2.9 percent p.a.), process industry (2.7 percent p.a.), electronics and electrical systems (3.3 percent p.a.), and information and communications (3.9 percent p.a.) (Geissbauer et al., 2014).

Based on the survey done by Swiss manufacturing companies (Deloitte, 2014), business segments that have undergone the greatest transformation in line with Industry 4.0 are (see Figure 2): research and development (60 percent of respondents that have undergone strong or very strong transformation); procurement and purchasing (43 percent); and logistics and warehouse (39 percent). These are business segments that traditionally embrace new and innovative technologies the fastest. The business segments that Swiss manufacturing companies see as

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PPKP_2017_book.indbKP_2017_book.indb 3232 221/11/20171/11/2017 06:5206:52 Figure 2. Business segments that respondents see as offering great or very great potential to benefit from Industry 4.0

80% 78% 74% 73% 72% 70% 69% 60% 60% 56% 50% 43% 40% 39% 35% 31% 32% 30% 20% 10% 0% Research and Warehouse Production Services Procurement Sales Development and Logistics and Purchasing Great transformation Great potential Source: Deloitte, 2014.

offering the greatest potential for benefiting from Industry 4.0 are: research and development (78 percent of respondents see great or very great potential); warehouse and logistics (74 percent); production (73 percent) and services (72 percent).

2.2 Sectoral structure change in manufacturing

The shift from selling products to selling measurable outcomes will redefine the whole industry structure (Rossi, 2016). Huge economic value can be unlocked by rapid technology adoption, retraining and redeployment of labor (Manyika et al., 2017).

The transaction to Industry 4.0 is expected to further boost economic development (Memedovic and Lapadre, 2009). Similarly, as in the past, also this industrial revolution will be marked by significant sectoral changes. These will not appear overnight – in fact, some have already become a constituent part of manufacturing or services, while others are still emerging.

The manufacturing sector is growing faster than mining and agriculture (Memedovic and Lapadre, 2009). The demand for IT services will be higher with the increase in investment and overall higher growth rate, as opposed to civil engineering and electronic equipment, and clothing, furniture and automobiles (Wolter et al., 2015).

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PPKP_2017_book.indbKP_2017_book.indb 3333 221/11/20171/11/2017 06:5206:52 Functional and spatial fragmentation of global value chains has weakened the interdependence of economic activities within the national borders and the tendency observed is exporting rather than expanding domestic manufacturing production. Sectors like textile and clothing, machinery, equipment and electrical machinery, whose productive capacities moved away from these regions to benefit Asian countries, have raised the role of all developing countries in international trade (Memedovic and Lapadre, 2009).

Export-oriented industries, such as automotive, chemicals and machine building, shipping and logistics, and agriculture industries are changing their structure to adapt to the changing environment – continuous innovative activities aligned with complex and dynamic value chains – and rethink the entire production control system across all economic sectors. According to Heng (2014), these innovative processes will upsurge in the automotive industry, agriculture and the manufacturing sector, whereas in industries such as pharma- ceutical they will not be implemented as quickly. Particularly ICT and machine building areas will be the most affected, since they are the pillar and the most responsible for the development of the Industry 4.0 infrastructure (Wolter et al., 2015). Population declines/stagnations will benefit from automation as it helps maintain living standards, but countries with high birth rates will struggle with their working-age population to find new jobs (Manyika et al., 2017).

Industry 4.0 has also led to a vertical disintegration of production in many industries (related to functional and spatial fragmentation of production, consumption and their reintegration through trade). As a consequence, trade in intermediate goods has grown faster that in final goods. The trend to de- industrialized or vertical specialization due to rising competitiveness of countries’ export and the integration of new international division of labor will offer competitive advantage and change in the structure as well. This is seen in the following sectors: agriculture, mining and utilities, manufacturing, construction, “transport storage and communications”, “wholesale and retail trade, restaurants and hotels” and “other activities” (Memedovic and Lapadre, 2009).

The sectoral shifts will also cause changes in the labor market and the nature of work. This is causing a restructuring in the labor market – increasing the demand for highly qualified manpower and decreasing the demand for less qualified workers performing routine jobs. The manufacturing professions that are expected to be decreasing and most affected by job cuts are metal construction, system engineering, sheet metal construction, installation, and assembly workers in the electrical and construction trades; wood, plastics pro-

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PPKP_2017_book.indbKP_2017_book.indb 3434 221/11/20171/11/2017 06:5206:52 cessing and treatment professions; other processing, manufacturing and repair professions; machine and system controllers and service professions. With the on-set of the new technological revolution, new jobs, such as IT, data analyst, and many others have been created, while several menial jobs have been or are being replaced by machines. Service-oriented professions are also rising, such as retail trade and sales, hotel and restaurant industry professionals, and technology and science area professionals (teaching and business consultants). Industry 4.0 has also led to changes in the work environment. Requirements such as independency, self-organization, expertise in problem solving, and abstract thinking-skills are vital, but not much more expected than the virtually end-to-end IT skills (Wolter et al., 2015).

3 Industry 4.0 and services

At the moment, there is a lack of a conceptual understanding about the impact of Industry 4.0 on service industries in the future and a general lack of research on the topic as well (Hofmann and Rüsch, 2017; Sanders et al., 2016).

3.1 General impact of Industry 4.0 on services

The service industry is moving from a “reactive” service (solving adversities) to “proactive” service (predicting disruption in organizations) (Whitelam, 2017). Services such as education, travel agencies, consultancy, management, legisla- tive services, security, media, and medical services assign 45 to 80 percent of their time distributing information (Gölpek, 2015). According to Population Matters (2016), the service sectors in which technologies related to Industry 4.0 are being implemented the fastest are construction business services, wholesale and retail services, finance and public services. However, as seen in Table 1, the biggest loser sectors (retail, finance and insurance, and telecommunication and media) in Industry 4.0 are all coming from the service area.

The main fields where robotization is taking charge are - not necessary by order: Construction and Material sector (high levels of automation and robotics in welding painting and assembly (Whitelam, 2017)); Hospitality and Health- care sector (application of robotics is taking place such as surgery robots (The Kiplinger Washington Editors, 2016; Whitelam, 2017)); Military and Public Safety sector (drones in safety and surveillance areas and robots in military units (The Kiplinger Washington Editors, 2016)); Agriculture (application of

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PPKP_2017_book.indbKP_2017_book.indb 3535 221/11/20171/11/2017 06:5206:52 machinery, along with autonomous advancements (jobs initially done by hand)); Food Processing sector (jobs are open to robotic replacement); Education sector (application of humanoid robot (The Kiplinger Washington Editors, 2016)); Biorobots sector (covers fields of cybernetics, bionics and genetic engineering (Kanda, 2012)); Mining sector (reliant on robots and technologies for safety environment); Entertainment and Household Service (dominant in specific areas of the world, such as South Korea and Japan, regarding their openness towards ‘humanoid-looking’ robots (Lear, 2015)).

In services, it is expected that around 30 percent of processes will rely on new technologies that are emerging, such as the robot-as-a-service business model known as RaaS (Violino, 2016). Such service software and AI will enable automation of operations and will soon assist customers virtually. For example, by 2022, chatbots will provide up to 80 percent accurate answers and be heavily used as a search tool surface. Additional instances of changes in customers’ needs are due to knowledge centers (instead of reaching out to a human for support, the existence and consumer preferences of more self-service such as FAQ, help centers and other self-service knowledge platforms); as well as mes- saging clients and the new social media being oriented around online chatting options (e.g. by 2019, request and customer support is predicted to come via these technological vehicle) (Gori, 2017; Smart Service, 2017).

3.2 Sectorial structure change in services

The nature of most services has changed due to the introduction of modern technologies, leading to the increased use of capital in the process. The services are being highly improved due to developed automation, usage of electronics, IT and digitalization processes (Gölpek, 2015). For that reason, real-time integration and analyzing data in order to optimize the processes are supported by some key technologies, such as mobile computing, big data (BD), cloud computing and IoT. Moreover, Industry 4.0 is finding solutions as service industries are becoming more complex and more knowledge intensive (Lu, 2017).

The service industry’s value chain is being changed by the tight collaboration with customers and other stakeholders. Increasingly, customers are expecting products with associated services (Rennung et al., 2016), demonstrating the development of traditional industries to more complex ones. These are mainly retail, healthcare, travel and financial services (Sanders et al., 2016). Not only have services changed their process, Industry 4.0 has led to the standardization

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PPKP_2017_book.indbKP_2017_book.indb 3636 221/11/20171/11/2017 06:5206:52 of services on the one hand, as well as personalization on the other, overall both leading to serving the customer better. Namely, with the standardization of services, the market is divided into two: (a) a standard services market (minimum standard service towards a large consumer that emerged), and (b) a tailored services market (meeting specific needs of a niche). These results stimulate the demand for services which are not available in the market - companies are continuously investing to shorten the life-cycle or to strive for innovation to improve and renovate the already available services and processes or develop a completely new service (Gölpek, 2015).

Furthermore, the industry requires a high level of specialist knowledge which is complex and individualized (Oesterreich and Teuteberg, 2016). It is now possible to combine physical and virtual services in the form of needs-based “as a Service” services. A big sum of investment will also be directed to IT services, continuous education and consulting services, indicating the increase of costs for training over the following years – leading to a price development in the learning and education sector. Changes are anticipated also in catering, cleaning and waste management services, legal, management and economic professions, media science professions, professions in humanities and social sciences, artistic professions, healthcare professions, social professions and teaching professions (Wolter et al., 2015).

Conclusion

Every industrial revolution has changed the economic structure substantially as seen from sections one and onwards. The first revolution caused a shift from agriculture to the industrial and manufacturing systems, while the second one introduced the production line. The third revolution transformed analogue and mechanical systems to digital ones, whereas Industry 4.0 is now filling the gap between the analogue production and the digital world. This disruptive technological paradigm has affected not only manufacturing with its mass customization, improved yield, more efficiency and increased productivity in the winner sectors such as electronics and automotive, but has demonstrated results and changes in services with improved quality, differentiation and innovation in the market and increased demand in areas such as IT, healthcare, and transportation and logistics sectors. However, the biggest loser sectors are still found in the service area, with retail, finance, telecommunications and media sectors being the most visible. The change of the economic landscape – the emergence of new and profitable industries and stagnation of others such as manufacturing,

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PPKP_2017_book.indbKP_2017_book.indb 3737 221/11/20171/11/2017 06:5206:52 mining and agriculture – is accelerating the structural change towards more services where “servitization of manufacturing” is taking place. The increase of export-oriented industries is also leading to a trend of de-industrialized production and vertical specialization, bringing structural changes as well. As seen in the findings of this chapter, Industry 4.0 is changing all future fields of applications and redesigning the manufacturing and services.

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PPKP_2017_book.indbKP_2017_book.indb 3838 221/11/20171/11/2017 06:5206:52 References Collins, S., Eckert, G., Hauer, V., Hubmann, C., Pilon, J., Poulton, G., and Polke-Markmann, H. 2016. “Global Risk Dialogue. Analysis and insight from the world of corporate risk and insurance.” Munich: Allianz Gobal.

Columbus, L. 2016. “Industry 4.0 Is Enabling A New Era Of Manufacturing Intelligence And Analytics.” Forbes. URL: https://www.forbes.com/sites/louiscolumbus/2016/08/07/industry- 4-0-is-enabling-a-new-era-of-manufacturing-intelligence-and-analytics/#1189d7e77ad9.

Deloitte. 2014. “Industry 4.0. Challenges and solutions for the digital transformation and use of exponential technologies.” URL: https://www2.deloitte.com/content/dam/Deloitte/ ch/Documents/manufacturing/ch-en-manufacturing-industry-4-0-24102014.pdf.

Geissbauer, R., Schrauf, S., Koch, V., and Kuge, S. 2014. “Industry 4.0 - Opportunities and Challenges of the Industrial Internet.” URL: https://www.pwc.nl/en/assets/documents/ pwc-industrie-4-0.pdf.

Geissbauer, R., Vedsø, J., and Schrauf, S. 2016. “A Strategist’s Guide to Industry 4.0.” Strategy plus Business. URL: https://www.strategy-business.com/article/A-Strategists-Guide-to- Industry-4.0?gko=7c4cf.

Gerbert, P., Lorenz, M., Rüßmann, M., Waldner, M., Justus, J., Engel, P., and Harnisch, M. 2015. “Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries.” BCG. URL: https://www.bcg.com/en-hu/publications/2015/engineered_products_proj- ect_business_industry_4_future_productivity_growth_manufacturing_industries.aspx.

Gölpek, F. 2015. “Service sector and technological developments.” Social and Behavioral Sciences 181: 125-130.

Gori, A. 2017. “Prepare for the future with these 4 CX strategies.” Zendesk. URL: https:// www.zendesk.com/blog/prepare-future-4-cx-strategies/.

Hofmann, E., and Rüsch, M. 2017. “Industry 4.0 and the current status as well as future prospects on logistics.” Computers in Industry 89: 23-34.

HPS. 2017. “The Impact of Industry 4.0.” URL: https://www.hps-pigging.com/the-impact- of-industry-4-0/.

Jain, P., and Mondal, T. 2017. “HfS Blueprint Guide: Industry 4.0 Services.” URL: https://www. accenture.com/ca-en/_acnmedia/PDF-52/Accenture-Industry-4-Excerpt-for-Accenture- Report.pdf.

Kanda. 2012. “Relation to Other Fields.” Human-Robot Interaction. URL: http://humanro- botinteraction.org/7-relation-to-other-fields/.

Lear, H. 2015. “Are You Being Served? The Influence of Robotics on the Service Industry.” Click Software. URL: https://www.clicksoftware.com/blog/are-you-being-served-the- influence-of-robotics-on-the-service-industry-2/.

Lu, Y. 2017. “Industry 4.0: A survey on technologies, applications and open research issues.” Journal of Industrial Information Integration 6: 1-10. — 39 —

PPKP_2017_book.indbKP_2017_book.indb 3939 221/11/20171/11/2017 06:5206:52 Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P., and Dewhurst, M. 2017. “A Future That Works: Automation, Employment and Productivity.” McKinsey & Company. URL: https://www.mckinsey.com/global-themes/digital-disruption/harnessing- automation-for-a-future-that-work. Memedovic, O., and Lapadre, L. 2009. “Structural Change in the World Economy: Main Fea- tures and Trends.” Unido. URL: https://www.unido.org/fileadmin/user_media/Publications/ Pub_free/Structural_change_in_the_world_economy.pdf. Oesterreich, T., and Teuteberg, F. 2016. “Understanding the implications of digitalization and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry.” Computers in Industry 83: 121-139. Population Matters. 2016. “The impact of robotics on future societies.” URL: https://www. populationmatters.org/impact-robotics-future-societies/. PwC. 2016. “Industry 4.0: Building the digital enterprise. 2016 Global Industry 4.0 Survey.” URL: https://www.pwc.com/gx/en/industries/industries-4.0/landing-page/industry-4.0- building-your-digital-enterprise-april-2016.pdf. Rennung, F., Luminosu, C., and Draghici, A. 2016. “Service Provision in the Framework of Industry 4.0.” Social and Behavioral Sciences 221: 372-377. Rossi, B. 2016. “What are the business and security impacts of Industry 4.0?.” Infor- mation Age. URL: http://www.information-age.com/business-security-impacts-industry-4-0-123463772/. Sanders, A., Elangeswaran, C., and Wulfsberg, J. 2016. “Industry 4.0 Implies Lean Manufactur- ing: Research Activities in Industry 4.0 Function as Enablers for Lean Manufacturing.” Journal of Industrial Engineering and Management 9(3): 811-833. Sharma, A. M. 2017. “Smart Services – What are they? Germany.” GTAI. URL: http:// industrie4.0.gtai.de/INDUSTRIE40/Navigation/EN/Topics/Smart-service-world/smart- services.html. Smart Service. 2017. “Future Trends for the Field Service Industry.” URL: https://www. smartservice.com/smart-service-blog/field-service-industry-trends/. The Kiplinger Washington Editors. 2016. “6 Fields Where Robots Are Taking Charge.” Kiplinger. URL: http://www.kiplinger.com/slideshow/business/T057-S005-robots-taking- charge/index.html. The Manufacturer. 2016. “Servitization in manufacturing today.” URL: https://www.theman- ufacturer.com/articles/servitization-in-manufacturing-today/. Violino, B. 2016. “The future of robotics: 10 predictions for 2017 and beyond.” ZDNet. URL: http://www.zdnet.com/article/the-future-of-robotics/. Wee, D., Kelly, R., Cattell, J., and Breunig, M. 2015. “Industry 4.0: How to navigate digitiza- tion of the manufacturing sector.” McKinsey Digital. URL: https://www.mckinsey.de/files/ mck_industry_40_report.pdf. Whitelam, P. 2017. “The Future of Service Delivery: How IoT and AI Optimize the Customer Experience.” RFID Journal. URL: http://www.rfidjournal.com/articles/view?15889. — 40 —

PPKP_2017_book.indbKP_2017_book.indb 4040 221/11/20171/11/2017 06:5206:52 Wolter, M., Mönnig, A., Hummel, M., Schneemann, C., Weber, E., Zika, G., Neuber-Pohl, C. 2015. “Industry 4.0 and the consequences for labour market and economy.” URL: http:// doku.iab.de/forschungsbericht/2015/fb0815_en.pdf. World Robotics Report. 2016. “Executive Summary World Robotics 2016 Industrial Robots.” URL: https://ifr.org/img/uploads/Executive_Summary_WR_Industrial_Robots_20161.pdf.

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PPKP_2017_book.indbKP_2017_book.indb 4242 221/11/20171/11/2017 06:5206:52 II. ROBOTIZATION

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PPKP_2017_book.indbKP_2017_book.indb 4444 221/11/20171/11/2017 06:5206:52 Ada Guštin, Matjaž Koman, Aleksander Bobić, Peter Emri, Katarina Kern Pirnat

GLOBAL TRENDS IN ROBOTIZATION

Introduction

Robotization has inevitably developed into a $35 billion global business that is estimated to grow to $87 billion by 2025 (Boston Consulting Group, 2017a). The replacement of labor activities has further elevated the integration of [industrial] robots in value chains, as unit sales of such robots were increasing at high-single-digit growth rates 2005-2015, and are estimated to grow by 13 percent year-over-year through the end of the decade. While businesses had been the primary ‘consumer’ of robots, with the new millennia, service robots gained ground, growing at double the rate of industrial robots (IFR, 2016b). In fact, certain industry experts argue that service robots are poised to overtake industrial robots in the coming decades (Ceccarelli, 2012).

The structure of the paper is as follows. In the first part, we examine the major global trends in robotics on the demand side that have been critical for the development thereof, followed by a detailed analysis of the distribution of industrial robots by total volume, region and industry. In the second part, we shift focus to the supply side of robotics, where we present the key suppliers and funding initiatives as well as the major factors affecting the robotics market. In the conclusion, we highlight some key implications in respect of the emergence of robotics.

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PPKP_2017_book.indbKP_2017_book.indb 4545 221/11/20171/11/2017 06:5206:52 1 Users of new technologies

1.1 Key findings on adoption of robotization

Industrial robots have seen a steady rise in orders, they reached unit sales of 254,000 and 290,000 in 2015 and 2016, respectively. It is projected that unit sales may well reach 400,000 by 2019. As the global economy largely recovered from the global financial crisis, orders for industrial robots have almost tripled since 2009. The major geographical shift in sales of industrial robotics has occurred in Asia-Pacific (two thirds of total unit sales), namely in China and South Korea. Today, China represents the largest market by demand for robotics, as well as the most attractive market for robotization of business processes on account of high degree of labor involvement in production. Furthermore, the traction of robot demand implies a stable trend across various industries, such as the automotive industry retaining the position of the largest customer (45 percent of unit orders), and consumer electronics (31 percent of unit orders) on account of staggering growth rate. The importance of investment in industrial robotics by the afore mentioned industries lies in the productivity gains – both indicate highest benefits in efficiency, followed by transportation equipment in general. While investment into robotics within companies has increased, the market value of robotics has a somewhat different structure. Interestingly, two thirds of the market value of industrial robots ($33.5 billion) actually represent the value of intangibles in relation to industrial robotics, thus the industry is heavily dependent on the know-how rather than development of physical robots (IFR, 2016a). A detailed analysis of data on macroeconomic trends in industrial robots is discussed below.

1.2 Data analysis of automation and robotization

1.2.1 Global sales

Figure 1 exhibits a notable increase in worldwide demand for industrial robots in the past decade, suggesting an uptrend towards investing in Industry 4.0 technologies, including robotics. The annual demand for industrial robots has exhib- ited historical compound annual growth rate (CAGR) of 7.79 percent (2005-2015), wherein projections imply double-digit annual growth of 12.60 percent (2016e- 2019e). Alternatively, the projected increase in quantity of supplied industrial robots is estimated to be approximately 40 thousand additional industrial robots installed, primarily in Chinese, European and American markets. The current

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PPKP_2017_book.indbKP_2017_book.indb 4646 221/11/20171/11/2017 06:5206:52 Figure 1. Global annual demand for industrial robots and market size

Market Size Actual Market Size Annual Supply (thousands) 50 500

40 400

30 300

20 200

10 100 Market value in billion USD

0 0 Annual supply of industrial robots in thousand units thousand in robots industrial of supply Annual 2010 2011 2012 2013 2014 2015 2016e 2017e 2018e 2019e Source: IFR, 2016a. market value based on sales of industrial robots has grown steadily to $11.1 billion in 2015. However, the estimate of the actual market size takes into account the cost of software, peripherals and systems engineering. Hence total market value of industrial robots, including the aforementioned items, is estimated at US$35 billion (2015), wherein the market should grow by US$10 billion by 2019 (IFR, 2016a).

1.2.2 Sales by industry

Sales of industrial robotics have all been subject to a severe economic down- turn due to the global recession in 2008-2009. However, sales quickly recovered and have been stable due to robust demand. The automotive industry, as can be seen in Figure 2, has been the dominant stakeholder in industrial robotics as the nature of the business is capital-intensive and involves high labor costs in the production process. It was growing at CAGR of 31.1 percent from 2009 to 2015, or five-fold since 2009, to 97,500 units p.a.

The electronics industry has risen at CAGR of 34.52 percent in the same period due to an uptrend in mobile devices and increasingly complex nature of production. The industry consolidated and was subject to in-house integration efforts to develop software and hardware within. Electronics have grown six- fold in the same period, to 64,600 units.

Other industries, such as metal, chemical/rubber/plastics, and food industry, represent an important role in industrial robotics, but have been overshadowed largely by the automotive and electronic industries (IFR, 2016a).

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PPKP_2017_book.indbKP_2017_book.indb 4747 221/11/20171/11/2017 06:5206:52 Figure 2. Unit sales of industrial robots by industry 100000

80000

60000 Units 40000

20000

0 2009 2010 2011 2012 2013 2014 2015 Year

Automotive Electronics Metals Chemical/Rubber/Plastics Food Source: IFR, 2016a.

1.2.3 Sales by region

In the late 1990s, Japan carried out sales of almost half of the industrial robots, however, the dynamics of robot installations has nowadays been shifting to emerging markets. Figure 3 shows that Asia-Pacific has grown at fastest pace at CAGR of 15.44 percent since 2008, followed by Americas (largely due to Mexico, Canada and Brazil) at 11.20 percent. In Europe, demand for industrial robots has increased well below the global average level in the same period Figure 3. Unit sales of industrial robots by region

300000

250000

200000

150000 Units

100000

50000

0 2008 2009 2010 2011 2012 2013 2014 2015 2016e Year Americas Asia/Australia Europe Africa Other Total Source: IFR, 2016a.

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PPKP_2017_book.indbKP_2017_book.indb 4848 221/11/20171/11/2017 06:5206:52 (5.73 percent), however, the highest relative growth rates were discovered in Central and Eastern Europe, even though Western Europe still maintains an important market for industrial robots. China represents the largest market for industrial robots, taking 27 percent of global supply (IFR, 2016a). While the developed economies in North America and Western Europe represent a large proportion of sales of industrial robots, Asia-Pacific has outpaced its peers in terms of number of industrial robots due to increased production in the region as well as relocation of production resources to Asia-Pacific. China represents the biggest market potential for automation, while Central and Eastern Europe have also been indicating strong demand in recent years.

1.2.4 Industrial robot density

With unit sales by region interpreting growth trends in respective markets, robot density measures the degree of adoption of robots in business processes. While sales by region and industry may suggest that markets have been increasingly more attractive to industrial robot manufacturers, the latter hardly implies the development of a region. Robot density (Figure 4 and 5) thus measures the number of industrial robots, present in a respective region or country, relative to the employed population.

On average, 69 industrial robots are installed per 10,000 employees in the manufacturing industry globally. Figure 4 shows that regionally, Europe is leading in robot density at 92 units per 10,000 employees in the manufacturing industry, followed by the Americas and Asia-Pacific at 86 and 57, respectively (IFR, 2016a).

The most automated economies are South Korea (531 units), Singapore (398 units), Japan (305 units) and Germany (301 units). The volume has significantly increased in the aforementioned countries in the electronics and automotive in-

Figure 4. Number of multipurpose industrial robots per 10,000 employees in 2015 by region

Europe 92

Americas 86

Asia-Paciﬁc 57

Source: IFR, 2016a.

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PPKP_2017_book.indbKP_2017_book.indb 4949 221/11/20171/11/2017 06:5206:52 Figure 5. Number of multipurpose industrial robots per 10,000 employees in 2015 by country 600 531 500

400 398 305 300 301 212 200 190 188 176 169 160 150 136 128 127 126 120 119 110 93 100 86 79 71 69

0 South KoreaSingaporeJapan GermanySwedenTaiwan DenmarkU.S. BelgiumItaly Spain Canada Austria France Finland NetherlandsSwitzerlandSloveniaCzech RepublicAustraliaSlovakiaUK Average

Source: IFR, 2016a. dustry. Although the U.S. represents a top market for industrial robots in terms of supply and demand, its robot density stands at 176 in 2015, while China as the largest market for industrial robots belongs notably below the world average at 49 units, possibly implying significant growth potential.

2 Producers of new technologies

The supply side of robotics industry has been exposed to: (a) rapid downward price pressures in the late 1990s and early 2000s; (b) increasing private equity investment activity and; (c) increasing regional public-private funding initiatives to boost the competitiveness of respective regions. Continued investment in robotics suggest rapid developments in functionalities and universality of use, all of which are the key drivers for growth in unit sales, however, at the expense of exponential price bottoming of industrial as well as service robots. This sub-chapter briefly discusses the supply-side trends of industrial robotics, its’ major suppliers in the industry by size and their prospects, and highlights the key funding initiatives that favor the development of robotics (Acemoglu and Restrepo, 2017).

2.1 Trends in the production of industrial robotics

The rapid decline in robot prices led to increased utilization of robots (which we dub ‘robot densification’) in a range of different industries. Moreover, industry-country pairs which saw more rapid increases in robot density from 1993-2007,

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PPKP_2017_book.indbKP_2017_book.indb 5050 221/11/20171/11/2017 06:5206:52 Figure 6. Robotics potential by industry

Highly Adoption in high-wage economies only Most likely to lead global adoption automatable Plastics and rubber products Machinery Transportation Miscellaneous Computer and electronic products equipment Electrical equipment, appliances and components Ability to automate based on currently Textile products Furniture available Apparel Fabricated metals Primary metals technology Yarns and fabrics Leather Paper Food Chemicals Wood Beverage and Nonmetallic products tobacco products mineral products Printing Limited ability to automate Laggards Technologically limited -40 -20 0 20 40 Deviation from global average manufacturing wage (%) Source: Boston Consulting Group, 2017b. experienced larger gains in labor productivity (Graetz and Michaels, 2015). At the same time, findings suggest that larger increases in robot density are translated into increasingly smaller gains in productivity, suggesting that there are dimin- ishing marginal gains from increased use of robots. However, with respect to the productivity gains, Boston Consulting Group (2017b) demonstrates the respective industries and the viability of robotics, therein considering the wage as a leading cost driver (Figure 6). While apparel, processed foods and tobacco amongst other traditional industries lack the potential for automation, the demand for industrial robotics lies primarily in machinery, transportation, appliances and electronics, according to the analysis. The producers of such industrial robots have shifted their focus towards the robots in the latter industries, as there is the highest potential for growth. Also, another attractive sector for robotics are industries that have been experiencing severe labor shortages, namely farming. The substitution of human labor in developed economies may through time have notable immigra- tion implications as robots might replace certain business aspects.

2.2 Key suppliers and funding initiatives

2.2.1 Key suppliers

Roughly half of the estimated revenues represent top ten producers of industrial robots worldwide. Figure 7 exhibits their respective market share by revenue in 2016, wherein the majority of key producers are located in Europe (ABB, b+m, KUKA), followed by Japan (Fanuc, Yaskawa). While these sup-

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PPKP_2017_book.indbKP_2017_book.indb 5151 221/11/20171/11/2017 06:5206:52 Figure 7. Key producers of industrial robots by revenue in 2016 Revenue in million USD 0 2000 4000 6000 8000 ABB b+m Fanuc Yaskava KUKA Nachi Wittmann Yamaha Slasun Universal Robots IGM Source: Statista, 2017.

pliers initially produced robots for automotive production lines, their revenue streams increasingly derive from robots for consumer electronics. The shift, according to some of them, comes from the need for reshoring production facilities to developed economies, which reduces logistical complexities. Moreover, the robotics industry is somewhat consolidated, on account of increasing merger and acquisition (M&A) activity, thereby pushing growth through price-bottoming on account of stable private and funding initiatives, discussed in the following section (Acemoglu and Restrepo, 2017).

2.2.2 Funding initiatives

Given the rapid growth in orders of industrial robots, five markets represent 70 percent of total volume shipments: China, Japan, the U.S., South Korea and Germany. Along with growth prospects, the interest by investors has risen as well. Venture capital investment in the U.S. has risen from $30 million in 2010 to $172 million in 2013. Investment activity has included companies outside traditional manufacturing industries. Recent examples include M&A activity in high-tech companies that have acquired robotic system start-ups, such as Google taking over 8 robotic companies since 2013 and Amazon purchasing Kiva Systems in order to optimize its warehousing processes. Foxconn, on the other hand, invested $118 million to boost its manufacturing capacity capabilities in order to improve the value of its components that are supplied to high- tech companies like Apple and Samsung Electronics (RAS UK, 2016).

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PPKP_2017_book.indbKP_2017_book.indb 5252 221/11/20171/11/2017 06:5206:52 Several funding initiatives across the globe have taken place by public as well as private institutions. RAS UK (2016) highlights several key developments, highlighted in the following:

• Europe The European Commission along with 180 enterprises and research groups called euRobotics, initiated a robotics research program SPARC as a regional robotics hub, wherein 700 million EUR have been invested by the European Commission and €2.1 billion EUR by euRobotics. SPARC is expected to create 240,000 new jobs and ultimately increase the market value of robotics in Europe by four billion euro (RAS UK, 2016). The ARTEMIS platform, which promotes R&D projects in relation to enhancement of manufacturing and production automation, received an influx of €2.4 billion. €1.2 billion have been awarded to the Public-Private Partnership Initiative “Factories of the Future,” supporting Smart Factories, such as ICT-driven manufacturing.

• The United States Advanced Manufacturing Partnership (AMP) serves as a private institution organizing cooperative initiatives amongst business, research and political groups to improve the “course for investing and furthering the development of the emerging technologies” and support reshoring of manufacturing jobs. In 2013 budget, advanced manufacturing funding increased by 19 percent to $2.2 billion. The National Institute of Standards and Technology (NIST), the leading institution for standardization, has been awarded $100 million to provide technical support for the domestic manufacturing industry through the provision of research facilities and know-how. NIST is also responsible for AMP to further facilitate the networking between government, research and private initiatives. Jobs and Innovation Accelerator Challenge initiative is investing $20 million in the further ten Public-Private Partnerships in the field of advanced manufacturing. The US Department of Defense (US DoD) proposed the ARM to focus on building US leadership in smart collaborative robotics, where advanced robots work alongside humans seamlessly, safely, and intuitively to do the heavy lifting on an assembly line or handle with precision intricate or dangerous tasks. The US DoD indicated assistive robotics has the potential to change a broad swath of manufacturing sectors, from defence and space to automotive and health sectors, enabling the reliable and efficient production of high-quality customised products. ARM, the 14th and last Manufacturing USA Institute to be announced by the Obama administration was named on 13 January 2017. It will be headquartered in Pittsburgh, and the proposal group was convened by Carnegie Mellon

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PPKP_2017_book.indbKP_2017_book.indb 5353 221/11/20171/11/2017 06:5206:52 University. The institute will bring together a very large team, including 84 industry partners, 35 universities and 40 other groups in 31 states. Federal funds plus industry and state cost sharing will total some $250 million; the federal commitment is for $80 million. Clemson University’s Center for Workforce Development will lead the new institute’s workforce training programmes.

• China The 12th Five-Year Plan (2011-2015) aims to reduce dependency on foreign technology and pursue global technology leadership in seven ‘strategic industries’ including High-End Equipment Manufacturing and a New- Generation Information Technology. In doing so, the Chinese government introduced a €1.2 trillion fund to stimulate supply and demand through subsidies, tax breaks and other financial incentives. They also intend to increase R&D investment as a proportion of GDP from 1.5 to two percent by 2015. In the machine tools sector, priorities include themes such as intelligent manufacturing equipment, intelligent control systems, high-class numeri- cally controlled machines and industrial control and automation. The School of Software at Dalian University of Technology established a research group as long ago as 2009 with a remit that includes the investigation of CPS applications in automation engineering (RAS UK, 2016).

Conclusion

While the industry was estimated at the actual market value of $42 billion in 2016 (IFR, 2016a), BCG expects the market of robotics to increase two-fold by 2025, as the use of robotics is increasingly becoming widespread and at- tractively priced (Boston Consulting Group, 2017a). Declines in robot prices increase robot adoption, and this in turn raises productivity and wages, and decreases output prices (Graetz and Michaels, 2015). More specifically, the inclusion of industrial robotics has added 0.37 percentage points to the GDP by eliminating low-skilled labor and other costly processes within the value chain, thereby reducing production costs by up to ten percent (OECD, 2017).

Robotization creates net positive effect on labor demand in Europe (IFR, 2016c), however, some critics of robotics (Unite the Union, 2015) highlight that elimination of low-skilled jobs further deteriorates the income inequality gap at the cost of ill-educated workforce. To further the stance, the use of robotics in medical field appears to be driven by economics and marketing even though

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PPKP_2017_book.indbKP_2017_book.indb 5454 221/11/20171/11/2017 06:5206:52 it increases the cost for patient, wherein the outcome of robotic surgeries is at times unclear (Kirkner, 2014). Nonetheless, there have been some indicators that have shown that the labor shortages in certain industries, such as farming, have substituted human labor due to the lack thereof (Chaffin, 2017).

While the adoption of robots is becoming increasingly widespread, the integration of robotics in operations is inevitable, especially in electronics, transportation and industrial machinery. Nevertheless, the key issue is whether the use of robotics will improve the performance of business processes or is it going to lag the desired productivity and other economic outcomes.

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PPKP_2017_book.indbKP_2017_book.indb 5555 221/11/20171/11/2017 06:5206:52 References Acemoglu, D., and Restrepo, P. 2017. “Robots and Jobs: Evidence from US Labor Markets.” MIT. URL: https://economics.mit.edu/files/12763. Boston Consulting Group. 2017a. “Global Spending on Robots Projected to Hit $87 Bil- lion by 2025.” URL: https://www.bcg.com/d/press/21june2017-gaining-robotics-advantage-162604. Boston Consulting Group. 2017b. “Industries and Economies Leading the Robotics Revo- lution.” URL: https://www.bcgperspectives.com/content/articles/lean-manufacturing- innovation-industries-economies-leading-robotics-revolution/. Ceccarelli, M. 2012. “Service Robots and Robotics: Design and Application.” Her- shey: Engineering Science Reference. URL: https://books.google.si/books?id=nn L0bo2NawkC&pg=PA3&lpg=PA3&dq=engelberger+service+robots+will&sourc e=bl&ots=nn2ybd1jFR&sig=I4oWwbfAqf7LzgQVMP53N1foDbs&hl=sl&sa=X&ved=0ah- UKEwiK66vtiYHXAhXJnBoKHRfSCGkQ6AEIYTAI#v=onepage&q=engelberger%20service%20 robots%20will&f=false. Chaffin, J. 2017. “Farm robots ready to fill Britain’s post-EU labour shortage.” Financial Times. URL: https://www.ft.com/content/beed97d2-28ff-11e7-bc4b-5528796fe35c. Graetz, G., and Michaels, G. 2015. “Robots at Work.” SSRN. URL: https://ssrn.com/abstract=2589780. International Federation of Robotics. 2016a. “Executive Summary World Robotics Indus- trial Robots.” URL: https://ifr.org/img/uploads/Executive_Summary_WR_Industrial_Ro- bots_20161.pdf. International Federation of Robotics. 2016b. “Executive Summary Service Robots.” URL: https://ifr.org/downloads/press/02_2016/Executive_Summary_Service_Robots_2016.pdf. International Federation of Robotics. 2016c. “The Impact of Robots on Employment.” URL: https://ifr.org/img/office/IFR_The_Impact_of_Robots_on_Employment.pdf. Kirkner, R. M. 2014. “Rush Robotic Surgery Outpaces Medical Evidence Critics Say.” Managed Care. URL: https://www.managedcaremag.com/archives/2014/5/rush-robotic-surgery- outpaces-medical-evidence-critics-say. RAS UK. 2016. “Manufacturing Robotics – The Next Robotic Industrial Revolution.” EPSRC. URL: http://hamlyn.doc.ic.ac.uk/uk-ras/sites/default/files/UK_RAS_wp_manufactur- ing_web.pdf. Statista. 2017. “Leading companies in the global industrial robot market in 2016, based on revenue from industrial robot sales (in million euros).” URL: https://www.statista.com/ statistics/257177/global-industrial-robot-market-share-by-company/. OECD. 2017. “The Next Production Revolution.” URL: http://www.keepeek.com/ Digital-Asset-Management/oecd/science-and-technology/the-next-production- revolution_9789264271036-en#.WcSgcEx7Fn4. Unite the Union. 2015. “Productivity – A Comparison in Manufacturing: UK, France, Germany and USA.” URL: http://www.unitetheunion.org/uploaded/documents/Comparison%20 In%20Manufacturing%20Booklet11-25509.pdf. — 56 —

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PPKP_2017_book.indbKP_2017_book.indb 5858 221/11/20171/11/2017 06:5206:52 Mitja Kovač, Janez Prašnikar, Tjaša Redek, Jakob Döller, Lara Flegar, Tamara Žarković

ROBOTIZATION IN DENMARK, AUSTRIA AND SLOVENIA

Introduction

Robotization is a globally present phenomenon, but its implementation varies in intensity and speed between countries and industries. To understand the differences in the intensity of robotization, it is important to identify the drivers and enablers of the process, inhibitors to robotization at large, and country and industry specific factors. To do so, the process robotization has been closely examined in three small open European economies. The first was Denmark, where robotics as well as robotics industry is well established, followed by Austria, where the use of robotics as well as the robotics industry are less developed than in Denmark but catching up fast, and Slovenia, where the use of robotics and the development of such technologies is the least intense. The comparative differences in their economic structure and geo-economic location allow also a deeper understanding of the nature of drivers and inhibitors to robotization (IFR, 2016a).

The chapter first provides an in-depth overview of the level of robotization in all three countries. Afterwards, the country-specific drivers as well as clusters and producers are examined. At the end, the chapter provides useful lessons based on the experiences in the three countries. In particular, we focus on what Slovenia, as the least developed in terms of robotization, can learn from the two more developed countries.

1 Intensity of robotization in Denmark, Austria and Slovenia

The intensity of robotization differs significantly between the three countries, as is evident from the data on the number of multipurpose industrial

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PPKP_2017_book.indbKP_2017_book.indb 5959 221/11/20171/11/2017 06:5206:52 Table 1. Number of multipurpose industrial robots per 10,000 employees in the manufacturing industry Year Country Percentage 2008 2010 2012 2015 2016 change 2016/2015 Austria 81 97 108 128 144 12.50 Denmark 110 150 167 188 211 12.23 Slovenia 39 56 81 110 135 22.73 European average 69 77 82 94 99 5.32 Source: IFR, 2016a.

robots1 per 10,000 employees in the manufacturing industry over the past nine years (Table 1) and industry-level differences in operational stock of industrial robots (number of robots in use) in Austria, Denmark and Slovenia (Table 2). The Danish robot density increased remarkably in the recent years, from 110 robots in operation per 10,000 employees in the manufacturing industry in 2008 to 211 units in 2016, meaning it almost doubled in eight years. Overall, Denmark is the third European country by robot density, following Germany with 309 units and Sweden with 223 units. Having in mind that only a few small automotive part suppliers operate in Denmark and adding really small operational stock under this industry, this is correspondingly high robot density. Of the 5,119 industrial robots used in Denmark, 29 percent of all are used in the metal industry, 18 percent in the plastic and chemical industry, and 15 percent in food production. Additionally, the wood and furniture industry, as well as the medical, precision and optical instruments industry use a large share of all industrial robots (Table 2) (IFR, 2016a).

Austrian robot density is also high with 144 robots per 10,000 employees in 2016, significantly exceeding also the EU average of 99 robots per 10,000 employees (Table 1). The intensity of robotization has been especially high since 2012. The Austrian economy is characterized by a well-established, high-tech automotive industry, which has been the main contributor to an increase in the demand as well as the supply of robots in Austria in the past few years. The automotive industry accounted for 30 percent of the total operational stock in 2014 and by 2016 reached a level of 846 robots per 10,000 employees. The very high investments of the automotive industry in robots in 2016 also contributed most to the overall increase in robots in Austria in that year. Other industries with high operational stock of robots are the plastic and chemical industry with

1 Industrial robot as defined by ISO 8373:2012. An automatically controlled, reprogrammable, multipurpose manipulator programmable in three or more axes, which can be either fixed in place or mobile for use in industrial automation applications (IFR, 2016b). — 60 —

PPKP_2017_book.indbKP_2017_book.indb 6060 221/11/20171/11/2017 06:5206:52 Table 2. Percentage of total operational stock of industrial robots per industry in Austria, Denmark, Slovenia and the EU in 2014, and the total operational stock of robots used in the economy Country Industry Austria Denmark Slovenia EU Agriculture 0.07 1.89 0.49 0.13 Wood and Furniture 0.70 4.96 0.55 0.76 Plastic and Chemical Products 21.58 17.58 15.17 11.94 Metal 22.68 29.40 13.25 15.36 Medical, Precision, Optical Instruments 0.23 1.52 0.05 0.54 Food and Beverages 1.73 14.69 3.79 5.90 Automotive 28.20 3.54 44.80 43.58 Total operational stock of robots 7,237 5,119 1,819 411,062 Source: IFR, 2016a.

1,562 units and the metal industry with 1,641 units, each representing roughly 22 percent of the entire robot stock (Table 2) (IFR, 2016a).

Slovenia is, according to data, also a well automated country and its application of robotics in the automotive industry is comparable to other European countries. On average, Slovenian manufacturing used 135 robots per 10,000 employees in 2016 (Table 1). In 2015, Slovenia exceeded the European average of 94 robots per 10,000 employees for the first time, and recorded also a 23 percent increase over the previous year. The operational stock of robots in the automotive industry accounts for almost 45 percent of the total operational stock in 2014. The other two industries with relatively high operational stock of robots are, similarly as in Austria, the plastic and chemical industry and the metal industry (IFR, 2016a).

2 Denmark

2.1 Denmark: A bottom-up approach to robotization with (passive) government support

The first important steps in the development of the Danish robotics industry were made in 1980s, when A. P. Moller, a shipping magnate, was planning to build an advanced shipyard in Odense. Robotic welding and advanced software for operating self-programming robots were supposed to play important role in

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PPKP_2017_book.indbKP_2017_book.indb 6161 221/11/20171/11/2017 06:5206:52 the production of ships. To stimulate also the collaboration with the research sector, a sizeable donation by private investors was made to the University of Southern Denmark (SDU), where at that time many of the key people from today’s robotics scene were being educated. This collaboration resulted in AM- ROSE, a software company owned by A. P. Moller, where SDU was developing advanced programs for robots. SDU assisted also in the creation of Universal Robots. These were the beginnings of the development of a well-connected community, which further excelled with the establishment of the highly influential Odense robotics cluster and RoboCluster (Robohub, 2016).

The rise of robotics in Denmark can thus be largely attributed to enterprising individuals who wanted to create automation solutions, but on the other hand, to government support as well. A forum on cluster and network policy in Denmark was established between eight ministries and six regional growth forums in 2013 as part of the national innovation strategy. The forum is expected to support cooperation between local, national and international cluster efforts in robotics (The Danish Ministry of Science, Innovation and Higher Education, 2013). Denmark strongly supports interaction between institutions and companies to promote knowledge-sharing and open new business opportunities for all, developing a national system which is much more cooperative than the US system (Robo- hub, 2016). The Danish flat social structure, short chains of command and great flexibility resulted in the development of several strong networks and clusters, supporting the overall technological development and robotization (The Danish Ministry of Science, Innovation and Higher Education, 2013).

The demand of Danish companies that have a general need for fast adaptability of production lines, their high flexibility and variability due to small series production, acts as an important push factor for robotics development. Robotization also improves productivity while keeping the production in-house rather than out-sourcing it (The Danish Ministry of Science, Technology and Innovation, 2006). 44 percent of Danish firms used at least one industrial robot in their factories in 2009 (European Commission, 2012).

High robot-supported productivity also sustains the high level of wages and living standards in Denmark. The Danish also acknowledge the changes in the labor market with possible extinction of menial tasks, while an increase in the number of jobs that include creativity and personal interaction, which are also less prone to automation, is expected. Robotization will create new jobs, most of them in the service sector which is characterized by a high share of non- automatable tasks (McKinsey & Company, 2017).

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PPKP_2017_book.indbKP_2017_book.indb 6262 221/11/20171/11/2017 06:5206:52 The next driver of robotization is their high-quality educational system, which specializes also in robotics-related fields. Aalborg University offers a robotics program that specializes in three major fields: manipulators, actuators, and sensors. It also offers a master’s degree in control and automation. Two specializations, advanced robotics and unmanned aerial systems technology, are offered as a part of the master’s program in robot systems at the University of Southern Denmark. The Danish Technological Institute (DTI), which focuses on developing, applying, and transferring robotics technologies to industry and society, has a lot of influence in robotics research (Robotics Business Review, 2016c). Interestingly, despite the increasing supply of highly-educated individuals, the need for such employees is increasing even faster. Being aware that the lack of human capital can inhibit future development, the clusters are actively involved in finding solutions and promoting suitable educational programs (Nielsen, 2017).

Last, the global commercial market for drones is expanding rapidly, and Denmark is the leader in the EU in this segment. Since August 2015, a new Unmanned Aerial Systems Drone Center is located in the city of Odense, at the University of Southern Denmark. The university offers a master’s program in advanced robotics and drone technology, which allows students to take part in the development of new technologies, for example drones that inspect buildings, help in agriculture, deliver post and food, or find people who have been injured in natural diseases. Many projects are also focusing on drone safety and easier control for commercial use, which will increase the number of potential uses as well as users (Robotics Business Review, 2016a).

Overall, the robotics industry in Denmark has been developing from bottom- up with the supportive role of the government. Development of robotization in Denmark so far has not required strong non-stop involvement of the government because clusters played an important role as drivers of growth and innovation through their policies. However, the automation age will be increasing the role of the government and its policies in the future. 40 percent of working hours in the Danish labor force could be automated, corresponding to one million full time equivalent jobs and the economy is likely to face a significant skill gap, primarily a shortage in the key areas, such as data science and engineering. Therefore, the government is partnering with the private sector to ensure these shortages are better addressed, accompanying these activities also with a shift in education policy towards lifelong learning, ensuring that the workers can keep up with new technologies. Additionally, the government should also support the development of the new-generation digital infrastructure and direct investment through government innovation and research programs. Because

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PPKP_2017_book.indbKP_2017_book.indb 6363 221/11/20171/11/2017 06:5206:52 of this, it can be expected that in the automation bigger interventions from the government and a more top-down approach might be required in some fields in the future (McKinsey & Company, 2017).

2.2 Major clusters and producers

The major determinants of Denmark’s success in robotics are its strong robotics clusters, robotics manufacturers and innovation networks that are supported by educational programs (Universal Robotics, 2017). Denmark is home to several robotics clusters that are mainly located around universities and are characterized by strong collaborative relationships between universities and the robotics industry. The most developed robotics cluster can be found in Odense, while other important robotics organizations have been established in Aalborg, Aarhus, Copenhagen and Sønderborg (The Robot Report, 2017).

Odense Robotics cluster today comprises more than 100 companies, around 2,600 employees, as well as more than ten research and innovation institutions and more than 40 educational programs. Companies in Odense specialize in collaborative robots, food automation and logistics. The Odense cluster has the world’s only startup program in which startups don’t have to sell off ownership and intellectual property and are still provided with lab and office facilities, as well as technical and business mentoring (Odense Robotics, 2017).

In order to further support robotics startups, Odense Robotics StartUp Hub (ORSH) has been established. The support is given through free development space, free meeting rooms, affordable ways of testing, designing and marketing their new products, but even access to investor capital. Only an idea is needed for a startup to join the hub and 24 months are given at their disposal to prove themselves. Copenhagen is the second biggest hub for robotics in Denmark, with several educational programs at the Technical University of Denmark. Further educational programs have also been established at the University of Aalborg (The Robot Report, 2017).

RoboCluster is an innovation network, bringing together the robotics environment by providing several services, like access to funding and bringing together development partners. The RoboCluster network receives grants from the Ministry of Higher Education and Science of Denmark, and is partnering with the University of Southern Denmark, the Danish Robot Network, the Dan-

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PPKP_2017_book.indbKP_2017_book.indb 6464 221/11/20171/11/2017 06:5206:52 ish Technological Institute, the Danish Automation Society and many more (RoboCluster, 2017).

Universal Robots (UR) is the result of many years of intensive research in Denmark’s successful robotics clusters. It was founded in 2005. Universal Robots is successfully producing cobots (collaborative robots) and was sold to Teradyne Inc. in 2015 for $340 million. UR is producing different collaborative robotic arms which are very flexible and can be used for several different tasks. In 2015, UR had around 45 percent of sales in Europe, 30 percent in Americas, and 25 percent in Asia (Universal Robotics, 2016). The company’s goal is to make robotics accessible to medium- and small sized companies. It was ranked No. 25 on the 2015 MIT Technology Review list of the world’s 50 “smartest companies.” (Robotics Business Review, 2016b).

Mobile Industrial Robots ApS (MiR) is another successful robot producing company from Denmark. It is targeting the healthcare and manufacturing industries with a mobile robot used for internal transport and logistic purposes. MiR entered the US market in 2016 with a robot providing similar functionalities as the existing competition but at almost half the price. MiR was able to increase the revenues in the second half of 2016 by 500 percent and currently has more than 25 employees. MiR generates more than 80 percent of its revenues from exports (Haugaard Christiansen, 2017).

Blue Ocean Robotics is the leader in developing robot technologies for end- users, partners and the robotics community itself (Blue Ocean Robotics, 2017a). By incubating the projects, Blue Ocean Robotics takes the lead of the projects When the timing is right, Blue Ocean Robotics sells or licenses the intellectual property rights of the projects and remains a strategic partner (Robotics Busi- ness Review, 2017b). Blue Ocean Robotics had a profit of almost €380,000 in 2016 and 45 employees. It is internationally very present with 12 international joint venture agreements, including the USA, Hong Kong, and Germany (Blue Ocean Robotics, 2017b).

Blue Workforce is producing robots for pick-and-place operations and has recently received funding from a Chinese investor. This allowed them to significantly increase the production capacity (Robotics Business Review, 2017a). Currently, there are more than 25 people employed at Blue Workforce. The company is growing fast and has recently expanded to China (Pedersen, 2017).

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PPKP_2017_book.indbKP_2017_book.indb 6565 221/11/20171/11/2017 06:5206:52 3 Austria

3.1 A combination of the bottom-up and top-down approach

The Austrian robotics development has resulted from several drivers, similarly as in Denmark. The major incentives for the Austrian robotics industry come from the corporate sector, especially the automotive industry as seen in Table 2. The next important driver is the networks of research/educational institutions, such as GMAR (Association for Measuring, Automation and Robot Technologies), consisting of experts from the industry, and also several experts from the leading universities like the Technical University in Vienna, the Technical University in Graz, the Johannes Kepler University in Linz, the University of Applied Science in Linz, and many more (GMAR, 2017). The increasing demand for experts in the field of robotics is also tackled by governmental initiatives like the promotion of STEM (science, technology, engineering and mathematics) subjects at the universities (Bundesministerium für Wirtschaft, Familie und Jugend, 2016).

Austria is very active in robotics-related research. Table 3 shows the Aus- trian contributions to euRobotics, a public-private partnership that connects the European Commission, researchers, the industry and the end-users in the area of robotics under the Horizon 2020 Program. euRobotics developed a Ro- botics Strategic Research Agenda for Europe for the years 2014–2020 to push the market potential of robotics and identify relevant research areas. Austria is clearly not only a noteworthy driver of robotization but also covers various fields of robotics (Hofbaur et al, 2015).

Another driver of the Austrian robotics industry is Smart Automation Aus- tria, the Austrian only industrial automation technology trade fair. In 2016, there were 330 exhibitors from different countries of Europe, connecting and driving the robotics industry (Smart Automation Austria, 2017).

Austria is a front runner in implementing Industry 4.0, according to the assessment based on a report by Roland Berger (2014). This is not surprising, due to the large number of Industry 4.0 initiatives by the government in recent years. “Plattform Industrie 4.0” was launched in 2014 by the Austrian Ministry for Transport, Innovation and Technology (BMVIT). In 2015, “Industrie 4.0 Öster- reich” was founded with the purpose of connecting initiatives and activities within the field of intelligent production (EPTA, 2016). In January 2017, the government presented “Digital Roadmap Austria”, a digital strategy that was created with the

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PPKP_2017_book.indbKP_2017_book.indb 6666 221/11/20171/11/2017 06:5206:52 Table 3. Contributions by Austrian research institutes to the achievement of the European Research Area Roadmap objectives Institution Research Area Market Domains Robot Categories Robot Abilities Joanneum Computer Vision, Production, Commercial Manipulators, Mobile Interaction, Cognition, Research Sensors, Actuators, Robotics, Logistics Platforms, Interior, Configuration, Human-Robot Exterior, Cooperative, Manipulation, Collaboration, Robot Autonomous Reliability Safety Johannes Modelling Complex Production, Healthcare, Manipulators, Mobile Motion Control, Kepler Systems, Optimal Logistics Platforms, Interior, Manipulation, Universität Control, Control Preprogrammed Autonomy, Interaction Engineering Universität Kinematics, Robot Production, Consumer Manipulators, Interior, Configuration, Motion Innsbruck Architecture, 3D Robots Cooperative Planning, Adaptation, Computer Vision, Manipulation, Learning, Grabbing Cognition, Interaction Universität Linked and Autonomous Agriculture, Civil Robots Flying and Ground Cognition, Autonomy, Klagenfurt Systems, Unmanned Robots, Exterior, Interaction Aerial Vehicles, Multi- Preprogrammed, Robot Systems Autonomous Technische Camera-Based SLAM, Civil Robots, Flying and Ground Configuration, Universität Computer Vision, Commercial Robots, Robots, Interior, Adaptation, Autonomy, Graz Learning, Neural Consumer Robots Exterior, Tele-Operated, Cognition, Reliability Information Processing, Preprogrammed, Reasoning, Planning, Autonomous Diagnosis, Testing, Control Architectures Technische Automation, Cognitive Production, Commercial Manipulators, Mobile Interaction, Cognition, Universität Robotics, Computer Robots, Consumer Platforms, Interior, Manipulation Wien Vision, Navigation, Robots Preprogrammed, Manipulation, Human- Autonomous Robot Interaction Profactor Industrial Assistance Production, Commercial Mobile Manipulators, Interaction, Systems, Computer Robots Interior, Manipulation, Cognition Vision, Human- Preprogrammed, RobotInteraction Cooperative, Autonomous Source: Hofbaur et al., 2015. help of over 100 experts and points out the current challenges and different activities planned within the field of digitalization (Digital Roadmap Austria, 2017). In July 2017, the Austrian government founded the Robotics Council, which will develop the robotics strategy for Austria. It will also try to improve the assessment of the risks related to the new technologies and determine which actions have to be taken for Austria to become the leading robotics country. The Robotics Council was also established due to the Austrian public’s strong support (two thirds of the surveyed Austrians) of the increased role of the government (Bundesministerium für Verkehr, Innovation und Technologie, 2017).

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PPKP_2017_book.indbKP_2017_book.indb 6767 221/11/20171/11/2017 06:5206:52 Despite these recent developments, there is still a lack of a nationwide robotics framework that would unite the dispersed industry and allow interdisciplinary robotics projects. Research also suggests that the demand for robotics experts will increase, and the existing supply of suitable graduates in Austria is not yet sufficient (Hofbaur et al., 2015). The general public expects more involvement of politics in the field of robotics, for example, even 67 percent of the Austrian population is demanding a comprehensive strategy for handling robots (SORA, 2017).

3.2 Major players in robotics in Austria

In comparison to the Danish robotics cluster, the Austrian associations and partners, as well as the companies within the field, are not as geographically concentrated. There are several associations and companies which focus on robotics and potential applications.

The Austrian Association for Measuring, Automation and Robot Technologies (GMAR) is a network that intends to connect institutions within the mentioned field, as well as to represent them nationally and internationally. GMAR promotes the scientific and economic development of robotics and represents the interests of several institutions from the science and business worlds. The association comprises several members from renowned universities and companies like: Techni- cal University of Vienna, Technical University of Graz, Festo GmbH, Joanneum Research Forschungsgesellschaft mbH, FH Technikum Wien, and many more. GMAR has been organizing several events in Austria related to robotics, like the Austrian Robotics Week and the Austrian Robotics Workshop (GMAR, 2017).

Another organization within the field of robotics is the association for the promotion of automation and robotics (FA-R). The organization was founded in 2008 with the purpose of establishing a platform for technical services and know-how within the field of automation and robotics. It has several reputable members from the private industry like: ABB AG Robotics Austria, Buxbaum Automation GmbH, Smart Automation Austria, etc. (F-AR, 2017).

One of the major producers for robots is IGM Robotersysteme AG, which produces customized solutions for automated welding and cutting processes. IGM Robotersysteme was established 50 years ago and has currently around 300 employees and €50 million revenues, mostly generated through exports to Asia, Germany, Spain, France, India, Russia and the USA (IGM, 2017).

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PPKP_2017_book.indbKP_2017_book.indb 6868 221/11/20171/11/2017 06:5206:52 FerRobotics Compliant Robot Technology GmbH is producing flexible, intuitive robotic equipment with a special focus on flexible automation. It has received several awards for their innovative and high-quality products (e.g. the Young Entrepreneurs’ Award, the Strategic Manufacturing Award and the State Prize for Innovation). FerRobotics’ robots automatically adjust their work to suit the surface at the moment they make contact. It currently employs 16 employees and has estimated annual revenues of €3.4 million. The company works with Audi, BMW, Trumpf, VW, Renault, etc. (FerRobotics, 2017). FerRobotics exports more than 90 percent of products to Europe, Asia and the US (Naderer, 2017).

Profactor GmbH is an applied research company with expertise in the field of robotics, especially in the field of industrial assistive systems. The company acts as an interface between science and business, and is the most important in applied production research in Austria. Profactor GmbH is also assessing the potential of the Austrian robotics industry for the Austrian Ministry for Trans- port, Innovation and Technology. In 2015, it had revenues of six million euro with 72 employees, but is at the moment operating primarily in the domestic market (exporting only five percent of sales) (Profactor, 2017).

4 Slovenia

4.1 Intensive but still fragmented development of robotics in Slovenia

The origins of Slovenian robotics can be found in 1970s when researchers from the Jozef Stefan Institute (IJS), together with the company Gorenje, developed the first series of industrial robots called Goro. Simultaneously, the Faculty of Electrical Engineering in Ljubljana and Iskra established small electrical robots for folding. Gorenje was particularly interested in becoming a global player in the field of robot production (Lenarčič, 2017a).

While IJS’s scientific research was covering robotization on a broad scale, the Faculty of Electrical Engineering at the University of Ljubljana specialized in rehabilitation robotics. Furthermore, the Faculty introduced robotics as a regular course and later also as a separate study program. Even nowadays, 20 graduated engineers yearly finish this program. Similarly, the Faculty of Mechanical Engineering at the University of Ljubljana has made substantial efforts in the field of robotics application and developed their own programs in

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PPKP_2017_book.indbKP_2017_book.indb 6969 221/11/20171/11/2017 06:5206:52 robotics. Lately, robotics was included also in the curriculum at the Faculty of Mechanical Engineering at the University of Maribor (Lenarčič, 2017a).

Robotization in Slovenia was successfully promoted also by the corporate sector, especially in the automotive and metal industries (discussed later). The companies can prosper in the implementation of robotics most when engineers work closely together with experienced workers (Leban, 2017). The Slovenian state has also been supporting robotics through industrial policy measures. PORS (Posebne raziskovalne skupnosti), which represented a combination of the state and companies financing research activities in the second part of the 1980s, was the first such successful step (Novak and Demšar, 2014). These years represented the “good years” of collaborative research between companies and research organizations. In that period, the robotics industry in Slovenia was keeping pace with the most developed countries in the field of robotics (Lenarčič, 2017a).

In the first period (1990-1999) after Slovenia’s independence, the industrial policy was mainly aimed at stabilizing the economy in the new environment. This pragmatic approach resulted in the preservation of a relatively high portion of manufacturing in the structure of the Slovenian economy and thus sustained also sporadic robotics-related activities (Kovač et al., 2014).

In 1999, the Ministry of the Economy formulated a new concept of industrial policy (Cepec et al., 2014). The goal was to encourage entrepreneurship and corporate growth in an improved business environment. The policies focused on developing social capital through promoting partnerships between companies, universities and research institutions. After almost ten years of disconnection from the robotics industry, technological centers and industrial clusters started to be formed. Between 2004 and 2009, Slovenia supported the emergence of Centers of Competences and Centers of Excellence, which were expected to facilitate and promote research in specific scientific and technology areas, where the Slovenian economy had a reasonable prospect of developing competitive advantages. Re- gardless of the fact that Slovenia established strong “basic research” units, there was an obvious lack of applying their findings in practice. The evaluations showed that Centers of Excellence engaged in extremely narrowly focused research areas which might have contributed to science but had limited applicability for business (Bučar et al., 2014). The European Commission was also critical towards practical applicability of Centers of Excellence (Jakše, 2014).

In the near future, the industrial policy will be shaped by the Smart Special- ization Strategy (S4). S4 is a platform for concentrating development invest-

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PPKP_2017_book.indbKP_2017_book.indb 7070 221/11/20171/11/2017 06:5206:52 ment in areas where Slovenia has the critical mass of knowledge, capacities and competences. One of the defined priority areas is Industry 4.0, focusing on production management and control, quality assurance, regulation and data processing, intralogistics, automation, smart machines and equipment, mechatronic systems, actuators and smart sensors. S4 defines robotization as one of the key enabling technologies, with robotics being related to the factories of the future. The objective is to raise the level of digitalization with automation and robotization in manufacturing. Since in the automotive industry the rate of robotization is comparatively high, the emphasis will be on deploying automation. Another objective is to increase export of automated industrial systems and equipment by at least 25 percent by 2023, in particular in the tool industry, robotics and smart industrial mechatronic systems (Government Office for De- velopment and European Cohesion Policy, 2015).

Aging is another influential driver of robotization. In the next decade, the population in Slovenia is expected to increase by about 60,000 and at the same time the age structure of the population will drastically change. The proportion of people over 65 years is projected to increase from 17 to 22 percent in the next decade and the share of the work contingent (20-64 years) is expected to decline from 64 to 54 percent by 2034 (Repovž, 2017).

In 2016, Slovenia was chosen as a venue of the European Robotics Forum, the most influential meeting of the European robotics community. There is a gap in relation to robotics activities in the Western Balkans in comparison to the EU, which should not widen, and it is important that regional competences are kept and integrated into robotics. As was pointed out in the forum, Slovenia has potential to undertake the leading role in setting up a network of innovation hubs in the Western Balkans (European Robotics Forum, 2016).

4.2 Major players in robotics in Slovenia

There are no robotics clusters in Slovenia. But since the automotive industry is continuing to be the true powerhouse of the national economy, the Slovenian automotive cluster has had an important role in connecting users of robotics in Slovenia. One of the initiatives, led by the Automotive Cluster of Slovenia, pertaining to the area of Factories of the Future is the ACS4ICOMP initiative, which brings together the Slovenian companies and R&D institutions with the goal of developing smart factories in the automotive industry (Chamber of Commerce and Industry of Slovenia, 2017). According to Dušan Bušen, the di-

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PPKP_2017_book.indbKP_2017_book.indb 7171 221/11/20171/11/2017 06:5206:52 rector of ACS, cooperation among its members is a key for the success of such an initiative (Dragović and Read, 2011). However, this is difficult to achieve as there are no leading firms which would integrate different producers in the value chain proposition context. As most members have only limited roles in their value chains, they are more competitors to each other, which hinders collaborative efforts in the cluster (Leban, 2017).

Besides the firms in the automotive industry (e.g. Kolektor, TPV, Hydria, KLS, MAHLE Letrika, etc.), other progressive companies in the field of robotics, such as Unior, Domel, Lama Automation, and Gorenje, can be found in Slo- venia (TPV, Domel and Kolektor are studied more in detail in this book). There is also a number of enterprises that are specialized in producing components used in robots, the robot work-cells, as well as automated production lines, for domestic industry and exports (i.e. Zarja Elektronika), some of them also being global players. Besides domestic companies, Yaskawa, a global producer, also studied in detail in this book, is located in Slovenia.

The Jozef Stefan Institute (IJS), the leading Slovenian research organization in robotics, has also been intensively involved in promoting technological and economic development in Slovenia, both through the education of the personnel

Box 1. Kolektor and robotics Kolektor is a phenomenon in highly specialized industrial production. It is a global company with its seat in Slovenia and a widely spread network of companies and subsidiaries in Europe, the USA and Asia. Currently, Kolektor is among the leading companies in the automotive industry in commutators, magnetics, and hybrid components, as well as in the electric power engineering and engineering and technological systems. As robotics is already very well incorporated in their daily activities, they are focused on finding breakthrough solutions in the field of industrial digitalization, more precisely in the industrial internet of things, solutions for smart factories. They are focusing on areas where they have domain knowledge and competences and are trying to develop new solutions. They see their opportunity in building a platform for smart factories that would help the existing plants in transition to digitalization. Kolektor’s corporate fund, Kolektor Ventures, is rapidly investing in startups operating in the field of the industrial internet of things and solutions for Industry 4.0. Their purpose is to establish a link between innovators, startups, experts and research institutions and therefore become a contact point for people with ideas and people who master machine learning, analytics, artificial intelligence, robotics, sensorics, or virtual reality. They want to connect companies or startups vertically, and institutes and universities horizontally. They are looking for solutions in the value chain of smart solutions in the segments of smart sensors, smart objects, and applications or platforms. Source: Kupec, 2017.

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PPKP_2017_book.indbKP_2017_book.indb 7272 221/11/20171/11/2017 06:5206:52 as well as supporting R&D activities. In order to foster knowledge transfer, which is necessary to reduce the technology gap between more and less developed Euro- pean countries, the IJS Technology Park has been established. By bringing together companies which are research oriented, the Institute also aims to create conditions in which young research talents and innovators could contribute to transferring knowledge and modern technology into the economy. IJS is financed entirely through national and international projects, which the Institute obtains through various tenders or directly through its commercial activities. Due to austerity measures and decreased public resources for research, IJS turned to EU projects, which has in a way diminished its presence in the Slovenian society (Lenarčič, 2017b).

Conclusion

Throughout this chapter, we closely examined the robotics industry within three different European countries; Denmark, Austria and Slovenia. The different approaches to robotization in these three countries allow us to compare their robotics industries and point out similarities and differences in order to provide policy recommendations.

Within the past decade, the number of industrial robots per 10,000 employees in Denmark and Austria almost doubled, whereas in Slovenia the number of robots more than tripled. While in Austria and Slovenia the robotics industry is driven by the automotive industry, the Danish robotics industry is not characterized by a main contributor.

The Danish robotics clusters have been developed through a strong influence from the bottom-up, with especially the Odense cluster being a major driver in the beginning. The Danish robotics industry is very flexible, produces small quantities and is based on advanced technologies and a very advanced educational system. Moreover, the government had a constructive role in the development of the robotics in Denmark.

Austria’s robotics industry is currently facing a turning point, with increasingly growing influences from the top-down, but still remains characterized by a few industries, especially the automotive industry. The Austrian robotics clusters are geographically dispersed and technologically advanced. The educational system for robotics is not sufficiently developed and needs more governmental support. It is crucial to further develop the robotics industry to allow the society to maintain the high income and productivity levels.

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PPKP_2017_book.indbKP_2017_book.indb 7373 221/11/20171/11/2017 06:5206:52 The Slovene robotics industry is mainly influenced from the bottom-up, especially through collaborations between educational institutions, the Jozef Stefan Institute, and companies. In the last decades, also foreign firms such as Yaskawa have stepped in as suppliers of innovative cutting-edge technological solutions for companies. Similar to the Austrian experience, the automotive industry can be considered a key-driver. The involvement of the state in the robotics industry at the moment is low and needs to be increased in order to address also the demographic changes, especially the aging, and support Slo- venia’s transition to a high income society.

At the moment the key question is, whether Slovenia is establishing robotics clusters that could help the robotics industry to grow and prosper. Let us start on a positive note. First, robotics clusters in Slovenia could help build upon cooperative approach through supporting interaction between institutions and companies, and promote knowledge-sharing similarly to Denmark. Further on, hubs for small and medium sized companies in production and the use of robotics could be opened under clusters. Startups should be given the opportunity to prove their ideas through free development space, affordable ways of testing, designing and marketing their new products and, what matters the most, access to investors’ capital. Robotics clusters in Slovenia would stimulate the quality of robotics- related education and R&D activities. The potentially leading role of Slovenia in robotics in the Western Balkans is another reason in favor of clusters development because it can help shrink the gap between the EU and Slovenia in robotics.

However, although in the past, Slovenia demonstrated its capacity for cooperation in robotics between companies and research organizations (PORS), the times have changed and the Slovenian economy is no longer as homogenous as it was in the past. Many firms have become part of large international corporations (MAHLE Letrika, Revoz, Knauf Insulation, Danfoss, BSH Hausgeräte GmbH, etc.) with their own R&D centers. Yaskawa has just recently announced building its own robotics R&D center in Slovenia. Also, the past experience shows that firms are rather competitors for state resources in clusters than making a joint co-operative effort. Despite the decent development of robotics in the recent years, Slovenia still struggles with establishing a relationship between companies, universities and research organizations. Overall, the lack of general drive and cooperation for automation is a major drawback to faster robotization in Slovenia. But the positive experiences of several strong, modern companies, which are also global players and are renowned as good employers, could help turn the table, stimulate other companies to follow the lead, and increase both the government as well as the general public’s support for robotics.

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PPKP_2017_book.indbKP_2017_book.indb 7474 221/11/20171/11/2017 06:5206:52 References Blue Ocean Robotics. 2017a. “About us.” URL: https://blue-ocean-robotics.com/about/. Blue Ocean Robotics. 2017b. “Blue Ocean Robotics reaches new financial heights.” URL: https://blue-ocean-robotics.com/blue-ocean-robotics-reaches-new-financial-heights/. Bundesministerium für Verkehr, Innovation und Technologie. 2017. “Digitalisierung - Österreich bekommt einen Roboter-Rat.” URL: https://www.bmvit.gv.at/presse/aktuell/ nvm/2017/0824OTS0052.html. Bundesministerium für Wirtschaft, Familie und Jugend. 2016. “Ausbau des Fachhochschul- sektors Studienjahr 2018/19.” URL: https://wissenschaft.bmwfw.gv.at/bmwfw/wissenschaft- hochschulen/fachhochschulen/ausbau-des-fachhochschulsektors-studienjahr-201819/. Bučar, M., Stare. M., and Udovič, B. 2014. “Centri odličnosti in kompetenčni centri. Evalvacija instru- mentov.” Univerza v Ljubljani. URL: http://www.mizs.gov.si/fileadmin/mizs.gov.si/pageuploads/ Znanost/doc/Strukturni_Skladi/Centri_odlicnosti/Centri_odlicnosti_in_kompetencni_centri.pdf Cepec, J., Beširević, H., Černe, A., Golle, L., and Jelen, K. 2014. “Industrial policy under the market “euphoria.” period (2003-2008)” In Prašnikar, J. (ed.): Industrial policy in retrospec- tive, Časnik Finance, 2014. Chamber of Commerce and Industry of Slovenia. 2017. “Industry 4.0 for the future of manufacturing in the EU.” URL: http://adapt.it/Industry4EU/Industry4EU_CCIS_Slove- nian_National_Report.pdf. Digital Roadmap Austria. 2017. “Die digitale Strategie der österreichischen Bundesregier- ung.” URL: https://www.digitalroadmap.gv.at/. Domadenik, P., Grandovec, T., Guštin, A., and Rožman, T. 2014. “Finland and Slovenia: In- dustrial policy and the role of science.” In Prašnikar, J. (ed.): Industrial policy in retrospec- tive, Časnik Finance, 2014. Dragović, M., and Read, C. 2011. “A Cluster of Opportunities.” The Slovenia Times. URL: http://www.sloveniatimes.com/a-cluster-of-opportunities. European Commission. 2012. “Report: Public Attitudes towards Robotics.” URL: http:// ec.europa.eu/commfrontoffice/publicopinion/archives/ebs/ebs_382_en.pdf. European Parliamentary Technology Assessment (EPTA). 2016. “The Future of Labour in the Digital Era: Ubiquitous Computing, Virtual Platforms, and Real-time Production.” URL: epub.oeaw.ac.at/ita/ita-projektberichte/EPTA-2016-Digital-Labour.pdf. European Robotics Forum. 2016. “Slovenia to host the most important European robotics conference in 2016 – ERF2016.” URL: https://www.eu-robotics.net/robotics_forum/ newsroom/press/slovenia-to-host-the-most-important-european-robotics-conference- in-2016-erf2016.html?changelang=3. FerRobotics. 2017. “About FerRobotics.” URL: http://www.ferrobotics.com/en/company/. Förderung der Automation und Robotik (F-AR). 2017. “Der F-AR - Ein Netzwerk mit Perspe- ktive.” URL: http://www.f-ar.at/journal. Gesellschaft für Mess-, Automatisierungs- und Robotertechnik (GMAR). 2017. “Robotik.” URL: http://www.gmar.at/fachbereiche/robotik/. — 75 —

PPKP_2017_book.indbKP_2017_book.indb 7575 221/11/20171/11/2017 06:5206:52 Government Office for Development and European Cohesion Policy. 2015. “Slovenia’s Smart Specialization Strategy.” URL: http://www.svrk.gov.si/fileadmin/svrk.gov.si/pageuploads/Dokumenti_za_objavo_na_vstopni_strani/S4_document_2015_ENG.pdf Haugaard Christiansen, J. 2017. “God sommerferie: Mobilrobot i vild vækst - omsætningen 15-dobles.” Fyens. URL: http://www.fyens.dk/erhverv/God-sommerferie-Mobilrobot-i-vild- vaekst-omsaetningen-15-dobles/artikel/3167183. Hofbaur, M., Müller, A., Piater, J., Rinner, B., Steinbauer, G., Vincze, M., and Wögerer, C. 2015. “Making Better Robots – Beiträge Österreichs zur Europäischen Robotics Research Road- map. ” e & i Elektrotechnik und Informationstechnik 132(4-5): 237-248. IGM. 2017. “Schweissroboter und Schweisstechnik made in Austria.” URL: http://www. igm-group.com/de. International Federation of Robotics (IFR). 2016a. “World Robotics Report 2016.” URL: https://ifr.org/ifr-press-releases/news/world-robotics-report-2016. International Federation of Robotics (IFR). 2016b. “Industrial Robots – Definition and Types WR 2016.” URL: https://ifr.org/img/office/Industrial_Robots_2016_Chapter_1_2.pdf. Jakše, L. 2014. “Kar 361 NE-jev iz Bruslja. Evropska komisija ima številne pripombe na Operativni program za finančni okvir 2014-2020.” Delo. URL: http://www.delo.si/novice/ politika/kar-361-ne-jev-iz-bruslja.html. Kovač, M., Breg, N., Rudl, N., Volf, M., and Vuga, U. 2014. “Industrial policy under the transition to the market economy (1991-2002).” In Prašnikar, J. (ed.): Industrial policy in retro- spective, Časnik Finance, 2014. Kupec, B. 2017. “(intervju) Valter Leban, Kolektor: Biti moramo hitri, in start-upi so v tem mojstri.” Finance. URL: https://startaj.finance.si/8854539/%28intervju%29-Valter-Leban- Kolektor-Biti-moramo-hitri-in-start-upi-so-v-tem-mojstri. Leban, V. 2017. Personal communication, October 26, 2017. Lenarčič, J. 2017a. Personal communication, October 27, 2017. Lenarčič, J. 2017b. “Javno financiranje RR v Sloveniji v zadnjem desetletju.” IJS Working Paper. McKinsey & Company. 2017. “A future that works: the impact of automation in Denmark.” URL: https://www.mckinsey.com/global-themes/digital-disruption/harnessing-automation-for-a-future-that-works. Naderer, R. 2017. “TV Servicetipps - Exportchampions - FerRobotics GmbH.” Export Center OÖ. URL: https://www.wko.at/site/export-center-ooe/exportland-oesterreich/TV-Service- tipps---Exportchampions---FerRobotics-GmbH.html. Nielsen, B. 2017. Personal communication, August 31, 2017. Novak, P., and Demšar, F. 2014. “Zgodovina financiranja raziskovalne in razvojne dejavnosti v Sloveniji.” URL: http://www.arrs.gov.si/sl/analize/publ/inc/Zgodovina-financ-rrd-1.pdf. Odense Robotics. 2017. “What is Odense Robotics?” URL: https://www.odenserobotics.dk/. Pedersen, J. 2017. “Blue Workforce Hires Vestas Chief.” Blue Workforce. URL: http://blue- workforce.com/news/blue-workforce-hires-vestas-chief/. — 76 —

PPKP_2017_book.indbKP_2017_book.indb 7676 221/11/20171/11/2017 06:5206:52 Profactor. 2017. “About us.” URL: https://www.profactor.at/en/about-us/. Repovž, E. 2017. “Kakšna je demografska napoved za Slovenijo čez 10 oziroma 20 let?” Delo. URL: http://www.delo.si/druzba/delova-borza-dela/kaksna-je-demografska-napoved-za- slovenijo-cez-10-oziroma-20-let.html. RoboCluster. 2017. “Network.” URL: https://en.robocluster.dk/network.aspx. Robohub. 2016. “A look at Danish Robotic Cluster.” URL: http://robohub.org/a-look-at-a- danish-robotics-cluster/. Robotics Business Review. 2016a. “New Drone Center in Denmark Sparks Inspiration for Growing Industry.” URL: https://www.roboticsbusinessreview.com/research/new_drone_ center_in_denmark_sparks_inspiration_for_growing_industry/. Robotics Business Review. 2016b. “Denmark Is Driven to Lead European Robotics.” URL: https://www.roboticsbusinessreview.com/research/denmark_is_driven_to_lead_euro- pean_robotics/. Robotics Business Review. 2016c. “Top 5 Reasons Why European Robotics Thrives in Den- mark.” URL: https://www.roboticsbusinessreview.com/research/top_5_reasons_why_eu- ropean_robotics_thrives_in_denmark/. Robotics Business Review. 2017a. “Danish Robot Company Blue Workforce Expands in Asia.” URL: https://www.roboticsbusinessreview.com/supply-chain/danish-robot-company-blue- workforce-expands-asia/. Robotics Business Review. 2017b. “Blue Ocean Robotics ApS.” URL: https://www.roboticsbusinessreview.com/company/blue-ocean-robotics-aps/. Roland Berger. 2014. “Industry 4.0. The new industrial revolution. How Europe will succeed.” URL: http://www.iberglobal.com/files/Roland_Berger_Industry.pdf. Smart Automation Austria. 2017. “The Future under Control.” URL: http://www.smart-wien. at/en/exhibitor/. SORA Institute for Social Research and Consulting, Ogris & Hofinger GmbH. 2017. “Wie steht Österreich zu Robotik und KI.” URL: http://www.sora.at/nc/news-presse/news/news- einzelansicht/news/wie-steht-oesterreich-zu-robotik-und-ki-801.html. The Danish Ministry of Science, Innovation and Higher Education. 2013. “Strategy for Den- mark’s Cluster Policy.” URL: https://ufm.dk/en/publications/2013/strategy-for-denmarks- cluster-policy. The Danish Ministry of Science, Technology and Innovation. 2006. “Technology Fore- sight on Cognition and Robotics.” URL: https://ufm.dk/en/publications/2006/files-2006/ technology-foresight-cognitions-robotics.pdf. The Robot Report. 2017. “Global Map.” URL: https://www.therobotreport.com/map/. Universal Robotics. 2016. “Universal Robotics: Denmark’s largest robot manufacturer de- livers 91% growth in revenue.” URL: https://www.universal-robots.com/about-universal- robots/news-centre/91-growth-in-revenue/. Universal Robotics. 2017. “The success of the Danish robotics cluster.” URL: https://blog. universal-robots.com/the-success-of-the-danish-robotics-cluster. — 77 —

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PPKP_2017_book.indbKP_2017_book.indb 7878 221/11/20171/11/2017 06:5206:52 Matej Černe, Petra Ajdovec, Robert Kovačič Batista, Matjaž Vidmar

CORPORATE STRATEGY AND INDUSTRY 4.0

Introduction

Industry 4.0 refers to the current and upcoming changes occurring in the industrial development that are changing the way businesses operate. Zhou et al. (2015) noted that it includes future trends aimed at achieving superior and more intelligent manufacturing processes. These radical changes have an impact on companies and their business models, and contemporary firms are faced with having to change their corporate strategies and organizational structures in order to adapt to the changes and survive on the market (Chesbrough, 2010). Which factors determine firms’ use of new technologies, be it push (stemming from the firm itself) or pull (based on market changes), remains to be examined, as does how organizations respond to those changes with innovations in organizational structures/processes and business models.

The aim of this chapter is the analysis of the major push and pull factors that influence the implementation of new technologies in the companies. Do- ing so, we contribute to the extensive research by showing connections and common thoughts within various literature areas, identifying the main theoretical influxes into the field, and making informed suggestions for its future development. Furthermore, as the discussion of push and pull factors is closely related to firms’ business models (BMs) and their innovations, where not all push and pull factors are equally important for all types of BMs, the chapter elaborates the advantages, disadvantages, effects and possible implications of the new robotization era processes on companies’ business model transformation and changes in organizational structures, with the emphasis on the firms’ strategies and the management behind them. The key outcome of the chapter is the development of a framework model presenting the push and pull factors, and firms’ potential responses to them.

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PPKP_2017_book.indbKP_2017_book.indb 7979 221/11/20171/11/2017 06:5206:52 The structure of the chapter is as follows. Section 1 lists and describes the major push and pull factors for the implementation of new technologies as well as their overlap. Section 2 delves into firms’ responses to push and pull factors in the form of business model innovation, followed by an analysis of the main changes to corporate strategic management and business process management within In- dustry 4.0. We conclude by summarizing the main findings of the chapter.

1 Major push and pull factors for the implementation of new technologies

Push factors, such as firms’ drive to grow in revenue, gain market share, engage more customers, conquer new potential markets, etc. are all factors that influence the development of processes and products that derive from an organization to the market. On the other hand, pull factors, e.g. increasing productivity, maximizing efficiency, capitalizing on (big) data analysis, changes in legislation, process standardization, etc. can be understood as vis-a-vis push factors, thus diametrically opposite, moving from the market to the organization. A push factor for one pool of organization can also become a pull factor for other firms. An accurate general theoretical background cannot be conducted based on the extensive literature, but instead we could literally allocate the push and pull factors for each specific company, since the applicability of these factors are relative to a peculiar circumstance of facts. Pull strategies should be applied only to high competitive intensity sectors due to the laborious, often impossible long-term planning and market dynamism. Yet it is not doable per se, considering that push and pull factors have to be identified based on distinct industry, market, process or organization. The aftereffect is that two connected diametric rationales of push and pull factors within a single company’s process can be found (Corniani, 2008). The general overlap factors are customer satisfaction, understanding market requirements, prioritizing business need, improvement in rapid solving capabilities, labor reduction, and finally, product and service customization.

Because of the complexity and intertwined subject matter, we have created a rough mind map that illustrates all noteworthy factors identified in the literature (Figure 1). In the following diagram, we have divided all our notable factors into two main pools, push and pull. The overlap represents all factors that could not be directly separated into push or pull pools.

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PPKP_2017_book.indbKP_2017_book.indb 8080 221/11/20171/11/2017 06:5206:52 Figure 1. Most important push and pull factors

Push Pull Revenue growth Increase productivity Market share gaining Maximizing eﬃciency Customer engagement Data collecting Development of new Data analysis products and services Legislation adaptation Enhancing compliance Process standardisation Horizontal integration Overlap Increasing competition Vertical integration Increased data security Improved management Transparency High complexity of Instant Monitoring processes and products Employee satisfaction Governmental support through subsidies Greater consumer power Higher accuracy Better process insights Higher turnover Short delivery times Less errors

Overlap Customer satisfaction Understanding market requirements Prioritizing business needs Flexibility to changes Rapid solving capabilities Labour reduction Product and service customization

Source: Our own interpretation based on the extensive literature.

1.1 Push factors

Organizations create and develop a product and present it to a specified pool of customers or purchasers. The supply of goods or services is accordingly provided to the market by the organization or firm (Corniani, 2008). We could deduct that successfully executing this process is a matter of good trend forecasting, which shows the expectation of innovation needs from the market. A firm can, in this way, enlarge its market share or obtain competitive advantage. A company’s performance is thus dependent on whether it is able to introduce and develop new technological innovations or not. Organizations are pushing new innovations for various reasons. One is the increase in market share, for example Google’s two-sided dynamic search engine developed in 2003. Char-

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PPKP_2017_book.indbKP_2017_book.indb 8181 221/11/20171/11/2017 06:5206:52 acteristic for most technological innovations, this was not just a technological leap, but rather an invention of a new business model needed to support technological breakthroughs (Baden-Fuller and Haefliger, 2013). In the following, we further explore other push factors.

Innovation or more specifically, new product and process development, is a typical push motive. The catalysts for organizational innovations can be found internally or externally. Predominantly, the aim of an innovation is to reach commercial or non-commercial use of the new know-how through the outburst of new technology. There are two paths firms can take in achieving such outburst, either through radical innovations (‘technology push’) or incremental innovations (‘market pull’) (Brem, 2008). The evolution of new production systems and new products is joined and harmonized with product life cycles, allowing new alliances to emanate between development and production sections (Schlaeper and Koch, 2014). Additionally, this is a way of market penetration, many times creating a new one and finally increasing revenue and growth or just gaining a competitive advantage.

In addition to growing revenue and gaining market share through innovation development, additional notable push factors include consumer engagement and consumer satisfaction, which are vital for organizations. The implementation of new technology helps us find the suitable value proposition for individual customer or groups of customers. For instance, consulting firms are specialized in interacting with specific clients, while automobile assemblers and food suppliers and computer retailers are dealing with mass production format (Baden- Fuller and Haefliger, 2013). Firms’ dedication towards customer engagement helps to improve the flexibility of response towards customers, integrate diverse skills and expertise and aids in solving complex tasks. In essence, the focus on the factor of customer engagement describes collaboration between business model choice and guidance of technology innovation. Organizations strive towards recognizing customer clusters and understanding their requirements in order to monetize them. Within the business model framework, this factor is described as value delivery. Contemporary open innovation platforms, such as IDEO, InnoCentive or Imaginatik, combine both sides, customers and companies, through engaging approaches in order to create a satisfaction benefit as well as greater revenues. Taken together, new technologies have the potential to please customers’ requirements by optimizing turnover, utilization, quality in marketing, production, development and strategy (Prangnel and Wright, 2015).

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PPKP_2017_book.indbKP_2017_book.indb 8282 221/11/20171/11/2017 06:5206:52 There are many other push factors (listed in Figure 1), such as revenue growth, creation of new markets or obtaining a bigger portion of market share, and even horizontal and vertical integration.

1.2 Pull factors

Contrasting the logic of push factors, pull factors originate from the processes within the market that influence organizations. The demand is on the market side and hence pulls it from the organization. The markets demonstrate the requirements that need to be satisfied and thus companies adapt by developing products as a response to the pull action of demand (Corniani, 2008). In many cases pull factors force companies to implement new technology or some specific processes in order to maintain the desired market position or achieve a better one. One of the advantages of pull factors could be the fact that organizations can acquire or invest in new technology when demand is already present in the market, hence the mentioned technology development can facilitate new business models (Baden-Fuller and Haefliger, 2013). On the other hand, this can lead to a deficit, since the competitors can be a foot in front with the implementation, ergo taking the market share.

A pull factor related to the necessity of maximizing efficiency and increasing productivity could have been noticed when some big European truck-logistics companies started to use a program called TimoCom, which is a platform for sharing information about cargo and available trucks. Many middle or small companies followed this trend in order to gain higher revenues, inventory turnover or obtain cost reduction. Another specific example of a pull factor with the background of cost savings, increased accuracy, higher productivity and consequently higher profits, is the implementation of robot-computers for trading and analysis at BlackRock, Inc. Its president, Mr. Fink, said, “The de- mocratization of information has made it much harder for active management. We have to change the ecosystem — that means relying more on big data, artificial intelligence, factors and models within quant and traditional investment strategies” (Landon, 2017).

Another important pull factor, a frequently emphasized factor within shared services and global business services is cost reduction or increased savings by implementing robots or other forms of new technology into organizations. The usage of robots and artificial intelligence (AI) grants brisk and scalable welfare from process automation with relatively low investment and a ninth of the

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PPKP_2017_book.indbKP_2017_book.indb 8383 221/11/20171/11/2017 06:5206:52 full-time employee’s cost, as opposed to the substitution of the whole system or relocating processes to near shore or far shore locations. Smart manufacturing initiatives can help save some cost (Geissbauer et al., 2016). In addition, it can help improve management tools. This is a very important factor, which can lead to reducing overhead and fixed costs. Implementing robots is associated with higher turnover, more accuracy, less errors, faster cycles with greater volumes, risk reducing, more affordable coverage of peak periods, easier controlling and analysis, less employees, and last but not least, humans can focus on more challenging work and customer satisfaction (Prangnel and Wright, 2015).

Outside limits of cost savings, a benefit that has to be mentioned is data aggregation and the yield of process data derived from automation, a large and important impact on process optimization that leads to maximizing efficiency, strengthening security, reducing error rates, obtaining work process standardization and improving strategic management. Additionally, it could enhance the particular expertise and skills of SMEs. It is essential to focus on the vast field of data analyzes, assisting to reach better insights and meaning-making from the gathered information. By capturing human motion and goods motion, firms can shorten customer decision time. It can even be used for product development and to penetrate new markets (Brown et al., 2015). The reachability of this information or data is available at all stages of product’s life cycle, facilitating potentially new process models (Schlaeper and Koch, 2014). We can already find predictive maintenance of key assets, made out of algorithms, to improve repair systems, schedules and asset uptime (Geissbauer et al., 2016). About 72 percent of the companies interviewed by PwC expect that the use of data analytics will considerably optimize product life cycles, customer relationship and finally customer engagement, which can open many new collaborations. Additionally, the rationale is that companies will be able to produce personalized products and services that will provide higher margins. PwC predicts that industry will impair push models and augment customer pull models with creating a norm where customers are involved with the manufacturers directly (Geissbauer et al., 2016). Cost saving and data gathering are empowering process standardization, which can allow human transition to perform more creative and talent-needed tasks. Indeed, it reduces workforce volume and obviously costs (Brown et al., 2015).

To conclude, a push factor for implementing new technologies, such as standardization of process workflows that leads to simplified processes (optimization), where a company achieves better process insights and finally reaches a better level of compliance and management, can easily become a pull factor

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PPKP_2017_book.indbKP_2017_book.indb 8484 221/11/20171/11/2017 06:5206:52 for a company that is seeking a superior market position or trying to restrain one. On the other hand, this can boost complex process opportunities tackling, which upgrades the flexibility of an organization, since the demand is quite dynamic and hard to overlook.

Despite the existence of many additional pull factors listed in Figure 1, there is another one with a heavy impact on the business environment that needs to be mentioned. We are referring to regulations and legislation changes adaptation. We must emphasize the supportive role of a state – the government, which can contribute to designing a viable environment for innovations to flourish by giving subsidies to innovative and technologically advanced organizations.

In order to keep up with the rapid changes that occur within and outside the firms and organizations on the basis of either push or pull factors, firms and organizations are forced to find innovative ways to adapt their business models on a daily basis and remain competitive actors in the market. Organizations thus need to adapt to changes by shifting the logic on which they capture value, innovate and renew their business strategies, and adapt their business processes and organizational structures. All these organizational responses can be por- trayed through the logic of business model innovation.

2 Business model innovation

Business model (BM) innovation denotes changes in decisions that collec- tively determine how a business earns its revenue, incurs its costs and manages its risks. Some common central characteristics of a BM are: a BM’s focus is on the value creation logic for all stakeholders; the consideration of crucial value creating activities that are performed by parties external to the company, such as suppliers and customers; a holistic approach is used to explain the value creation logic of a company; and the fact is that BMs emerge as a new unit of analysis in academia (Zott and Amit, 2013).

Zott and Amit (2013) constructed a chart, presented in Figure 2, which consists of nine components and provides a synthesis of different and highly regarded BM concepts. Those components (cost structure, partner network, value configuration, core competencies, relationships, target customers, distribution channels, revenue model and value proposition) can be used as a base to determine push and pull factors that influence the actions of a certain firm or organization, since companies respond to push and pull factors by chang-

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PPKP_2017_book.indbKP_2017_book.indb 8585 221/11/20171/11/2017 06:5206:52 Figure 2. Components constituting a BM

concerns on Value allows promote mantained with conﬁguration Relationships receive relies on value for

allow deliver Partner Core Value deliver Distribution to Target network competencies based proposition channels customers on

supports

Cost built on Revenue structure model

Source: Zott and Amit, 2013, in Arnold et al., 2016.

ing and adapting their BM. In summary, BM innovation relates to changes in the following decisions: what the firm’s offerings will be, when decisions are made, who makes them, and why. Successful changes along these dimensions improve the company’s combination of revenue, costs and risks.

2.1 Business models in relation to push and pull factors

According to Aversa et al. (2015), we can distinguish between four different types of business models (BMs); BM1: Internal knowledge transfer, BM2: External knowledge transfer, BM3: Supply, and BM4: Talent. BM1 refers to the exchange and transfer of knowledge that is created within the firm or the organization and serves as the ground for technological innovations. BM2 refers to the firms and organizations that influence other firms or organizations from the outside and can belong to the same industry or come from more distant areas but bring valuable knowledge that helps creating technological innovations that would otherwise be beyond the capabilities of an observed firm or organization. BM3 refers to the model which is focused on selling technological components to other firms (inside or outside the industry). BM4 refers to talent which can in some companies or organizations be a precious source of competitive advantage; moreover, several firms have implemented structured business models to scout, train and keep the best employees.

Those four types of BM serve as a base for determining which push and pull factors can be found in a respective BM (Table 1). Due to the complexity and interconnection of both, BM types and push and pull factors, this analysis is pro-

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PPKP_2017_book.indbKP_2017_book.indb 8686 221/11/20171/11/2017 06:5206:52 Table 1. Push and pull factors most frequently appearing in a respective business model type Business model type Common push factors Common pull factors Problem solving capabilities, increase productivity, Implementing new technology to higher turnover, maximizing efficiency, tackling maintain the desired market position, BM1 – Internal complex process opportunities, competitive competition’s performance. knowledge transfer advantage, better focus on goals, increased data security, better process insights, better management, creating new products, improved data analysis. BM2 – External Customer engagement, tackling complex process Demand for problem solving, legislation, knowledge transfer opportunities, better market penetration, creating potential untapped markets. new products. Maximizing efficiency, improved operational Inadequate satisfaction of customer’s costs, customer engagement, improved quality, needs, demand for problem solving, competitive advantage, improved outcomes, better competition’s performance, fluctuation of BM3 – Supply process insights better standardization of process cost of resources, customer’s demand and workflow, optimization, better market penetration, suggestions. understanding the market requirements, creating new products. Problem solving capabilities, less errors (higher Demand for problem solving, invent-to- BM4 – Talent accuracy), improved quality, competitive advantage, order, competition’s working conditions, increased data security, better management, creating labour legislation. new products, improved data analysis. Source: Own analysis and conceptualization based on characteristics of push/pull factors and BM types.

visional and illustrative and calls for additional confirmatory research. Nonethe- less, based on the logic describing each push and pull factor, and the characteristics of each BM type described in the previous paragraph, we have attempted to conceptualize the connections between specific push/pull factors and BM types.

2.2 Analysis of the main changes to corporate strategic management and business process management within Industry 4.0

Digitalization and big data science have already disrupted traditional business models (Weil and Woerner, 2015). Rising challenges and complexities that go along with technological development have an impact on corporate strategic management and business process management, since new processes have to be incorporated in order not to fall behind competition. Decision making related to specific changes in strategy formulation, business processes and organizational structure is becoming increasingly complicated as a result of the enormous number of alternatives and multiple conflicting goals (Tonelli et al., 2016).

Fundamental principles that need to be taken into account when developing or renewing a strategy due to push or pull factors related to changes in the

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PPKP_2017_book.indbKP_2017_book.indb 8787 221/11/20171/11/2017 06:5206:52 industrial landscape are the following: knowledge of the principles of system- oriented digitalized model of processes and process elements based on Industry 4.0; clear vision in according to the digitized operations, data delivery safety, data complexity, digital standards, norms and certificates; standardization of maintenance system for computerized and digitalized processes and operations (Chromjakova, 2017). Each type of business model that aims to address specific push or pull factors and engages in strategic renewal and subsequent changes in processes and structures should reflect those principles. With digitalization and big data science, traditional hierarchical work structures based on slow vertical communication and authority channels fall apart and give opportunity to new increasingly flexible in-house and networked structures (Zammuto et al., 2007). Complementing these changes in business processes and organizational structures, human resources (HR) are an important asset that can represent key success factors in BM renewal in response to Industry 4.0, because “/.../ an organization’s performance and competitiveness greatly depend on how its employees are managed.” In order to fulfill present and future market needs, companies will have to include development of competencies of their workforce in their strategies and address the identification of emerging challenges, the deduction of competencies to face those challenges, and the visualization of required competencies with the help of a suitable instrument (Hecklau et al., 2016).

Contrasting those key success factors related to business process adaptation, changes in organizational structure and developing key HR competencies to address industrial changes, there are also certain obstacles to strategic renewal and business model innovation that companies find hard to overcome. Experiences from several strategic orientation workshops with various companies have shown that companies have serious problems to grasp the overall idea of Industry 4.0 and particular concepts hereof. Companies are sometimes not able to relate the concepts of Industry 4.0 to their specific domain and their particular business strategy. Furthermore, they experience problems in determining their state-of- development with regard to the Industry 4.0 vision and therefore fail to identify concrete fields of action, programs and projects (Schumacher et al., 2016).

Schumacher et al. (2016) recommend that in order to overcome these obstacles, new methods and tools are needed to provide guidance and support to align business strategies and operations. They relate to understanding the nuances of strategic renewal, i.e. the process, content, and outcome of refreshment or replacement of attributes of an organization that have the potential to substantially affect its long-term prospects (Agarwal and Helfat, 2009). Schumacher et al. (2016) offer a maturity model and a specific tool useful to systematically as-

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PPKP_2017_book.indbKP_2017_book.indb 8888 221/11/20171/11/2017 06:5206:52 sess manufacturing companies’ state-of-development in relation to the Industry 4.0 vision. It defines nine dimensions and assigns 62 items to them for assessing Industry 4.0 maturity. Dimensions “Products”, “Customers”, “Operations” and “Technology” have been created to assess the basic enablers. Additionally, dimensions “Strategy”, “Leadership”, Governance, “Culture” and “People” allow for testing the organizational aspects and recognizing the current state of readiness the firms possess in order to successfully adapt their BMs and subsequent changes in strategy, business processes and organizational structure to proactively cope with changes related to Industry 4.0.

Conclusion

The key findings of this chapter show that in order to seize the full potential of industry 4.0 the companies will need to adapt business models to seize the moving value pools, build solid foundations for robotized and digital transformation and strive to higher operational effectiveness. After a thorough elaboration of push and pull factors for implementing new technology, we can say that factors are in a way interlaced. While some factors are clearly push or pull factors, there is some overlapping of the factors presented as well. The perspective by which it is looked at or observed can put the opposite light on the same matter or substance. Nevertheless, push factors are mainly associated with big equity firms that have highly developed research sectors, while pull factors are more represented by actions of the firms that are following market trends or firms that are constrained by new market regulations.

Therefore, companies and organizations are forced to find innovative ways to adapt their business models (BM) in order to take into account both push and pull factors and keep up with the rapid changes that happen inside and outside the firms and organizations on a daily basis in order to remain competitive in the market. We can distinguish between four different types of business models; BM1: Internal knowledge transfer, BM2: External knowledge transfer, BM3: Supply, and BM4: Talent (Aversa et al., 2015). Companies and organizations have to include the rapid changes that go along with the uprise of Industry 4.0 into their corporate strategies, which includes adapting their business models as well. In order to fulfill the present and future market needs, companies will also have to include development of competencies of their workforce into their strategies.

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PPKP_2017_book.indbKP_2017_book.indb 8989 221/11/20171/11/2017 06:5206:52 References Agarwal, R., and Helfat, C. E. 2009. “Strategic renewal of organizations.” Organization Sci- ence 20(2): 281-293. Arnold, C., Kiel, D., and Voigt, K. I. 2016. “How Industry 4.0 changes business models in different manufacturing industries.” Proceedings of ISPIM Conferences. URL: http:// eds.b.ebscohost.com.nukweb.nuk.uni-lj.si/eds/detail/detail?vid=2&sid=9942d254-12ff- 4686-9919-3a4e157cf754%40sessionmgr102&bdata=Jmxhbmc9c2wmc2l0ZT1lZHMtbGl 2ZQAN=117204958&db=edb. Aversa, P., Furnari, S., and Haefliger, S. 2015. “Business model configurations and performance: A qualitative comparative analysis in Formula One racing.” Industrial and Corporate Change 24(3): 655-676. URL: https://academic.oup.com/icc/article-lookup/doi/10.1093/ icc/dtv012. Baden-Fuller, C., and Haefliger, S. 2013. “Business Models and Technological Innovation.” Long Range Planning: 419-426. Brem, A. 2008. “The Boundaries of Innovation and Entrepreneurship. Conceptu- al Background and Essays on Selected Theoretical and Empirical Aspects.” Betrieb- swirtschaftlicher Verlag. URL: https://link-springer-com.nukweb.nuk.uni-lj.si/content/ pdf/10.1007%2F978-3-8349-9679-4.pdf. Brown, H. R., Roehrig, P., and Malhotra, V. 2015. “The Robot and I: How New Digital Tech- nologies Are Making Smart People and Businesses Smarter by Automating Rote Work.” Cognizant. URL: https://www.cognizant.com/whitepapers/the-robot-and-I-how-new- digital-technologies-are-making-smart-people-and-businesses-smarter-codex1193.pdf. Chesbrough, H. 2010. “Business model innovation: opportunities and barriers.” Long Range Planning 43(2): 354-363. Chromjakova, F. 2017. “Process stabilization – key assumption for implementation of Indus- try 4.0 concept in industrial company.” Journal of Systems Integration. URL: http://www. si-journal.org/index.php/JSI/article/viewFile/295/299. Corniani, M. 2008. “Push and Pull Policy in Market-Driven Management.” SYMPHONYA Emerging Issues in Management: 45-64. URL: ftp://ftp.repec.org/opt/ReDIF/RePEc/sym/ PDF/symjournl117.pdf. Geissbauer, R., Vedso, J., and Schrauf, S. 2016. “Industry 4.0: Building the digital enterprise.” PWC. URL: https://www.pwc.com/gx/en/industries/industries-4.0/landing-page/ industry-4.0-building-your-digital-enterprise-april-2016.pdf. Hecklau, F., Galeitzke, M., Flachs, S., and Kohl, H. 2016. “Holistic Approach for Human Resource Management in Industry 4.0.” Procedia CIRP. URL: http://www.sciencedirect.com.nukweb.nuk. uni-lj.si/science/article/pii/S2212827116308629?_rdoc=1&_fmt=high&_origin=gateway&_do canchor=&md5=b8429449ccfc9c30159a5f9aeaa92ffb. Landon, T. 2017. “At BlackRock, Machines Are Rising Over Managers to Pick Stocks.” New York Times. URL: https://www.nytimes.com/2017/03/28/business/dealbook/blackrock-actively- managed-funds-computer-models.html?mcubz=3. — 90 —

PPKP_2017_book.indbKP_2017_book.indb 9090 221/11/20171/11/2017 06:5206:52 Prangnel, N., and Wright, D. 2015. “The robots are coming.” Deloitte. URL: https://www2. deloitte.com/content/dam/Deloitte/uk/Documents/finance/deloitte-uk-finance-robots- are-coming.pdf. Schlaeper, R., Koch, M. 2014. “Industry 4.0: Challenges and solutions for the digital transformation and use of exponential technologies.” Deloitte. URL: https://www2.deloitte. com/content/dam/Deloitte/ch/Documents/manufacturing/ch-en-manufacturing-industry-4-0-24102014.pdf. Schumacher, A., Erol, S., and Sihn, W. 2016. “A Maturity Model for Assessing Industry 4.0 Readiness and Maturity of Manufacturing Enterprises.” Procedia CIRP. URL: http://www. sciencedirect.com.nukweb.nuk.uni-lj.si/science/article/pii/S2212827116307909?_rdoc=1&_ fmt=high&_origin=gateway&_docanchor=&md5=b8429449ccfc9c30159a5f9aeaa92ffb &ccp=y. Tonelli, F., Demartini, M., Loleo, A., and Testa, C. 2016. “A Novel Methodology for Manu- facturing Firms Value Modeling and Mapping to Improve Operational Performance in the Industry 4.0 Era.” Procedia CIRP: 122-127. URL: http://www.sciencedirect.com.nukweb.nuk. uni-lj.si/science/article/pii/S2212827116311751?_rdoc=1&_fmt=high&_origin=gateway&_ docanchor=&md5=b8429449ccfc9c30159a5f9aeaa92ffb. Weill, P., and Woerner, S. 2015. “Thriving in an increasingly digital ecosystem”. MIT Sloan Management Review 56(4): 27-34. URL: http://resolver.ebscohost.com.nukweb.nuk.uni-lj.si/ openurl?sid=EBSCO%3aedsglr&genre=article&issn=00094978&ISBN=&volume=52&issue= 6&date=20150201&spage=1025&pages=1025-1025&title=CHOICE%3a+Current+Reviews+f or+Academic+Libraries&atitle=Westerman%2c+George.+Leading+digital%3a+turning+tec hnology+into+business+transformation&aulast=Dantes%2c+A.&id=DOI%3a&site=ftf-live. Zammuto, R., Griffith, T., Majchrzak, A., Dougherty, D., and Faraj, S. 2007. “Informa- tion technology and the changing fabric of organization”. Organization Science 18(5): 749-762. URL: http://eds.a.ebscohost.com.nukweb.nuk.uni-lj.si/eds/pdfviewer/ pdfviewer?vid=3&sid=70f8be09-5f87-4c53-a266-7f9a9c2c9f98%40sessionmgr4010. Zhou, K., Liu T., and Zhou, L. 2015. “Industry 4.0: Towards Future Industrial Opportunities and Challenges.” FSKD. Zott, C., and Amit, R. 2013. “The business model: A theoretically anchored robust construct for strategic analysis.” Strategic Organization 11(4): 403-411.

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PPKP_2017_book.indbKP_2017_book.indb 9292 221/11/20171/11/2017 06:5206:52 Matjaž Koman, Tjaša Redek, Samo Knafelj, Nina Kovač, Jan Ratej

TPV GROUP

Introduction

The automotive industry has always been a field of most rapid transformations and fastest pace of technological development. Not surprisingly, this industry is where the very first industrial robot operated, in General Motors back in 1961 (Robotic Industries Association, 2017). In South-Eastern Slovenia, however, the first robot was used by a company called TPV Group (“TPV”), a global player in the automotive industry (V. I., 2017).

TPV is a development supplier in the automotive industry with an exemplary corporate practice concerning implementations of new technologies. Their programs are grouped in three business divisions: Vehicles, Trailers, and the core business division – AvtoIN. Currently, they have five production sites in Slovenia and one in Serbia and employ over 1,200 people (TPV Group, 2017). They were the first company to use a welding robot for assembling seats for Re- nault Five in 1992. Since then, robots have been a big part of their development process, with even greater plans prospected for the future. Company’s driving force, as well as its vision, is progress in innovation. Following the existing trends, they are shifting from robot users to robot suppliers as well, targeting a promising niche in the fast-growing automatization industry.

In this chapter, our aim is to present TPV as an example of good corporate practice on a global scale. Relying on theoretical foundations and benchmark- ing company’s activities towards competitors’, we analyze their state of robotization, automatization and digitalization, the core components of Industrial Revolution 4.0.

Following the introduction, a general description of the studied company is provided, with a focus on their core business’ characteristics. The implementation of robotization and digitalization is presented, followed by a study of main

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PPKP_2017_book.indbKP_2017_book.indb 9393 221/11/20171/11/2017 06:5206:52 Figure 1. TPV Breakdown

Source: TPV Group, 2017.

drivers and obstacles for the new technologies implementation. Next, future plans are studied. The chapter concludes with recommendations for similar companies.

1 Company description

TPV was established in 1989 as part of the former IMV (“Industrija motornih vozil”), which was broken into Revoz, Adria Caravan, TPV and TADO. The name originates from their initial specialization in military jeep development, even though the vehicle was never constructed due to the downfall of Yugosla- via. At that time, the expectations for the company were grim. However, the management was ambitious and employees did not lack the energy, knowledge or drive for further development. From then on, they have been reaching development milestones every year. They have set the goal of having their components installed in all major-brand cars on the market. The goal was achieved a few years ago (TPV, 2017a).

1.1 Product programs

TPV’s product programs are grouped in three business divisions: TPV Avto – Vehicles division, TPV Šumadija – AvtoIN division and TPV Prikolice – Trail-

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PPKP_2017_book.indbKP_2017_book.indb 9494 221/11/20171/11/2017 06:5206:52 Figure 2. Revenue and EBITDA for AvtoIN and TPV in ten million euro for years 2006-2016 AvtoIN Revenue TPV Revenue AvtoIN EBITDA TPV EBITDA

6 x 10000000 4

-2 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Source: TPV, 2017a.

ers division (Figure 1). The Vehicles division deals with official car dealership for Renault, Nissan and Dacia, the AvtoIN division produces car part components and TPV Trailers develops and produces light transport trailers. TPV is a sole owner of TPV Vehicles and AvtoIN. TPV Trailers is partially owned by Böckmann (Germany, 49 percent) (TPV, 2017a).

1.2 Corporate performance

In 2015, gross added value per employee was 32 thousand euro and 35.8 thousand euro in 2016, with a five percent growth forecasted for 2017. In 2016, the revenues amounted to 139.86 million euro, exceeding the previous year’s result by approximately ten million euro. Due to the promising trends in the automotive industry, they forecast their revenues to experience a five percent growth in the next business year (TPV, 2017a). In 2016, profit was 3.3 million euro and 27.90 million EBITDA (Figure 2).

The core business division AvtoIN successfully recovered after the crisis in 2008 and ended the year 2016 with little bellow 1.3 million euro in profit i.e. 20.90 million EBITDA (Figure 2).

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PPKP_2017_book.indbKP_2017_book.indb 9595 221/11/20171/11/2017 06:5206:52 1.3 Core business – AvtoIN

The company finds their core business within the AvtoIN division, where they devise, develop and produce body and chassis components, such as: door latch system, door hinges, manual transmission shift levers, etc. Part of their product portfolio are also seat components like seat frames, back, head and arm- rest metal structures, height adjuster components and many more. In addition, engine gaskets for the needs of OEMs (original equipment manufacturers) and leading system suppliers are produced. Just recently, a new pillar of automated guided vehicles was added to the core business division (TPV, 2017b).

Their products are installed in most of the luxury automotive brands: Rolls Royce, Ferrari, Jaguar, BMW, Mercedes, Audi and Volvo, as well as middle class brands like Renault, Volkswagen, Nissan, Citroën, Fiat, Opel, Toyota and Kia. Their main customers are either the aforementioned car manufacturers directly, or they can be large chassis and seat suppliers like Brose, Benteler, Faurecia, Mahle (TPV, 2017b). Depending on the nature of the project, they act as both, direct/development supplier or predevelopment supplier, however, they endeavor to be the former.

The company, initially part of IMV, sold primarily to Yugoslav markets but invested significant efforts in the shift towards European and global markets. Today, their core markets are Europe, the USA and China, with business with Mexico, Brazil, Argentina, South Africa, Turkey, etc. increasing. The company is much better known and recognized for their qualities in global markets than at home, with the USA forecasted to be the main growing market of the future (TPV Group, 2017).

Their main competitors are companies like Adient in Novo mesto and Daim- ler’s Starkom in Maribor. They share similarities in the product structure, and, ultimately, both are part of TPV`s customers-competitors list (Adient, 2017 and Starkom, 2017). Out of the two, Starkom represents a higher concern; their revenues grew from 18 to 65 million euro in the last half of the decade and are expected to grow further (Starkom, 2017). On the other hand, the biggest global players in the automotive industry like Benteler and Faurecia are their rivals as well. However, those companies are much bigger; Benteler, e.g., created 7.4 billion euro in revenues and employed 28 thousand employees in the last year, outranking also in terms of financial performance (Benteler, 2017).

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PPKP_2017_book.indbKP_2017_book.indb 9696 221/11/20171/11/2017 06:5206:52 TPV’s success so far was based on their competitive advantages - knowledge and innovative solutions for product-specific problems that companies face. Specifically, they specialize in weight reduction and innovative design. The competitive disadvantages, however, were found in the size of the company, fewer references, lack of global production locations and weaker selling channels (TPV, 2017a and TPV, 2017b).

2 Implementation of Industry 4.0 technologies

2.1 Robotization

The first robot to join the company, Iskra welding robot, was used already in 1992. After the initial implementation of welding robots, TPV has gone through three generations of automated production lines:

The first generation included only the “feeder system” responsible for the transfer of material between different operations. The installation of such system was usually done manually, which took between eight and 16 hours. Such systems were used before the year 2000.

Next came the servo robots which were working on operations that were programmable. Their installation was almost fully automated, except for some final parameters that had to be set manually. Production had to wait for about one hour for the installation to be completed.

The last generation of automatization in the company remains to the present date.

There are two automated lines, each including 13 robots for all kinds of operations: welding, screwing, transporting, controlling, etc. Only a few operations still need to be done manually.

Today, all of the production lines are at least partially automated. The company is trying to mechanize the physically demanding operations to achieve the best results and remain competitive in their highly-demanding industry. From 1992, when the first Yaskawa robot was installed, to 2000, the number of robots increased to eight. By 2010, there were 29 (Figure 3). In that time, robots were used primarily to replace human labor in processes, either ergonomically harmful or volume/deadline demanding. A typical example is serial production

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PPKP_2017_book.indbKP_2017_book.indb 9797 221/11/20171/11/2017 06:5206:52 Figure 3. Number of robots in TPV in years 1992, 2000, 2010 and 2017 100 90 90 80 70 60 50 40 30 29 20 10 8 1 0 1992 2000 2010 2017

Source: TPV, 2017b.

(in millions of units) with high human error probability. Since then, the usage of robots in the workplace continued and developed in different ways; from welding, weld control, element cooling and much more. In comparison, robots in the competitors’ manufacturing halls are in different stages of development – from performing holistic tasks like quality control in Adient, to only one-stop operations, such as welding in Starkom.

TPV has established long-term contracts with three different robot suppliers: Yaskawa, ABB and Kuka. TPV provides their robot suppliers with physical requirements regarding the robots; the programming of applications is later done in-house. The final integration of the new robots into manufacturing processes is completed in collaboration with the supplier’s engineers (TPV, 2017b). A numerical proof of TPV’s success in robotization is a minimal scrap percentage of 0.2, whereas a qualitative one is the diversity of the developed robot applications. For example, in collaboration with their subcontractors, they have developed a smart package-assembly machine and an automated welding machine, both fully functional in their daily operations (TPV, 2017a).

However, TPV has taken a step forward in robotization. They have connected their robots in a collaborative system and are working hard on its full implementation in the future. In such a system, each robot works in a robotic cell and groups of such cells need to work consistently. To achieve that, demanding software specifications have already been developed. While TPV is hoping to finalize the implementation of collaborative robots in the next five years, their

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PPKP_2017_book.indbKP_2017_book.indb 9898 221/11/20171/11/2017 06:5206:52 competitor Benteler already had them in operation in 2016. Benteler’s robots are allowed to drive at full speed of 750 mm/s with no fences, a highly-demanded technological advancement on the market (Benteler, 2017).

TPV’s empire of robots is expanding with a goal of “one robot per each employee”. Besides the obvious financial demands, they are experiencing some problems with their software tools. Software codes were developed a long time ago, with a limited ability of further updates. Current requirements for add-ins are very complicated and cannot be connected with basic software code bases. To solve that, TPV has thought of changing the basic software completely, but could not execute that due to too many and too complicated connections of the software network (TPV, 2017b).

2.2 Digitalization

Digitalization facilitates better and simpler production control. Therefore, the company aims at combining automatization with full digitalization. Digi- talization is present in control and tracking; barcodes and scanners are used to provide all required documentation in production. Each barcode contains a link to product documents and specifications, where an employee is redirected after scanning the barcode. Digitalized system is also used as a transparent checklist for marking the work outcomes – units completed, test reports, faulty products, etc. By using such devices connected in a system, TPV builds an extremely powerful database for internal and external statistical purposes.

Another very important product of digitalization is a digital supply chain. In relation to the worldwide guidelines for digital supply chain, TPV is on the right path. The first and most important is understanding the problem and setting the strategy. Then the appropriate map with warehouse management system (WMS) and manufacturing equipment system (MES) is developed. That brings a company to the final step of developing and controlling the impact (Schrauf, 2017). Currently, TPV fully exercises the benefits of the digital supply chain, together with the inventory-tracking. They are using both, the WMS and MES to get a real-time insight into the manufacturing processes, as well as the inventories. That allows them to eliminate any unnecessary buffers in the process related to the lack of resources of any kind.

Another component of the digitalization is the electronic data interchange (EDI), which allows one company to send information to another company

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PPKP_2017_book.indbKP_2017_book.indb 9999 221/11/20171/11/2017 06:5206:52 electronically rather than on paper (Union Pacific, 2017). Adient is an example of a company that holds full automatization of the EDI (Adient, 2017).

TPV practices automated receiving and processing of customers’ orders, issuing electronic delivery notes and invoices in the frame of EDI. Last but not least, the company’s current digitalization system also includes a business intelligence system (BI) and Intranet for internal use.

3 Motivation for automatization, digitalization and robotization

Agile supply chain, ability of self-monitoring, capacity for customization and network flexibility are the most important benefits of Industry 4.0 in automotive industry (Masters, 2017). TPV is strongly relying on the first three, but could increase the use of the last one.

TPV had been continuously implementing different elements in the area of automatization, digitalization and robotization long before the trend took its current pace. That came as a consequence of different motivational factors, which fall into one of the two systems – push or pull (Kirkwood, 2009).

The company’s main push factor for automatization, robotization and digitalization is their corporate value of constant improvement through innovation. The biggest pull factor is the demanding price war they need to keep up with to remain competitive in the global market. In line with the Business model types (BM) from Chapter 5, TPV fits best within the BM3 - model which is focused on selling technological components to other firms. Push and pull factors of TPV are in line with the common ones of the BM3.

3.1 Push factors and internal obstacles

The push system is a schedule-driven system. JIT (Just in Time) delivery as a part of the push concept uses a centralized approach designed to calculate exactly what is needed. Complex parts like seats, bumper systems or front and rear axles require late configuration and sequential delivery to OEM (Original Equipment Manufacturer) plants (Christopher, 1998). The position of TPV as a supplier of the aforementioned parts implicates that TPV is part of the so- called push system.

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PPKP_2017_book.indbKP_2017_book.indb 100100 221/11/20171/11/2017 06:5206:52 Innovation in TPV comes as a consequence of primarily internal push factors. They are eagerly ambitious with innovation being one of their core corporate values. The company has a strong innovative internal culture embedded in their roots and internal processes organized specifically to enhance internal innovation. TPV fosters innovation in all hierarchical levels for all areas of implementation. The employees are motivated to submit ideas for improvement by TPV’s reward system – every year they hold a corporate event where the best innovations are announced and rewarded. In addition to such internal innovation system, they outsource innovation to their strong collaboration network of suppliers, customers and start-up companies (TPV, 2017b).

There are several tough internal obstacles that need to be dealt with in order for the development to continue. Surprisingly, they are not financial in nature, since returns on investment of such implementations have been very high. The main internal obstacle is high specificity of the equipment together with high complexity of processes and products. Also, the current state of robotics is known to be fairly user-unfriendly, which needs to be improved before robots can be more broadly used in the company.

TPV or any company trying to exercise robotization has to overcome an escalating social issue – a commonly shared public perspective that robots are killing jobs for humans. Some experts say that by 2045, machines will be able to perform a significant fraction of the work a man can do. Therefore, the question that the public asks itself is “If machines are capable of doing almost any work humans can do, what will humans do?” (Connor, 2016).

TPV is working hard to battle the idea of robots being a competition to humans or a substitute for human labor. They present robots as a relief of physically and mentally burdening jobs and a way to create more pleasant workplaces. The main point that the management is trying to bring forward is “Without robots, there is no jobs for humans”. They can support the claim by the increase in the number of employees altogether in the period when the majority of robots was implemented.

From 2010 to the current date, TPV has added over 60 robots within its Av- toIN division. Outside the common variations in the number of employees, the employee base in the division has increased by around 140 people in the same period (Figure 4).

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PPKP_2017_book.indbKP_2017_book.indb 101101 221/11/20171/11/2017 06:5206:52 Figure 4. The numb er of AvtoIN employees from 2011 to 2016 800 710 734 722 700 680 600 584 525 500 400 300 200 100 0 2011 2012 2013 2014 2015 2016 Source: TPV, 2017a.

Also, the financial data shows that TPV created 357,620 euro in revenue per employee in the year 2009 with the number increasing to 808,682 euro in 2016, as the process of robotization continued (TPV, 2017a). For all of the stated reasons, plant workers of TPV do not feel endangered by the increasing number of robots implemented but see them as assistants and an essential part of a brighter future in the labor market (TPV, 2017b).

3.2 Pull factors and external obstacles

In contrary to the push system, theory identifies pull logistics as a demand- driven system. A typical pull system is the Kanban system. Kanban uses a decentralized statistically-based approach to control internal and external supply. The aim is to produce no more than the required quantity (Christopher, 1998). A pull-driven demand comes from sales and marketing operations and is then translated into operating strategy.

With the rise of China and other fast-developing technology-oriented economies, a Slovenian company would lose its competitiveness in the market instantly, had they not found a way to keep up with the lowering prices. Whoever is able to lower the market price the most, wins. And the only way to achieve that is through the use of technology.

Specific external i.e. pull factors in automotive industry and TPV are cost- efficiency, short delivery times and minimum level of faulty products. All of that is achieved through minimizing scrap and human error, fewer buffers in the system and shorter production times. There are several other pull factors

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PPKP_2017_book.indbKP_2017_book.indb 102102 221/11/20171/11/2017 06:5206:52 that TPV mentions, such as flexibility of the processes that allows quick adaptation to customers’ needs and responsiveness to the market, precision, speed and simplicity of the processes (TPV, 2017b).

The main external obstacle is a lack of qualified personnel that would continue the technological development. In one of his interviews, Mr. Savsek, the assistant manager of TPV, stresses, “We find it very difficult to find prospective technical staff, such as engineers, technicians, operators, etc. The problem is they either lack the appropriate education and skills or working experience. This issue is not prospected to resolve anytime soon” (Lokar, 2012).

Besides the pressing human resource management issue, which is further elaborated in the next chapter, there is also a more general external problem; a sufficient level of standardization is needed for the implementation of Indus- try 4.0 components. It poses a real challenge for the players in the automotive industry to standardize processes enough because the customers’ requirements are high in volume and complexity (TPV, 2017b).

4 Future plans

Innovation is the driving force of progress in TPV and one of the core values of the company. They are aware of the exceptional importance of automatization and digitalization of business processes. Therefore, they actively introduce new technological and business solutions into their operations.

4.1 General strategy elements related to Industry 4.0

In the future, they will be focused on two key pillars; internal innovation processes and global technology trends. They will implement an innovation platform allowing open innovation, internal and external flow of ideas and philosophy of lean operations. In order to triumph on a global market, they will pursue research efforts and use of lightweight materials. They plan to further develop the area of automatization and even more intensively join the trend of digitalization. Currently, they invest five percent of their yearly revenue for the purpose of innovation with the percentage forecasted to increase in the future (TPV, 2017a). Starkom, to compare, invested what represented almost ten percent of their 2016 revenues for new production lines’ innovations (Starkom, 2017).

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PPKP_2017_book.indbKP_2017_book.indb 103103 221/11/20171/11/2017 06:5206:52 Generally, the plan is to have one robot per one employee. Specifically, the directions that they want their robots to develop in are: • Bin picking – currently being developed: picking and sorting pieces based on their characteristics from a scattered state; • Machine sight: used for bin picking and other operations – enables the machine to recognize 3D characteristics much like the human eye, which leads to a fully autonomous performance of such a machine; • Collaborative robots – already being used in serial production

Within the robot or any other new technology development, they stick to the mentality that the safety of customers should never be endangered. Called “the wall of quality”, superior quality, on-time delivery, etc. must always be met before new technologies are implemented and widely used. Therefore, a pilot project is usually performed first and only then followed by the final manufacturing and development.

Future digitalization plans include real-time insight into their geographically-dispersed business units, warehouses, manufacturing facilities, sales, purchasing, etc. In order to be more interconnected, they also want the technology to provide level-appropriate reporting content, useful for strategic planning.

4.2 Automated Guided Vehicle (AGV)

TPV as a very ambitious company always seeks for blue oceans of innovation and niche markets for their ideas. With such a corporate philosophy, they have developed a promising technology for the automatization of internal logistics. They are now striving to position themselves as a supplier of the so-called automated guided vehicles (AGVs).

AGV, an autonomous robot, is navigated by magnetic tape and RFID (radio- frequency identification) technology. It is used for transporting carts from point to point on demand. Its main distinctive factor is its dimensions because it fits under the most common forms of transportation packaging: pallets, gitter boxes or carts. That eliminates any need for modifications, saving the customers a lot of time and finances. Further, the carefully thought-off dimensions allow the wheels of the carts to rotate without problems, making turning and re-routing an easy task.

AGV is a complete, effective, simple and robust internal logistics solution, meaning the purchase includes hardware, software and the transfer of know-how

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PPKP_2017_book.indbKP_2017_book.indb 104104 221/11/20171/11/2017 06:5206:52 to the customer. After TPV’s experts make a thorough analysis, calculations and a presentation of the most appropriate plan for logistics automatization, they also teach customers everything they need to know about usage, modification and servicing.

The robots are adapted to customers’ needs and their level of automation – either: • full level of automation with Central Control System connecting AGV commands with existing WMS (Warehouse Management System), MES (Manufacturing Equipment System), SAP (Systems, Applications and Products), and complete peripheries, • supporting PLC (Professional Learning Community) or • just simple solutions driving from point A to B.

A great variety of programming commands like stop, release, rotate, turn, accelerate, brake, etc., is linked to a specific tag, directing the vehicle. The whole system is guided by CCS (Central Control System), which receives commands from the employees and forwards them to the available AGV using Wi-Fi or cable. CCS is also responsible for traffic management, charging of the batteries, maintenance, and communication with the operators. It comes in six models but follows one of the three core systems: tunnel, tug and conveyer.

Much like how TPV has automatized and robotized their internal logistics, so have Benteler and Adient. Starkom, however, still uses normal forklift trucks (Starkom, 2017). On the other side, Kolektor, one of the biggest domestic players, managed to organize the production lines in the following way: internal logistics is executed with conveyors between robots and only truck loading operations are carried out by normal forklifts (Kolektor Group, 2017).

The internal logistics automatization department in TPV as a part of the AvtoIN division is expected to grow in the future together with the percentage of dedicated expenses to that source. The department is forecasted to deliver two million in revenue in the next year and ten million by 2020 (TPV, 2017b).

4.3 TPV Smart Factory

In April 2017, TPV started constructing a new smart factory that is expected to be finished by 2020. It should be an environment where machinery and equipment are able to improve processes through automation and self-optimization.

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PPKP_2017_book.indbKP_2017_book.indb 105105 221/11/20171/11/2017 06:5206:52 The benefits also extend beyond just the physical production of goods and into functions like planning, supply chain logistics and even product development (Otto, 2017). TPV is leaning more into the direction of focusing on supply chain logistics and automatization, leaving self-optimization and automated product development open for further development.

The total estimated amount of their investment in the period from 2017 to 2020 is 20.3 million euro. The investment will include exterior arrangement, construction of a new building, advanced technology production lines, production management system, system of automated internal logistics, digitalized storage system, and system of digital interaction with suppliers and customers.

In the frame of this new investment, 110 new jobs of different educational levels will be created. The process of digitalization and automatization will help TPV reduce their production costs, increase productivity and manufacturing accuracy, answering the aforementioned pull factors perfectly (TPV, 2017a).

5 Recommendations and conclusion

Car-sharing, responsible for the decreasing number of cars worldwide, imposes a threat on all the players in the automotive industry. In TPV, they recognize the problem but emphasize that there are a lot of underdeveloped markets, where the number of cars needs to increase before it can start falling. We believe their main focus should be on further development of robotization, especially bin-picking and collaborative robots, which are already in use in other competitive companies. That would enable the company to further compete on new emerging markets. Electrification and other transformations in fuel types should not pose a problem for TPV’s components, since these are independent on the type of fuel.

There is a desire and an expectation that the trend of robotization in the future will evolve around a more user-friendly experience. That will result in lower-educated people being able to work in the field of automatization and robotics. The company should focus on attracting such people through collaboration with educational institutions. A longer-term cooperation with students from technical schools will bring better correspondence between labor supply and demand. It would be very welcome to get some incentives from the government as well.

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PPKP_2017_book.indbKP_2017_book.indb 106106 221/11/20171/11/2017 06:5206:52 In conclusion, the company is succeeding in keeping its step in time with the global trend-setters and should keep trying to stay on track with the pace of technological development in the future. Automated guided vehicle is definitely a step in the right way but collaborative robots should become part of their production as soon as possible as well. That would help them achieve better results also in operations that are still limited to human labor.

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PPKP_2017_book.indbKP_2017_book.indb 107107 221/11/20171/11/2017 06:5206:52 References Adient. 2017. “Annual report 2016.” URL: http://investors.adient.com/financial-information/ annual-reports. Anderson, J. Q. 2015. “The Future of Work? The Robot Takeover is Already Here.” Medium Corporation. URL: https://medium.com/@jannaq/the-robot-takeover-is-already-here- 5aa55e1d136a. Benteler. 2017. “Annual report 2016.” URL: https://www.benteler.com/fileadmin/corporate/ Group/Anual_Reports/2016/BENTELER_GB16_EN.pdf. Christopher, M. 1998. “Logistics and Supply Chain Management.” Prentice Hall, Harlow. Connor, S. 2016. “Robots ‘will make majority of humans unemployed within 30 years’.” Independent UK. URL: http://www.independent.co.uk/life-style/gadgets-and-tech/news/ robots-will-make-majority-of-humans-unemployed-within-30-years-a6872486.html. FANUC. 2017. “Benteler Automotive in Schwandorf, Germany is using the collaborative Robot CR-35iA in an automated movement, without safety fences, and at maximum speed.” URL: http://www.fanuc.eu/de/en/customer-cases/benteler. Faurecia. 2017. “Registration document 2016.” URL: http://www.faurecia.com/files/media/ site_com_corporate/AMF/AG-2017/faurecia_ddr_2016_veng.pdf. Gattorna, J. L. 1998. “Strategic Supply Chain Alignment.” Gower, Aldershot. Kirkwood, J. 2009. “Motivational factors in a push-pull theory of entrepreneurship.” Gender in Management: An International Journal 24(5): 346-364. URL: https://doi. org/10.1108/17542410910968805. Klug, F. 2006. “Synchronized Automotive Logistics: an optimal mix of pull and push principles in automotive supply networks.” Munich University of Applied Sciences, Germany. URL: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.464.2233&rep=rep1&type=pdf. Kolektor Group. 2017. Personal communication, September 19, 2017. Lokar, S. 2012. “Nujne potrebe po inženirjih in kvalificiranih poklicih.” Dnevnik. URL: https:// www.dnevnik.si/1042540247. Masters, K. 2017. “The Impact of Industry 4.0 on the Automotive Industry.” Flexis. URL: https://blog.flexis.com/the-impact-of-industry-4.0-on-the-automotive-industry. Otto, M. 2017. “What is the smart factory and its impact on manufacturing?” URL: https:// www.ottomotors.com/blog/what-is-the-smart-factory-manufacturing. Robotic Industries Association. 2017. “The History of Robotics in the Automotive Industry.” URL: https://www.robotics.org/blog-article.cfm/The-History-of-Robotics-in-the-Automo- tive-Industry/24. Scemama, S., Harbour, R. 2017. “Surprise: Robots Aren’t Replacing Humans In Key Areas Of Manufacturing.” Forbes. URL: https://www.forbes.com/sites/oliverwyman/2017/02/03/ surprise-the-correct-answer-is-not-always-to-go-with-the-robot-just-ask-some- automakers/#2a62a310120a. — 108 —

PPKP_2017_book.indbKP_2017_book.indb 108108 221/11/20171/11/2017 06:5206:52 Schrauf, S. 2017. “Industry 4.0: The Five Steps Towards a Digital Supply Chain.” Forbes. URL: https://www.forbes.com/sites/strategyand/2017/03/21/industry-4-0-the-five-steps- towards-a-digital-supply-chain/#32f49eac5287. Singh, S. 2016. “The Five Pillars of Digitalization That Will Transform The Automotive Indus- try.” Forbes. URL: https://www.forbes.com/sites/sarwantsingh/2016/10/19/the-five-pillars- of-digitisation-that-will-transform-the-automotive-industry/#7901069018f2. Starkom. 2017. “Annual report 2016.” URL: http://www.starcomsystems.com/investors/ company-regulatory-documents. TPV. 2017a. “Annual report 2016.” Acquired from the company representatives. TPV. 2017b. Personal communication, August 28, 2017. TPV Group. 2017. “Company profile.” URL: http://www.tpv.si/en/media-center/publications/. Union Pacific. 2017. “What is EDI?” URL: https://www.up.com/suppliers/order_inv/edi/ what_is_edi/. V., I. 2017. “Največ robotov je med Dolenjci.” Dolenjski list. URL: http://www.dolenjskilist. si/2017/04/13/174064/novice/dolenjska/Najvec_robotov_je_med_Dolenjci/.

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PPKP_2017_book.indbKP_2017_book.indb 110110 221/11/20171/11/2017 06:5206:52 Tomaž Čater, Miha Dominko, Domen Gulič, Simon Pangeršič, Rok Štemberger

DOMEL

Introduction

Automatization, robotization and digitalization are the core concepts of Indus- try 4.0. In this chapter we analyze how company Domel is adapting to the new technological changes that Industry 4.0 is bringing. The chapter is organized as follows. First, we briefly describe company Domel. Next, we present their process and technological advances in the area of digitalization and robotization. In Section Three, we discuss Domel’s motives for making technological advances. Finally, we conclude.

This study case is based on the interviews with Domel’s employees, especially with the Managing Director of Domel, the Director of Research and Develop- ment, and the Director of General Affairs. Secondary sources were used as well.

1 About Domel

Domel is a closed business group that is mostly owned by its current and retired employees and its shares are not listed on a stock exchange. The organizational structure of the Domel Group is shown in Figure 1 (Domel, 2017a).

The company Domel is operating in the industry which is classified as C27.1. Its main activities are manufacturing of electric motors, vacuum cleaner units, universal collector engines, DC-motors and electronically commutated motors. Furthermore, Domel is also present in automotive programs, gardening equipment, air handling units, laboratory equipment and hydrogen technology. It is specialized in producing highly energy-efficient electric motors that can be customized for each individual company’s requirements (Domel, 2017b; Domel Group, 2016).

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PPKP_2017_book.indbKP_2017_book.indb 111111 221/11/20171/11/2017 06:5206:52 Figure 1: Organizational structure of the Domel Group

Domel Holding, d.d.

Domel IP, d.o.o. Domel, d.o.o. Domel Energija, d.o.o.

Domel Electric Motors Domel elektronika, d.o.o. Domel Inc. Suzhou Co. Ltd.

Business unit Business unit Domel Tehnologije, d.o.o. Laboratory Systems Vacuum cleaner Motors

Business unit Business unit DH Ventilatorji, d.o.o. EC Systems Automotive program

Business unit Business unit Domel RR, d.o.o. Motors Components and Tools

Source: Domel, 2017a.

Domel’s products are not placed individually on the market under their own brand but are rather installed into the end products of other manufacturing companies. They have many business partnerships, some already lasting for decades. Domel is present in many markets, however their biggest market in 2016 was Eu- rope (65 percent market share), more specifically Germany 22 percent (indirectly more than 50 percent), Romania ten percent, Hungary nine percent, Slovenia eight percent, and Italy seven percent (Domel, 2017b; Domel Group, 2016).

In Table 1 you can see the revenues, profit and the number of employees in Domel in the past four years. As can be seen, the revenues and profit were steadily increasing throughout the years. The number of employees was growing each year as well.

2 Domel’s diagnostic system and digitalization

Domel began with the digitalization of its production and control processes in 2004. The main goal was to enable flexible production by introducing self- tuning features, real-time detection of deviations, product and production tracking, and easy access to historical data (Debenjak et al., 2017).

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PPKP_2017_book.indbKP_2017_book.indb 112112 221/11/20171/11/2017 06:5206:52 Table 1. Key numbers about Domel 2013 2014 2015 2016 Revenues (€) 87,450,701 97,293,143 111,126,407 127,659,451 Profit (€) 2,637,580 5,525,835 7,907,383 10,141,196 Number of employees 986 1,018 1,023 1,149 Source: Domel Group, 2016. In the remaining of this section we describe the process of Domel’s diagnostic system, with the purpose of showing in which stage of digitalization Domel is right now. The presented system of production analytics integrates information from various production steps and processes. The system is designed by taking into account the concepts of enterprise application integration. The communication backbone of the system is an enterprise service which handles all the data transfer among the modules and agents involved. The sources of information are components of the production process and the enterprise resource planning system. The processing is performed by agents that are implemented within the company’s on-premises cloud. The implemented modules can enable commis- sioning and versioning of testing protocols, self-tuning of quality limits, statistical analysis of production data, alarm generation and processing, production tracking, and report generation. The system includes several Domel production lines at three geographical locations, two of which are in Slovenia and one in China (Debenjak et al., 2017). Furthermore, Domel is seeking new possibilities to connect with what would provide value added to Domel’s processes.

Domel, in collaboration with the Jožef Stefan Institute, has taken a step forward in trying to make an algorithm for the characterization of an individual engine. Their assembly of motors runs on a pallet system which records data and information through different processes with the help from the integral memo- rial unit. The data from the pallet system and from the diagnostic system are accumulated at the end of each production line in the central database and are bound to the serial number of the motor. Based on the engine characteristics, the computer algorithm determines the engine status at the control diagnostic stations. While the diagnostic system gathers and analyzes data, it also assesses whether the item fulfils the quality requirements. These are predefined by development, quality assurance or the assembly line regulator. In this regard, the system can be considered as “pre-Industry 4.0” but with new upgrades to the diagnostic system on their advanced industrial blowers. This enables communication with the central database and brings Domel one step closer towards Industry 4.0 and digitalization (Debenjak et al., 2017).

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PPKP_2017_book.indbKP_2017_book.indb 113113 221/11/20171/11/2017 06:5206:52 Domel has started to invest in the field of electronically commutated motors and blowers in order to establish some general conditions for the installation of the Internet of Things (IoT) into their products. Simultaneously, they are developing sensors that will process data from the motors through certain algorithms. The sensors will then send the data to the central database via the internet. By using a predictive algorithm, the motor will be able to notify the end user and enable advanced maintenance planning based on the received data. Cloud-based data will enable Domel to have constant access to data on the motors and thus predict different states of motors in advance (Debenjak et al., 2017).

The advantage of this solution lies in the unlimited and simple scalability and easy usage of the already established solutions (agents and applications) for the new production processes. The major challenges ahead represent interaction and aggregation of multiple variables of technological stations within the assembly process and variables of the diagnostic control device (Debenjak et al., 2017). With such knowledge, we can proceed to analyze the motives for automatization, robotization and digitalization from the perspective of push and pull factors.

3 Analysis of the motives for automatization, robotization and digitalization

In this section we analyze the motives that drive companies towards greater automatization, robotization and digitalization of their processes, with an emphasis on Domel. They have already started implementing changes in this area in order to stay competitive in the market and reap the benefits of it. That came as a consequence of different motivational factors, which fall into one of the two categories – push or pull (Kirkwood, 2009).

Matjaž Čemažar, the Director of Research and Development of Domel d.o.o., and five other employees in relevant positions at Domel were asked to evaluate the importance of pull and push factors from one to ten, ten being the most important. Based on each individual assessment of every factor, we have derived the averages and classified them by their importance in Table 2. With this we have tried to analyze the factors for the implementation of digitalization in Domel.

The company’s main pull factor for automatization, robotization and digitalization is increased quality and reliability (9.0), and the biggest push factor is additional revenues and increased productivity (8.8). Based on the observed data we believe that Domel is more motivated by pull factors than push factors, since the

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PPKP_2017_book.indbKP_2017_book.indb 114114 221/11/20171/11/2017 06:5206:52 Table 2. Evaluation and categorization of motives Average Categorization Motives for digitalization in Domel evaluation (Push or Pull) Increased quality and reliability 9.0 Pull Lack of suitable workforce 8.8 Pull Additional revenues and increased productivity 8.8 Push Competition 8.3 Pull Better horizontal and vertical integration 8.3 Push Reduced costs 8.2 Pull Strengthening structural/organizational competences 7.8 Push Strengthening technological competences 7.2 Push New potential markets 5.2 Push Development facilitation by the government and other subventions 3.8 Push Source: Domel, 2017b.

average of pull factors (8.6) is greater than the average of push factors (6.9). We therefore first analyze the pull motives and then continue with the push motives.

3.1 Pull motives

Increased quality and reliability. Having competed in the market with high quality products, it is not surprising that increased quality and reliability is the most important factor for Domel to embark on the digital transition. Domel is known for a long tradition of providing the highest quality products and services. This focus is also demonstrated by its quality management systems certificates. Having their processes automatized and supported by technologically efficient high-end machines also provides less space for human mistakes and consequently bad quality of their products. They offer greater reliability due to automatization of the transfer and processing of the information, data and report making (SAP) (Domel, 2017b). Efficient interaction between all automation components means requirements for optimized processes to enable shortening of the time to market and improvement in quality (Siemens, 2014). At Domel, they also respect the strictest international quality and environmental standards (Domel Group, 2016).

Domel has more problems each year in obtaining suitable workforce. Therefore, lack of skilled workforce is the second most important pull factor, since processes and operations within the company require advanced knowledge from their employees.

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PPKP_2017_book.indbKP_2017_book.indb 115115 221/11/20171/11/2017 06:5206:52 Also, new companies are emerging in the area of Železniki. Since 2008, there has been more than 100 new companies founded in this area (SURS, 2017). Companies like Domel are therefore usually forced to start thinking about digitalization due to the lack of skilled labour that needs to be replaced by robots. While digitalization cannot be implemented throughout the production due to its high cost, alternatives should take place. That is why caring for education and training of their employees is a constant. They enable their employees to study while working in order to improve the educational structure of the company (e.g. in 2016, 61 of their employees were enrolled in a study program). Many employees also attend seminars, courses and workshops. In the previous year, an average of 30 hours was spent on internal and external professional education per employee. Domel also grants scholarships for education in regular programs as well as offers the possibility of practical training (practice) that is mandatory in many high school and faculty programs (Domel Group, 2016).

Domel is facing a stronger competition also in the produkt market. The competition in the product market is connected with the quality of the products. Domel can only compete by quality and not by price. Their margin is five to eight percent, which is low compared to some other industries. Therefore, Domel cannot lower their prices in new or already established markets. Moreover, Domel is turning to robots where operations are focused on precision and demanding procedures with immense accuracy in order to provide competitive qualitative products. More and more companies are turning to robotization to avoid mistakes and high production costs, which allows them to sell high quality products at a lower price, especially companies from countries where robotization is already highly developed, e.g. Germany, Denmark, etc. (Domel Group, 2016).

Cutting down costs and controlling them is essential for Domel in order to maintain and improve competitiveness, was what we were told by the Chairman of the Management Board of Domel Holding, d.d., (Rejec, 2017). The main areas of cost reduction in Domel are the reduction of costs of materials, reduction of costs due to low quality products, and savings that are the outcome of ongoing improvements. They have exceeded the planned reduction in material costs and started including suppliers in the development processes. They have also collaborated with knowledge institutions on demanding development projects (Domel Group, 2016).

3.2 Push motives

Additional revenues and increased productivity is the most important push factor. The business year 2016 was for Domel again even better than the year

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PPKP_2017_book.indbKP_2017_book.indb 116116 221/11/20171/11/2017 06:5206:52 before. They reached a 12 percent growth in sales volume. The largest part was created in the Domel manufacturing company and sales growth was especially evident in new programs, where automatization took place. They also achieved a five percent increase in productivity by automating production processes and organizational changes (Domel Group, 2016). According to the 2016 Global In- dustry 4.0 Survey done by PwC, digitalization and interconnection of products and services (IoT/IoS) will contribute strongly to ensuring competitiveness and additional revenues.

Another significant driver for the advance of Industrial Internet solutions lies in the opportunity to integrate and better manage horizontal and vertical value chains. At Domel, they are striving towards complete integration throughout the value chain, but they are aware that a lot of time and investments are still needed. They have already started to work on downstream integration, which would enable their customers to get better support services, therefore improve their competitiveness. Even though, tracking of the downstream information is hard, tracking the upstream information from their suppliers would be even harder (Domel, 2017b). Achieving total integration throughout the value chain would reduce their costs, increase efficiency and make them more competitive overall.

With digitalization, the boundaries between departments in Domel are getting more and more blurry - horizontal integration. Therefore, strengthening structural/organizational competences is a very important push factor for Domel. The need for constant cooperation demands different departments to work with one another. Domel’s departments that had no connection in the past now work closely together, e.g. the department for developing new machinery is in constant contact with all other departments, which would not be possible without mutual trust and reliance. It is hardly surprising that the information technology department is the one that needs to integrate the most with the rest of the departments. Even though they stated that this can be time consuming at first, it will result in lesser mistakes, having better grip on their dates, smaller inventories needed, etc. The reorganization and optimization of Domel’s processes will continue in 2018 with the goal of making the entire company more flexible, transparent and adapting to the constantly changing market conditions (Domel, 2017b).

Strengthening technological competences. Through the use of QR codes, the advanced diagnostic systems of Domel can track the motors through the assembly line and personalize them to their customers’ specifications, including the automatic upload of firmware through the SAP information system. The uploaded firmware and the already established possibility of connecting the product with

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PPKP_2017_book.indbKP_2017_book.indb 117117 221/11/20171/11/2017 06:5206:52 the internet (IoT) is the base for products of the new industrial revolution. The established concept enables a fully individual analysis of separate blowers, from assembly to final control and programming. This is the foundation for the individual analysis of every stakeholder in the process of motor production (Domel, 2017b). In today’s business world, it is essential to be technologically ready and in line with technological leaders in the market. Eventually, it will also become crucial to obtain “Industry 4.0” certificates in order to work with other advanced companies as a viable business partner (Deloitte, 2015).

New technologies open many possibilities for new cooperation within the supply chain and for expanding to new markets. Domel is currently present in the European and Chinese markets, with ambitions for the American market, which is unexpectedly more optimistic than the actual situation. The company is not reaching the level of success that they wish for, due to cultural differences and big local competition. At Domel, managers are aware of the great potential they have in expanding their business to new markets. The first to mention is the Indian market, which is similar to the Chinese market, where Domel is already present, but is unfortunately facing difficulties around local legislation. Fairly unknown markets to Domel are the African and South-American markets, where they do not have any plans for expansion in the future. As long as they see the opportunities in the northern hemisphere, they want to focus on these markets, with the priority of the EU and American markets (Domel, 2017b).

Development facilitation by the government and other subsidies. In order to create an innovative economy, investment in research and technological development are one of the key factors for the competitive ability of companies. Therefore, development facilitation by the government and other subsidies is crucial. In Slovenia, the government offers companies tax incentives for investment in research and development (R&D) to increase entrepreneurial investment in R&D, promote employment, and build an innovation infrastructure in support of the national innovation system (Ministry of Economic Development and Technology, Republic of Slovenia, 2017). The European Union also offers support for individual projects related to the EU policies, which are usually awarded through a public tender. In Domel, 80 percent of all received subsidies come from the EU funds and 20 percent from the national level. Recently, they have applied for the subsidy from the GOSTOP program (blocks, tools and systems for future factories), co-financed by the EU and Slovenia. Investment at Domel have been aimed at introducing new products, automating production, increasing productivity, improving working conditions and product quality. A large part of the investment was the purchase and renovation of business prem-

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PPKP_2017_book.indbKP_2017_book.indb 118118 221/11/20171/11/2017 06:5206:52 ises for a new production location in Trata, Škofja Loka, which will be highly automatized (Domel, 2017b).

Based on the conversation with the representatives of Domel, we establish that even though there are many push motives driving Domel towards digital transition, the pull ones play a bigger role in this case. Providing high quality products/services and constant fight in the labor market are all together dictat- ing the digital transition of Domel.

4 Obstacles for implementing changes and new technologies

Obstacles for introducing Industry 4.0. have been already discussed in literature by PwC, OECD and others. In the following paragraphs, we focus on the obstacles that Domel is facing. These obstacles are: initial investment in infrastructure, lack of suitable workforce, adapting to change in employee’s routine, and managing big data and how to secure it.

The question of resources is key for companies facing digital transformation to Industry 4.0. Therefore, initial investment in infrastructure is important. Since margins in industries where Domel operates are low, they are very cautious when investing. The management of Domel is well aware that new investment into Industry 4.0 are all about timing; if you are too eager and you over invest into new technologies too quickly, you will be the first to arrive and there will be no benefits and profits to reap, but on the other hand, if you are too slow, you will miss the train of Industry 4.0, which could result in a huge loss. Industry 4.0 is becoming a fashionable word. Everybody is dealing with this. It is important to find a niche where you can achieve a better result with the “right” use of the “right” tools. Of particular importance in this context is the appropriateness of the existing IT infrastructure and the availability of the necessary talented and skilled employees, as already mentioned above. Domel’s funding opportunities are limited. By the European law, the Slovene state cannot directly invest into Domel. Also, their ownership structure is not beneficial when it comes to getting funded. A substantial part of Domel’s owners are retired employees who prefer to receive dividends as opposed to distributing profit for investment. Getting support from business partners is also something they can hardly see happening. They got some support through applying for public tenders and EU grants for the development of the processes, and from other projects supporting the smart industry - development towards Industry 4.0 (Domel, 2017b).

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PPKP_2017_book.indbKP_2017_book.indb 119119 221/11/20171/11/2017 06:5206:52 PwC’s 2016 Global Industry 4.0 Survey shows that lack of skills or/and competencies in the company’s workforce is one of the biggest challenges companies deal with. Even Domel is lacking suitable workforce. They are increasingly searching for more educated and skillful workforce, employees who are ready to take additional seminars and lectures and are willing to adapt to the changes that are bound to happen. Domel is investing a lot into education of their own employees while working; development of communication skills, delegating, mentoring, technical knowledge, etc. The company is searching for competent and highly skilled lecturers who will pass on their knowledge to the employees. They have already acknowledged that the structural changes can be seen but they are going to continue developing further as the technology progresses. They also believe that a structural reform of our educational institutions needs to take place in order to cover the future employment needs (Domel, 2017b).

Some experts (Brynjolfsson and McAfee, 2014) suggest that the technological change we are experiencing in this ‘second machine age’ not only risks displacing some specific types of jobs but could also lead to a decline in overall employment. Not only will routine tasks continue to be automated but cognitive tasks that until recently were considered non-automatable are now at risk, for example, writing standard reports on stock market changes (OECD, 2016). However, at Domel the social aspect is of high importance and they feel responsible for the people in their vicinity, providing jobs and security for them. The company is expanding every year, providing many job positions. The number of employees has increased by more than ten percent since last year, contributing to the increasing employment rates in the region of Železniki; there was a 1.4-percentage-point increase in the last year in the region. With 3.3 percent of registered unemployment rate in June 2017, the municipality really stands out from the Slovenian average of 9.1 percent (Employment Service of Slovenia, 2017). It is one of the biggest drivers while still achieving economic success (Domel, 2017b).

Adapting to change in the employee’s routine. A complete implementation of the IoT system or any other new technology to a company is usually a difficult and long-lasting process that requires employees’ full commitment. The employees will have to learn how to use new programs and adapt to the new system. This often means changing the employees’ routine and learning how to work with the new technology. This can be hard for some employees, especially the senior ones who have been working there for a long time being used to the settled working environment. Moreover, the majority of people tend to dislike change. This is why it is important to involve all employees in the process of implementing new

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PPKP_2017_book.indbKP_2017_book.indb 120120 221/11/20171/11/2017 06:5206:52 technologies and provide them with appropriate training for easier transition into their future work in the company (Bartel and Lichtenberg, 1987).

Digital ecosystems and the broad use of data also raises important issues around cybersecurity (PwC, 2014). The internet of things, services, data and people open up new avenues for data theft, industrial espionage and attack by hackers. At Domel, the threat of managing big data and how to secure it has been ranked as the second biggest, just after natural disasters. Even though technical information regarding their processes is valuable to them, the theft of their business data would have an even bigger negative impact on the company. Not all of the data is saved in one place; some is kept in SAP, some of it is on a cloud storage system, and there are also servers for data storage. The cost of storing data using these new kinds of technology is becoming less and less expensive, however, the amount of data grows rapidly (Domel, 2017b). To secure their data, they outsource it to an external institution, which has proved to be successful for them so far. Third-party assurance is an important way to confirm that systems are robust, which strengthens trust among system participants in one’s platform’s integrity. They also make sure that all the machinery and computers installed are appropriately coded, so there is no fear of information leakage. They are also regularly educating their employees in order to prevent such attacks.

Conclusion

In this paper we have presented Domel’s digital journey towards Industry 4.0. There are many motives for companies to embark on the transition. At Domel they have already made the first steps and acted proactively upon it to reap the benefits of digitalization. Every change comes with obstacles and companies need to overcome them in order to achieve digitalization. Investment needed for the digital transition to occur can be very expensive, therefore companies need to make smart decisions and find additional ways to help them implement digital changes. Companies need to provide the right environment to welcome the changes, equip themselves with suitable workforce and assure the safety of big data that comes along with digital transition. To conclude the initial notion of this case study, timing is everything and passing on an opportunity can sometimes be the best option. It is up to the companies to decide when they will cash in their train ticket, join the digital journey and get the most out of it.

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PPKP_2017_book.indbKP_2017_book.indb 121121 221/11/20171/11/2017 06:5206:52 References Bartel, A., and Lichtenberg, F. 1987. “The comparative advantage of educated workers in implementing new technology.” The review of economics and statistics 69(1): 125-132.

Brynjolfsson, E., and McAfee, A. 2014. “The Second Machine Age: Work, Progress and Pros- perity in a Time of Brilliant Technologies.” New York: W.W. Norton & Company.

Debenjak, A., Boškoski, P., Musizza, B., Kern, M., and Biček, A. 2017. “Informacijska arhitek- tura za proizvodnjo analitiko.” Ventil 23: 214-218.

Deloitte. 2015. “Industry 4.0. Challenges and solutions for digital transformation and use of exponential technologies.” URL: https://www2.deloitte.com/content/dam/Deloitte/ch/ Documents/manufacturing/ch-en-manufacturing-industry-4-0-24102014.pdf.

Employment Service of Slovenia. 2017. “Stopnja registrirane brezposelnosti.” URL: https:// www.ess.gov.si/trg_dela/trg_dela_v_stevilkah/stopnja_registrirane_brezposelnosti.

Domel. 2017a. “Trajnostne inovativne rešitve.” URL: http://www.domel.com/sl.

Domel. 2017b. Personal communication, August 31, 2017.

Domel Group. 2016. “Konsolidirano letno poročilo skupine Domel za poslovno leto 2016.” URL: http://www.gvin.com/PodjDok/podjdokf/LP/2016/1294156_20170907_KLP.pdf.

Kirkwood, J. 2009. “Motivational factors in a push-pull theory of entrepreneurship.” Inter- national Journal 24(5): 346-364.

Ministry of Economic Development and Technology, Republic of Slovenia. 2017. “Davčne olajšave za vlaganja v raziskave in razvoj.” URL: http://www.mgrt.gov.si/si/delovna_po- drocja/tehnoloski_razvoj/spodbujanje_inovacij_in_tehnoloskega_razvoja/davcne_ola- jsave_za_vlaganja_v_raziskave_in_razvoj/.

OECD. 2016. “Automation and Independent Work in a Digital Economy.” Paris: OECD Pub- lishing. URL: https://www.oecd.org/els/emp/Policy%20brief%20-%20Automation%20 and%20Independent%20Work%20in%20a%20Digital%20Economy.pdf.

PwC. 2014. “Industry 4.0 - Opportunities and Challenges of the Industrial Internet.” URL: https://www.PwC.nl/en/assets/documents/PwC-industrie-4-0.pdf.

PwC. 2016. “2016 Global Industry 4.0 Survey. Industry 4.0: Building the digital enterprise.” URL: https://www.PwC.com/gx/en/industries/industries-4.0/landing-page/industry-4.0- building-your-digital-enterprise-april-2016.pdf.

Rejec, L. 2017. Personal communication, August 24, 2017.

Siemens. 2014. “Quality, Reliability, Performance. SIMATIC IPC: The More Industrial PC.” URL: https://w5.siemens.com/spain/web/es/industry/automatizacion/simatic/PC_industriales/ Documents/SIMATIC_IPC_Brochure_e_6ZB5370-1BF02-0BC5.pdf.

SURS. 2017. “Podjetja po občinah, Slovenija, letno.” URL: http://pxweb.stat.si/pxweb/ Dialog/varval.asp?ma=1418807S&ti=&path=../Database/Ekonomsko/14_poslovni_sub- jekti/01_14188_podjetja/&lang=2. — 122 —

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PPKP_2017_book.indbKP_2017_book.indb 124124 221/11/20171/11/2017 06:5206:52 Barbara Čater, Marko Jakšič, Kristian Groznik, Rok Lavrič, Tjaša Skubic

YASKAWA

Introduction

The robotics suppliers are those who are playing an important part in enabling companies and their customers to embrace the full potential of the new technologies. The paper discusses the Yaskawa Electric Corporation, a Japanese manufacturer of servos, motion controllers, alternating current (AC) motor drives, switches and industrial robots, and its division Yaskawa Motoman, a producer of robotic automation for industry and robotic applications (Yaskawa Global, 2017). In order to make this case study relevant, we went through various secondary sources as well as visited the Yaskawa’s subsidiary in Slovenia, where we conducted an interview with head of development Erih Arko, production manager Klemen Kastelec and CEO dr. Hubert Kosler, who gave us relevant insight information about their business strategy.

The purpose of this chapter is to explain how robotics suppliers are increasing their competitiveness in the global market by getting closer to the customers and satisfying diverse customers’ needs by offering unique solutions. The thesis is that in order to become successful globally, robotics suppliers need to focus on local development of products for local markets.

This chapter is structured as follows. After the introduction, Yaskawa’s global markets with the focus on the EU region are explored. This is followed by the presentation of Yaskawa’s business strategy that enables them to continuously adapt to external changes in order to satisfy customers’ demand in the local markets. In light of this, their expansion of production to Europe is discussed with the focus on the case of Yaskawa in Slovenia as an example of such a move.

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PPKP_2017_book.indbKP_2017_book.indb 125125 221/11/20171/11/2017 06:5206:52 1 Market overview

1.1 Yaskawa in the global markets

Yaskawa Electric Corporation is a Japanese manufacturer that began its journey in 1915 as Yaskawa Electric Manufacturing Co. by creating motors for the coal mining industry. Over the years, they introduced many innovative products, such as the first all-electric industrial robot called Motoman-L10 in 1997, used for welding. In 1991, the company’s name was changed to Yaskawa Electric Corporation. Since then, it has helped to drive automation in the mining, steel, automotive, woodworking, and textile industries and many more (Yaskawa Global, 2017).

Today, Yaskawa provides high-end mechatronic and robotic solutions to companies throughout Asia, Europe, Africa and America. Through their core technologies they are offering new customized solutions and are striving towards being close to their customers to responsively cater to their needs. The company currently employs 11,810 people and has more than sixty subsidiaries worldwide. Their net income for the year 2016 was 394.8 billion yen (approximately three billion euros) and overseas sales represented 66 percent of their revenue (Yaskawa, 2017).

Figure 1. Net sales of Yaskawa Corporation 2012-2016 in billion of yen; breakdown by business segments

1500

1200 Other 900 System Engineering

600 Robotics Billions of yen Motion Control 300

0 2012 2013 2014 2015 2016 Year Source: Yaskawa, 2017.

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PPKP_2017_book.indbKP_2017_book.indb 126126 221/11/20171/11/2017 06:5206:52 In order to understand Yaskawa’s business in more detail, its business segments are presented next. Their business is focused on four larger segments: motion control (alternating current (AC) servos and AC drivers that are, among other uses, used in factory automation and robotics); robotics (vertically articu- lated robots which are important for automation of welding, painting, assembly, transfer, etc.); system engineering (steel plant, social system, environment & energy and industrial electronics business); and other (information-related businesses, logistics services, etc.). Motion control is the largest business segment, followed by robotics, system engineering, and others (Yaskawa, 2017) (Figure 1).

Even though motion control is responsible for the largest part of Yaskawa’s net sales, the robotics segment is particularly interesting for this case study because robots are the key component of production systems automation. Their products are in 77 percent sold overseas (motion control 71 percent). In total, this segment represents 35 percent of Yaskawa’s net sales. They are manufacturing Motoman robots for diverse applications, such as welding, cutting, palletizing, handling, painting, cleanroom, etc. In order to stay competitive they continue to contribute to automation for mass production and also seek to develop a breakthrough technology for customized production of various products in areas where full automation is difficult (Yaskawa, 2017).

Yaskawa’s customers are spread between various fields of industry, and therefore, in order to fulfil their requirements, the company needs to be on track with the newest trends and investment in all of those areas. To be ahead of their competitors they need to adjust and improve their business model accordingly. One typical example of this is investment made in auto-related markets. Those have been constantly increasing in the past few years and there is also an increasing trend in demand for the automation, automotive, energy and medical industries forecasted for the period 2015 - 2020. In particular, professional solutions as well as finished goods will be of great importance. These investment and trends are shaping a positive future for the demand in robotics segment (CBI, 2017).

Yaskawa’s biggest markets around the globe are defined next. Figure 2 shows that Asia represents the biggest market, followed by Americas, Europe and others. Their overall sales increased from 2012 to 2015, with a slight decrease in the following year. Nevertheless, sales increased tremendously if we look at the difference between the years 2012 and 2016. This trend applies also to the European market, where the sales have grown from the initial 30 billions of yen (approximately 224 million euros) in 2012 towards the peak of approximately

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PPKP_2017_book.indbKP_2017_book.indb 127127 221/11/20171/11/2017 06:5206:52 Figure 2. Overseas sales of Yaskawa 2012-2016 in billion of yen

America Europe Asia 150

120

60 Billions of yen

0 2012 2013 2014 2015 2016 Year Source: Yaskawa, 2017.

50 billions of yen (373 million euros) in 2015. Overall, that means an increase of approximately 67 percent in five years (Yaskawa, 2017).

In order to better understand Yaskawa’s position in Europe, this topic is presented in the next two sections.

1.2 European robotics market

In the race for a change in manufacturing and automation, Europe is currently one of the global market leaders. Half of the top ten nations with the most industrial robots per 10,000 employees belong to the European Union. This is evident from the demand and high robot density existing in the automotive industry, where Yaskawa is also present. The strongest growth in Europe is currently happening in the Eastern and Central Europe. The demand among customers for industrial robots is driven by a whole assortment of factors: new materials, efficiency, better developed automation concepts, the actual production plant and the virtual world to be connected with one another, as per the definition of Industry 4.0. Another factor that has increased the demand for industrial robots is the increase in multiple tasks a robot is able to do (IFR, 2016).

The recognition of increasing demand in Europe for robotic products has led to a shift in Yaskawa’s business model with more efficiency and higher responsiveness through domestic production plant (Yaskawa Slovenia, 2017).

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PPKP_2017_book.indbKP_2017_book.indb 128128 221/11/20171/11/2017 06:5206:52 1.3 Yaskawa’s business in Europe

Yaskawa Europe GmbH is headquartered in Eschborn, Germany. In Eu- rope, they have R&D, sales and aftersales services, as well as production they are working on to develop even further. The company provides professional mechatronic and robotic solutions for companies all over Europe, the Middle East, Africa and the countries of the former Soviet Union. They have seven production sites (in which servo drives, frequency converters, controllers and wind generators are manufactured) and 30 offices around Europe. Their own sales and service centers are located in various European countries, such as the Netherlands, the Czech Republic, Germany, Austria, Finland, Sweden, France, Spain, Italy, the United Kingdom, Poland, Russia, and Slovenia, while having partners in many other countries as well (Yaskawa Europe, 2017). Besides those countries, their subsidiaries are also located in Israel and South Africa. European sales presented 13 percent of their all consolidated net sales in the year 2017. By having subsidiaries and production sites in some of the countries in these regions, they are always close to customers and are able to react upon urgent inquiries as fast as possible (Yaskawa Global, 2017).

Their business volume was 368 million euros in 2015; drives and motion segment presented 132 million euros, robotics 196 million euros, the other 40 million euros were from VIPA (electronic parts supplier in Germany). They have a plant construction and robot systems located in Slovenia as well as Ger- many, and a controller assembly, positioner and a portal construction in Sweden (Yaskawa Europe, 2015).

With its innovations, Yaskawa is seeking to more actively penetrate the EU robotics market, which is characterized by high growth and competition of brands with a strong local presence. Its presence in the European robotics market is limited due to the lack of local production capabilities (their robotics plants are located in Japan and China), while its main competitors The Fanuc Corporation, ABB and Kuka Group are all present in Europe with sales as well as manufacturing facilities. Fanuc Europe Corporation has its headquarters located in Luxembourg. From there it provides its 17 European subsidiaries with various services, such as European sales and service, product support, supply chain, parts and repairs. The company positions itself as the only company in the industry developing and manufacturing all its major components in-house. They state that their availability is their main advantage (Fanuc, 2017).

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PPKP_2017_book.indbKP_2017_book.indb 129129 221/11/20171/11/2017 06:5206:52 Figure 3. Global industrial robot sales by suppliers

Others Fanuc 34% 28%

ABB Yaskawa 17% 21%

Source: Muñoz-Delgado et al., 2017.

ABB has its facilities for research, development and manufacturing located in Sweden, the Czech Republic, Norway, Mexico, Japan, the USA and China. They are highlighting their fast and flexible response, lifecycle management, performance improvements, and operational excellence as their core competences (ABB, 2017). KUKA Roboter GmbH, with its headquarters in Augsburg, ranks among the world’s leading suppliers of industrial robots. They are present in around 30 countries globally. Production plants are located in Germany and China. Their offer is ranging from individual components to fully automated systems (AMS, 2017). Fanuc corporation has globally the biggest share of sales with 28 percent, followed by Yaskawa with 21 percent and ABB with 17 percent (Figure 3).

Yaskawa’s competitive advantage lays in high robot precision. Their high positioning and path accuracy have positive returns in terms of economic efficiency. Their easy programmability contributes to this aspect, as it does not require costly training of employees. At the core of Yaskawa’s competitiveness is an all-encompassing commitment to ensured quality (Yaskawa Europe, 2017).

2 Yaskawa’s business strategy

2.1 General strategy

Yaskawa’s global vision is to become the number one turnkey solution pro- vider in the existing core business and delivering revolutionary industrial automation for their customers. However, satisfying each individual customer relies heavily on providing customer-specific customized and “localized” solutions, thus the way this needs to be done is through increasing local presence in target markets. The key step towards improving their customer service and increasing operation efficiency by improving logistics in Europe lies in establishing local

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PPKP_2017_book.indbKP_2017_book.indb 130130 221/11/20171/11/2017 06:5206:52 production capabilities and core product R&D. With the decentralization of their business model, they will achieve essential flexibility and lower transportation costs (Motoman, 2017b).

Development and integration of technology through small and fast moving global companies is the best first step to successful localization. It goes in line with the trend that robotics suppliers must become truly global, therefore a crucial step in satisfying customers’ needs in their local area is to evolve new products and technology by using local research and development. If a company wants to go global, they have to think local (Dudovskiy, 2017). In the end, this approach might be the key success factor for robotics solution providers.

While distribution on a global scale is important for robotics companies, customization of offering is paramount. By applying the region-specific finish- ing touches at a facility closer to the final destination, the robot producers are able to ensure that the final customers receive a product customized to their region/country and their unique needs. However, localization does not stop with product delivery. The system where physical products, robots in our case, are adapted when there is a need, has to be established. By localizing international return capabilities, both retailers and manufacturers are able to develop a more streamlined supply chain. Because of that the end customers receive better and more efficient service (Virden, 2017).

Another thing to highlight here is long-term support that goes beyond regular sales and aftersales resources. Every robotics company should retain as much customers as possible, since it costs them from five to twenty-five times more to acquire a new one (Gallo, 2014). A great example of that is Yaskawa because it ensures all the stages of product life cycle with support and product service that fits any need. Yaskawa’s total customer support covers applications, products, systems and processes to increase productivity, availability and to instantly retain everything in top condition. By providing comprehensive support they strive towards customer satisfaction, which is their top priority (Yaskawa Slovenia, 2017).

2.2 Expansion to Europe

The company is expanding its European activities by investing in the already existing robot businesses and also electric drive technology. The most recent extension of their business model was through strategic acquisitions in the area

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PPKP_2017_book.indbKP_2017_book.indb 131131 221/11/20171/11/2017 06:5206:52 of industrial control technology and green energy, thus pursuing their ambitious expansion strategy in Europe. In doing so, the company is bucking the international trend towards relocating technological know-how and production competence from Europe to Asia. Yaskawa is proposing to intensify its presence in Europe, with the aim of becoming one of the leading manufacturers of industrial robots (Yaskawa Europe, 2016).

Today they employ about 350 development and application engineers in Europe and are planning to expand their business in this area by investing in a new robot plant. They have chosen Slovenia as the country for this investment. New production and development capacities will permit even faster customized robotic solutions in Europe (Yaskawa, 2017).

This will also strengthen the cooperation with original equipment manufacturers in these markets. As Manfred Stern, CEO of Yaskawa Europe GmbH said: “Europe is a leader in many technologies, and we want to consistently demonstrate to our customers that we are happy to be here and engage in the joint development of even more efficient solutions. The investment in the Eu- ropean robot plant is of strategic importance for Yaskawa. Slovenia is a good combination of the shortest distance for component supply from the Far East and a good location to distribute finished robots to customers in EMEA” (The Switch, 2016). This statement which proves strategic thinking by Yaskawa goes in line with the decentralized business model for shortening their supply chain.

2.3 Yaskawa in Slovenia

Upon entering the Slovenian market, Yaskawa Slovenia began its story with sales and aftersales services, and later expanded into a B2B automation systems integrator. Yaskawa has two corporate entities in Slovenia, the first one is Yaskawa Ristro, d.o.o. (research and development center), where the majority of employees are employed, the second one is called Yaskawa Slovenija, d.o.o. (responsible for sales and aftersales). They are specialists in integration of robotics systems into companies in the central and eastern parts of Europe and constantly strive towards ensuring the best possible turnkey solutions for their customers. They utterly strive to providing customers with efficiency, competitive prices, and flexibility in customized solutions. There is no production line of robots in Slovenia (yet), thus their focus there is selling and providing the best possible post-purchase customer services, moreover, there is also a support team for the Yaskawa company in Italy (Yaskawa Slovenia, 2017).

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PPKP_2017_book.indbKP_2017_book.indb 132132 221/11/20171/11/2017 06:5206:52 Figure 4. Total sales and net profit of Yaskawa Ristro 2012-2016 in million of euro Total sales Net proﬁt 35 30.426 30 29.728

25 20.047 20.341 20 19.536

15 Millions of € 10

5 0.541 0.665 0.997 1.979 1.674 0 2012 2013 2014 2015 2016

Source: Yaskawa Ristro, 2016. Year

Yaskawa Ristro is the development centre of the entire Europe. They deliver robotic solutions to as many as thirteen subsidiaries of Yaskawa Europe (euRobotics AISBL, 2016). As Figure 4 shows, they have been increasing sales throughout the years, exhibiting a positive trend in increasing demand for robotic solutions in Europe.

Yaskawa Ristro is currently focusing on serving larger clients with built- to-order products and solutions. Their client base comes from all parts of Europe and even Africa. Yaskawa’s advanced solutions and innovations have had a significant impact on the development of industrial robotics and production processes in Slovenia and abroad. For example, they helped the German company BMW with manufacturing “car front-axle beams” and managed to decrease their workstations, operating staff, and consequently costs for its 7 Series. Another example is the production of a new “robot welding system” for the UK customer Alpha Manufacturing Ltd, resulting in production cost savings as well as quality improvements (Motoman, 2017a).

Recognition of excellence for the past work is the reason behind the strategic decision to build a new production plant in a Slovenian town called Kočevje. By building a complete structure for its robotics business locally that is equipped with development, manufacturing, and sales, it will bolster its relationship with customers from Slovenia and customers from Europe through strengthened offers of products and solutions and thus ensuring a stronger competitive position (Yaskawa, 2017).

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PPKP_2017_book.indbKP_2017_book.indb 133133 221/11/20171/11/2017 06:5206:52 Another advantage is that delivery time will shorten and they will also be able to react more quickly to customers’ demands due to the spread of fast communication and transport means. It will become easier to serve their customers within and outside the country. Localization will lead to promotion and growth of the Slovenian subsidiary (Economics concepts, 2017). For Slovenia, such an investment will represent the potential for further local development and new jobs. The Ministry of Education, Science and Sport has already confirmed that there will be an additional mechanical engineering program at the local education center in Kočevje to meet the demand for trained professionals in that area. As said by Mr. Cerar, Slovenian Prime Minister, other important steps would be taken to revitalize the region’s economy. Work on the railway line began last autumn, with trains set to run to Kočevje again after a gap of 46 years. A functioning rail connection is essential for Yaskawa and other businesses in the region (Vlada RS, 2015).

Combining all of the events, strategies and trends in the robotics industry that Yaskawa Slovenia and Yaskawa Global are facing, it is evident that continuous evolution and improvement of their future business model is necessary. Effec- tive selling and marketing of products or services requires a comprehensive and cohesive strategy that addresses the marketing and sales strategy, which together create clear market differentiators that propel market acceptance and revenue growth. But in order to do that, the company has to keep improving its business model after global presence, focus on producing locally, be responsive to the fast-paced changing market and be closer to the customers (Yaskawa Slovenia, 2017).

One of the main aims of Yaskawa in Slovenia is to help customers adopt new technologies. Whenever a customer comes to Yaskawa for help, the R&D department develops new or improves older products, which makes them more competitive. Now, with localized production and the R&D department, Yaskawa will also have a new option to produce products or parts on stock for products with higher demand. However, components and parts required in the local manufacturing facility will still be shipped from plants in Japan and China. They will continue ordering components from the East until they establish reliable logistics, find qualified workforce, and only afterwards they will transfer production from Asia to Kočevje in order to supply the European region. Con- sequently, long distance supplying of the local plant will still postpone their full effectiveness in the short term (Yaskawa Slovenia, 2017).

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PPKP_2017_book.indbKP_2017_book.indb 134134 221/11/20171/11/2017 06:5206:52 Conclusion

Our main purpose throughout this case was to explain how robotics suppliers are increasing their competitiveness in the global market by getting closer to the customers. We have discovered and provided solutions how robotics producers or suppliers can improve their local presence around the globe with decentralization of traditional business models and collaboration with their customers. The primary goal is to achieve long-term competitive advantage by providing innovative cutting-edge technological solutions with user-friendly and reliable implementations of robotics. We need to be aware of the fact that global companies must not neglect the influence of the local market. In the current fast-paced global economy, robotics manufacturers have to address the local needs rapidly.

Yaskawa is a proven actor of the statement “go global - think local.” Lo- calization of business is not only an advantage for the company itself in terms of efficient local operations, but it also positively affects the local community. We have discussed Yaskawa’s localized investment in Europe, focusing on the implications of the investment in the manufacturing facility in Slovenia with a purpose of meeting increasing demand for robotics industry. This strategic move should bring positive long-term effects to the company’s higher domestic effectiveness, responsiveness, and consequently business advantage with localized production. These actions will significantly improve relationships between customers from Slovene regions as well as others from the whole Europe, thus they could easier retain and even increase their market share.

To conclude our case, companies are facing challenges to implement “go global - think local.” It is of value for the robotics international corporations to create successful regions with seizing the opportunities by localization, as well as simultaneously expanding their international business. Detailed knowledge of the local market, proactive approach and amending from traditional rigid business models to decentralized flexible ones to meet dynamic demand will play even a greater role in the future. The competition in robotics market is fierce and to increase market share, bold investments such as Yaskawa’s will be necessary in the long haul.

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PPKP_2017_book.indbKP_2017_book.indb 135135 221/11/20171/11/2017 06:5206:52 References ABB. 2017. “ABB’s global webpage.” URL: http://new.abb.com/. AMS. 2017. “KUKA.” URL: https://automotivemanufacturingsolutions.com/ams-directory/ kuka-roboter-4. CBI. 2017. “What trends offer opportunities on the market for electronics and electrical engineering?” URL: https://www.cbi.eu/market-information/electronics-electrical-engineering/trends/. Dudovskiy, J. 2017. “Think Globally, Act Locally: A Critical Analysis.” Research Methodol- ogy. URL: https://research-methodology.net/think-globally-act-locally-a-critical-analysis/. Economics concepts. 2017. “Localization of Industries.” URL: http://economicsconcepts. com/localization_of_industries.htm. euRobotics AISBL. 2016. “Development in robotics brings competitive advantage and economic growth.” European Robotics Forum 2016 in Ljubljana, March 21-23, 2016. Fanuc. 2017. “Fanuc’s European website.” URL: http://www.fanuc.eu/pt/en/who-we-are/ why-fanuc. Gallo, A. 2014. “Retaining Customers.” Harvard Business Review. URL: https://hbr. org/2014/10/the-value-of-keeping-the-right-customers. IFR. 2016. “IFR Press Conference Frankfurt 2016.” URL: https://ifr.org/downloads/press/02_2016/Presentation_market_overviewWorld_Robot- ics_29_9_2016.pdf. Motoman. 2017a. “Yaskawa Ristro d.o.o. website.” URL: http://www.motoman.si/. Motoman. 2017b. “Yaskawa Slovenia product examples.” URL: http://www.motoman.si/sl/ resitve/oblocno-varjenje/primeri-uporabe/. Muñoz-Delgado, C., Salcedo, S., Tanaka, N., Coronel, D., Morillas, A., and Bombarelli, P. 2017. “Robotics Sector Analysis: Entering in a new era.” BE International. URL: http://madi.uc3m. es/en/international-research-en/markets-and-industries-en/robotics-sector-analysis/. The Switch. 2016. “Advancing the World with Electrical Drive Trains.” URL: http://theswitch. com/2016/12/18/yaskawa-announces-ambitious-expansion-plan-in-europe/. Virden, S. 2017. “The Importance of Country-Specific Customization and Localization.” ModusLink. URL: https://www.moduslink.com/importance-country-specific-customization-localization/. Vlada RS. 2015. “Premier dr. Cerar ob 100. obletnici Yaskawa Electric Corporation o uspešnem primeru tuje naložbe v Sloveniji.” URL: http://www.vlada.si/predsednik_ vlade/sporocila_za_javnost/a/premier_dr_cerar_ob_100_obletnici_yaskawa_electric_ corporation_o_uspesnem_primeru_tuje_nalozbe_v_sloveniji_290/. Yaskawa. 2017. “Yaskawa annual report 2017.” URL: https://www.yaskawa.co.jp/en/ir/materials/annual. Yaskawa Europe. 2015. “Masters in robotics and motion control.” Company presentation. Acquired by company representatives. — 136 —

PPKP_2017_book.indbKP_2017_book.indb 136136 221/11/20171/11/2017 06:5206:52 Yaskawa Europe. 2016. “Japanese technology group Yaskawa to invest in Europe.” URL: https://www.yaskawa.eu.com/en/news-events/news/article/news/japanese-technology- group-yaskawa-to-invest-ineurope/?tx_news_pi1%5Bcontroller%5D=News&tx_news_pi 1%5Baction%5D=detail&cHash=a05f85942fc2969718aedc25c7272a5c. Yaskawa Europe. 2017. “Yaskawa’s European website.” URL: https://www.yaskawa.eu.com/. Yaskawa Global. 2017. “Yaskawa’s global website.” URL: http://www.yaskawa-global.com/. Yaskawa Ristro. 2016. “Annual report 2016.” URL: https://www.ajpes.si/jolp/podjetje. asp?maticna=5967732000. Yaskawa Slovenia. 2017. Personal communication, August 24, 2017.

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PPKP_2017_book.indbKP_2017_book.indb 138138 221/11/20171/11/2017 06:5206:52 Ljubica Knežević Cvelbar, Enya Caserman, Eva Erjavec, Ana Rita Fernandes

SLOW ADOPTERS: ROBOTIZATION IN THE HOSPITALITY INDUSTRY

Introduction

Tourism contributes nine percent to global GDP and accounts for one in 11 jobs worldwide (UNWTO, 2013). Tourism is also an industry that has in the last decades been transformed by the advancement of technology. Technology has not only become an integral part of tourism but has also revolutionized the way travel is planned and conducted (Buhalis, 2003). The technology has enabled: smart devices, real-time analytics, customization, digital interaction, big data analytics, IoT (Internet of Things) and lately blockchain innovations. However, tourism is being a slow adopter of technological solutions.

Furthermore, tourism is a highly seasonal economic activity employing low skill workers and paying below average salaries (Eurostat, 2017). Polarization of the labor force on low-skill low-paid and high-skill high-paid jobs, taking place in other industries is also present in tourism (Autor, 2010). The majority of jobs in tourism (80 to 90 percent) are low-skill and low-waged (European Commission, 2010); however, in comparison to other industries where low-skill jobs are being replaced with the machines, tourism in its essence needs a “human touch” and is being slow in replacing humans with machines.

This chapter presents the existing and potential future transformation of tourism due to technological change and, specifically, sheds light on the impact of robotization on the development of the hospitality industry. Following the introduction, the development trends in tourism are presented; Section 2 is dedicated to robotization in the hotel industry, while Section 3 describes the first fully robotized Henn-na Hotel, operating in Japan. The chapter ends with the discussion on the future development, and challenges robotization in the hospitality and tourism industry.

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PPKP_2017_book.indbKP_2017_book.indb 139139 221/11/20171/11/2017 06:5206:52 1 Tourism current and future development

According to the latest UNWTO World Tourism Barometer, International tourist arrivals grew by 3.9 percent to reach a total of 1.2 billion (UNWTO, 2017). It is expected that this growth will continue and we will reach 1.8 billion tourists until 2020 (UNWTO, 2017). The major trends that will impact the tourism development until 2020 stated by PwC Middle East (2017) are summarized in Table 1.

Table 1. Demand and supply trends in tourism

Generation Growing middle Emerging Political issues Demand Silver hair tourists Y & Z class destinations and terrorism

Technological (R) Health & Healthy Supply evolution Digital channels Loyalty v.X.0. lifestyle Sustainability Source: PwC Middle East, 2017.

On the demand side, due to the demographic changes, the segment of silver hair tourists will be growing along with millennials. Emerging destinations will be attracting more tourism demand, however, political issues and terrorism will shape global tourism flows. On the supply side, technological progress, as well as concern about health and sustainability, will be the major drivers of the development (Table 1).

Millennials – generations Y & Z – are technology adopters and are recognized as users of different technological solutions. This growing tourism segment demands real-time information and high level of personalization and customization. The key technologies in tourism transformation are: smart devices, real-time analytics, customization, digital interaction, IoT, blockchain and robots. Robots (maintenance, guest service, and room service), holograms with avatars (reception, staff), interactive displays, smartphones and gadgets have already been introduced by some hotels (PwC Middle East, 2017).

Generally, two major technological trends are transforming tourist experiences today. The first is the idea of co-creation, which recognizes active and empowered consumers in the co-creation of their own experiences (Prahalad and Ramaswamy, 2004). Consumers use social media to interact with organizations and other travelers, making them active participants in shaping their experiences. The second is the proliferation of ICTs, implying that tourist experiences can maximize the consumer value if technologically empowered

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PPKP_2017_book.indbKP_2017_book.indb 140140 221/11/20171/11/2017 06:5206:52 (Tussyadiah and Fesenmaier, 2009). By taking full advantage of the different ICTs available, technology becomes the key element of a tourism experience (Buhalis et al., 2013).

Travel & Tourism have gone through digitalization of all processes which have enabled the creation of applications, such as flight tracking system, computer reservation system, global distribution system1, biometric passport (E-Passport), CRM, mobile technology and social networks (Wahab, 2017). Additionally, the technological evolution has enabled for GIS2 to take place (Mason, 2003). GIS can be useful for data analysis, modeling and forecasting (Cvelbar et al., 2017), allowing the tourism sector to be better informed and planned than ever before.

Despite such technological developments, tourism is often viewed as a slow adopter of technology (Lubetkin, 2016). However, terms such as Smart Tour- ism have been coined for the past years. The term “smart” has been used to describe technological, economic and social developments fueled by technology in different sectors – including tourism (Sigala et al., 2015). In Smart Tourism, technology is seen as an infrastructure, rather than as individual information systems (Washburn and Sindhu, 2010). It rests on the ability to not only collect enormous amounts of data but to store, process, combine, analyze and use big data (Sigala et al., 2015). Instant information collection can be analyzed to reveal patterns and trends. Therefore, destinations are able to offer personalized services to each type of tourists.

The realization of the Internet of Things (IoT) will be crucial for creating the environment to enable Smart Tourism, a technological environment that encompasses connected physical and digital infrastructures, will provide a shift in service provision to always-responsive situated services (Sigala et al., 2015). Besides IoT, some hotels are testing the machine or robot solutions that will be discussed in the following section.

2 Robotization in the hotel industry: Hotel 4.0

Looking at the trends in Industry 4.0, the next stage of technological development would be related to development in the field of robotization. Some hotel companies are already using robots in their operations and are presented

1 Linkage between the service providers in the travel industry, such as airlines, hotels and car rental companies. 2 Geographic Information System – computerized systems for handling and processing data. — 141 —

PPKP_2017_book.indbKP_2017_book.indb 141141 221/11/20171/11/2017 06:5206:52 in Table 2. Those hotels can be found in Japan, China, Singapore, the United States and northern countries of Europe – Belgium and the Netherlands (Table 2). Hotels that are using service robotization are three to four star hotels, as five star hotels require superb service and their customers expect excellence. Overall, the level of robotization is still very basic in most of the hotels, with the exception of the Japanese Henn-na Hotel, which is fully operated by robots (presented in Section 3). Hotels are generally using delivery robots, butler robots, concierge, or entertainment robots. Delivery robots are the simplest and their tasks are mainly related to hotel delivery operations. Butler robots are more advanced; they can give orders using artificial intelligence features – politeness, dependability and trustworthiness. Similarly, concierge robots are mainly in charge of the meetings, incentives, conventions and exhibitions, they greet guests and speak multiple languages, answer questions about the hotel and give basic recommendations. Besides the stated, hotels worldwide are investing into the automatization of services, including digital keys/keyless entry, mobile check-in and environmentally friendly technologies using available automatized processed to improve operations and marketing activities. The level of robotization varies among them. In order to evaluate customers experience we have looked into the guest service evaluations (using TripAdvisor data). The general response is rather positive. The guests accepted robots well and recognized added value of robots to their experience when staying at the hotel. On average, the price range of hotels is from 100 to 150 euros. This supports our previous claims that robots are suitable for hotels offering low to medium service quality. So far, no five star hotels have been using service robots. Based on our analysis of the global hotel chains, the most progressive in introducing robotization into their operations is Hilton Hotels & Resorts (Hilton McLean Tysons Corner Hotel, 2017). They have invested in the innovation hub where they test, adapt robots and up-grade robots. All the hotels using service robots are listed in Table 2.

Estimations for the future are that every fifth job in hospitality could be in the domain of robots, without any drastic technology improvement (Autor, 2010). However, for robots to take over more socially oriented tasks, they should include more advanced artificial intelligence features in their operations. While introducing service robotization the safety of humans must be assured. It is also questionable whether guests are willing to accept that kind of impersonal communication, so further research in this area should be held. There is also a question of morality, “Can we really teach robots right from wrong”. In this sense it can be expected that the service industries will lag behind the manufacturing industries in the implementation of artificial intelligence solutions into

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PPKP_2017_book.indbKP_2017_book.indb 142142 221/11/20171/11/2017 06:5206:52 their operations. Why? Because they ultimately deal with people and ethical and moral challenges of robotization have to be resolved before fully adopting those technologies. Table 2. Hotels using service robots Level of TripAdvisor / Booking Price Hotel Country robotization comments comparison Henn-na Hotel Nagasaki & Tokyo, Fully staffed • 7.6 125-200€ Japan by robots • Unique experience • Funny but still far to go • Overpriced, overrated • Kids loved it • Still have human staff in offices to help if needed • Robots need improvement Pengheng Space China Waiters, • 7.8 11€ Capsules Hotel reception • Small room capsules desk staff and doormen robots • Futuristic vibe Marriott Fairfield Inn California, US Delivery robot • 9.2 120-160€ and Suites • Children enjoyed the robot Hilton McLean Tysons Virginia, US Robot concierge • 8.8 80-90€ Corner Aloft Cupertino California, US Robot butler • 8.9 100-140€ Delivery robot • Everybody seem to like the robot delivery Ghent Marriott Hotel Belgium • 9.0 200-250€ Residence Inn by Louisiana, US Robot butler • 8.8 215-300€ Marriott • Liked the robot Holiday Inn Express California, US Delivery robot • 8.8 150-200€ Redwood City-Central Hotel EMC2, Illinois, US Delivery robot • 9.4 180-200€ Autograph Collection • The hotel of the future M Social Singapore Singapore Robot butler • 8.2 100-130€ • Robot makes it unique and special • Robot is polite and cute Sheraton Gateway California, US Delivery robot • 8.0 150-200€ Hampshire Hotel Belgium and Multi-talented • 7.1 120-160€ Netherlands humanoid robot YOTEL New York New York, US Robot to store • 8.3 170-200€ luggage Source: Hotels’ websites.

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PPKP_2017_book.indbKP_2017_book.indb 143143 221/11/20171/11/2017 06:5206:52 3 Henn-na Hotel, the first 4.0 hotel in the world

The first fully robot-staffed hotel opened in Japan in July 2015. The name of the hotel is Henn-na and it means “strange or weird” in Japanese. The hotel has more than 200 robots in use, including an English-speaking dinosaur at the reception desk, robot porters, a giant robotic arm in the robot cloak room, a concierge and personal robots in the rooms (Robarts, 2015). Besides the available secondary data we interviewed the public relations manager of Henn-na Hotel, Mr. Yuko Nakano, to find more about hotel robotization and motivation for opening such a hotel.

So, what does a day at Henn-na Hotel look like? When coming to the reception desk, there is a Japanese speaking female humanoid and a multilingual dinosaur receptionist. The entire process of check-in is still mainly done by using a tablet; however, the guests found the robot receptionists to be an interesting and entertaining part of the registration process. After the check-in there is an automated trolley porter to deliver luggage to the room and an available storage room operated by a robotic arm. In the lobby a concierge robot provides guests with the information about the restaurant menus, events and other useful information about the neighborhood. There is also an entertainment robot in the lobby pretending to play the piano. All rooms are equipped with a small personal robot with a voice recognition system which answers simple questions about the time, weather, restaurants, and controls room lighting as there are no switches on the walls. The hotel has also introduced other technological improvements, such as fa- cial recognition system, meaning there is no need to use keys, grass cutter robots, vacuum cleaner robots, delivery robots and drones. The main tasks and guests’ responses to service robots are summarized in Table 3 (Henn-na Hotel, 2017).

There are still some operations at the hotel that must be performed by humans, e. g. robots still cannot make the beds. Another thing is security. The hotel uses security cameras and is supervised by humans, preventing any offensive acts in the hotel. Mr. Nakano told us how the visitors initially felt worried because they could not see any staff in the lobby, but when they realized that the staff would come as soon as they were needed, they stopped worrying. Accord- ing to Mr. Nakano, the goal of almost totally robot-staffed hotel was to save labor costs. The hotel management wanted to reach the highest productivity level, and since this project made them worldwide leaders in hospitality robotization, they have achieved substantial economic benefits. Further motivation for them to invest in this hotel was to pamper families with children, since the younger ones are the population that is amazed by the robots working in the hotel.

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PPKP_2017_book.indbKP_2017_book.indb 144144 221/11/20171/11/2017 06:5206:52 Table 3. Henn-na Hotel service robots Service Type of robot Tasks Guests’ comments Reception Humanoid/Multilingual Check-in/Check-out Interesting and entertaining, receptionist use of tablet, sometimes help of human staff needed Luggage Automated trolley porter Storage and room luggage delivery Lobby Concierge robot Providing useful information Entertaining, fun Entertainment robot about hotel Playing the piano Room Personal robot Telling the time and weather Kids enjoyed it, but more features Managing the lighting provided in Japanese, sometimes interrupting guests during conversation Room service Robot butler Bringing food and toiletry kits Source: Hen-na Hotel’s website and interview with hotel management.

To get more insight into guests’ impressions about the hotel we have analyzed guests’ comments on TripAdvisor. Most guests stated that the hotel was unique and gave them an interesting experience. Some of them were worried because there were no human employees, while others complained that the check-in process was conducted by humans, so they missed the “authentic robot experience”. Guests who struggled with the reception service pointed out, “Yes, there were robots at the check-in but they had problems scanning our non-Japanese passports so a human had to assist.” (TripAdvisor, 2017). In general, guests concluded that the hotel was much fun for the children and recommended it to families with small children, which was the owners’ initial motivation for this investment. There were also comments related to the hotel price, “The novelty of the robots does not make up for a very mediocre hotel.” (TripAdvisor, 2017). In terms of technological solutions, some of them found them useful, while others struggled because they didn’t work. Overall, most of the comments were positive and the guests claimed that the hotel is something special but still needs improvement, “Unique, but I feel it is not there yet!” (TripAdvisor, 2017).

Henn-na Hotel is ultimately an outstanding innovation in the hospitality industry making a cutting-edge progress in hotel management and operations. The hotel management is planning further development of the robots. They are also aware that this is a long-term process and that fully robotized hotels are still “far away”, since human power is still absolutely needed.

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PPKP_2017_book.indbKP_2017_book.indb 145145 221/11/20171/11/2017 06:5206:52 4 Future challenges related to technological trends in tourism

Although at the moment, service robots are still in the implementation phase in tourism, there is a potential in incorporating the novelties into tourism and hospitality. Tourism is an industry that employs mainly low skilled workers and there is a potential to replace some of those functions that do not require human contact with robots. Hotels worldwide are introducing some machine based functions, such as delivery robots, butler robots, concierge and entertainment robots, and it is estimated that every fifth job in hospitality could be replaced by a robot at this very moment (Autor, 2010).

Besides being utilized for monotonous and repetitive tasks, robots can perform operations with accuracy and have the ability to work continuously as opposed to a human worker. However, the potential threat of robotization is definitely social impact, specifically issues related with ethical and moral standards. One has to ensure that the machines perform as planned and that people can’t overpower them to use them for their own needs (World Economic Forum, 2016). Another matter of concern is security that has to be related to human actions.

From the consumers’ point of view, recent studies have been inconclusive. Some of them are showing that customers would not mind having robotized services if those made their travels easier and more comfortable and customized, while others find that people do not feel comfortable when robots are involved in emotional and personal issues (European Union, 2017). Furthermore, there is a potential threat of losing “human jobs”; however, with tourism booming in the last decades and jobs being low-skilled and low-paid, robotization is actually more of a solution than a threat.

In terms of economic impact, the introduction of new technologies would potentially reduce financial costs. The relevant question is the development costs that are still unknown and the question of robot taxation, which most of the countries still do not have a clear standpoint on. The potential winner of the technical changes might be the environment, since current practices have shown that significant reductions of water, waste and energy can be reached with robotization in tourism and hospitality.

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PPKP_2017_book.indbKP_2017_book.indb 146146 221/11/20171/11/2017 06:5206:52 Conclusion

To finalize, in the tourism and hospitality industry robotization is still in its infancy. It is not to be expected that tourism will be the leader in implementing new technologies into its operations. It will be more of a slow adopter, primarily due to the moral and ethical issues, since the tourism industry is ultimately about the people. Furthermore, the introduction of robots is to be expected in the areas where human contact is not crucial and in low to middle income tourism offers where cost-efficiency is more relevant than service quality.

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PPKP_2017_book.indbKP_2017_book.indb 147147 221/11/20171/11/2017 06:5206:52 References Autor, D. 2010. “The Polarization of Job Opportunities in the U.S. Labor Market.” Washing- ton: The Brookings Institution. Booking - Hotels’ comments. August 19, 2017. URL: https://www.booking.com. Buhalis, D. 2003. “eTourism: Information technology for strategic tourism management.” Harlow: Pearson Education. Buhalis, D., Neuhofer, B., and Ladkin, A. 2013. “A Typology of Technological-Enhanced Tour- ism Experiences.” International Journal of Tourism Research 16: 340-350. Cvelbar, K. L., Mayer, M., and Vavpotic, D. 2017. “Using user-generated content to identify visitor flows in tourism.” Journal of Hospitality Marketing & Management 18: 66-94. European Commission. 2010. “Europe, the world’s no. 1 tourist destination – a new political framework for tourism in Europe.” URL: http://www.iaapa.org/docs/gr-archive/tour- ism_implementation_plan_comments_jan_2011.pdf?sfvrsn=0. European Union. 2017. “Public Attitudes towards Robots.” URL: https://data.europa.eu/ euodp/data/dataset/S1044_77_1_EBS382. Fairfield Inn & Suites San Diego North/San Marcos. August 20, 2017. URL: http://www. marriott.com/hotels/travel/sanes-fairfield-inn-and-suites-san-diego-north-san-marcos/. Ghent Marriott Hotel. August 8, 2017. URL: http://www.marriott.com/hotels/travel/gnemc- ghent-marriott-hotel/. Hampshire Hotel. August 21, 2017. URL: https://www.hampshire-hotels.com/en. Henn-na Hotel. August 21, 2017. URL: http://www.h-n-h.jp/en/. Hilton McLean Tysons Corner Hotel, McLean, United States. August 21, 2017. URL: http:// www3.hilton.com/en. Holiday Inn Express Redwood City-Central. August 19, 2017. URL: https://www.ihg.com/ holidayinnexpress/hotels/us/en/redwood-city/rwcca/hoteldetail. Hotel EMC2, Autograph Collection. August 18, 2017. URL: http://www.marriott.com/hotels/ travel/chidx-hotel-emc2-autograph-collection/. Lubetkin, M. 2016. “Tourism and The Internet of Things — IoT.” Linkedin. URL: https://www. linkedin.com/pulse/tourism-internet-things-iot-meni-lubetkin. M Social Singapore Hotel. August 21, 2017. URL: http://www.hotelsone.com/singapore- hotels-sg/m-social-singapore.html. Mason, P. 2003. “Tourism Impacts, Planning and Management.” Oxford: Butterworth- Heinemann. Pengheng Space Capsules Hotel, Shenzhen. August 21, 2017. URL: http://pengheng-space- capsules-hotel.hotel-shenzhen.com/en/. Prahalad, C., and Ramaswamy, V. 2004. “Co-Creation Experiences: The Next Practice in Value Creation.” Journal of Interactive Marketing 18: 5-14. — 148 —

PPKP_2017_book.indbKP_2017_book.indb 148148 221/11/20171/11/2017 06:5206:52 PwC Middle East. 2017. “The five megatrends that will impact the Middle East’s travel and tourism industry: PwC report.” URL: https://www.hospitalitynet.org/performance/4082368. html. Residence Inn Los Angeles LAX, Century Boulevard. August 21, 2017. URL: http://www.marriott.com/hotels/travel/laxax-residence-inn-los-angeles-lax-century-boulevard/. Robarts, S. 2015. “New Japanese hotel has robot staff and no room keys.” New Atlas. URL: https://newatlas.com/henn-na-hotel-robot-staff/38577/. Sheraton Gateway Los Angeles Airport Hotel. August 19, 2017. URL: http://www.shera- tonlax.com/. Sigala, M., Gretzel, U., Xiang, Z., and Koo, C. 2015. “Smart tourism: foundations and developments.” St. Galen: Institute of Information Management. TripAdvisor - Hotels’ comments. August 25, 2017. URL: https://www.tripadvisor.com/. Tussyadiah, I. P., and Fesenmaier, D. R. 2009. “Mediating Tourist Experiences.” Annals of Tourism Research 36(1): 24–40. UNWTO. 2017. “Sustained growth in international tourism despite challenges.” URL: http:// www2.unwto.org/press-release/2017-01-17/sustained-growth-international-tourism- despite-challenges. Wahab, I. N. 2017. “Role of Information Technology in Tourism Industry.” Bangalore: Garden City University. Washburn, D., and Sindhu, U. 2010. “Helping CIOs Understand “Smart City” Initiatives.” Cambridge: Forrester Research. World Economic Forum. 2016. “Top 9 ethical issues in artificial intelligence.” URL: https:// www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence/. YOTEL New York. August 19, 2017. URL: http://www.yotel.com/.

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PPKP_2017_book.indbKP_2017_book.indb 150150 221/11/20171/11/2017 06:5206:52 III. INDUSTRY 4.0 AND CHANGES IN THE LABOR MARKET

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PPKP_2017_book.indbKP_2017_book.indb 152152 221/11/20171/11/2017 06:5206:52 Polona Domadenik, Špela Drnovšek, Anej Peter Lah, Urban Smolar

LABOR MARKET POLARIZATION: WELLPAID HIGHTECH JOBS VS. LOWPAID SERVICE JOBS?

Introduction

Every major technological event in the economy, such as every industrial revolution, has caused displacement in the labor market, and with Industry 4.0, we are once more at the brink of such changes, yet for the first time they threaten not only completely routinized jobs, but are seeping farther and farther into areas that have long been deemed irreplaceable by machines. However, while the experts agree that these changes will cause further labor market polarization towards either high-skill, high-paid jobs or low-skill, low-paid service jobs (Frey and Osborne, 2015; Goos et al., 2010), it is important to closely examine the available data to truly understand and clearly see the actual differences that such changes bring. Based on the analysis of the available data, this chapter explores the case of such polarization in the European Union in general and in the most digitalized and robotized EU countries in particular.

After the introduction, we briefly review what the current literature covers regarding Industry 4.0 and labor market polarization, which is followed by a description of our data as well as the results of the analysis, while the final section summarizes and concludes our findings.

1 Literature review

In order to better understand the current and future trends in respect of labor market polarization and its connection to digitalization, it is important to view them in a historical context (Institute of Development Studies, 2017). Most lit-

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PPKP_2017_book.indbKP_2017_book.indb 153153 221/11/20171/11/2017 06:5206:52 erature agrees that in the past two decades, we have witnessed the polarization of jobs – much faster employment growth in highest and lowest parts of the skill and income occupations spectrum (mainly non-routine cognitive and non-routine manual occupations), while middle-skill, middle-income occupations saw much slower growth (Frey and Osborne, 2015; Institute for Development Studies, 2017; Goos et al., 2010). This seems to be closely connected with digitalization, since these ‘’in-the-middle jobs’’ tend to be the most routinized and therefore most vulnerable to automatization (Frey and Osborne, 2015). Some literature even recognizes technological changes paired with offshoring as an explanation for around 70 percent of increases in employment share of low-paying and high-paying jobs against middle-paying jobs between 1993 and 2006, where routinization suppos- edly played a major role for those increases (Goos et al., 2010).

While the current trend of digitalization is much slower than the digital disruption caused by consumer internet (McKinsey & Company, 2015), it is important to note that in the current trend, digitalization and robotization are changing the very nature of work across occupations, industries and countries. While presenting new employment opportunities for highly skilled workers, the increasing scope of digitalization and robotization increasingly substitutes ordinary workers in a number of domains. This implies that the digital age may bring an increasing share of workers worse off in the long-run. To that end, most literature also agrees that the actual gains or losses for each individual due to digitalization and automatization are highly dependent on their skill level and degree of skill-bias technological change (Frey and Osborne, 2015). In the current trend of digitalization, this means that such changes increase the demand for educated workers, which does not only harm the middle-skill, middle-income occupations, but as opposed to trends in the past, seems to put low-skill, low-income occupations (with the exception of occupations intensive in human interaction) at even greater risk of automatization than the aforementioned ‘’middle jobs’’ (Frey and Osborne, 2015; Acemoglu and Restrepo, 2017). This finding points toward the fact that while no industry is im- mune to the effects of digitalization, the susceptibility of each industry depends on the structure of its occupations and their probability of being automated. As an illustration, Frey and Osborne (2015) rank the industries in the U.S. based on their employment share at high risk of automatization, the top three at risk being Accommodation and Food Services (86.7 percent), Transportation and Warehous- ing (75 percent) and Real Estate and Rental & Leasing (67.2 percent).

Most literature agrees that the changes in wages do not directly reflect the changes in employment structure, where Frey and Osborne (2015) explain that with the difference of difficulty between moving from middle-skill jobs to low-

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PPKP_2017_book.indbKP_2017_book.indb 154154 221/11/20171/11/2017 06:5206:52 skill jobs against moving from middle-skill jobs to high-skill jobs. This is a consequence of the so called ‘race between technology and education’, where the first occurs rapidly, while the second takes far more time to take hold. Autor (2015), on the other hand, mainly names three mitigating forces as the reasons for the difference described above, which are complementarity, demand elasticity and labor supply. Both of them therefore suggest that the situation regarding the changes in wages due to digitalization and robotization is more complex than the implications for employment growth rates.

The final point that most of the literature suggests regarding the connection between labor market polarization and digitalization is that it is rather difficult to make generalizations of the relationship as a whole and that attempts to link the two need to be well defined in scope as well as the specific parameters used to draw conclusions from.

With that in mind, this chapter will be focusing on presenting the differences that can be observed between the most digitalized countries on one hand and the most robotized countries on the other and the rest of the EU countries, as well as the EU average, which could at least partially be explained by the effect of Industry 4.0. The main research questions of this chapter will therefore be: • differences in employment growth rates in both industry and services due to robotization and digitalization, • changes in educational structure of the working population to meet new demands in the labor market, • differences in income growth as a result of Industry 4.0, • shifts in employment growth in sectors that have been outlined by literature to be at risk due to Industry 4.0; manufacturing, as well as certain service sectors (wholesale and retail trade, transport, accommodation and food services).

2 Data description

The data for our research was acquired from international institutions offering statistical data which is related to the topic of polarizing effect of digitalization and robotization of the industry, namely EUROSTAT, International Labor Or- ganization and the Organization for Economic Co-operation and Development. These institutions offered statistical data on most general parameters required for the analysis during the period between 2010 and 2015. Since the literature review

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PPKP_2017_book.indbKP_2017_book.indb 155155 221/11/20171/11/2017 06:5206:52 explores polarization regarding both income and employment respective to skills, we gathered data on income growth, education levels and employment rates divided into industry and services. The most revealing statistical data for the topic of polarization of the labor market is the measure of percentile of enterprises in a European country that has an automatic link to either supplier or buyer activities and employs at least ten people. This measure was used in the analysis as a way to determine the country’s degree of digitalization in its economy or private sector, which allowed the analysis of the correlation between the degree of highly digitalized countries and other aforementioned statistical data. The acquisition of this data revealed the ranking of the European countries by the degree of their digitalization and the top five countries, used as an example, are: Denmark, Germany, Belgium, Croatia (which proved as a huge outlier displaying counter- intuitive data due to the misreport of the tourism sector and was, in this analysis, replaced by Finland, which is the sixth on the list) and Lithuania, respectively. To avoid biases in the aforementioned analysis, such as the difference in the size of the countries and their economies, as well as different starting positions of the countries based on their situations during the financial crisis, growth rates were used as the most relevant data point in this regard.

Other complementary information was gathered from the executive summary of World Robotics Industrial Robots and Service Robots (2016). The annual report of World Robotics provides robust measure of robotization in countries around the world, proxied by the number of multipurpose or reprogrammable robots per ten thousand employees in either industry or services. World Robotics ranks top five most robotized countries in Europe as follows: Germany, Italy, France, Spain and the United Kingdom, respectively, and later explains that the main force for robotization in a country is the share of industry involved in automotive production.

However, before presenting the results of our analyses, it is important to stress that readers should be aware of potential shortcomings due to endogeneity of digitalization and robotization and the crisis affecting most of the countries listed above differently than the rest of the EU members.

3 Labor supply and demand response to Industry 4.0’s digitalization and robotization

This chapter analyzes the degree of digitalization and robotization and the labor market developments in order to discover whether the described job polarization is occurring in the EU. The analysis is based on a series of scatter

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PPKP_2017_book.indbKP_2017_book.indb 156156 221/11/20171/11/2017 06:5206:52 plots with respective correlation lines, unraveling the growth of employment in either industry or services, the structure of employees in regards to their level of education, and the wage growth divided among the levels of education.

3.1 Employment growth changes due to digitalization in industry and service sectors

We began by analyzing how the degree of digitalization affects the two different sectors regarding replacing jobs and lowering employment; for that purpose, we divided the data into either the industry or the service sector. The following two scatter plots try to display the correlation in employment of either industry or services based on the degree of digitalization which is represented by the x-axis.

Based on the data from the five most robotized and five most digitalized countries we can identify a positive correlation between the degree of digitalization of a country and the percentage growth of employment in both industry (Figure 1) and service sectors (Figure 2). Surprisingly, employment in services grows much faster with the degree of digitalization than it does in industry.

These results seem to confirm what some authors hypothesize, that this effect may be due to digitalization in industry translating into robotized, which can increase employee efficiency manyfold, thus requiring less employees, while on the other hand, digitalization in the service sector has so far appeared to be mainly an efficiency and productivity enhancing tool. Figure 1. Industry employment percentage change in relation to digitalization rate, observed EU countries, 2010-2015 6

-2

Industry employment [% change] [% Industry employment -4

-6 0 5 10 15 20 25 30 35 40 Digitalization rate [%] Source: Eurostat, 2017.

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PPKP_2017_book.indbKP_2017_book.indb 157157 221/11/20171/11/2017 06:5206:52 Figure 2. Services employment percentage change in relation to digitalization rate, observed EU countries, 2010-2015 5 4 3 2 1 0 1 2 Services employment [% change] 3 4 0 5 10 15 20 25 30 35 40 Digitalization rate [%] Source: Eurostat, 2017.

3.2 The effect of digitalization and robotization on educational structure of the working population

As stated in the literature review, the polarization occurs when low-skill jobs are being replaced by digitalization or robotization, thus the evidence should state that less and less low educated employees will be needed. The following three scatter plots try to map out the relationship between the degree of employees with primary, secondary and tertiary education and the degree of digitalization.

An obvious fact that stands out in Figure 3 immediately is the decline in the share of employees with primary level education as the degree of digitalization gets larger.

The growth in the share of employees with secondary level education in regards to the degree of digitalization (Figure 4) almost mirrors the decline in the share of employees with primary level education.

Lastly, we see a positive correlation with the employment of tertiary educated employees (Figure 5), yet the difference is not as dramatic as seen in the case of secondary and primary educated employees. The effect of low-skill jobs slowly disappearing in job polarization is quite apparent, since the correlation shows similar outcomes as hypothesized in the literature review.

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PPKP_2017_book.indbKP_2017_book.indb 158158 221/11/20171/11/2017 06:5206:52 Figure 3. Percentage of working population with primary level education in relation to digitalization rate, observed EU countries, 2010-2015 45 40 35 30 25 20 15 10 5 Employed with primary eduaction [%] 0 0 102030405060 Digitalization rate [%] Source: Eurostat, 2017.

Figure 4. Percentage of working population with secondary level education in relation to digitalization rate, observed EU countries, 2010-2015 70

10 Employed with secondary eduaction [%] with Employed 0 0 102030405060 Digitalization rate [%] Source: Eurostat, 2017.

3.3 Do digitalization and robotization affect the income growth as well?

The other side of the coin of job polarization in Industry 4.0 may at first glance suggest that rise or decline in employee’s income is also connected with the degree of digitalization.

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PPKP_2017_book.indbKP_2017_book.indb 159159 221/11/20171/11/2017 06:5206:52 Figure 5. Percentage of working population with tertiary level education in relation to digitalization rate, observed EU countries, 2010-2015 50 45 40 35 30 25 20 15 10 5 Employed with tertiary with eduaction [%] Employed 0 0 102030405060 Digitalization rate [%] Source: Eurostat, 2017.

Regarding the income of the working population with primary education, a clear downward line depicts a negative correlation with the degree of digitalization; after a certain degree their income starts to experience negative growth.

Unlike the population with primary education, the working population with secondary education experiences rising growth rates in correlation to higher de- grees of digitalization.

Figure 6. Income growth changes of working population with primary level education in relation to digitalization rate, observed EU countries, 2010-2015 10.0

5.0

0.0

-5.0 Income growth rate [%]

-10.0 0 5 10 15 20 25 30 35 40 Digitalization rate [%] Source: Eurostat, 2017.

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PPKP_2017_book.indbKP_2017_book.indb 160160 221/11/20171/11/2017 06:5206:52 Figure 7. Income growth changes of working population with secondary level education in relation to digitalization rate, observed EU countries, 2010-2015 6.0

4.0

2.0

0.0

-2.0 Income growth rate [%] -4.0

-6.0 0 5 10 15 20 25 30 35 40 Source: Eurostat, 2017. Digitalization rate [%]

Lastly, the working population with tertiary education experiences the fastest growth rate of income, the higher the digitalization rate. While most literature notes the changes in income do not necessarily reflect changes in employment structure, the correlation lines do seem to present an image in favor of polarization, as the scatter plots depict a decline of income for the population with a low education level and growth for the population with higher levels of education.

Figure 8. Income growth changes of working population with tertiary level education in relation to digitalization rate, observed EU countries, 2010-2015 6.0

4.0

2.0

0.0

Income growth rate [%] -2.0

-4.0 0 5 10 15 20 25 30 35 40 Digitalization rate [%] Source: Eurostat, 2017.

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PPKP_2017_book.indbKP_2017_book.indb 161161 221/11/20171/11/2017 06:5206:52 4 The impact of digitalization and robotization on employment growth by sector

While observing the changes in employment growth in different sectors among EU28 countries we found out that some sectors stood out based on the trends in employment. These are manufacturing and the wholesale and retail trade sector, transport, accommodation and food services (Eurostat providing employment data for all of them as a single sector) – all of which fall under the sectors seen as most vulnerable to digitalization and robotization (Frey and Osborne, 2015). We compared employment growth in these sectors among the five most digitalized and five most robotized countries with the employment growth average in the EU in order to understand the actual influence of robotization and digitalization on the labor market.

4.1 Manufacturing

As can be seen in Figure 9, employment growth in manufacturing was decreasing between 2011 and 2013 not only in EU28 in general but also in the five most digitalized and five most robotized countries. This was most likely the consequence of the first attempt to recover after the crisis, since economies were still not prepared for a turnaround in the economy. However, after 2013, we can see an increase in employment growth in manufacturing as a result of a real boost in the economies. Despite this general trend, as well as the fact that

Figure 9. Employment growth in manufacturing in EU28, 5 most digitalized, and 5 most robotized countries, 2010-2015 EU28 Digitalized Robotized 2

-1

-2 -3

-4

Percentage change on previous period previous on change Percentage -5

-6 2010 2011 2012 2013 2014 2015 Source: Eurostat, 2017.

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PPKP_2017_book.indbKP_2017_book.indb 162162 221/11/20171/11/2017 06:5206:52 both observed groups of EU countries had lower employment growth than the EU average, there are certain differences between the most digitalized and the most robotized countries in the EU. In general, in the most digitalized countries employment growth was much higher than in the most robotized economies in manufacturing before the real boost of countries’ GDP (for approximately two percentage points), which may indicate that robotized processes needed fewer workers, since they were substituting labor with new technologies. We can also observe that after 2013, employment was growing in both country groups, probably as a consequence of economic growth in general, as we can observe the same trend in employment growth for the EU average (Figure 9); however, both groups of countries have seen a visibly lower employment rate, which may point towards an impact by Industry 4.0.

4.2 Wholesale and retail trade, transport, accommodation and food services

Again, we can observe similar general trends in wholesale and retail trade, transport, accommodation and food services (Figure 10). Not focusing on crisis effects, we can see that until 2014, in the most digitalized EU countries employment growth was higher in the wholesale and retail trade sector than in the most robotized countries (by more than one percentage point). However, in 2015 the situation turned around and the lately robotized countries started to employ much more in wholesale and retail trade than digitalized economies

Figure 10. Employment growth in wholesale and retail trade, transport, accommodation and food services, 2010-2015 EU28 Digitalized Robotized 2.0

1.5

1.0

0.5

0.0 -0.5

-1.0

Percentage change on previous period previous on change Percentage -1.5

-2.0 2010 2011 2012 2013 2014 2015 Source: Eurostat, 2017.

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PPKP_2017_book.indbKP_2017_book.indb 163163 221/11/20171/11/2017 06:5206:52 due to already saturated labor market in previously digitalized wholesale and retail trade. Nevertheless, the average EU employment growth in this sector after 2014 was higher than employment growth in most digitalized and robotized countries. This phenomenon is most likely the consequence of other EU countries that are not as robotized and digitalized, and therefore need more labor to satisfy the needs of boosting economies.

Another thing we can observe for these sectors is that a large part of their employment structure can be seen as fairly routinized positions, meaning that the fact that the most robotized and the most digitalized countries have either lower or even negative employment could hint at the connection between In- dustry 4.0 and the polarization towards high-skilled occupations, which falls in line with the observations and predictions made in the literature we reviewed regarding specific sectors. It is however important to, again, stress that these connections are only implied, considering the fact that the crisis impacted these countries differently than most of the EU, as well as the endogeneity of robotization and digitalization.

Conclusion

Keeping in mind the limitations to the conclusiveness of the analysis, based on our analysis we can conclude that the share of employees with primary education in more digitalized and robotized countries fell, while on the other hand, the share of employees with secondary and tertiary education increased more than on average in the EU – implying a potential connection between Industry 4.0 and the educational structure of labor force. The results also seem to imply that higher levels of digitalization are accompanied by income growth for the secondary and tertiary educated and much smaller or even negative growth for primary educated employees.

Our results also imply that certain sectors outlined in the literature do show the described shifts in employment growths and do seem to be vulnerable to Industry 4.0 – sectors with a large portion of routinized, middle and low-skilled occupations.

Perhaps the most important result of our analysis, however, is that there appear to be visible differences between the impact of digitalization based on how it is applied as either substitution or augmentation, which differs in manufacturing and service sectors. And while both, higher levels of digitalization

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PPKP_2017_book.indbKP_2017_book.indb 164164 221/11/20171/11/2017 06:5206:52 (services) and robotization (manufacturing), implied a larger polarization in those countries than the EU average, the actual degree and depth of polarization varies between the more heavily digitalized or robotized countries – which seems to at least hint towards how further implementations of Industry 4.0 could shape the labor market in the future.

References Acemoglu, D., and Restrepo, P. 2017. “Robots and Jobs: Evidence from US Labor Markets.” URL: https://economics.mit.edu/files/12763. Autor, D. H., 2015. “Why Are There Still So Many Jobs? The History and Future of Workplace Automation.” Journal of Economic Perspectives 29(3): 3-30. Eurostat. 2017. “Eurostat Database.” URL: http://ec.europa.eu/eurostat/data/database. Frey, C. B., and Osborne, M. 2015. “Technology at work: The future of Innovation and Em- ployment.” Citi GPS: Global Perspectives and Solutions. Goos, M., Manning, A., and Salomons, A. 2010. “Explaining job Polarization in Europe: The Roles of Technology, Globalization and Institutions.” London: Centre for Economic Performance. Institute of Development Studies. 2017. “Digital Development summit 2017 – the Future of of Work, background paper.” Brighton: Institute of Development Studies. International Federation of Robotics. 2016. “Executive Summary World Robotics 2016 Industrial Robots.” URL: https://ifr.org/img/uploads/Executive_Summary_WR_Indus- trial_Robots_20161.pdf. McKinsey & Company. 2015. “Industry 4.0: How to navigate digitalization of the manufacturing sector.” McKinsey Digital. URL: https://www.mckinsey.de/files/mck_industry_40_re- port.pdf.

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PPKP_2017_book.indbKP_2017_book.indb 166166 221/11/20171/11/2017 06:5206:52 Marko Pahor, Nada Zupan, Katja Avsenik, Domen Boštjančič, Nina Jagodic

HUMAN CAPITAL FOR THE FUTURE

Introduction

In today’s economy the technology is moving from digital to cognitive, causing companies, business models, products and processes to change. A wide range of capabilities such as artificial intelligence, processing of natural language, human-computer interaction and deep learning have evolved over the last few years with exponential speed (Rometty, 2017). These technological shifts and advances known under the name Industry 4.0 are challenging organizations to introduce changes to their business processes, including Hu- man Resource Management (HRM), where both the occupational mix as well as skill mix are swiftly being altered or humans are being replaced by robots or artificial intelligence. Consequently, they pose a major challenge especially to the field of talent management (Arntz et al., 2016).

The aim of this chapter is, first, to show which jobs and skills that are today still prevalent in any organization might become obsolete, where most job creations can be expected and what the emerging skills and occupations are. Second, we investigate how the changes in the skill and occupation mix will impact HRM in the corporate world.

Next, the prospective development of the existing and emerging occupations is studied, followed by a more detailed examination of the new competencies required to support efficient integration of smart machinery in everyday work life. Last, the chapter addresses the challenges of HRM at corporate level, focusing on finding the solutions to an increasing miss-match of workers’ competencies and job requirements in the swiftly changing environment.

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PPKP_2017_book.indbKP_2017_book.indb 167167 221/11/20171/11/2017 06:5206:52 1 Major technology-induced changes in the labor market

Machine learning, robotics and cyber-physical systems (CPSs) are leading us towards increasingly complex and intelligent systems, characterized by the merging of the physical and virtual worlds, dynamic formation of system-of- systems, context-dependent and autonomously operating systems, cooperative systems with decentralized control and extensive human-system-collaboration (Schuh et al., 2014). Following these advances in artificial intelligence, natural language processing, and inexpensive computing power, jobs that were once considered as poor candidates for automation are suddenly becoming automatized. However, while technology has made certain occupations and industries redundant, it has caused a wide array of new occupational fields as well as become a complement to human labor, helping workers to become more productive (Spandas, 2016).

1.1 The perspective of occupations

There is no straightforward answer to the question whether automation can replace human workers, however, some things are certain – new technologies and the use of information technology will reshape our way of work and the use of our skills. The effects of automation might be slow at the macro level, within entire sectors or economies, but they could be quite fast at the micro level, for individual workers whose activities are automated or for companies whose industries are disrupted by competitors using automation (Manyika et al., 2017). Nowadays, the use of machinery and computers has replaced manual skills and craftsmanship and these former workers are now considered as machine operators (Burke and Ng, 2006).

Reflecting back to the technological advances of the previous century, the feared massive unemployment did not happen due to accompanying creation of new types of work and other effects of the increased productivity. For example, the large-scale deployment of bar-code scanners and associated point-of-sale systems in the United States in the 1980s reduced labor costs per store by an estimated 4.5 percent and the cost of the groceries consumers bought by 1.4 percent. It also enabled a number of innovations, but cashiers were still needed; in fact, their employment grew at an average rate of more than two percent between 1980 and 2013 (Chui et al., 2016). The assumptions of Industry 4.0 are that while occupations with high percentage of routine jobs will be cut back, occupations with lower percentage and work that cannot be conducted according

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PPKP_2017_book.indbKP_2017_book.indb 168168 221/11/20171/11/2017 06:5206:52 Figure 1. Can machines replace human workers? WHY IT CANNOT? WHY IT CAN? Highly qualified Creative workers: Software (Artificial intelligence): competencies Machines are unable to reproduce artistic Routine intellectual operations of any creativity, professional expertise and skill in complexity can be easily put together into an details. algorithm and controlled by software that can process arrays of data many times greater than those available to humans. Low qualified Migrant worker: Robot: competencies Migrant labor is cheaper than the production, Heavy physical labor, work in harsh conditions, operation and maintenance of robots for low- and routine manual labor can be, predictably qualified work. and more effectively performed by automatic devices. Source: Luksha et al., 2015. to programmable standards will increase. ‘As processes are transformed by the automation of individual activities, people will perform activities that complement the work that machines do, and vice versa’, said the report performed by McKinsey’s specialists. The technical integration basing on CPS and IoT into industrial processes is resulting in impacting the value chain, business models, as well as downstream services and work organisation (Wolter et al., 2015).

So we can expect (following also the historical experience) that the occupational structure will change; some occupations will become fully obsolete by automation, while some cannot be expected to be replaced. Figure 1 offers a short overview of where and how machines might replace human workers and in which cases human workers will still prevail.

The automation of activities can enable businesses to improve performance by reducing errors and improving quality and speed, and in some cases achieving outcomes that go beyond human capabilities. Contrarily to the overall belief that only low-skill, low-wage work could be automated, almost every occupation has partial automation potential. Middle-skill and high-paying, high-skill occupations also have a degree of automation potential, because every occupation includes more than one type of activity, of which each has different requirements for the automation. According to Chui et al. (2016), the so called technical feasibility, which is defined as a percent of time spent on activities that can be automated by adapting currently demonstrated technology, helps forecast the automation potential and job obsoleteness.

However, momentarily at the existing level of technological development, less than five percent of occupations are candidates for full automation. The

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PPKP_2017_book.indbKP_2017_book.indb 169169 221/11/20171/11/2017 06:5206:52 study also showed that data collection, data processing and predictable physical work have the highest technical feasibility, therefore, they will eventually be automated. Jobs that are most likely to lose out or change due to automation and technological advancement are in industries such as accommodation and food services (cooks, dish cleaners, room maids, etc.), manufacturing (welders, cutters, braziers, etc.), agriculture (farmers, plant workers, etc. and transportation and warehousing (warehouse workers, drivers, logistic operators, etc.) (Chui et al., 2016).

Jobs with routine tasks in the manufacturing sector, which are lost due to increases in productivity, are accompanied by growth in occupations with non- routine tasks, which on average require a higher level of qualifications. Basing on this trend, a shift towards a service-based economy is assumed. This also implies that to a great extent mainly a job ‘switch’ between sectors, occupations and qualifications will occur. While particularly in the manufacturing sector (jobs such as system and machinery control and maintenance) around half a million jobs is expected to be lost, the demand for service professions is predicted to increase. In particular, IT and scientific professions could benefit from the investment in the automation process in the longer run. Moreover, the demand for highly qualified manpower should increase at the expense of the demand for persons with secondary education. Due to the increasing demand for highly qualified workers, new jobs are also expected in teaching professions, including professionals for adult education who will support the required continuous life-long education and training to make the workers adequate to work side by side with robots. Furthermore, due to changes of business models, business consultancy should benefit already in the short term (Wolter et al., 2015).

While some “traditional” jobs will be replaced, this cannot be generalized. Namely, the U.S. Department of Labor occupational database, the Occupational Informational Network (O*NET), projects that between 2014 and 2024 most of the projected job openings will be in traditional occupations like salespersons, cashiers, food preparation workers, waiters, customer service representatives, laborers and similar. These jobs are also part of the service sector with high technical feasibility, but the mentioned jobs still have projected average growth of two to eight percent per year. For now, these traditional jobs are unlikely to be replaced by new technology; workers will more likely (increasingly) work alongside machines, robots and new technology, making their work easier (O*NET, 2017).

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PPKP_2017_book.indbKP_2017_book.indb 170170 221/11/20171/11/2017 06:5206:52 The shift towards the service economy is just one of the emerging trends for the future, while sustainability is also a very prosperous trend. Moreover, O*NET data reveal a significant number of new and emerging jobs resulting from the green and sustainable economic development trends, such as recycling coordinators, nano systems engineers, geothermal technicians, sustainability specialists, etc. The United Nations Environment Programme (UNEP) has also pointed out the sectors that create the most sustainable jobs; those are research and development (R&D), administrative and service activities, and sectors, such as construction, recycle and waste management, which produce a vast amount of pollution. Jobs among the top selected sustainable jobs certainly show changes that are affected by Industry 4.0, with jobs such as electricians, machinists, industrial machinery mechanics, engineers, and software developers that are already using technology and machines as a tool in their workplaces, and will potentially become co-workers of robotic machines’ (Valenti et al., 2016).

The Health Care and Social Assistance industry jobs are also expected to grow (O’NET, 2017). Jobs like nurses, medical assistants, personal care aides, physicians and surgeons will be highly employable in the next eight years. Fast job growth projections in these industries are a consequence of the ageing world population, with people’s life expectancy extending. The projections for the future show that by 2050 there will be 25 percent of people that are 60 or above, in Europe even more, since Europe already has 25 percent of people aged 60 or above (United Nations, 2017).

Technology will induce significant changes in the labor market. But an even bigger change can be expected in the field of competencies and skills needed for these future jobs.

1.2 The perspective of competencies for the future

Competencies also have a limited life-span, similarly as some occupations, but the changes appear even faster. Sooner or later some competencies become less important and new emerging competencies take their roles (Spencer et al., 1992). Many studies are trying to identify the skills for the future. While their results differ, they all stress that some of the most important future competencies are innovation, knowledge of information technology, use of digitalization, foreign language, and critical thinking (Farčnik et al., 2016).

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PPKP_2017_book.indbKP_2017_book.indb 171171 221/11/20171/11/2017 06:5206:52 Figure 2. Anticipated skill needs due to sectoral/job restructuring, 2015-25, EU-28 Share of all jobs requiring the listed skills Jobs with rising projected employment Jobs with declining projected employment

28,4% Consumer interaction 23,3% 34,4% Technologies Product innovation 30,7% 38,3% Routine tasks 41,9% 76,1% Planning/organisation 70,8% 76,6% Learning to learn 75,3% 82,0% Problem-solving 78,4% Skills 64,9% Customer service 55,3% 80,1% Teamworking 78,4% 79,5% Communication 75,1% 73,9% Technical/job-speciﬁc 72,9% 20,2% Advanced ICT 13,9% 45,2% Foreign language 41,0% 55,3% Advanced literacy 44,4% 71,9% Vocational studies 73,9% 41,8% High Education 25,0% 46,7% Medium 57,4% 11,5% Low 17,6% Source: Cedefop, 2017.

The European Centre for the Development of Vocational Training has performed a skills survey on a Pan-European representative sample, with 49,000 respondents aged between 24 and 64. The results show that skills such as advanced literacy, problem solving, customer service and foreign language seem to be part of the important skill set in the future (Figure 2). Interestingly, at the moment, 87 percent of students are happy with the transversal skills they obtained through vocational training and education. Transversal skills are skills developed through training and are a mixture of cognitive and non-cognitive skills, such as problem solving, decision-making, risk assessment, management of own feelings, creativity, critical thinking, etc. (Cedefop, 2017). These skills will certainly be very important for workers in the future. Figure 2 shows how

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PPKP_2017_book.indbKP_2017_book.indb 172172 221/11/20171/11/2017 06:5206:52 Figure 3. Top 20 technology tools required in job applications for emerging jobs Number of job categories, marked as new and emerging, that list the use of this tool 0 100 200 300 400 500 600 700 800 900 Microsoft Excel Microsoft PowerPoint Microsoft Outlook Microsoft Access Data entry software SAP Autodesk AutoCAD Microsoft Project Microsoft Visio Adobe Systems Adobe Acrobat Adobe Systems Adobe Photoshop Structured Query Language SQL The MathWorks MATLAB Microsoft SharePoint SAS C++ ESRI ArcGIS software IBM Notes Microsoft Visual Basic Microsoft Publisher

Source: O*NET, 2017.

some jobs had a rising projected employment and others declining projected employment, according to the respondents who choose the skills which they think are important for different jobs.

According to O*NET data (2017), only reading comprehension was considered more important than critical thinking, with 92 percent of respondents evaluating it as more important at the same level of difficulty. Other competencies that were found as most common amongst the new and emerging jobs were active listening, speaking, writing, judgment and decision making, monitoring and complex problem solving, with more than 50 percent of participants of the survey seeing them as the most essential competencies for the new and emerging jobs.

Naturally, in the time of technological advances, the use of technology is also one of the key competencies for the future. Based on the O*NET data on Tools and Technology, Figure 3 provides us with top 20 searched tech skills posted in more than 9,800 job offers.

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PPKP_2017_book.indbKP_2017_book.indb 173173 221/11/20171/11/2017 06:5206:52 Knowledge of Microsoft Office, in particular Microsoft Excel is most wanted by employers, it was listed in job offers more than 800 times, out of 9,844 job listings that were included in O*NET database (2017). Enterprise resource planning and data entry software such as SAP, design software such as AUTOCAD or Adobe Photoshop were in high demand as well, followed by the knowledge of programing languages, such as SQL, MATLAB, C++ and others.

But these are not the only competencies being part of emerging occupations. In today’s dynamic environment new jobs and different skills arise every day and with them new potential competencies emerge that we need to possess to be competitive in the market. Consequently, these developments of competencies also lead to changes of the role that HRM is playing in the companies.

Pew Research Center conducted a large-scale canvassing of professionals in different fields which have shown what their expert opinion on the significance of training in the workplace by 2026 is. The study confirms that humans possess several skills that machines seem unable to replicate and this is where humans have an open window in keeping their jobs. The education system should nurture and develop these skills which employees could further develop in the years of their employment by different trainings. All of this is aimed at preparing workers to work efficiently alongside artificial intelligence and machines and make the most use out of the technology advancements (Pew Research Center, 2017). Furthermore, innovative training approaches as well as revitalizing some of the traditional techniques is helping to bridge the skill gap. With revitalizing apprenticeship, employers are remodelling the education system and adding value to potential employees (Association for Advancing Automation, 2017).

2 Technology-induced to human resource management

HRM is a field that is constantly facing new pressures for change by new technologies, globalization, increased generational diversity as well as other global trends (Stone et al., 2015). New technologies had a continuous impact on HRM in the past and future automation will unquestionably take part in this as well. One of the things that have changed in this field is the transition from the traditional human resources strategies to the so-called talent management. The CEOs who have taken part in the PwC Survey say that the main challenge nowadays is ensuring the right human capital with befitting competencies for the organization’s objective, meaning that talent management is of crucial importance (PwC, 2014). Furthermore, ‘Rather than focus on the traditional HRM

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PPKP_2017_book.indbKP_2017_book.indb 174174 221/11/20171/11/2017 06:5206:52 activities and processes, such as designing compensation package, reviewing contracts, talent management is focusing on contributing fundamental strategic capabilities that are rather cross-functional than HR process-based’ (Sparrow et al., 2015).

Looking at e-HR, this sets a good example of how employees are taking on a much more complex role than they have ever before and many aspects are not exclusively in the HRM domain anymore. For examples, tasks which were once carried out by the HR department have now been transferred to the employees themselves, such as writing reports, gathering feedback, etc. With e-HR and its advancements, managers and employees at all levels have been able to access information and use it to affect overall organizational effectiveness (Lengnick- Hall and Moritz, 2014).

Internal HR Development is another thing that is rapidly developing with the development of new technologies and will unavoidably keep changing in order to keep up with robotization. The organization’s goal should be long-term integration of the company’s strategy and its employees’ personal development. By driving the two together, the employees will easily adapt to a new position when necessary (Feng, 2016). With automation, employees at all levels will need to adapt to the new technology and will need to be trained in order to understand and make better use of the additional advancement. The labor market will be in desperate need of specific training. The Pew Research Center survey shows that 87 percent of surveyed workers believe that they will have to develop new skills and get specific training in order to keep pace with the evolving workplace which will be affected by automation (Pew Research Center, 2017).

The Eurostat’s Continuing Vocational Training Survey (CVTS) that has been conducted in 4 waves so far (1993, 1999, 2005 and 2010) collects information on enterprises’ investment in the continuing vocational training of their staff. Continuing vocational training (CVT) refers to education, training or activities which are financed in total or partly by the enterprise (Eurostat, 2010). As we can observe from the Figure 4, the EU’s total training enterprises as a percentage of all enterprises has been somewhat higher than 60 percent. This differs through each of the industries and is the highest in information and communication; financial and insurance activities.

Interestingly, Slovenia ranks better than the EU average, which suggests that Slovenian enterprises invest on average more in the continuing vocational training of their staff than an average EU enterprise (Eurostat, 2010).

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PPKP_2017_book.indbKP_2017_book.indb 175175 221/11/20171/11/2017 06:5206:52 Figure 4. Continuing vocational training in enterprises – training enterprises as a percentage of all enterprises in 2010 90 80 70 60 50 40 30 20 10 Training enterprise as a % of all enterprises enterprise Training 0 Total - all NACE Industry (except Construction Wholesale and retail Information and Real estate activities; activities construction) trade, transport, communication; professional, accomodation and ﬁnancial and scientiﬁc and food service insurance technical activities; European Union (28 countries) activities activities administrative and Slovenia support service activities; arts, entertainment Source: Eurostat, 2010. NACE and recreation

Furthermore, Eurostat’s percentage of all enterprises providing CVT courses is comparing years 2005 and 2010 and it can be clearly seen that the percentage has increased in the EU average but has decreased in Slovenia (Figure 5). This can be due to many reasons, however, the average increase can be explained by the increased awareness of the need for training of the employees, which has affected the enterprises’ culture and has increased the CVT courses for the employees (Eurostat, 2010).

More and more industries are pushed to increase automation due to increasing labor costs but the challenge for corporate HRM that consequently arises from this is finding new roles for redundant workers and making sure that they are equipped with knowledge that will enable them to work alongside machines and AI efficiently. According to Grant Thornton (2015), 43 percent of businesses expect automation to lead to job losses. One of the direct implications of this statistic is that some countries might be under a lot of pressure not to reduce staffing, especially in smaller economies. New Zealand is a good example for this as a small economy, where any job losses can attract negative headlines. Since low-skilled workers will be particularly at risk this poses a serious threat on the already rising inequality in the nowadays world. On the other hand, Perkins also shows that automation will enable low-skilled workers to pursue

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PPKP_2017_book.indbKP_2017_book.indb 176176 221/11/20171/11/2017 06:5206:52 Figure 5. Continuing vocational training in 70 enterprises – training enterprises as a percentage of all enterprises 60 in 2005 and 2010 – CVT courses 50

European Union (28 countries) 20 Slovenia 10 Training enterprise as a % of all enterprises enterprise Training 0 2005 2010 Source: Eurostat, 2010. CVT Training

higher-value roles. Businesses intend to either retrain workers or redeploy them to other areas where they will be of bigger value and will therefore not be made redundant. As Steve Perkins, global leader for technology at Grant Thornton says: “Increased dialogue between governments, businesses and educational institutions will help us understand where gaps in the labor market will exist, to ensure we have a pipeline of educated and trained people to fill those roles” (Grant Thornton, 2015).

Conclusion

Based on the reviewed literature, we can argue that the role played by technology in boosting employment often goes overlooked because of its more conspicuous destructive effects. However, while technology is killing jobs, at the same time only technology can save them. And while technology adoption and its attendant short-term job loss certainly transformed those historical economies, evolution of work did not lead to mass unemployment as much as to a transformation of the work being done (Heater, 2017). Historically, the industrial revolutions have boosted employment in knowledge-intensive sectors such as medicine, accounting and professional services, which is in accordance with the outlook for future jobs based on our research. Sectors which are the source of technological innovation are expanding rapidly, already demanding increased labor (Deloitte, 2015).

Taking historical events into consideration, a positive narrative about technology and progress has dominated. The past technological developments af-

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PPKP_2017_book.indbKP_2017_book.indb 177177 221/11/20171/11/2017 06:5206:52 fecting the job market demonstrate that when a machine replaces a human the result is, in fact, faster growth and, in time, rising employment. Another important finding is that technology rarely automates major occupations completely. Nevertheless, it is assumed that job automation will continue to result in some form of job losses. The only way to fight job losses is to train the talent that we have, because we cannot avoid embracement of robotics in the future that will have enormous effect on the structure of work (Heater, 2017). The work of future is likely to be varied and have a bigger share of social interaction and empathy, creativity and skill. Such a change is going to require some massive investment in education, both on the part of the corporations looking to move valued employees into new roles and the education system preparing workers for the real world (ibid.).

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PPKP_2017_book.indbKP_2017_book.indb 178178 221/11/20171/11/2017 06:5206:52 References Arntz, M., Gregory, T., and Zierahn, U. 2016. “The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis.” OECD Social, Employment and Migration Working Paper No. 189. Burke, J. R., and Ng, E. 2006. “The changing nature of work and organizations: Implications for human resource management.” Human Resource Management Review 16: 86-94. Cedefop. 2017. “People, Machines, Robots and skills.” URL: http://www.cedefop.europa. eu/en/publications-and-resources/publications/9121. Chui, M., Manyika, J., and Miremadi, M. 2016. “Where machines could replace humans–and where they can’t (yet).” McKinsey & Company. URL: https://www.mckinsey.com/business- functions/digital-mckinsey/our-insights/where-machines-. Deloitte. 2015. “Technology and people: The great job-creating machine.” URL: https:// www2.deloitte.com/uk/en/pages/finance/articles/technology-and-people.html. Eurostat. 2016. “Continuing vocational training in enterprises.” URL: http://ec.europa.eu/ eurostat/cache/metadata/en/trng_cvts_esms.htm. Farčnik, D., Kaše, R., Mihelič, K., Ograjenšek, I., Pahor, M., Redek, T., Sotenšek Š., and Zu- pan, N. 2016. “Model za napovedovanje dolgoročnejših potreb po kompetencah: Primer elektronske in elektro dejavnosti.” Ljubljana: Ekonomska fakulteta Univerze v Ljubljani. Feng, Y. 2016. “Future development possibilities of talent management under the influence of ‘Industry 4.0’.” Tritonia. URL: https://www.tritonia.fi/fi/e-opinnaytteet/tiivistelma/6900/ Future+Development+Possibilities+of+Talent+Management+Under+The+Influence+of+ %E2%80%98Industry+4.0%E2%80%99. Grant Thornton. 2015. “Automation: the pros & cons.” URL: https://www.grantthornton. global/en/insights/growthiq/automation/. Heater, B. 2017. “Technology is killing jobs, and only technology can save them.” Tech- Crunch. URL: https://techcrunch.com/2017/03/26/technology-is-killing-jobs-and-only- technology-can-save-them/. Lengnick-Hall, M. L., and Moritz, S. 2014. “The Impact of e-HR on the Human Resource Management Function.” Cougar Communications. URL: https://cougarcommunications. wikispaces.com/file/view/The+Impact+of+E-HR+on+the+Human+Resource+Managem ent+Function.pdf. Manyika, J., Chui, M., Miremadi, M., Bughin, J., George, K., Willmott, P. and Dewhurst, M. 2017. “Harnessing automation for a future that works.” McKinsey & Company. URL: https:// www.mckinsey.com/global-themes/digital-disruption/harnessing-automation-for-a- future-that-works. O*NET. 2017. URL: https://www.onetonline.org/. Luksha, P., Luksha, K., Varlamova, D., Sudakov, D., Peskov, D., and Korichin, D. 2015. “Atlas of emerging jobs.” Moskow: Skolkovo. Pew Research Center. 2017. “The Future of Jobs and Jobs Training.” URL: http://www.pewin- ternet.org/2017/05/03/the-future-of-jobs-and-jobs-training/. — 179 —

PPKP_2017_book.indbKP_2017_book.indb 179179 221/11/20171/11/2017 06:5206:52 PwC. 2014. “Talent management in manufacturing: The need for a fresh approach.” URL: https://www.pwc.com/gx/en/industrial-manufacturing/publications/assets/pwc-talent- management.pdf. Rometty, G. 2017. “Danes digitalno, jutri kognitivno.” Global 17(5): 8. Schuh, G., Potente, T., Varandani, R., Hausberg, C., and Franken, B. 2014. “Collaboration Moves Productivity to The Next Level.” Procedia CIRP 17: 3-8. Spandas, L. 2016. “6 jobs that new technologies are creating.” The Business Insider. URL: / www.businessinsider.com.au/6-jobs-that-new-technologies-are-creating-2016-3. Sparrow, P., Hird, M., and Cooper, C. 2015. “Do We Need HR?: Repositioning People Man- agement for Success.” UK: Palgrave MacMillan. Spencer, L. M., McClelland, D. C., and Spencer, S. M. 1992. “Competency Assessment Meth- ods: History and State of the Art.” Boston: Hay/McBer. Stone, D., and Deadrick, D. 2015. “Challenges and opportunities affecting the future of human resource management.” Human Resource Management Review 25(2): 139-145. URL: http://www.sciencedirect.com/science/article/pii/S1053482215000042. United Nations, Department of Economic and Social Affairs, Population Division. 2017. “World Population Prospects: The 2017 Revision.” New York: United Nations. Valenti, A., Gagliardi, D., Fortuna, G., and Iavicoli, S. 2016. “Towards a greener labour market: occupational health and safety implications.” Ann Ist Super Sanita 52(2): 415-423. Wolter, I. M., Monnig, A., Hummel, M., Schneemann, C., Weber, E., Zika, G., Helmrich, R., Ma- ier, T., and Neuber-Pohl, C. 2015. “Industry 4.0 and the consequences for labour market and economy. Scenario calculations in line with the BIBB-IAB qualifications and occupational field projections.” IAB. URL: http://doku.iab.de/forschungsbericht/2015/fb0815_en.pdf. Association for Advancing Automation. 2017. “Work in the Automation Age: Sustainable Careers Today and into the Future.” URL: https://www.a3automate.org/docs/Work-in-the- Automation-Age-White-Paper.

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PPKP_2017_book.indbKP_2017_book.indb 182182 221/11/20171/11/2017 06:5206:52 IV. BROADER SOCIAL ISSUES ON INDUSTRY 4.0

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PPKP_2017_book.indbKP_2017_book.indb 184184 221/11/20171/11/2017 06:5206:52 Miha Dominko, Matjaž Koman, Fabijan Leskovec, Anastasia Liakhavets, Andrei Petukh, Dimitrii Sazonov

SOCIAL CHALLENGES RELATED TO INDUSTRY 4.0

Introduction

The Fourth Industrial Revolution is not only changing what we are doing, but it is changing us (Schwab, 2017). Similarly, as other major historical technological disruptions, the technologies related to Industry 4.0 are not only changing the nature of industrial production but are also changing the nature of industrial relations and impacting broader social development.

The aim of this chapter is to show the potential social consequences of technological changes linked to Industry 4.0. Special attention is devoted to social changes related to potential changes in the labor market. It is not yet clear whether technological changes will reduce the number of jobs or increase them, but the structure will change; and structural changes accompanied with job creation or job destruction will require suitable government intervention.

The chapter is structured as follows. First, we discuss previous industrial revolutions and their social consequences. In Section Two we analyze the social changes of Industry 4.0. We mainly focus on the consequences in terms of labor market, education, social security, inequality issues, life style issues and security issues. We conclude in the last section.

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PPKP_2017_book.indbKP_2017_book.indb 185185 221/11/20171/11/2017 06:5206:52 Table 1. The main characteristics (technological and social) of the first three industrial revolutions The periods of the greatest concentration of qualitative shifts The last third of the 19th Elements Late 18th – early 19th – the beginning of the The middle of the 20th of technical century (The First Industrial 20th century (The Second century (The Third progress Revolution) Industrial Revolution) Industrial Revolution) Transport Rail transport on steam Diesel ships, road and air Development of unified locomotive, steamer. transport, improvement of rail transport systems, transport. containerization, jet transport, and rocket technology. Means of Postal services. Telecommunications Radio communication and communication (telegraph, telephone). electronics, the Internet. Education The spread of literacy and Mass general and special A significant (several times) the emergence of vocational education. increase at the average level of training. education, rapid development of higher education. Standards of Mass urbanization, social Increase of living standards, Growth of living standards, living, lifestyle stratification, disruption of creation of consumer society, government programs in stereotyped social roles. social mobility, creation of different countries to provide different classes and middle homes, education, medical class, increase in overall services to people, increase in efficiency of reproduction. care of the elderly, diseased, homeless, and incapable, partly solving the problem of world‘s hunger. Human rights The beginning of women‘s fight Women could work on a Progress in human rights‘ for equal rights. par with men; people who problems. previously could be considered unable to work became able to find a job. Labor market Unemployment, massive use of Increasing the need for skilled Creation of opportunities for women and children‘s labor. workers, growth in labor self-employment and self- productivity and intensity, improvement, improvement formation of non-physical of „no country of production“ workers‘ groups. concept (one part of product is Structural change: mass job made in one country, second in transition from agriculture to another, etc.). manufacturing. Structural change: mass job transition from manufacturing to service. Security - Job insecurity. Loss of job insurance. Privacy - Wiretapping by special forces. Increasing possibilities to “steal” private information. Source: Dean, 1979; Crafts, 2010; Smelser, 1959; Mokyr, 1999; Rifkin, 2011; He, 2012; Sarma and Girão, 2009; Purcell, 2014.

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PPKP_2017_book.indbKP_2017_book.indb 186186 221/11/20171/11/2017 06:5206:52 1 Major technological and social changes in industrial revolutions

In Table 1, the main changes (technological and social) of the first three industrial revolutions are shown. The First Industrial Revolution is characterized by two major inventions: improvement of the loom in 1733 by John Kay and especially creation of a steam engine in 1778 by James Watt (Deane, 1979). The invention of the steam engine made transportation improvements which eventually led to mass urbanization (Crafts, 2010). Other distinguishing features of the First Industrial Revolution were improvement of the quality of education and the massive use of women’s labor to perform work that did not require qualification. In that period, women started their battle to get equal rights compared to men. Also, the disruption of stereotyped social roles started in this era (Smelser, 1959).

The major discoveries in the Second Industrial Revolution were: large-scale machine production, the invention of electricity (creation of electric motor), the telegraph and the telephone. These inventions led to rapid growth in labor productivity, an increase in the overall efficiency of reproduction and an increase in the standard of living (Mokyr, 1999). As a result of increased productivity, employment in agriculture decreased substantially, while employment in manufacturing increased substantially. The Second Industrial Revolution entailed another change in the social sphere. The society increasingly focused on material values and consumption, i.e. the consumer society emerged. It was characterized by a high degree of social mobility. A new phenomenon in the social structure of Western society was the formation of the middle class (He, 2012). Additionally, important changes had occurred in the nature of work: the need for skilled workers increased. Women could work on a par with men. In addition, people who previously could be considered unable to work became able to find a job (Mokyr, 1999).

The Third Industrial Revolution was technologically marked by the introduction of computers, and later, of Internet technologies as well as transition to renewable energy sources (sun, wind, water, geothermal sources). The third revolution smoothed social differences in society. New technologies made a great impact on people’s life. The general standard of living grew and the cost of consumer goods declined and the level of people’s education increased (Rifkin, 2011). Internet opened a lot of opportunities for self-development and self-improvement. In addition, it helped people in many aspects, such as buy- ing things online, possibilities to find jobs, quick access to the information of interest or news, and one of the most common things – possibilities to talk with

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PPKP_2017_book.indbKP_2017_book.indb 187187 221/11/20171/11/2017 06:5206:52 Table 2. Potential social consequences Social Potential social consequences aspects Positive or neutral Hard to assess Negative Labor A sustainable global society Growth of cognitive and creative A squeeze of middle class; market through a smart approach to job jobs; global growth of low class with distribution. higher demand for social and lower in-class diversity; emotional skills. loss of two-thirds of all jobs in developing countries. Disbalances The shift from maximizing profits - Huge social inequality; of shareholders to maximizing benefits of productivity gains are value for society to build skewed toward the owners of sustainable societies. capital (accumulation of wealth by robot owners); asymmetries in power due to asymmetries in understanding technologies; social unrest and spread of in-country conflicts with the aim of income redistribution due to structural changes in the labor market. Lifestyle 4-hour working day; Blurred boundaries between The rise of useless self-administered mobile clinics work and free time; (unemployable) social class; controlled by AI; changes in attitudes toward the virtual world is preferred to lower mortality and longer life necessity to work (new the real world; expectancy; ideology); the shift from capitalism to transportation and travel are significant changes in human modern feudalism. more affordable and safer. values; more leisure lifestyle; a comfortable life without owning a house, car, appliances or clothes. Security “Flexicurity” and “activity Universal basic income. Crimes and social violence accounts”. due to chronic poverty and unemployment. Privacy Higher safety. Significantly low privacy level Technological and informational due to IoT (surveillance). inequality; lower citizen empowerment. Human Electronic personhood for the Robots have rights and are - rights most advanced forms of AI. considered as a part of society; cyborgization of people for strengthening human abilities over a robot. Source: Kapoor, 2017; World Economic Forum, 2016a; World Economic Forum, 2017a; The Guardian, 2016; The Guardian, 2017a; The Guardian, 2017b; Technologist, 2016; Schwab, 2016; Forbes, 2017a; Forbes, 2017b; Karsten and West, 2015; Atlas of emerging jobs, 2017; European Commission, 2017; Lim, 2015; Hutson, 2016; O’Sullivan, 2016; The World Bank, 2017; Rieland, 2017; RT, 2017; The Economist, 2017.

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PPKP_2017_book.indbKP_2017_book.indb 188188 221/11/20171/11/2017 06:5206:52 people and find old and new friends via social networks or messengers (Kons- bruck, 2003; Castells, 2014). However, this revolution, as well as the previous one, led to big structural changes in the labor market. There was a mass transition of employment from manufacturing to the service sector (Gatti et al., 2012).

2 Broader social effects due to Industry 4.0

The three major technological drivers of the Fourth Industrial Revolution (EY, 2017) are: digital (characterized by the Internet of Things (IoT), robotization, digital platform, big data, cloud computing, artificial intelligence (AI), and machine learning), physical (characterized by 3D printing and autonomous cars), and biological (characterized by neurotechnology and genetic engineering) (Li et al., 2017).

The technological inventions of the Fourth Industrial Revolution might lead to social changes which are summarized in Table 2.

2.1 Social and educational changes related to labor market shifts

The Fourth Industrial Revolution will have a significant impact on the labor market. However, there is a big disagreement about the size of the impact that automation technologies will have on the workforce. While some warn of astonishing unemployment, others point out that technology may create new job vacancies that will employ displaced workers. A third group argues that robotization will have little effect on employment in the future. Any policy measures that address the future of employment must account for the uncertainty of outcomes on employment (Karsten and West, 2015).

It is clear that Industry 4.0 will cause some jobs to disappear. The number of telemarketers, data entry keyers, library technicians, loan officers, cashiers, agriculturalists, woodworking machine setters, real estate brokers, etc. is likely to decline. On the other hand, healthcare social workers, audiologists, surgeons, dentists, human resource managers, etc. will survive or expand in number with high probability (also due to causes unrelated to Industry 4.0 but rather related to ageing). But most importantly, a number of new professions will be created (Frey and Osborne, 2013) – similarly as in other industrial revolutions. Sixty- five percent of children entering primary school now will find themselves in occupations that today do not exist (World Economic Forum, 2017b). Moreover,

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PPKP_2017_book.indbKP_2017_book.indb 189189 221/11/20171/11/2017 06:5206:52 Table 3. Skills for the future jobs: top ten required skills in 2015 and 2020 Top ten skills in 2015 Top ten skills in 2020 1. Complex Problem Solving 1. Complex Problem Solving 2. Coordinating with Others 2. Critical Thinking 3. People Management 3. Creativity 4. Critical Thinking 4. People Management 5. Negotiation 5. Coordinating with Others 6. Quality Control 6. Emotional Intelligence 7. Service Orientation 7. Judgement and Decision Making 8. Judgement and Decision Making 8. Service Orientation 9. Active Listening 9. Negotiation 10. Creativity 10. Cognitive Flexibility Source: World Economic Forum, 2016a. a number of jobs related to new technologies will emerge; studies forecast new jobs in the sphere of space tourism, space miners, exobiologists, and space junk recyclers (University of Kent, 2017). The need for living system designers, bio- ethicists, ergonomic designers of wearable security devices, robot attendants, virtual world designers, and game educators is also likely to increase (Atlas of emerging jobs, 2017). Moreover, people will need to upgrade skills and turn to more sophisticated work (Population Matters, 2017).

Complex problem solving, critical thinking and creativity will become the top three skills that people will need to have (Table 3). Industry 4.0 will therefore demand social skills as well. This process will be similar as in the past, but it will more likely happen at a faster pace. Professions that require social skills grew by 24 percent from 1980 to 2012, while math-intensive jobs grew by only 11 percent. Negotiation and flexibility are high on the list of skills for 2015, but in 2020 they will begin to drop from the top ten, as machines using masses of data will begin to make decisions instead of people. Also, active listening, considered a core skill in 2015 and today, will disappear completely from the top ten. On the other hand, emotional intelligence, which does not feature in the top ten today, will become one of the top skills needed by all (World Economic Forum, 2016a).

To provide the relevant skills in the rapidly changing environment, the education system is going to be changed through the active role of AI. People will be able to get a very personalized education by getting a unique mini-curriculum based on their particular interests, abilities and society’s needs. These changes will require the advent of new technologies, the infrastructure of classrooms and teachings skills. AI will not only help children in areas where they are most likely to succeed but it will also help teachers shape the most effective way for

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PPKP_2017_book.indbKP_2017_book.indb 190190 221/11/20171/11/2017 06:5206:52 individual students to learn, based on data from thousands of other students. Children are going to be taught with data monitoring. Teachers will become more data scientists who understand AI and can evaluate the data about how students are learning (Rieland, 2017).

Industry 4.0, especially robotization, will have a substantial impact on the social class structure as well. Middle class will be squeezed, while low class will increase (Table 2). A small part of the current middle class will become a part of high class. Low class will be less diversified, due to the prevalence of low-skills and low wage jobs (World Economic Forum, 2017a).

2.2 Lifestyle changes

The widespread values in society are the basics of social behavior patterns. The Fourth Industrial Revolution will change these values significantly, in the way that people will have more free time for creative activities, hobbies, nature, family, friends, volunteer jobs, etc. Second, the boundaries between work and free time are going to be blurred due to an increase of freelance workers; the total freelance pool is predicted to be at 40 percent by 2020 (Forbes, 2017a). Another prediction says that by 2050 a new useless class of people might emerge. This class will be composed of not just unemployed but unemployable people. They will get more excitement and emotional engagement from 3D virtual reality worlds rather than from the “real world” outside (The Guardian, 2017c).

Robots will become our every-day help, used in care for the elderly and children, help in households. We will be treated for disease by robots, and self-driving cars will be our chauffeurs. Over the next 20 years, more than fifty percent of the population in the OECD countries will be over 50 years old. The ageing population requires more medical treatment and caregiving. It is estimated that for example Japan might meet the shortage of about 380,000 caregivers for the elderly. Solutions for all these problems are going to be continuously accompanying care-robots and smart-healthcare (Financial Times, 2017).

In 2017, the Artefact Group Company presented their vision of medical technologies in the future: a self-administered mobile clinic under the management of AI. This clinic can carry out diagnostics, do various procedures, give conclusions, set an appointment with the required doctor, connect with the on-call doctor-consultant and even deliver the patient, if necessary, to the nearest hospital (Space, 2017). At the IFA exhibition in Berlin in September 2017, the Chinese

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PPKP_2017_book.indbKP_2017_book.indb 191191 221/11/20171/11/2017 06:5206:52 firm Qihan presented a smart robot Sanbot, which is capable of doing everything from housekeeping to protecting the life and health of the owner (The Telegraph, 2017). 3D printing will allow us to print buildings faster and cheaper so that real estate for people will become cheaper and more affordable (Zaleski, 2017).

The transportation system is also going to be transformed significantly by self-driving vehicles. They will prevent deadly car accidents, provide mobility to the elderly and disabled people, as well as increase road capacity, save fuel, and lower emissions. It is estimated that they might increase people’s life-span. Consequently, meeting, working, sleeping or reading in the self-driving vehicles will be just part of society’s daily routine. A driver’s license will no longer be obligatory for using autonomous cars; transportation will become available for anyone despite their age, skills, physical or psychological conditions (Fagnant and Kockelman, 2015).

AI will bring substantial changes to our life. For example, Alibaba’s Jack Ma says in 30 years people will only work four hours a day, since AI and robots will take over so much of people’s work (RT, 2017). Therefore, people will have substantially more leisure time. However, John Maynard Keynes made similar predictions in his “Economic Possibilities for our Grandchildren” in 1930, but we currently still have a 40-hour work week (Forbes, 2017b).

2.3 Inequality and consequences of social disbalances

Inequality is one of the most important problems society is facing nowadays. Since 2015, the richest one percent has owned more wealth than the rest of the planet. One of the biggest concerns of our society is how the new industrial revolution with its technological advances will affect earnings distribution (O’Sullivan, 2016). As mentioned in Table 2, middle-income routine and repetitive jobs will be suffering the most, while low-income manual occupations as well as high-income cognitive and creative jobs will grow (Schwab, 2016).

Undoubtedly, social inequality is closely intertwined with unemployment. The increasing automation in manufacturing in high-income countries will result in lower demand for manufacturing work in emerging markets (The World Bank, 2017). Developing countries could lose about two thirds of all jobs, because industrial robots increasingly undertake manufacturing, which is negative if another job opportunity is not created (Kapoor, 2017). In addition to a decrease in the labor market, social and demographical changes will compel the world economy

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PPKP_2017_book.indbKP_2017_book.indb 192192 221/11/20171/11/2017 06:5206:52 to employ more than 600 million people extra in the next 15 years (The World Bank, 2017). Globalization has connected economies so close that some countries like India, which once started to benefit from job export from high-developed countries, such as the US, might now get into trouble because of that. Namely, the US companies that lead the Fourth Industrial Revolution can now as easily export unemployment as they once exported jobs (Kapoor, 2017).

The Fourth Industrial Revolution is bringing challenges in societies not only at the local level but also at the global one. People in countries like Finland, Switzerland, Sweden, Israel, Singapore, the Netherlands, and the United States have a greater probability to succeed than people in African countries, which are unequipped for the transition and where just 20 percent of the population has regular access to the Internet (World Economic Forum, 2016b; Peyper, 2017).

A baffling complexity of up-to-date technologies might emerge inequalities in today’s information society, between tech-savvy individuals who understand and control them and those who are just passive users of technology they do not understand. As a result of it, significant asymmetries of power might appear due to information asymmetries (Schwab, 2016).

Perhaps the most important of them is the potentially tremendous emergence of excess labor due to automation, because it might result in even bigger social inequality. This, in turn, might create anxiety or even inability to get any level of prosperity for the people. According to Klaus Schwab, if people feel no chance of attaining any level of prosperity or meaning in their lives, it might lead to significant social risks (Schwab, 2016). Consequently, that might give incentives for people to fight. So all these conditions might cause local violent conflicts or, at worst, a global war. For instance, the Second World War helped the US to decrease unemployment by employing people for war needs and therefore decrease the social tensions. Put it differently, the war lightened transition from the agricultural sector to manufacturing. That labor transition mechanism has already been mentioned in the context of the Second and Third Industrial Revolutions, nevertheless, this explanation might also be applied to the Fourth Industrial Revolution (Gatti et al., 2012).

2.4 Privacy

Increasing income and technological inequalities might threaten citizen empowerment, one of the fundamental principles of a high-developed society. Fur-

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PPKP_2017_book.indbKP_2017_book.indb 193193 221/11/20171/11/2017 06:5206:52 thermore, governments are thinking about employing combinations of up-to-date technologies, legislation, and policies that shrink the space for civil society and restrict the independence of civil society groups and their activities. The tools of the Fourth Industrial Revolution enable new forms of surveillance and other means of control that run counter to healthy, open societies (Schwab, 2016).

These privacy issues oblige the society to be prepared. For example, the basic idealistic idea of the Internet of Things (IoT) is to make the pervasive presence of things around us communicate with each other to achieve common goals (Atzori et al., 2010). This idea might be transformed immediately into something potentially dangerous in the wrong hands. “Smart dust”, “micro motes”, “snakebots”, cyborg beetles with cameras, hummingbird drones – some of these things are already real, some are being developed right now. All these technologies might be easily adapted for surveillance despite motives of their users. Face-recognition technologies and the right software allow personal data brokers to gather and sell personal data. Besides, with the advent of the Internet of Things, appliances and gadgets will monitor many aspects of peoples’ lives, from what they eat to what they flush. Even thoughts could become hackable and some virtual-reality headsets can measure brain waves (Hutson, 2016). Moreover, AI becomes able to predict thoughts based on brain scans with 87 percent accuracy. It shows just how far learning has come. That might mean that super-powered machines are rising. Possibly in the future, mental freedom might be under threat (Regan, 2017). And eventually, it needs to be remembered that even unexpected things might be hacked: from an Instagram account to smart houses, from smart locks to pacemakers (Steinberg, 2017; Tom’s Guide, 2016; Franceschi-Bicchierai, 2017).

2.5 Robots like humans and humans like robots

A growing number of areas in people’s daily lives are increasingly affected by robotics, and AI is increasing their intelligence. Highly sophisticated robots on the streets should not be seen as a science-fiction movie anymore. As a result, the European Parliament’s Legal Affairs Committee issued a report that recom- mends creating a form of “electronic personhood” that would afford rights and responsibilities to the most advanced forms of AI, in order to ensure society from unpredicted accidents between humans and robots (The Guardian, 2017b).

Experts and scientists also predict that people will modify their bodies, because of several technological possibilities and threats. One of the negative

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PPKP_2017_book.indbKP_2017_book.indb 194194 221/11/20171/11/2017 06:5206:52 consequences, predicted by Elon Musk, is the assumption that if humans don’t augment their capabilities through a “merger of biological intelligence and machine intelligence”, they might become “house cats” to artificial intelligence. That is why “neuroprosthetics” is considered as the solution against AI. It will be possible to communicate complex ideas telepathically or give people additional cognitive (extra memory) or sensory (night vision) abilities (The Guard- ian, 2017a). Moreover, 3D printing, or in other words additive manufacturing (3D Printing Industry, 2014), is going to change lives much more than it seems. A huge social shift is expected to be due to bioprinting of organs. Literally, people will be able to exchange worn out organs with new ones, which will extend their lifetime (The Economist, 2017).

Conclusion

The Fourth Industrial Revolution will bring substantial social changes. Some of them will be positive, while some of them will be negative. On the positive side, up-to-date technology will allow us to build a sustainable global society, through the maximization of the value for all humans and shareholders, rather than just shareholders. Moreover, the Fourth Industrial Revolution might cause a four-hour work day, which might allow people to have more time for families, hobbies, traveling and self-development. Life expectancy is going to increase tremendously due to better and cheaper medical treatment and higher safety.

On the negative side, technology changes might results in a squeeze of the middle class, global growth of low class with lower in-class diversity, rise of useless (unemployable) social class, deep immersion into the virtual world, loss of two thirds of all jobs in developing countries, technological and informational inequality, lower citizen empowerment, accumulation of wealth by robot owners, asymmetries in power due to asymmetries in understanding technologies, crimes and social violence due to chronic poverty and unemployment, social unrest and spread of in-country conflicts and the shift from capitalism to modern feudalism.

The building of sustainable global society is going to be the biggest challenge for the world of the Fourth Industrial Revolution, that is why societal values need to be emphasized more than the shareholders’ ones (The Guardian, 2016).

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PPKP_2017_book.indbKP_2017_book.indb 195195 221/11/20171/11/2017 06:5206:52 References 3D Printing Industry. 2014. “The free beginner’s guide to 3D printing.” URL: https://3dprintingindustry.com/3d-printing-basics-free-beginners-guide. Atlas of emerging jobs. 2017. “Future Jobs.” URL: http://atlas100.ru/en/catalog/?aft_2020 =yes&otrasl=all. Atzori, L., Iera, A., and Morabito, G. 2010. “The internet of things: a survey.” Computer networks 54(15): 2787-2805. Castells, M. 2014. “The Impact of the Internet on Society: A Global Perspective.” MIT Tech- nology Review. URL: https://www.technologyreview.com/s/530566/the-impact-of-the- internet-on-society-a-global-perspective/. Li, G., Hou, Y., and Wu, A. 2017. “Fourth Industrial Revolution: Technological Drivers, Im- pacts and Coping Methods.” Chinese Geographical Science 27(4): 626-637. URL: https:// link-springer-com.nukweb.nuk.uni-lj.si/content/pdf/10.1007%2Fs11769-017-0890-x.pdf. Crafts, N. 2010. “Explaining the first Industrial Revolution: two views.” European Review of Economic History 15: 153-168. Deane, P. 1979. “The first industrial revolution.” Cambridge: Cambridge University Press. URL: https://books.google.si/books?hl=en&lr=&id=eMBG_soDdNoC&oi=fnd&pg=PA1& dq=first+industrial+revolution+inventions&ots=t2GP9zOAsO&sig=5k1DufOaBE3baQ0b xKbjSEMsklw&redir_esc=y#v=onepage&q&f=false. Ernst & Young. 2017. “Four things to know about the Fourth Industrial Revolution.” URL: https://betterworkingworld.ey.com/digital/four-things-to-know-about-the-fourth-industrial-revolution. European Commission, DG for Employment, Social Affairs & Inclusion. 2017. “Flexicurity.” URL: http://ec.europa.eu/social/main.jsp?catId=102. Fagnant, D. J., and Kockelman, K. 2015. “Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations.” Transportation Research Part A: Policy and Practice 77: 167-181. Financial Times. 2017. “Using robots to care for the elderly.” URL: http://undiscovered-japan. ft.com/articles/automation-and-ageing/?mhq5j=e1. Forbes. 2017a. “Brands Take Note! The Future Belongs to Lifestyle Influencers.” URL: https:// www.forbes.com/sites/annabelacton/2017/05/08/why-the-future-belongs-to-lifestyle- influencers-brands/#35cbabff1a2d. Forbes. 2017b. “I Don’t Think He’s Right - Alibaba’s Jack Ma On The Future Of Work.” URL: https://www.forbes.com/sites/timworstall/2017/06/21/i-dont-think-hes-right-alibabas- jack-ma-on-the-future-of-work/. Franceschi-Bicchierai, L. 2017. “465,000 Patients Need Software Updates for Their Hackable Pacemakers, FDA Says.” MotherBoard. URL: https://motherboard.vice.com/en_us/article/ nee5bw/465000-patients-need-software-updates-for-their-hackable-pacemakers-fda- says?mc_cid=ae72c46711&mc_eid=975e3da6fe. — 196 —

PPKP_2017_book.indbKP_2017_book.indb 196196 221/11/20171/11/2017 06:5206:52 Frey, C. B., and Osborne, M. 2013. “The Future of Employment: How susceptible are jobs to computerisation?” Oxford Research. URL: http://www.oxfordmartin.ox.ac.uk/downloads/ academic/The_Future_of_Employment.pdf. Gatti, D., Gallegati, M., Greenwald, B. C., Russo, A., and Stiglitz, J. E. 2012. “Sectoral Imbal- ances and Long-run Crises.” In: Allen, F., Aoki, M., Fitoussi, J. P., Kiyotaki, N., Gordon, R., and Stiglitz, J. E. (eds): The Global Macro Economy and Finance, Palgrave Macmillan, 2012. He, C. 2012. “Modernization Science. The Principles and Methods of National Advance- ment.” Berlin: Springer. Hutson, M. 2016. “Even Bugs Will Be Bugged.” The Atlantic. URL: https://www.theatlantic. com/magazine/archive/2016/11/even-bugs-will-be-bugged/501113/. Kapoor, A. 2017. “Innovation is the key to India surviving next industrial Revolution.” NewsGram. URL: https://www.newsgram.com/innovation-is-the-key-to-india-surviving- next-industrial-revolution/. Karsten, J., and West, M. D. 2015. “How robots, artificial intelligence, and machine learning will affect employment and public policy.” Brookings. URL: https://www.brookings.edu/ blog/techtank/2015/10/26/how-robots-artificial-intelligence-and-machine-learning-will- affect-employment-and-public-policy/. Konsbruck, R. L. 2003. “Impacts of Information Technology on Society in the new Century.” Zurich: IBM Research. URL: https://www.zurich.ibm.com/pdf/news/Konsbruck.pdf. Lim, J. 2015. “The Social, Economical and Political Impact of UBI.” Session 17 Cognitive Capitalism and Basic Income. URL: http://bien2016.org/en/wp-content/uploads/2015/10/ BIEN2016_Sess17_JHLim_en.pdf. Mokyr, J. 1999. “The Second Industrial Revolution, 1870-1914.” Storia dell Economia Mon- diale: 219-245. O’Sullivan, M. 2016. “Global Wealth Databook 2016.” Research Institute. URL: http://publications.credit-suisse.com/tasks/render/file/index.cfm?fileid=AD6F2B43-B17B-345E- E20A1A254A3E24A5. Peyper, L. 2017. “Africa lags in ICT, not ready for 4th Industrial Revolution – report.” Fin- 24Tech. URL: http://www.fin24.com/Tech/africa-lags-in-ict-not-ready-for-4th-industrial- revolution-report-20170507. Population Matters. 2017. “The impact of robotics on future societies.” URL: https://www. populationmatters.org/documents/impact_of_robotics.pdf. Purcell, B. M. 2014. “Big data using cloud computing.” Journal of Technology Research 5(8): 1-8. Regan, T. 2017. “Scientists made an AI that can read minds.” Engadget. URL: https://www. engadget.com/2017/06/29/scientists-made-an-ai-that-can-read-minds/. Rieland, R. 2017. “Is Artificial Intelligence the Key to Personalized Education?” Smithsonian Magazine. URL: http://www.smithsonianmag.com/innovation/artificial-intelligence-key- personalized-education-180963172/. Rifkin, J. 2011. “The Third Industrial Revolution: How Lateral Power is Transforming Energy, the Economy, and the World.” New York: Palgrave Macmillan. — 197 —

PPKP_2017_book.indbKP_2017_book.indb 197197 221/11/20171/11/2017 06:5206:52 RT. 2017. “Jack Ma predicts in 30yrs people will work 4 hours a day.” URL: https://www. rt.com/business/393542-jack-ma-workers-four-hours/. Sarma, A. C., and Girão, J. 2009. “Identities in the Future Internet of Things.” Wireless Per- sonal Communications 49(3): 353-363. Schwab, K. 2016. “The Fourth Industrial Revolution.” New York: Crown Business. Smelser, N. J. 1959. “Social changes in the industrial revolution. An application of theory to the British Cotton Industry.” Chicago: The University of Chicago Press. Space. 2017. “A mobile clinic administered by AI.” International News. The Earth Chronicles of Life. URL: http://earth-chronicles.com/science/a-mobile-clinic-administered-by-ai.html. Steinberg, J. 2017. “6 Million Instagram Accounts Hacked: How to Protect Yourself.” Inc.. URL: https://www.inc.com/joseph-steinberg/6-million-instagram-accounts-hacked-how- to-protect.html. Technologist. 2016. “The last word: Modern feudalism of the sharing economy.” URL: http://www.technologist.eu/the-last-word-modern-feudalism-of-the-sharing-economy/. The Economist. 2017. “Printed human body parts could soon be available for transplant.” URL: https://www.economist.com/news/science-and-technology/21715638-how-build- organs-scratch. The Guardian. 2016. “Will jobs exist in 2050?” URL: https://www.theguardian.com/careers/2016/oct/13/will-jobs-exist-in-2050. The Guardian. 2017a. “Elon Musk says humans must become cyborgs to stay relevant. Is he right?” URL: https://www.theguardian.com/technology/2017/feb/15/elon-musk-cyborgs- robots-artificial-intelligence-is-he-right. The Guardian. 2017b. “Give robots ‘personhood’ status, EU committee argues.” URL: https://www.theguardian.com/technology/2017/jan/12/give-robots-personhood-status- eu-committee-argues. The Guardian. 2017c. “The meaning of life in a world without work.” URL: https://www. theguardian.com/technology/2017/may/08/virtual-reality-religion-robots-sapiens-book. The Telegraph. 2017. “IFA 2017: The best and weirdest gadgets from Europe’s biggest tech conference.” URL: http://www.telegraph.co.uk/technology/2017/08/31/ifa-2017-best- weirdest-gadgets-europes-biggest-tech-conference/. The World Bank. 2017. “The Future of Jobs and the Fourth Industrial Revolution: Business as Usual for Unusual Business.” URL: http://blogs.worldbank.org/psd/future-jobs-and-fourth- industrial-revolution-business-usual-unusual-business. Tom’s Guide. 2016. “75 Percent of Bluetooth Smart Locks Can Be Hacked.” URL: https:// www.tomsguide.com/us/bluetooth-lock-hacks-defcon2016,news-23129.html. University of Kent. 2017. “Future Jobs.” URL: https://www.kent.ac.uk/careers/Choosing/ future-jobs.htm. World Economic Forum. 2016a. “The 10 skills you need to thrive in the Fourth Industrial Revolution.” URL: https://www.weforum.org/agenda/2016/01/the-10-skills-you-need-to- thrive-in-the-fourth-industrial-revolution/. — 198 —

PPKP_2017_book.indbKP_2017_book.indb 198198 221/11/20171/11/2017 06:5206:52 World Economic Forum. 2016b. “The Global Information Technology Report 2016.” URL: http://www3.weforum.org/docs/GITR2016/WEF_GITR_Full_Report.pdf. World Economic Forum. 2016c. “Welcome to 2030. I own nothing, have no privacy, and life has never been better.” URL: https://www.weforum.org/agenda/2016/11/shopping-i-can- t-really-remember-what-that-is/. World Economic Forum. 2017a. “5 charts that show what is happening to the middle class around the world.” URL: https://www.weforum.org/agenda/2017/01/5-charts-which-show- what-is-happening-to-the-middle-class-around-the-world/. World Economic Forum. 2017b. “The role of technology in the education of the future.” URL: https://www.weforum.org/agenda/2017/05/science-of-learning/. Zaleski, A. 2017. “How to print a house.” Curbed. URL: https://www.curbed. com/2017/5/3/15504458/3d-printed-houses-construction-apis-cor.

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PPKP_2017_book.indbKP_2017_book.indb 200200 221/11/20171/11/2017 06:5206:52 Polona Domadenik, Blaž Božič, Dimitrije Ivanović, Nikola Sionov

PERCEPTIONS OF ROBOTS AMONG THE GENERAL PUBLIC

Introduction

As the next technological revolution is knocking on the front door, people are starting to wonder, what will the impact on their jobs, wages and productivity be. Robotics is the key technology for the future that will boost efficiency not only in manufacturing but also in the service sectors, and it will contribute to employment as well. Public perceptions are however often influenced by misconceptions and fears. On one hand, Mark Zuckerberg argues that robots will boost jobs and improve people’s lives, but on the other hand, Elon Musk has a dire warn- ing that robots will be the downfall of humanity (Hashmi News, 2017). In order to improve the image of robots and to increase public support for technological progress in the field of robotization in the future it is important to understand the public opinion and identify the main determinants behind the negative attitudes of specific population groups.

The aim of the chapter is to identify the magnitude of positive and negative perceptions regarding automatization and robotization among people in different countries in different years and their demographic characteristics. After the introduction, we briefly review the current literature on potential advantages and disadvantages of robots, followed by data description and the presentation of our analysis. The final section summarizes and presents the main findings of our analysis.

1 The use of robots in different industries and general public perception: A literature review

Over the next decade it is expected that the number and variety of robots in the workplace will soar, taking over many jobs that are too dirty, too dull or

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PPKP_2017_book.indbKP_2017_book.indb 201201 221/11/20171/11/2017 06:5206:52 too dangerous for people to do. Already, about 1.4 million industrial robots are deployed around the world, as well as several million robotic devices designed for in-home consumer use (Kiplinger, 2013). The fast introduction of robots into different aspects of our lives is resulting from their numerous advantages, highlighted also in the literature (Broadbent, 2017; Nomura, 2017). Robots are particularly useful for industries and jobs which are too dangerous for humans, can be improved in terms of higher productivity, or there are not enough human resources due to increasing demand, like military actions, massive production, farming, space exploration and logistics. In this part of the chapter we discuss the use of robots in different industries followed by literature review of the public perception towards robots in general.

1.1 Where can robots be useful?

An autonomous robot is a machine that can operate and perform tasks by itself without continuous human guidance. The first robot was a digitally operated programmable arm used in the car industry in the 1950s (Broadbent, 2017). Since then, many different types of robots have been developed.

Military robots are some of the most high-tech and important robots used today, especially for tasks such as surveillance, bomb disposal, automated weap- onry, deactivation of improvised explosive devices and mines. These state-of- the-art machines save lives by performing extremely dangerous tasks without endangering humans. Industrial robots are increasingly used in manufacturing (mass production lines) to replace manual tasks. For example, they are used in the automobile industry to assist in car manufacturing. These high-powered machines have mechanical arms with tools, wheels and sensors that make them ideal for assembly line jobs. Not only do robots save more money in manufacturing costs, but they also perform tough tasks at a pace no human could possibly do. Like manufacturing jobs, farming robots have the ability to work faster, longer and more efficiently than humans in agriculture. Robots replace part of the human factor from this labor intensive and difficult work. They can be taught to navigate through farmland and harvest crops on their own. Robots can also be used for horticultural needs, such as pruning, weeding, spraying pesticide, and monitoring the growth of plants. In the last two decades social robots were developed, made to interact closely with humans as artificial companions and helpers in homes, hospitals, schools, shopping malls, and beyond. It is in this application that robots are being made to mimic humans most closely—in looks, mind, emotional expression, and behavior. In healthcare, robotic systems can

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PPKP_2017_book.indbKP_2017_book.indb 202202 221/11/20171/11/2017 06:5206:52 support humans in complex, exhausting or often repeated tasks. In fact, there is evidence that people do perceive robots to have advantages over humans in some aspects of healthcare; these advantages include increased perseverance, commitment, and availability, as well as decreased distraction from patients, in robots compared to human doctors (Broadbent et al., 2010). Application areas are both help during activities of everyday life (at home or work) and medical rehabilitation (Hill, 2017).

1.2 Attitudes and perceptions

Several empirical studies report that there is a high prevalence of negative attitudes towards robots. An experimental study on British data suggests that this is associated with different behavior styles (Syrdal et al., 2009). People being more in touch with humanoid robots have more positive attitudes towards interaction with robots and the social influences of robots (Weiss et al., 2009; Cramer et al., 2009). It is evidenced that the negative attitudes affect people’s acceptance of assistive robots at home and service robots in public places, with the Chinese, in general, having more negative attitudes towards robots than residents of the USA (Nomura et al., 2009; Wang et al., 2010). On the other hand, the Japanese have more positive attitudes if compared to people from the UK (Nomura and Kinoshita, 2016). The existing research suggests that these attitudes are affected by demographic factors, like gender, culture, age, as well as the experience of working with robots, types of robots, and different use of robots. Nomura et al. (2009), for example, stress in their study that the negative attitudes decrease as experience with robots increases, while on the other hand, Halpern and Katz (2012) report that exposure of participants to specific types of robots does not affect their attitudes. The very recent online survey in Japan reports that real experience with robots decrease negative attitudes towards robots and not being related with age or gender although young people have more expectations for humanoid robots (Nomura and Kinoshita, 2016). Similar but weaker influence is reported also in the case of experience with robots via media. However, these studies only report experience with robots in the short term and do not take into account the quality of experience such as duration and context. Recent experiments on the use of social robotic technologies to provide person-centered cognitive interventions reveal that majority of individuals had positive attitudes towards the socially assistive robot and its intended applications (Louie et al., 2014). However, real value added of robots perceived by humans will be possible to evaluate in long time.

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PPKP_2017_book.indbKP_2017_book.indb 203203 221/11/20171/11/2017 06:5206:52 2 General public perceptions: A cross-country comparison

In order to find out how robots are perceived by the population in the Euro- pean Union member states we used special Eurobarometer surveys on public attitudes towards robots and autonomous systems published in 2012 and 2015. In every member state the interest for science and technology is different, however, in all member states men (76 percent) are more interested in science and technology than women (65 percent). Figure 1 shows perception towards robots with 70 percent of the interviewees having a positive attitude, and less than a quarter sharing a negative view, while seven percent did not express their preferences (Eurobarometer, 2012).

The most positive attitudes were expressed by people in the Scandinavian countries (almost 90 percent of people shared a positive view on robots), while the least positive attitudes were recorded in Poland and some Mediterranean countries (Portugal, Spain, Greece), with only half of the people sharing positive attitudes. Therefore, we have tested whether countries with higher GDP per capita share a more positive view towards robots although we are aware that this might be endogenous. Figure 2 shows that there is a rather U-type of correlation between the share of citizens with a positive attitude towards robots and GDP per capita in particular country. We can suspect that poorer countries within the EU perceive robotization as a source of higher growth potential. Richer countries also share a positive view towards robotization, while countries in between are less positive about robotization. However, more research on this is needed in the future to confirm our initial findings (Eurobarometer, 2012; Eurostat, 2012).

It comes with no surprise that the fear and disapproval of robots increase as people get older, as is clearly shown in Figure 3. Of course, younger people interact more with robots than older people. 74 percent of young Europeans have a positive view towards robots and 72 percent of all Europeans (77 percent of young people) believe robots are good for society because they help people. Older people do not share the same beliefs, only 56 percent of people older than 55 have a positive view towards robots. As evidenced by some experimental studies highlighted in the previous section, the positive perception among the elderly increases after having real experience with robots (Eurobarometer, 2012).

Furthermore, the less educated have a much stronger opposition towards robots compared to those more educated (Figure 4). The difference in perceptions can be seen when comparing the responses. Only 44 percent of the least educated people but 72 percent of the more educated people have positive views

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PPKP_2017_book.indbKP_2017_book.indb 204204 221/11/20171/11/2017 06:5206:52 Figure 1. Perception towards robots Don't know 7% among EU-27 citizens Positive 70%

Negative 23%

Source: Eurobarometer, 2012.

Figure 2. Correlation between positive attitudes towards robots and GDP per capita in EU-27 countries in 2012 100% 90% 80% 70% 60% 50% 40% 30% 20% 10%

Positive attitudes towards robots (in percent) robots towards attitudes Positive 0% 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 GDP per capita (in EUR) Source: Eurobarometer, 2012.

Figure 3. General perceptions of robots by age in EU-27 countries in 2012 80% 74% 70% 67% 65% 60% 56% 50%

40% 34% 30% 26% 27% 21% 20% 8% 10% 10% 5% 7% 0% 15-24 25-39 40-54 55+

Source: Eurobarometer, 2012. Total Positive Total Negative Don't know

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PPKP_2017_book.indbKP_2017_book.indb 205205 221/11/20171/11/2017 06:5206:52 Figure 4. General perceptions of robots by educational attainment in EU-27 countries in 2012 80% 77% 72% 70% 63% 60% 50% 44% 45% 40% 30% 29% 22% 20% 18% 11% 10% 8% 6% 5% 0% Elementary school High school Bachelor/Faculty Still studying Source: Eurobarometer, 2012. Totally positive Totally negative Don't know

towards robots. The most positive about robots are those who are still studying. It is interesting to note that in 2014, the percentage of people with a lower level of education having negative views towards robots increased compared to 2012. In 2012, 36 percent of people who finished a lower level of education had a positive attitude, followed by an 18 percentage point increase (44 percent) in 2014. A similar change could be observed among people with a higher level of education. In 2012, 64 percent had a positive attitude, while in 2014, the proportion increased to 72 percent. On the other hand, also a negative attitude expanded among people with a lower level of education in 2012, 38 percent of them shared a negative attitude that increased by eight percentage points in 2014. These findings could be explained by the prevalence of jobs being automatized and robotized in the last decade – mostly routine jobs in manufacturing that were occupied by individuals with low education attainment. The least educated workforce does not perceive robots as assistive but as a danger to their workplace. Due to low competences and inadequate skills it would be difficult for them to find another job (Eurobarometer, 2012; Eurobarometer, 2015).

Although robots are becoming more and more present in people’s life, only a small percent of people are using them. The usage of robots increased in 2014 compared to 2012, as 13 percent of people owned some form of robots in 2012, while the percentage increased to 16 percent in 2014. As being reported in Fig- ure 5, people between ages 25-39 report the highest percent of robot usage (20 percent). In contrast, 90 percent of people who are older than 55 years do not use robots at all (Eurobarometer, 2012; Eurobarometer, 2015).

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PPKP_2017_book.indbKP_2017_book.indb 206206 221/11/20171/11/2017 06:5206:52 Figure 5. Share of population using robots by age in EU-27 countries in 2014 Yes No

15-24 17% 83%

25-39 20% 79%

40-54 16% 84%

55+ 9% 90%

Source: Eurobarometer, 2015.

Comparing the use of robots by educational attainment, it can be concluded that people with a higher level of education are the ones who use robots the most. In 2014, almost 20 percent of people with a higher education level or the ones who are still studying reported that they were using robots, while the corresponding share was only 12 percent in 2012. On the other hand, only eight percent of people with elementary education used robots in the same period. (Eurobarometer, 2012; Eurobarometer, 2015).

People in general have well defined and positive views about the robot application areas that are too difficult or too dangerous for humans, like space exploration (52 percent of interviewees agreed that robots should be used for that), manufacturing (50 percent), military and security (41 percent) and search and rescue tasks (41 percent). As evidenced in Figure 6, people have more confidence in robots used in heavy industries or hazard situations but less in services dealing with production of food, taking care of the elderly, children or

Figure 6. Percentage of people who agree on where the robots should be used, in the observed EU countries 60% Space exploration, 52% 50% Manufacturing, 50%

40% Search and rescue , 41% Military and security, 41% 30% Healthcare, 22% 20% Agriculture, 11% Transport/Logistics, 11% Domestic use (such as cleaning), 13% Share of people who agree 10%

that robots should be used as priority that Care of children, eldery and the disabled, 4% Education, 3% 0% Areas in which robots should be used as priority Source: Eurobarometer, 2012.

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PPKP_2017_book.indbKP_2017_book.indb 207207 221/11/20171/11/2017 06:5206:52 healthcare. Less than five percent of people agreed that robots could be used in education and care of children or the elderly (Eurobarometer, 2012).

Interesting is that on average 25 percent of people (from the age of 14-55) are totally comfortable having surgery performed by robots, although there is a significant difference between people with different educational attainment. 15 percent of low educated people are comfortable being medically treated by robots, and 33 percent of highly educated people share the same opinion. Fur- thermore, on average, people are skeptical and do not believe that robots will boost job opportunities. This opinion is especially evident in the case of the older, less educated and people who hold negative believes towards robots in general (Eurobarometer, 2012; Eurobarometer, 2015).

The economic situation in a given country and experience with robots are not the only things that shape public attitudes towards robots. Culture plays a vital role as well. In western cultures robots have been presented in a very negative light. Movies such as Frankenstein, The Terminator, I Robot and many others have created a negative image in people’s minds. This can be evidenced also in the Eurobarometer surveys (2012 and 2015), where respondents completely agreed with the statement that robots have to be carefully managed. On the other hand, in Japan people have more positive perceptions towards robots. Pop-culture has helped to create a positive image through animated cartoons where robots strive to become as human as possible, as well as help protecting their human counterparts. Also, in Japan engineers are exploring the idea of creating robots for entertaining purposes. In order to do that it is believed that robots should look like a human being. In the field of entertainment, design is more important than functionality, which is why Japan is the leader in humanoid robots (Rathmann, 2013).

3 Why are robots perceived as a danger?

Empirical studies show that when it comes to robots, people are hesitant in their opinion– are robots good for humanity or will they bring destruction and despair? Although, people are most afraid of losing their jobs due to robots replacing their labor, we could identify three major groups of reasons: labor market issues, sociological, and psychological reasons (Chang and Huynh, 2016).

Labor market issues. The most important fear that robotization and automatization cause is the question of replacing certain jobs. Identifying jobs that have

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PPKP_2017_book.indbKP_2017_book.indb 208208 221/11/20171/11/2017 06:5206:52 higher probability of being replaced by robots is important to understand fears and negative attitudes among people. In order to evaluate the likelihood of a certain job would being replaced by a robot, researchers analyzed four categories of jobs: routine, non-routine, manual, and cognitive with its connection to technology and potential shifts in demands for all categories of jobs. On one hand, jobs which include routine work and follow a set of rules and procedures tend to be more likely to be automated. On the other hand, jobs that are non-routine, abstract and require judgment or creativity, tend to be less automatable (Chang and Huynh, 2016). As the four job categories can be related to education attain- ments, we can again confirm an interesting, although not a surprising finding that attitude towards robots depends on the level of education.1 Moreover, data reveals that less educated people also have less experience in working with robots either at home or at work (Eurobarometer, 2012).

Sociological concerns. Since robots are designed to enter a range of personal spaces and contexts, the sociological investigation of how robots will integrate into people’s environments has been an important component of human-robot interaction (HRI) research (Young et al., 2008). Studies in the fields of sociolo- gy and gender studies highlight how technology, science and gender are tightly intertwined (Wajcman, 2009). Gender impacts how scientific knowledge (Schie- binger, 2008) and technologies are developed, appropriated, used (Venkatesh et al., 2000), and understood by society (Arvanitaki and Stratigaki, 1994). Some works suggest that people like the social presence of robots, at least in healthcare (Yoshikawa et al., 2011; Takano et al., 2008). According to the Yoshikawa and Takano studies, 50 percent of hospital pain clinic patients did not mind the presence of a female humanlike robot in their first consultation with the doctor, 33 percent preferred the robot’s presence, and only six percent preferred its absence. Nevertheless, Sharkey and Sharkey (2011) warn that if robots are used as nannies to care for young infants, then infants may not develop linguistically, socially, or emotionally due to insufficient human care.

Although the field of human-robot interactions is still dominated by engineers and computer scientists, psychological concerns are beginning to evolve as well, and the field has rapidly expanded in the past few years. Most research has focused on the technical side of robotics, and more research is needed on the ways humans respond to and work with robots. Turkle (2014) and Misra et

1 During the IMB study visit to Italy in 2017, the Tivoli Group was visited and the CEO introduced the company to the students and answered the questions that were raised by the students. One of the questions addressed the issue of automatization, since most of the tasks that the workers did were manual and routine. The CEO replied that in the future it is possible that a few tasks will be replaced by robots but most of the production will remain manual. Obviously the company will not implement robots because hand crafted products are perceived as luxurious by the customers and if robots replace workers, the competitive advantage will be lost. — 209 —

PPKP_2017_book.indbKP_2017_book.indb 209209 221/11/20171/11/2017 06:5206:52 al. (2016) argue that robots aim to develop a different culture in which relationships with mobile devices may come at the cost of relationships with each other, with the presence of cell phones linked to feelings of decreased empathy and closeness during dyadic conversations. The intensive use of technology causes concerns especially in the case of children who don’t develop the capacity to enjoy being by themselves and have no time for reflection. There are more concerns that using robots is unethical as they are deceiving people about their real nature because robots can only simulate love or friendship and should be used only when people express their wish to prefer robots over humans (Spar- row and Sparrow, 2006).

Conclusion

As more and more companies are starting to automate their production processes, it is no longer a debate whether robots will have an impact on the workplace but what its magnitude will be. However, the focus should be broader: how to incorporate robots in our lives and minimize any potential harm to labor market opportunities, as well as sociological and psychological impacts. Adapting to the new technology and fast learning should be the imperatives for workers to “survive” in the age of constant technological improvement with policy makers designing specific training programs. However, there is an opportunity to use new technology in order to overcome mismatch in supply and demand in the labor market regarding healthcare services. Due to the rapidly growing elderly population in the world, robots will substitute scarce resources and people. Robotization is perceived as the only vital resource in this case and we can assume that in the long run the positive attitudes will prevail.

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PPKP_2017_book.indbKP_2017_book.indb 210210 221/11/20171/11/2017 06:5206:52 References Arvanitaki, K., and Stratigaki, M. 1994. “Computerization in Greek Banking: The gendering of jobs and payment practices.” In Cockburn, C., and Furst-Dilic, R. (eds.): Bringing Technol- ogy Home: Gender and Technology in a Changing Europe, Open University Press, 1994. Broadbent, E. 2017. “Interactions With Robots: The Truths We Reveal About Ourselves.” Annual Review of Psychology 68: 627-652. Broadbent, E., Kuo, I., Lee, Y. I., Rabidran, J., and Kerse, N. 2010. “Attitudes and reactions to a healthcare robot.” Telemedicine and E-Health 16: 608-613. Chang, J. H., and Huynh, P. 2016. “Asia in transformation.” Bureau for Employers’ Activities Working Paper No. 9. Cramer, H., Kemper, N., Amin, A., Wielinga, B., and Evers, V. 2009. “Give me a hug: the effects of touch and autonomy on people’s responses to em- bodied social agents.” Computer Animation and Virtual Words 20: 437-445. Eurobarometer. 2012. “Public Attitudes towards Robots.” URL: https://data.europa.eu/ euodp/data/dataset/S1044_77_1_EBS382. Eurobarometer. 2015. “Autonomous Systems.” URL: https://data.europa.eu/euodp/data/ dataset/S2018_82_4_427_ENG. Eurostat. 2012. “GDP per capita EU 27.” URL: http://appsso.eurostat.ec.europa.eu/nui/sub- mitViewTableAction.do. Halpern, D., and Katz, J. E. 2012. “Unveiling robotophobia and Cyber-dystopianism: The role of gender, technology and religion on attitudes towards robots.” International Conference on Human Robots Interaction in Boston, March 5-8, 2012. Hashmi News. 2017. “Mark Zuckerberg calls Elon Musk’s AI predictions irresponsible.” URL: https://www.youtube.com/watch?v=sSu5Di93RUQ. Hill, C. 2017. “10 jobs robots already do better than you.” Market Watch. URL: https://www. marketwatch.com/story/9-jobs-robots-already-do-better-than-you-2014-01-27. Kiplinger. 2013. “Fields Where Robots Are Taking Charge.” URL: https://www.kiplinger.com/ slideshow/business/T057-S005-robots-taking-charge/index.html. Louie, W. G., McColl, D., and Nejat, G. 2014. “Acceptance and Attitudes Toward a Human-like Socially Assistive Robots by Older Adults.” Assistive Technology 26(3): 140-150. Misra, S., Cheng, L., Genevie, J., and Yuan, M. 2016. “The iPhone effect: the quality of in- person social interactions in the presence of mobile devices.” Environmental Behavior 48: 98 - 275. Nomura, T. 2017. “Robots and Gender.” Gender and the Genome 1: 18-25. Nomura, T., Kanda, T., Suzuki, T., Yamada, S., and Kato, K. 2009. “Influences of Concerns toward Emotional Interaction into Social Acceptability of Robots.” 4th ACM/IEEE International Conference on Human-Robot Interaction in La Jolla, March 9-13, 2009. Nomura, T., and Kinoshita, Y. 2016. “Gender stereotypes in cultures: experimental investigation of a possibility of reproduction by robots in Japan.” The International Conference on culture and computing in Kyoto, October 17-19, 2015. — 211 —

PPKP_2017_book.indbKP_2017_book.indb 211211 221/11/20171/11/2017 06:5206:52 Rathmann, M. 2013. “Cultural difference in Robotics: Japan and Germany - an overview.” Social Science Open Access Repository. URL: http://www.ssoar.info/ssoar/handle/document/40939.

Schiebinger, L. 2008. “Getting More Women into Science and Engineering - Knowledge Issues.” In Schiebinger, L. (ed.): Gendered Innovations in Science and Engineering, Stanford University Press, 2008.

Sharkey, A., and Sharkey, N. 2011. “Children, the elderly and interactive robots.” IEEE Robot- ics & Automation Magazine 18: 32-38.

Sparrow, R., and Sparrow, L. 2006. “In the hands of machines? The future of aged care.” Minds and Machines 16: 141-161.

Syrdal, D. S., Dautenhahn, K., Koay, K. L., and Walters, M. L. 2009. “The Negative Attitudes towards Robots Scale and Reactions to Robot Behaviour in a Live Human-Robot Interaction Study.” 1st Symposium on New Frontiers in Human-Robot Interaction: 109-115.

Takano, E., Matsumoto, Y., Nakamura, Y., Ishiguro, H., and Sugamoto, K. 2008. “Psychological effects of an android bystander on human-human communication.” International Confer- ence of Humanoid Robotics in Daejeon, December 1-3, 2008.

Turkle, S. 2014. “Objects of desire.” In Brockman, J. (ed.): What Should We Be Worried About? Real Scenarios that Keep Scientists Up at Night, Harper Perennial, 2014.

Venkatesh, V., Morris, M., and Ackerman, P. 2000. »A Longitudinal Field Investigation of Gender Differences in Individual Technology Adoption Decision-Making Processes.« Or- ganizational behavior and human decision processes 83(1): 33-60.

Wajcman, J. 2009. »Feminist theories of technology.« Cambridge Journal of Economics 34(1): 143-152.

Wang, L., Rau, P., Evers, V., Robinson, B. K., and Hinds, P. 2010. “When in Rome: The role of culture & context in adherence to robot recommendations.” 5th ACM/IEEE International Conference on Human-Robot Interaction in Osaka, March 2-5, 2010.

Weiss, A., Bernhaupt, R., Tscheligi, M., and Yoshida, E. 2009. “Addressing user experience and societal impact in a user study with a humanoid robot.” 1st Symposium on New Frontiers in Human-Robot Interaction in Sheffield, April 5-6, 2016.

Yoshikawa, M., Matsumoto, Y., Sumitani, M., and Ishiguro, H. 2011. “Development of an android robot for psychological support in medical and welfare fields.” IEEE International Conference of Robotics and Biomimetic in Phuket, December 7-11, 2011.

Young, J. E., Hawkins, R., Sharlin, E., and Igarashi, T. 2008. »Toward Acceptable Domestic Robots: Applying Insights from Social Psychology.« International Journal of Social Robot- ics 1: 95-108. — 212 —

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PPKP_2017_book.indbKP_2017_book.indb 214214 221/11/20171/11/2017 06:5206:52 V. POLICY PROPOSALS

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PPKP_2017_book.indbKP_2017_book.indb 215215 221/11/20171/11/2017 06:5206:52 — 216 —

PPKP_2017_book.indbKP_2017_book.indb 216216 221/11/20171/11/2017 06:5206:52 Matjaž Koman, Janez Prašnikar, Tjaša Redek, Matjaž Dolenc, Tadej Ocvirk, Klemen Pavačič

POLICY RESPONSES TO THE CHALLENGES OF THE FOURTH INDUSTRIAL REVOLUTION

Introduction

Robotization is bringing changes that will have significant implications for governments and business leaders around the globe. By 2025, a quarter of all processes in manufacturing will be completely automated. Today, an industrial robot investment of 100,000 euro yields twice the productivity than it did a decade ago, and this rapid pace of improvement is set to continue (Sirkin et al., 2015). Robotization promises to deliver a large contribution to economic growth as part of the Fourth Industrial Revolution, yet its effects on the labor market are a large cause for concern; one robot could eliminate over six human jobs in the economy (Acemoglu and Restrepo, 2017a). These issues have the potential to cause drastic changes in the fabrics of today’s societies, both developed and developing. Therefore, it is imperative for governments to be prepared and devise policies that will successfully tackle the coming transition period.

The aim of this chapter is to discuss the issues brought forth by the shift to a new development paradigm and suggest policy proposals that could address the challenges it brings about. In order to do so, we set the goal of systematically analyzing the implications of robotization within the context of the Fourth Industrial Revolution and formulating policy responses in eight areas, divided among five subchapters. Each subchapter also contains a summary table, identifying potential problems, as well as a list of suggested policy responses.

Finally, the impact and consequences on Slovenia, its economy and society are addressed with an attempt to provide some suggestions on how the country should promote robotization with insight from the experiences of other devel-

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PPKP_2017_book.indbKP_2017_book.indb 217217 221/11/20171/11/2017 06:5206:52 oped open economies, specifically Denmark, Austria, Germany, Japan and Singapore. Slovenia has something in common with all of them, be it the will to ensure high incomes through robotization, a shortage of labor, or demographic challenges. Their strategies and experiences present a learning opportunity for Slovene policymakers.

1 Policy responses

The Fourth Industrial Revolution is expected to significantly impact several major aspects of our lives, creating a number of challenges for policymakers, which should aim to support the positive outcomes and help overcome the negative ones. In this subchapter, we address the following challenges and provide suitable policy responses: productivity increases, labor market disruptions, education system challenges, inequality and some other issues.

1.1 Productivity increases

The proliferation of robots is one of the key elements of the Fourth Indus- trial Revolution (hereinafter: Industry 4.0). Modern industrial robots are autonomous, flexible and versatile and their capabilities are profoundly changing manufacturing. Their contribution to productivity is notable: between 1997 and 2007, robots contributed 0.37 percentage points to economic growth across 17 countries. Furthermore, recent evidence shows that robots are being increasingly used in developing countries as well – China is already the world’s leading buyer of robots, meaning the contribution of robots to worldwide growth will likely increase in the coming decades (Graetz and Michaels, 2015).

Industrial robots are spreading beyond large enterprises to SMEs, largely as a result of declining prices of robots; from 1990 to 2005, the prices of industrial robots have halved and between 2005 and 2014, they dropped by another 30 percent to an average of 133,000 US dollars (Graetz and Michaels, 2015). The average price is predicted to fall by another 22 percent by 20251 (Sirkin et al., 2015).

Moreover, the rise and increased use of collaborative robots is expected to drive the market in the coming years (IFR, 2016). The increasing robot density can be beneficial, as robot adoption has the potential to promote GDP growth

1 Not adjusting for inflation or improvements in quality. — 218 —

PPKP_2017_book.indbKP_2017_book.indb 218218 221/11/20171/11/2017 06:5206:52 Table 1. Subchapter summary: Potential impacts and policy responses related to productivity issues Impact Policy responses Governments need to ensure basic factors for productivity increases: • Facilitate trade Positive impact • Encourage FDI and mobility of skilled labor on economic growth, • Knowledge sharing, especially between business and scientific/higher education institutions potential to • Improve access to human and financial capital counteract aging • Minimize inefficiencies in resource reallocation workforces, cut labor costs, Policy responses which specifically target robotics: however, • Framework improvements for robotization and automation while addressing societal varying concerns for privacy and security productivity • Investment in next-generation digital infrastructure impacts by industry/region. • Grants and loans to companies • Establishment of competence centres/centres of excellence, which can drive adoption among SMEs • Leading by example – automation of public sector through increased use of robots and cobots Source: Graetz and Michaels, 2015; OECD, 2015a; Schröder, 2016; Sirkin et al., 2015; The Tuborg Research Centre for Globalisation and Firms and McKinsey & Company, 2017.

in economies with aging workforces (Acemoglu and Restrepo, 2017b). As demographic trends take an increasing toll on the labor supply and thus putting continued growth of developed countries in question, robotization and automation could counteract this stagnation and represent a new engine of economic growth (Manyika et al., 2017). On the other hand, Graetz and Michaels (2015) found that the marginal returns on increased robot densification seem to dimin- ish fairly rapidly, although robot adoption is still quite low overall.

Robotization will impact labor costs. According to Sirkin et al. (2015), robots have the potential to cut labor costs significantly, for example by as much as 33 percent in South Korea, 25 percent in Japan, 24 percent in Canada and 22 percent in the United States and Taiwan. However, the productivity increase observed thus far varies by industry. For example, according to Graetz and Michaels (2015), a 0.02 increase in the number of robots per million hours worked led to a three percent increase in productivity in the construction industry, however, an equal 0.02 increase in the number of robots per million hours worked in the utilities industry led to a 43 percent increase in productivity. The different productivity impacts of robotization on different industries present a challenge for policymakers.

The policy options that governments possess to facilitate the transition to the new paradigm are varied, but in order to devise the right policies, govern-

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PPKP_2017_book.indbKP_2017_book.indb 219219 221/11/20171/11/2017 06:5206:52 ments should understand which factors impact technology adoption and diffusion. The OECD (2015a) has identified the following: trade levels, foreign direct investments and mobility of skilled labor; the exchange of knowledge through interactions between businesses and scientific and higher education institutions; the extent of knowledge-based capital; and the availability of human and financial resources, among others. On the other hand, inefficient resource reallocation caused by a lack of competition, rigid labor markets, and barriers to growth restrict productivity increases. For example, the sensitivity of capital investment to a change in patent stock, a proxy for technological innovation in companies, is almost double in Norway, a country with efficient contract enforcement, relative to Italy, a country where contract enforcement is inefficient and costly (Andrews et al., 2014). Governments should therefore address these factors and ensure they are not a hindrance to the economy.

Governments can also help by designing targeted framework improvements to deal with issues that specifically hinder robot adoption and automation. Ensur- ing the right conditions for autonomous vehicles or delivery drones, for example, requires adjustment of the road or airspace infrastructure rules, vehicle safety regulations, and infrastructure investments to name a few. However, it is imperative that societal concerns regarding privacy and safety are addressed (The Tuborg Research Centre for Globalisation and Firms and McKinsey & Company, 2017).

While framework improvements are necessary for the successful implementation of robotization and automatization, governments should also promote early technology adoption to ensure that productivity gains will be enjoyed by a larger share of the economy and to prevent company complacency. Examples of such policies include investment in next-generation digital infrastructure, increasing direct investment in automation through government grants and loans, be it directly to companies or to centres of excellence, as well as attracting foreign capital, talent and experts (The Tuborg Research Centre for Globalisation and Firms and McKinsey & Company, 2017). These types of investment may be especially useful in developing countries or in rural areas, where current infrastructure may be subpar. Some European countries, e.g. Romania, that had very poor communication infrastructure in the past, are now among the top European countries in terms of internet speed, as a result of such investments (Kelly et al., 2017).

Furthermore, special care should be given to SMEs and their needs and abilities. There is a significant relationship between company size and implementation of robots. SMEs are less likely to be sufficiently prepared for Industry 4.0; they are poorly networked, lack awareness and subsequently also strate-

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PPKP_2017_book.indbKP_2017_book.indb 220220 221/11/20171/11/2017 06:5206:52 gies regarding Industry 4.0issues. Government or trade association-sponsored centres of excellence or competence centres could spread awareness and offer consultation services to SMEs (Schröder, 2016; The Tuborg Research Centre for Globalisation and Firms and McKinsey & Company, 2017).

Finally, a way for governments to support and promote robotization and automatization is to lead by example. While the public sector is less automatable than other sectors of the economy2, governments should introduce automated and robotised solutions in various public-sector settings (elderly care, healthcare, tax, etc.). While increasing innovation, providing know-how enhancement opportunities to the private sector and serving as a champion for Industry 4.0, such efforts also have the added benefit of improving public sector productivity (The Tuborg Research Centre for Globalisation and Firms and McKinsey & Company, 2017).

1.2 Labor market disruption

The changes brought about by robotization and automation could disrupt labor markets. Although the nature and extent of disruption is unclear at this point, several recent studies have come to conflicting results (Table 2 summarizes the main issues). Acemoglu and Restrepo (2017a), Frey and Osborne (2013), as well as Sirkin et al. (2015) claim that automatization and robotization will result in job losses. Acemoglu and Restrepo (2017a) performed a study focused on the impacts of growth in robotization on the labor market equilib- rium in the period between 1990 and 2007 and found that the implementation of one robot per thousand workers reduced the employment-to-population ratio by approximately 0.37 percentage points and led to a 0.73 percent lower wage growth, compared to a wage growth in an area with an absence of robots. In practical terms, this translates to one robot consequently reducing employment by as much as 6.2 workers, and one robot per thousand workers resulting in the reduction of the average yearly wages by 200 US dollars. Frey and Osborne (2013), who performed a study on the automatability of various professions, argue that by 2025, machines will perform more than 23 percent of all jobs.

Other authors tend to disagree, however, Autor (2015) claims that most middle-skill jobs assumed to be taken over by robots will persist or in some cases potentially even grow in number. He argues that only certain tasks, as op-

2 In Denmark, for example, automatable tasks currently represent only 27 percent of total work hours in the public sector, compared to 40 percent in the economy as a whole (The Tuborg Research Centre for Globalisation and Firms and McKinsey & Company, 2017). — 221 —

PPKP_2017_book.indbKP_2017_book.indb 221221 221/11/20171/11/2017 06:5206:52 Table 2. Subchapter summary: Potential problems and policy responses in the labor market Potential problems Policy responses Policymakers will be vital in ensuring that automation and robotization have a positive impact on the labor market. Policies need to target: 1. those entering the workforce, governments should facilitate cooperation between businesses and higher education institutions to establish specialized study programmes which: Authors disagree on • should promote creativity, social skills, systems thinking and other hard-to-automate skills whether robotization and automation will • focus on big data and analytics, augmented reality, additive manufacturing, the cloud impact job creation. technology, cybersecurity, industrial Internet of Things (IoT), horizontal and vertical system However, it will cause integration, autonomous robots. shifts in the labor 2. those already working, reskilling opportunities must be available. Policymakers can: market. • encourage cooperation between educational institutions and the private sector in the creation of reskilling programmes • follow best practices already employed in different countries • support transitioning workers through wage subsidies and other financial support.

Source: Acemoglu and Restrepo, 2017a; Arntz et al., 2016; Autor, 2015; Frey and Osborne, 2013; Rüßmann et al., 2015; Sirkin et al., 2015; The Tuborg Research Centre for Globalisation and Firms and McKinsey & Company, 2017.

posed to entire jobs, are susceptible to automation, leading to a likely scenario in which machine and worker co-exist to the degree that they are complements. Examples of such jobs are medical para-professions, such as radiology technicians, phlebotomists and nurse technicians, as well as certain repair occupations, including plumbers, builders, electricians, air-conditioning installers, and automotive technicians. A similar argument is put forth by Arntz et al. (2016), who claim that assuming a complete automation of jobs, as opposed to tasks, leads to an overestimation of job automatability. In a study of the impacts of robotization on German manufacturing carried out by Rüßmann et al. (2015), a positive trend of growth in employment has been found, which led them to predict a six percent increase in employment stimulated by robotization by 2025.

This set of conflicting impact assessments does not provide a clear input to policy-makers, but requires the policymaker’s flexibility and ability to react quickly and in a diversified manner. Nevertheless, one should not underestimate the power of policy when dealing with labor market disruption. In a study of the impact of automation in Denmark, The Tuborg Research Centre for Globali- sation and Firms and McKinsey & Company (2017) emphasized that it is the policymakers who will be vital in ensuring that automation leads to a positive impact on the labor market by increasing and promoting both skill development and job mobility to ensure a smooth workforce transition (Table 2).

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PPKP_2017_book.indbKP_2017_book.indb 222222 221/11/20171/11/2017 06:5206:52 To achieve that, policies need to be targeted at those entering the workforce, as well as those who are already working. In order to successfully reach the former, governments should facilitate a tighter cooperation between businesses and higher education institutions that should lead to the establishment of specialized study programmes. These programmes must present an improvement over the existing programmes, with the main adjustments being done to the curricula, education design and teaching methods. They should promote creativity, social skills, systems thinking and other hard-to-automate tasks (The Tuborg Research Centre for Globalisation and Firms and McKinsey & Company, 2017). The key areas of competency development should focus on the following technologies: big data and analytics, augmented reality, additive manufacturing, the cloud technology, cybersecurity, industrial IoT, horizontal and vertical system integration, simulation and the field of autonomous robots (Rüßmann et al., 2015).

On the other hand, to successfully transition people already in the workforce, reskilling opportunities must be available through programmes provided by cooperation between educational institutions and the private sector. Again, governments should play a role by ensuring the necessary regulatory framework, as well as facilitating the creation of reskilling initiatives and programmes. An example of good practice in the field of retraining is SkillsFuture, a Singapor- ean government-funded platform that collaborates with universities in providing reskilling courses to workers switching careers due to skills mismatch and low labor demand. In 2016 alone, 380,000 Singaporeans benefitted from the retraining, an increase of 30,000 over the previous year (Hui, 2017).

A similar example from Slovenia is a start-up called Smart Ninja, which offers a variety of coding courses to applicants with little-to-no prior experience in programming, and within months brings them to a level of proficiency that allows them to take on entry level jobs in web development and programming (Smart Ninja, 2017). It is also important to emphasize that workers’ transitions between jobs should be continuously supported by both labor unions as well as online job platforms and job centres. Lastly, transitioning workers should be eligible to receive wage subsidies during the switching period (The Tuborg Research Centre for Globalisation and Firms and McKinsey & Company, 2017).

However, in addition to workers, employers will have to adjust to new labor market conditions as well. In order to successfully tackle the increase in demand for new professions, manufacturers and organizations should formulate a strategic workforce plan that will allow them to properly assess the demand

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PPKP_2017_book.indbKP_2017_book.indb 223223 221/11/20171/11/2017 06:5206:52 for employees with newly developed skills and competencies. By doing so, they will be able to intervene early enough to include their existing employees in these training and development programmes and prepare them for adopting new roles (Rüßmann et al., 2015).

1.3 Education

Besides the labor market, the field of education will also face several challenges, primarily linked to the increasingly high pace of change in the demand skills, increasing the demand for specific skills, while other profiles will require retraining of existing skills (Lorenz et al., 2015). Table 3 summarizes the main challenges as well as possible policy solutions.

The existing gap between the educational profile of youth and the skills required by employers will continue to widen, negatively impacting the desir- ability of these profiles. A survey carried out in 2012 showed that a majority of employers reported that new hires were sufficiently prepared for the job in only three sectors: education, financial intermediation and healthcare. As demand for skills relating to science, technology, engineering and mathematics (STEM)3 continues to grow, the shortage of graduates will increase further. This will require a reassessment of education systems and a shift of priorities within these systems (Mourshed et al., 2013).

In Germany, the shortage of university graduates in the fields of engineering, robotics and other IT-related programmes due to additional demand, as a consequence of Industry 4.0, is expected to rise to 120,000 individuals by 2025 (Lorenz et al., 2015). Just as the continued shift from agriculture to industry made high school education standard at the beginning of the 20th century, the shift towards robotization and automation could similarly require a new educational standard for the 21st century (Goldin and Katz, 2008).

In order to successfully close the IT skills gap, education systems should work towards creating and implementing cross-functionally integrated programmes with an increased number of interdisciplinary study fields, with the main focus on integration of IT, engineering, business informatics and other underrepresented fields (Lorenz et al., 2015). Furthermore, universities should promote soft skills, such as creativity, problem solving, critical thinking, leadership, collaboration and adaptability, enshrine openness to continuing devel-

3 Specifically computer engineering, IT, robotics, mechanical engineering, big data management, among others (Mourshed et al., 2013). — 224 —

PPKP_2017_book.indbKP_2017_book.indb 224224 221/11/20171/11/2017 06:5206:52 Table 3. Subchapter summary: Potential problems and policy responses in the field of education Potential problems Policy responses Policymakers can reduce the skills mismatch and future-proof education by: • facilitating collaboration between private sectors and educational institutions in order to adapt Skills mismatch programmes and curricula will continue to widen, there is • adapting higher education programmes to follow technological trends and focus on hard-to- uncertainty about automate skills which skills students • promoting science, technology, engineering and mathematics (STEM) among prospective students of today will need • teaching life-long learning as a value and a skill in the workplace of tomorrow. • creating an education-to-employment system integrator to coordinate and integrate activities, as well as monitor outcomes • promoting employer investment in training and skills outside of specialized needs of each company Source: Crockett, 2017; Gehrke et al., 2015; Lorenz et al., 2015; Mourshed et al., 2013; WEF, 2016.

opment and ongoing innovation, as well as promote life-long learning as the guarantor of social mobility and opportunity (Crockett, 2017). These are the skills that separate human labor from robots. It is of vital importance that students are exposed to work prior to graduation through collaboration between universities and the industry, offering internships reserved by enterprises specifically for university students (Gehrke et al., 2015).

Internships provide students with an insight into the specific, technical knowledge and organizational management skills, and have a positive effect on the general trust and acceptance of the new technology (Gehrke et al., 2015). Education does not have to be limited to off-site locations. Academic representatives should make an effort to collaborate with business leaders and offer online platforms with programmes specifically created to meet the needs of current and future demand in various industries (Lorenz et al., 2015).

It is important to emphasize that not all future employees will go through a regular university education programme. To create the possibility of reemploy- ment of existing workers, long-term education systems should be complemented with shorter specific, urgent and focused reskilling programmes. The curriculum of such programmes should be closely related to the recent technologies and practices and should therefore be envisioned in tight cooperation of governments, education providers and most importantly, the businesses themselves (WEF, 2016).

An example of such cooperation is the National Skill Development Corpora- tion in India (NSDC), whose sole purpose is to support the systems required for skill development and is funded and supported by the Asian Development Bank.

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PPKP_2017_book.indbKP_2017_book.indb 225225 221/11/20171/11/2017 06:5206:52 By 2017, it has successfully trained over 5.2 million students and has over 230 private sector partnerships for training and capacity building (Ministry of Skill Development and Entrepreneurship India, 2017). Such programmes are vital to enhance employment opportunities of the existing workforce, say Lorenz et al. (2015), while pointing out that in Germany, skills upgrading could be needed by as much as 65 percent of workers. However, SMEs generally do not prioritize internal education, as they expect the skilled workforce to be readily available. Furthermore, the lack of sufficient financial resources hinders the possibilities of continuous education of their workforce. SMEs should make an effort to cost-effectively train their employees in-house, by offering on-site training or by cooperating with local trade centres and community colleges (Robertson, 2003).

1.4 Inequality

Recent empirical research has surprisingly found that technological changes could adversely affect the workers in the middle of the income distribution. Autor et al. (2006) and Autor and Dorn (2013) argue that it is the routine tasks performed by many middle-skill workers that are more efficiently done by artificial intelligence rather than low-skill workers (Hemous and Olsen, 2014). This could lead to a potential job polarization and an increase in income inequality (Autor et al., 2006; Furman et al., 2016), however, future trends are unclear. The outcomes could either lower the overall inequality or polarize the workers and thus further deepen the inequality (Manyika et al., 2017). Table 4 summarizes the main issues discussed in this section.

According to the OECD (2015b), redistributive policies lower inequality while having no impact on growth. This must be considered by policymakers, who need to involve reforms to tax and benefit policies. Instead, a re-examination of tax systems should be considered. This can be achieved not only by raising marginal tax rates, but also by improving tax compliance, eliminating or scaling back tax deductions that benefit high earners disproportionally, and by changing the role of taxes on all forms of property and wealth. However, it is more important to focus on inequality at the bottom. Social and government transfers are extremely important support channels that promote and increase the access to public services. Investments that will smooth inequality and foster upward mobility have to be designed on a long-term basis (OECD, 2015b).

An alternative option to tackle the rapid automation process is some form of robot taxation. Bill Gates argues that slowing the automation process would

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PPKP_2017_book.indbKP_2017_book.indb 226226 221/11/20171/11/2017 06:5206:52 Table 4. Subchapter summary: Potential problems and policy responses related to inequality Potential problems Policy responses Policymakers can address income inequality through various methods: • income redistribution Inequality could increase • providing equal access to education and other public services due to job polarization, • skill development programmes however, not all authors • The taxation required for income redistribution can be adapted to today’s challenges: agree. • improving tax compliance • addressing the problem of capital accumulation through taxation • robot tax as a way of taxing productivity improvements Source: Autor et al., 2006; Autor and Dorn, 2013; Furman et al., 2016; Hemous and Olsen, 2014; Manyika et al., 2017; OECD, 2015b; The Economist, 2017.

allow people who would likely fall into long-term unemployment to re-adjust and thus prevent a sudden job loss. This would also allow policymakers to prepare better solutions for the endangered by raising additional funding for new employment opportunities (The Economist, 2017).

Finally, the issue of universal basic income (UBI), unconditional income paid to everyone, should be considered as a means of reducing inequality. A prominent example of an actual UBI is the Alaska Permanent Fund dividend, which has been paid yearly to all Alaska residents since the 1980s. The main purpose has been to distribute the rent from oil production. The early 21st century was characterized by many serious political proposals to induce the UBI in Switzerland, Finland, and France (Borgnas et al., 2015). Universal basic income has the benefit of being a transparent and simple welfare system compared to existing welfare systems in the world today. However, it is also an ideologically charged proposal that has proven difficult to implement (De Wispelaere and Stirton, 2004).

1.5 Other issues

In addition to the four main issues already addressed, other issues and trends presented by Industry 4.0 might require government action. We present the results in Table 5. The changing structure of the economy, driven by phenomena such as servitization, the shift from product-centric consumption to services, could significantly change several industries. A good example is the automotive industry, which is currently seen as on the verge of a car sharing revolution. This would change the value chain and shift profits from car makers to technological firms, as car brands become less important (The Economist, 2016).

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PPKP_2017_book.indbKP_2017_book.indb 227227 221/11/20171/11/2017 06:5206:52 Table 5. Issues, potential problems and corresponding policy responses Issue Potential problems Policy responses Governments should help companies restructure: Changing Industry 4.0 will cause the obsolescence of • providing grants and loans economic certain industries and completely reshape others. • facilitating centres of excellences, either structure government or trade association-backed, which can spread awareness and provide consultation

Increasing development gap between areas Governments should encourage development Regional with better access to human capital, investment in rural areas through: development opportunities and infrastructure (usually urban) • tax incentives and those without (usually rural). • infrastructure investments Positive impact due to telemedicine and augmented reality. There are ethical issues Healthcare with regards to human augmentation, genetic / modification. Today’s interconnectedness presents a risk for Policy measures in order to address digital the economy. As the use of internet in smart vulnerabilities are: Digital factories and IoT will only grow in the coming • Industry 4.0 bill of rights vulnerability years, issues such as security breaches, data theft, system failure, loss of privacy could become more • Strengthening monitoring pressing. • Kill switches in robots Source: European Commission, 2016b; European Parliament, 2015; Furman et al., 2016; Grinin and Grinin, 2014; Lin et al., 2011; Stefan, 2015; The Economist, 2016; The Tuborg Research Centre for Globalisation and Firms and McKinsey & Company, 2017; Waslo et al., 2017.

Regional development is another issue policymakers will have to tackle as part of Industry 4.0. Automation and robotization will impact all regions, regardless of whether urban or rural, however, in different magnitudes. In Denmark, for example, the range of impact is within 15 percentage points of the share of work hours, driven by the local sector composition. Rural and less developed areas with more jobs in manufacturing and industry are more at risk of automation compared to urban areas, where there are more jobs in IT, finance and the public sector (The Tuborg Research Centre for Globalisation and Firms and McKinsey & Company, 2017). Unless addressed by policy, this threatens to exacerbate current regional economic differences and create regions that are “left behind” (Furman et al., 2016). We suggest infrastructure investments and tax incentives as a means to counteract these forces.

Finally, while the modern interconnection of digital supply networks, customers, smart factories and operations enables new possibilities in value creation, the risks posed by cyber threats become greater and potentially farther reaching and thus present a need for a fundamental change in how security is viewed in Industry

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PPKP_2017_book.indbKP_2017_book.indb 228228 221/11/20171/11/2017 06:5206:52 4.0, specifically with regards to robots (Waslo et al., 2017). Policy responses include a kill switch in robots, the strengthening of monitoring and a Industry 4.0 bill of rights (European Parliament, 2015; Lin et al., 2011). Countries are starting to take action – the EU’s NIS Directive is the first EU-wide act on cybersecurity as part of its Digital Single Market initiative and will mandate security incident response teams across member states, among other solutions (European Commission, 2016b).

2 Robotics policies

Robotics represents a special area within Industry 4.0 – it is probably one of its most visible aspects. The policy measures that were explored in the previous subchapter address specific concerns about robotization within the context of the Fourth Industrial Revolution as a global phenomenon. However, the consequences and benefits for countries will differ and subsequently the paths that countries will choose will differ too. Given the large implications of robotization, it is sensible to consider how to promote further robotization. To provide guidelines for future actions in Slovenia, an overview of the policy measures in other countries is warranted.

2.1 Overview of country policies

To study the policies related to robotization, the approaches pursued in Den- mark, Germany, Austria, Japan and Singapore are analyzed. The countries are all open, developed economies with some similarities with Slovenia.

Denmark, specifically the town of Odense, is home to a strong cluster of well- developed research and educational institutions and more than 100 companies with robotics and automation technologies as the core of their business (Odense Robotics, 2017). The industry is very flexible, targeting niches and using advanced technologies, making full use of a very advanced educational system. This cluster has driven robotization and has allowed Denmark to become one of the most automated countries in Europe (IFR, 2016). Robotization in Denmark has progressed in the bottom-up approach through organic development in the Odense cluster. This is similar to Slovenia, where robot adoption is also growing organically in a bottom-up manner. However, in order to help sustain the country’s high quality of life, high, robot-supported productivity is required. To ensure this, the movement was followed by a concerted effort to develop robotics further, where the government had a constructive role (Steno, 2016).

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PPKP_2017_book.indbKP_2017_book.indb 229229 221/11/20171/11/2017 06:5206:52 The flagship of the robotics cluster is Universal Robots, a company that specializes in collaborative robots (Steno, 2016). In 2008, it was a failing start-up until it received a financial injection and new management by a state investment fund and later on, the company’s development was assisted by a state innovation fund, as well as clear local government support (Innovation Fund Denmark, 2017; Steno, 2016; Vaekstfonden, 2017). The company’s history thus highlights the importance of effective and smart government policy for cluster development.

Germany is the fifth largest robot market in the world and the country with the highest robot density in Europe (IFR, 2016). The country’s approach to robotization can be characterised as much more top-down than Denmark’s. The formulation of the strategy started at the Federal Ministry for Economic Affairs and Energy, which addressed the rapid technological changes through the creation of a nationwide Industry 4.0 strategic initiative, thus beginning an active push towards robotization in the manufacturing sector (Plattform Industrie 4.0, 2017).

The German federal government sees Industry 4.0 as a major opportunity for Germany to establish itself as an integrated industry lead market and pro- vider. The basis for this strategy are two goals; (1) Germany to become one of the world’s most competitive and innovative manufacturers, and (2) Germany becoming a technological leader in industrial production research and development (Germany Trade and Invest, 2017). However, despite some investments by Germany’s largest companies, business in general has not responded to the challenge, with SMEs (the so-called Mittelstand) proving particularly problem- atic in terms of awareness and readiness (Karnitschnig, 2016).

Much like Slovenia, Austria’s adoption of robots has been spearheaded by a limited number of industries, chief among which is the automotive industry (IFR, 2017), as well as an active science community, especially its network of research and educational institutions. Austria’s approach can be characterized as much more top-down than Denmark’s and is similar to Germany with the government’s active role in the promotion of Industry 4.0 (Roland Berger, 2014).

As one of the leaders in the world of robotics and automation, Japan aims to spread the use of robotics beyond just the large-scale factories to every corner of the economy and society (Fensom, 2015), and doing so, establish and maintain the country’s position as an international superpower in the field of robotics (Edwards, 2015). The key reason behind the Japanese success in the field of robotics lies in the fact that the idea of a robot-fuelled future enjoys strong support by the Japanese government. In order to counteract the aging of the

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PPKP_2017_book.indbKP_2017_book.indb 230230 221/11/20171/11/2017 06:5206:52 population, falling birth rates and the decline in productivity, Prime Minister Shinzo Abe in 2014 turned to businesses with a five-year plan named “New Strategy for Robots”, which was backed by more than 400 companies and other organizations. Various experts were recruited to establish a Robot Revolution Initiative Council, a concept not only based on the German model, but also intended to support international collaborations, most importantly with Ger- many (German Research and Innovation Forum, 2015).

The strategy is based on three pillars: (1) enhancement of the Japanese ca- pability to create robots, (2) popularization of robot usage in everyday life, (3) expansion and development of the “Robot Revolution”. It is expected to result in an increase in the size of the Japanese robotics market from $5.47 billion to $20 billion by 2020 (Kemburi, 2016) and provide a significant boost in the productivity in manufacturing, supply chains, construction and infrastructure maintenance and healthcare (Ministry of Economy, Trade and Industry Japan, 2015).

Unlike Japan, Singapore is a small economy, but it, too, already has one of the most automated manufacturing sectors in the world with 398 robots per 10,000 employees in 2016 (IFR, 2016). Furthermore, Singapore has adopted the National Robotics Program to further develop and drive robotics research and development and adoption. The programme is built around a whole-of- government approach, with coordination and participation of various government institutions (Kok Kiang, 2016).

Between 2016 and 2019, Singapore plans to spend more than 450 million Singapore dollars on supporting robotics. The aims of the programme are to develop a globally competitive robotics industry, exploit advances in robotics to enhance the productivity and competitiveness of its manufacturing sectors and to support adoption of robotics to address local needs. An important element of this programme is ensuring that the adoption and benefits of robotics also reach SMEs, which are much less likely to opt for advanced robotic equipment (Government of Singapore, 2016).

2.2 Lessons for Slovenia

The increasing presence of robots in national economies has led to a divergent set of policies, organically developed in different countries based on their specific economic, social and other factors. However, one of the overarching themes has been the government’s active role in further developing robotics and harnessing

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PPKP_2017_book.indbKP_2017_book.indb 231231 221/11/20171/11/2017 06:5206:52 its benefits to the economy and society to the maximum effect. This is an important consideration – currently, government involvement in the robotics industry is low (Lenarcic, 2017). However, if the country wishes to harness robotics in order to tackle some of its upcoming challenges, such as an aging society or transition to a high-income society, this will have to change. The government has adopted the Smart Specialization Strategy (S4) in 2015, however, its scope is limited to the manufacturing sector (Government of Slovenia, 2015).

Robotization in Slovenia has progressed the most in the automotive industry. In order to better serve society and the economy, robot usage should spread to other industries and into wider society. While S4 does aim to increase robot density among non-automotive industries in the manufacturing sector, Japan and Denmark’s leads should be followed by aiming to spread robots into areas where they can improve the quality of life, not just productivity (healthcare, elderly care, etc.). While robots can help recover lost economic growth as society ages, they can also effectively serve the increasingly large elderly population. Furthermore, in terms of industry, special care should be given to SMEs, which are slower in adopting robotics technology.

Many of the analyzed countries have, like Slovenia, began the process of robotization from the bottom up, with an initial impetus from businesses or a combination of business and research institutions. This has led to the development of robotics clusters in Denmark and Austria, but not in Slovenia. It is difficult to build a cluster out of scratch. The driving force behind robot adoption is currently large companies, often a part of multinational companies with their own R&D capabilities. A precondition for a successful cluster formation is that there is a basic desire to cooperate among existing businesses and institutions. If the benefits of working together outweigh the costs of going on their own, companies should be encouraged by the government to cooperate further. The government can help by facilitating deeper ties between companies, research institutions and universities. This is important from the perspective of labor as well. Improved relationships between business, education and research can help shape educational programmes, address the skills gap and thus ensure a better fit between the skillset of entrants to the labor market and the required skills from companies. A greater focus on robotics and the inclusion of robot-related topics into other fields of study will help educate a new generation of Industry 4.0 workers.

The planning and implementation stages of future policy should also trans- parently address societal perceptions and concerns, especially with regards to safety and privacy. This is an important consideration: while the Tuborg Research

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PPKP_2017_book.indbKP_2017_book.indb 232232 221/11/20171/11/2017 06:5206:52 Centre for Globalisation and Firms and McKinsey & Company (2017) see current personal data legislation in the EU as an obstacle to developing new advanced analytics-based solutions, the European Commission (2016a) has found that 71 percent of EU residents do not support data sharing among companies without their permission, even when it helps companies to improve their services.

Lastly, it is important to emphasize good practices. Despite the natural tendency of companies to group together geographically, it is possible to achieve more balanced regional development. Yaskawa’s Kočevje investment is a case in point. Slovenia is a small country with short distances. This enables less developed regions to compete on a more equal footing with the help of government policy.

Conclusion

The Fourth Industrial Revolution promises to provide a boost to productivity around the world. Yet, not all countries will benefit equally – the regulatory framework will prove important yet again when harnessing the opportunities of Industry 4.0. The labor market implications will probably be the most disruptive, with economic and social repercussions, especially on inequality and regional development. Governments have the ability to both benefit from the positive effects and mitigate the negative effects with the right policies in place. An efficient bureaucracy, facilitated trade and knowledge exchange, access to human capital, framework improvements for automated machines, investment in next-generation infrastructure on one side, and investments in education and reskilling, as well as effective redistribution on the other, should allow countries to successfully transition to the new development paradigm.

Slovenia has a unique opportunity to obtain a sustained competitive advantage through robotics by being an avid early adopter and focus on the development of its own robotics cluster. It has the potential to become a robotics champion and leader for Southeastern European countries. The cooperation and collaboration of industry, government, the scientific community, and educational institutions will prove crucial in ensuring that the country fulfils its potential and that robotization turns out to be a success for its economy and its citizens.

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PPKP_2017_book.indbKP_2017_book.indb 236236 221/11/20171/11/2017 06:5206:52 Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., and Harnisch, M. 2015. “Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries.” Munich: Boston Consulting Group. Schröder, C. 2016. “The Challenges of Industry 4.0 for Small and Medium-sized Enterprises.” Bonn: Friedrich-Ebert-Stiftung. Sirkin, H. L., Zinser, M., and Rose, J. 2015. “How Robots Will Redefine Competitiveness.” BCG Perspectives. URL: www.bcgperspectives.com/content/articles/lean-manufacturing- innovation-robots-redefine-competitiveness/. Smart Ninja. 2017. “About Smart Ninja.” URL: https://www.smartninja.org/#about. Stefan, H. 2015. “eHealth: Industry 4.0 can serve as the model for digital healthcare.” Deutsche Bank. URL: https://www.dbresearch.com/PROD/RPS_EN-PROD/PROD0000000000441850/ eHealth%3A_Industry_4_0_can_serve_as_the_model_for_d.PDF. Steno, C. 2016. “The success of the Danish robotics cluster.” Odense: Universal Robots. URL: https://blog.universal-robots.com/the-success-of-the-danish-robotics-cluster. The Economist 2016. “The driverless, car-sharing road ahead.” URL: www.economist. com/news/business/21685459-carmakers-increasingly-fret-their-industry-brink-huge- disruption. The Economist. 2017. “How to make robots pay their share.” URL: www.economist.com/ blogs/economist-explains/2017/03/economist-explains-1. The Tuborg Research Centre for Globalisation and Firms, and McKinsey & Company. 2017. “A future that works: The impact of automation in Denmark.” URL: https://www.mckinsey. com/~/media/McKinsey/Locations/Europe%20and%20Middle%20East/Denmark/Our%20 Insights/A%20future%20that%20works%20The%20impact%20of%20automation%20 in%20Denmark/A-future-that-works-The-impact-of-automation-in-Denmark.ashx. Vaekstfonden. 2017. “Objectives and Strategy.” URL: http://www.vf.dk/om-vaekstfonden/ formaal-og-strategi.aspx. Waslo, R., Lewis, T., Hajj, R., and Carton, R. 2017. “Industry 4.0 and cybersecurity: Managing risk in an age of connected production.” Deloitte University Press. URL: https://dupress. deloitte.com/dup-us-en/focus/industry-4-0/cybersecurity-managing-risk-in-age-of- connected-production.html. World Economic Forum (WEF). 2016. “The Future of Jobs: Employment, Skills and Work- force Strategy for the Fourth Industrial Revolution.” URL: http://www3.weforum.org/docs/ WEF_Future_of_Jobs.pdf.

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PPKP_2017_book.indbKP_2017_book.indb 238238 221/11/20171/11/2017 06:5206:52 Authors (Alphabetical order)

Enya Caserman Nina Kovač Andreja Cirman Robert Kovačič Batista Barbara Čater Anej Peter Lah Tomaž Čater Rok Lavrič Matej Černe Fabijan Leskovec Katarina Čop Anastasia Liakhavets Matjaž Dolenc Denis Marinšek Jakob Döller Tadej Ocvirk Polona Domadenik Simon Pangeršič Miha Dominko Klemen Pavačič Špela Drnovšek Janez Prašnikar Peter Emri Andrei Putukh Eva Erjavec Jan Ratej Ana Rita Fernandes Tjaša Redek Lara Flegar Dmitrii Sazonov Kristian Groznik Nikola Sionov Domen Gulič Tjaša Skubic Ada Guštin Urban Smolar Dimitrije Ivanović Nejc Šaranović Nina Jagodic Rok Štemberger Marko Jakšič Matjaž Vidmar Katarina Kern Pirnat Aida Zukić Samo Knafelj Tamara Žarković Matjaž Koman Mitja Kovač

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PPKP_2017_book.indbKP_2017_book.indb 239239 221/11/20171/11/2017 06:5206:52 Published by Časnik Finance d. o. o.

Editors: Janez Prašnikar, Tjaša Redek, Matjaž Koman

Proofreader: Tanja Povhe

Technical Editor: Ciril Hrovatin

Graphic Designer: Ciril Hrovatin

Cover Designer: Laura Pompe Sterle

Printed by Plusbiro d.o.o.

CEO of Časnik Finance: Peter Frankl

Editor-in-Chief of Časnik Finance: Simona Toplak

First printing

Ljubljana, November 2017

Circulation: 450 copies

Under the Value Added Tax Act (Official Gazette of RD, No. 89/1998), the book belongs to products for which 9.5 % VAT is charged.

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