A SAVVY GUIDE TO THE DIGITAL SUPPLY CHAIN HOW TO EVALUATE AND LEVERAGE TECHNOLOGY TO BUILD A SUPPLY CHAIN FOR THE DIGITAL AGE APRIL 2018

GLOBAL SUPPLY CHAIN INSTITUTE Sponsored by NUMBER TWO IN THE SERIES TECHNOLOGY IN THE SUPPLY CHAINMANAGING RISKCATEGORIES IN THE GLOBAL OF INNOVATIONSUPPLY CHAIN 1 A SAVVY GUIDE TO THE DIGITAL SUPPLY CHAIN

TABLE OF CONTENTS Executive Summary 3 Introduction 6 Defining the Terms 8 INDUSTRY INSIGHT: Challenging Your Mindset About the Future 14 Research Findings 16 INDUSTRY INSIGHT: Automation, AI, and the Future of Work: More Good Than Bad 26 The SAVVY Framework 29 INDUSTRY INSIGHT: Mastering the Matrix: A Roadmap to Mastering Digital Transition 40 Conclusion 44 Digital Supply Chain Maturity Assessment 46 Glossary 48 Endnotes 54

2 MANAGINGCATEGORIES RISK OF IN INNOVATIONTHE GLOBAL SUPPLY CHAIN A SAVVY GUIDE TO THE DIGITAL SUPPLY CHAIN HOW TO EVALUATE AND LEVERAGE TECHNOLOGY TO BUILD A SUPPLY CHAIN FOR THE DIGITAL AGE

THE SECOND IN THE TECHNOLOGY IN THE SUPPLY CHAIN SERIES OF UT’S HASLAM COLLEGE OF BUSINESS SUPPLY CHAIN MANAGEMENT WHITE PAPERS

APRIL 2018

AUTHORS: A SAVVY GUIDE TO TED STANK, PhD THE DIGITAL SUPPLY CHAIN SHAY SCOTT, PhD BEN HAZEN, PhD TABLE OF CONTENTS Executive Summary 3 Introduction 6 Defining the Terms 8 INDUSTRY INSIGHT: Challenging Your Mindset About the Future 14 Research Findings 16 INDUSTRY INSIGHT: Automation, AI, and the Future of Work: More Good Than Bad 26 The SAVVY Framework 29 INDUSTRY INSIGHT: Mastering the Matrix: A Roadmap to Mastering Digital Transition 40 Conclusion 44 Digital Supply Chain Maturity Assessment 46 Glossary 48 Endnotes 54

MANAGING RISK IN THE GLOBAL SUPPLY CHAIN 1 THE GLOBAL SUPPLY CHAIN INSTITUTE WHITE PAPERS

THE TECHNOLOGY IN THE SUPPLY CHAIN SERIES

New Supply Chain Technology Best Practices

THE INNOVATIONS SERIES

Platform Lifecycle Best Practices

Selecting and Managing a Third Party Logistics Provider Best Practices

Creating a Transparent Supply Chain Best Practices

Transportation 2025 Megatrends and Current Best Practices

New Product Initiative Best Practices

End-to-End Supply Chain Collaboration Best Practices

THE GAME-CHANGERS SERIES

Game-Changing Trends in Supply Chain Bending the Chain: The Surprising Challenges of Integrating Purchasing and Logistics Managing Risk in the Global Supply Chain Global Supply Chains The ABCs of DCs: Distribution Center Management Supply Chain Talent: Our Most Important Resource

These white papers can be downloaded by going to the publications section at gsci.utk.edu. 2 MANAGINGCATEGORIES RISK OF IN INNOVATIONTHE GLOBAL SUPPLY CHAIN Executive Summary

upply chains are the lifeblood of any business, impacting everything from the quality, delivery, and costs of a business’s products and services to customer service and satisfaction Sto ultimately profitability and return on assets. The requirements and pressures on supply chain teams—including those for sustainability, cost We intend efficiency, and disruption and risk mitigation—are increasing and growing for this white paper to help in complexity. Unfortunately, most supply chain organizations are operating supply chain leaders make with systems built for another era. They lack the transparency and visibility sense of the tidal wave of needed to better predict and mitigate disruptions and imbalance. information confronting As a result, supply chain organizations struggle to collect and make sense them on potential disruptive of an overwhelming amount of data scattered across different processes, sources, and siloed systems. Under these conditions, it is exceedingly digital technologies. difficult to manage and monitor the complete supply chain—resulting in unnecessary risk exposure, disruptions, and delays, as well as increased costs. Recent studies by IBM supported this contention, noting:

n 84 percent of chief supply chain officers report that a lack of visibility is their biggest challenge.1

n 80 percent of all data is siloed, dark, and unstructured and high value data will double by 2020.2

n 87 percent of chief supply chain officers say it is difficult to predict and proactively manage disruptions.3

We intend for this white paper to help supply chain leaders make sense of the tidal wave of information confronting them on potential disruptive digital technologies that promise to fundamentally change the practice of supply chain management. We first define the most prominent digital technologies that supply chain managers are likely to confront over the next three years, including distinguishing automation from digital technology. These technologies will dramatically change the role of individual employees and organizations as cognitive analytics—leveraging the computational capabilities of artificial intelligence combined with the massive data flow from networked automation—takes on the role of managing routine decisions made throughout the supply chain, freeing humans to focus on the more strategic and exceptional decisions.

MANAGING RISKCATEGORIES IN THEEXECUTIVE GLOBAL OF INNOVATIONSUPPLY SUMMARY CHAIN 3 Next, we summarize the emerging trends we discovered from a series of field research interviews with senior supply chain and technology experts across multiple industries including automotive, heavy equipment, telecommunications and technology, healthcare, retail, fast-moving consumer goods, finance, education, and others. These trends include:

n Rethinking data

n Technology stacking

n Internal collaboration and realignment

n External collaboration

n New capabilities, businesses, and processes

n Sustaining human capital

n The changing nature of work

We then introduce a framework that will enable supply chain managers to become SAVVY about adopting and applying the benefits of supply chain digitization. Given the potential scale and complexity that supply chain digitalization brings for supply chain leaders, it is advantageous to develop a mental framework for how best to take advantage of this changing landscape.

As with any disruptive movement, digitalization tends to blur the picture quality that most leaders’ have for their organizations. In order to assist supply chain managers with sharpening their vision of their current and future supply chain environment, we have developed the SAVVY framework to help leaders make informed decisions about supply chain digitalization technology and to accelerate what Gartner has called the slope of enlightenment leading toward a plateau of productivity.

The SAVVY framework leads supply chain management executives through a process of assessing where digitalization might be successfully employed within their organizations on the following dimensions:

n Sources of data

n Analytical capabilities

n Variety of applications across the supply chain

n Value provided to the organization

n Your changing role Finally, we provide a digital supply chain maturity assessment to help you assess how your organization has transformed its supply chain to meet the challenges of the digital economy.

4 MANAGINGEXECUTIVE RISK SUMMARY IN THE GLOBAL SUPPLY CHAIN Our research suggests that supply chains, and the organizations which support them, are in the early stages of a digital transformation that will likely represent the biggest change in the integrated supply chain era. At this point, the picture of exactly how things will look is still understandably blurry, but supply chain leaders must begin to incorporate this new paradigm into their strategies, plans, and organizations, or risk being quickly and irreversibly left behind by competitors who do so. We hope that this white paper will help you up the learning curve to making better and more informed decisions regarding the digital supply chain technologies available to you to drive the performance improvements that Supply chains your organization demands. and the organizations which support them, are in the early stages of a digital transformation that will likely represent the biggest change in the integrated supply chain era.

MANAGING RISK IN THEEXECUTIVE GLOBAL SUPPLY SUMMARY CHAIN 5 Introduction

ny manager actively working in supply chain management during the last three decades likely recognizes that while the concepts underlying supply chain best practices remain relatively steady Aover time, the execution of those concepts changes constantly as technology evolves. From emailed exchanges of spreadsheets, to EDI, to RFID, to cloud computing, the march of technology has driven continued improvement and change in supply chain practice and helps separate high-performing supply chain organizations from laggards.

Supply chain managers recognize that these technological changes will continue to evolve at an increasing pace, yet a great deal of confusion remains as to how best to manage those changes. SCM World tracks supply chain leaders’ sentiments regarding disruptive technologies in the supply chain, and their findings show an escalating level of importance attributed to how digital capabilities are shaping supply chain strategy.4 The most recent survey results are shown in Figure 1.

Shrewd investment in digitalization holds the promise to leapfrog an organization over its competitors, enabling unimagined value to customers while streamlining response times, cutting through complexity, and dramatically reducing the assets needed to carry out operations. In fact, the digitized future promises to leverage sources of data and technological capabilities that are dramatically expanding in both volume and diversity to drive analytical decision-making and secure system interactions. This marriage of new data sources coupled with new analytical capabilities will have broad and deep

6 MANAGINGINTRODUCTION RISK IN THE GLOBAL SUPPLY CHAIN applications across the supply chain that can lead to transformative value for companies. Cognitive analytics, leveraging AI and data captured through network automaton, will assume routine supply chain tasks, changing the role of individual employees to focus more on strategic and exceptional decisions.

Digital technologies promise to fundamentally change the practice of supply chain management, and supply chain leaders need to understand the deluge of information at hand to properly meet this transformation. As such, we take care in this paper to clearly define the digital technologies that supply chain managers are likely to confront over the next three years, distinguishing automation technology from digital technology.

Cognitive An earlier Global Supply Chain Institute white paper covered the broad range analytics of automation technology in the physical supply chain.6 In this paper we focus on the impact of digital technologies on the supply chain. A series of interviews will assume routine supply with senior supply chain and technology experts from 17 companies revealed chain tasks, changing the trends in digitalization across multiple industries including automotive, heavy role of individual em- equipment, telecommunications and technology, healthcare, retail, fast-moving ployees to focus more on consumer goods, finance, education, and others. We also provide a framework strategic and exceptional enabling supply chain managers to become SAVVY about adopting and applying these trends to benefit their supply chain. decisions.

Figure 1 DISRUPTIVE AND IMPORTANT TECHNOLOGIES WITH RESPECT TO SUPPLY CHAIN STRATEGY

(SCM WORLD, 2017) SAMPLE SIZE = 1,415

84 14 2 Big Data Analytics

73 23 4 Internet of Things (IoT)

69 27 4 Cloud Computing

62 32 6 Machine Learning

62 30 8 Advanced Robotics

42 41 17 3D Printing

Drones/Self-Guided 41 43 16 Vehicles

35 50 15 Sharing Economy

25 61 14 Blockchain

n Disruptive and important n Interesting but unclear usefulness n Irrelevant

MANAGING RISK IN THE GLOBALINTRODUCTION SUPPLY CHAIN 7 Defining the Terms

rtificial Intelligence. Blockchain. Internet of Things. Predictive Analytics. Cloud Computing. Big Data.

These terms evoke strong feelings of both wonder and trepidation Afor today’s supply chain leaders. We know that most supply chain managers have read about the potential of supply chain digitalization, and are bombarded with disparate advice about why their company should adopt each new technology in order to remain competitive.

Digitalization As much as we hear about the promise and potential of digital technologies in technology the popular press, at conferences and trade shows, and through word of mouth, many supply chain executives are not clear as to how to employ them or even transforms and redefines what they really are. So where does one begin? What are these technologies value creation within the and what do they really do for the supply chain? Moreover, how will these supply chain. technologies shape how we manage supply chains in the future? Let’s try to answer some of those questions.

To begin, we must establish the difference between automation and digitalization technology. Automation applies advanced technology to achieve dramatic improvements in both efficiency and effectiveness in activities previously performed by human labor. For example, robots can be programmed to do the same task over and over, much faster than humans and with much greater precision, without tiring. Advances in the physical supply chain including automation are the subject of another recent GSCI white paper that can be found on our website at haslam.utk.edu/gsci. Digitalization technology, however, transforms and redefines value creation within the supply chain.7

8 MANAGINGDEFINING RISK THE INTERMS THE GLOBAL SUPPLY CHAIN Consider the simple example of a distributor of fresh seafood. Traditionally the value equation in the seafood distribution industry involved finding dependable fishing vendors that catch scalable numbers of fish that are in demand, getting those fish quickly through processing facilities and into restaurants within a short period of time to guarantee freshness and taste. Investment in automating equipment in the processing facility can improve productivity and speed time to market, thus likely improving revenue and margin, but the value equation remains the same and would likely generate a differential advantage for only a short period of time until the competition catches up.

But suppose the distributor embedded fishing boats, coolers, processing stations, trucks and kitchen facilities all along the supply chain from the time a fish is pulled from the water until it arrives on the diner’s plate with sensors that could capture the time, place, temperature, and other key metrics associated with locality, freshness, quality, and anything else a consumer or restauranteur would what to know?

Would a chef pay extra to know exactly when and where the fish was caught, by whom, and how long it spent in process until it arrived at her kitchen? Would a diner pay more for the same knowledge, possibly even seeing the face of the fisherman who caught the fish on an app on their smart phone? The value of the trust and authenticity that come with that knowledge may well be commercializable, particularly in food industries where the source and quality of product often becomes murky. In this case, digitalization can transform and redefine what a restaurant patron values and is willing to pay for. Even in this basic example, hints of how transformational the digital supply chain can be begin to emerge.

We focus our coverage in this white paper on digitalization technology and application as compared to advances in the physical supply chain such as robotics, machine learning, autonomous vehicles, and augmented reality. It is important to note, however, that digitalization and automation technology are often linked or stacked in a way that enhances value to a higher degree than if used alone. Thus, we provide a glossary for terms relating to automation technologies that are most closely linked to digitalization in the supply chain at the end of the paper. There, we define dditivea manufacturing/3D printing, automated guided vehicles/autonomous vehicles, augmented reality (AR), physical internet, robotic process automation (RPA), virtual reality (VR).

MANAGING RISK IN THE DEFININGGLOBAL SUPPLY THE TERMS CHAIN 9 In this section, we identify and describe digitalization technologies that participants in our field research interviews identified as the most promising for supply chain applications. As with most new business phenomena, we found a lack of common understanding with respect to what these technologies are, and what they actually do. So, we take some time to define and describe the most prevalent and potentially game-changing of these. There are many additional applications that build upon these technologies which we mention in our emerging trends section and define in a glossary at the end of the paper.

Given the nascent stage of digitalization, there is a lack of universal agreement in the area, which complicates the task of defining important terms. A core concept we discuss is the ability of technology to draw from a variety of data types to pull useful insights that allow us to better manage our supply chains. This concept is best described as cognitive computing; however, the term has not yet gained wide acceptance. It is also referred to as artificial intelligence, which is an older and more popular term but one that technically has a much more narrow definition. As our goal is not to debate terminology, we will use cognitive computing and artificial intelligence interchangeably throughout the paper, but we thought it important to define each.

ARTIFICIAL INTELLIGENCE (AI) AI forms possibly the most transformative and impactful technology on the list, combining the capabilities of all other technologies to create a synchronized system that simulates human intelligence and solve complex problems at speeds heretofore unimaginable. AI incorporates techniques such as machine and deep learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and

APPLICATIONS:

AI promises to have a dramatic impact on supply chain decision making, improving service elements, reducing cost, and speed inventory turns. Some of the most promising applications are:9

n Improving demand prediction to better match supply with demand at lower cost, less asset investment and with better response time

n Predicting maintenance needs in manufacturing and transportation

n Checking warehouse stocking levels to trigger re-orders

n Managing transportation mode and carrier selection by transaction

n Managing and/or mitigating risk and disruption

10 MANAGINGDEFINING RISK THE INTERMS THE GLOBAL SUPPLY CHAIN self-correction. Particular applications of AI include expert systems, speech recognition, and machine vision. AI forms the core of cognitive computing, using an AI system to provide information to help drive a decision or course of action.8

COGNITIVE COMPUTING Cognitive computing (CC) describes technology that attempts to mimic the function of the human brain with the goal of improving on the human decision-making process. Cognitive computing combines machine learning, natural language processing, speech and object recognition, human-computer interaction, and other technologies in an attempt to accurately respond to stimuli. Cognitive systems are characterized as:

n Adaptive, learning as information, goals, and requirements evolve, enabling them to resolve ambiguity and tolerate unpredictability

n Interactive, interacting easily with other processors, devices, and cloud services, as well as with people so that users can define their needs comfortably

n Iterative, defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. (They may also ‘remember’ previous interactions in a process and return information that is suitable for the specific application at that point in time.)

n Contextual, drawing on multiple sources of information, including structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided) to understand, identify, and extract meaning, syntax, time, location, appropriate domain, regulations, user’s profile, process, tasks, and goals.10, 11, 12

BIG DATA Big data in a basic sense refers to datasets marked by high volume (i.e., large), high velocity (i.e., continuously growing via real or near-real time data acquisition), and high variety (i.e., many types of data, especially unstructured text, pictures, etc.). Today’s big data often come in the form of unstructured data (i.e., data that are not organized in a pre-defined format, including textual data from emails or blogs, pictures, videos, audio, etc.) and can be derived from crowdsourcing, internet applications, direct from customers (i.e., point of sale, membership programs, etc.), and other sources. There are several uses for these large datasets such as in predictive and behavioral analytics.13

DEFINING THE TERMS 11 BLOCKCHAIN Originally developed for the digital currency of Bitcoin, blockchain is a continuously growing list of digital records which are linked and secured using cryptography. The data in these chains is not stored at any one centralized location. Instead, there are thousands of duplicated records across networks making the records public and easily verifiable. Additionally, this keeps information contained in blockchain from being controlled by any single entity and from having a single point of failure. Simply put, blockchain acts as a spreadsheet of information duplicated across networks that is regularly updated. For these reasons, blockchain shows promise in being used for endless applications such as transaction processing, records management, and other data driven tasks. Blockchain has the potential to be a valuable tool in today’s increasingly complex and data rich supply chains.14

CLOUD COMPUTING Cloud computing, often referred to as ‘the cloud’ is the practice of using a network of remote servers to access shared resources such as data servers, storage, applications, and other services. Cloud computing allows the user capabilities to store and process data in a privately owned cloud or a third-party server, making data readily accessible from virtually any location. Furthermore, this capability allows firms and individuals alike to minimize costs with regard to infrastructure and maintenance in information technology.15

INTERNET OF THINGS The Internet of Things (IoT) is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to connect and exchange data. Each thing is uniquely identifiable through its embedded computing system but is able to inter-operate within the existing internet infrastructure. The IoT gives objects the capability of being accessed or controlled remotely across the network and creates opportunities for integration of the physical world into computer-based systems. This compiling and exchanging of data through a network of connected devices results in improved efficiencies, accuracy, and economic benefit. These devices include things such as cameras streaming live video, automobiles with built-in sensors to improve performance, or medical applications such as implants that can transmit data while in use. Experts estimate that the IoT will consist of about 30 billion objects by 2020.16

12 MANAGINGDEFINING RISK THE INTERMS THE GLOBAL SUPPLY CHAIN PREDICTIVE ANALYTICS As described by SAS, predictive analytics refers to the employment of statistical and machine learning techniques to identify the likelihood of future outcomes based on historical data. Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making By applying for considered transactions. Predictive analytics provides a predictive score mathematical and (probability) for each individual unit being observed whether that be a customer, employee, component, or machines in order to determine, inform, or influence computational sciences to organizational processes.17 data gathered in earlier phases, prescriptive PRESCRIPTIVE ANALYTICS analytics seeks to select Prescriptive analytics extends beyond descriptive and predictive analytics by specifying the decision options available to take advantage of the results the best decision moving gathered previously. Prescriptive analytics look at both the actions necessary forward. to achieve predicted outcomes or mitigate risk and the interrelated effects of each decision. By applying mathematical and computational sciences to data gathered in earlier phases, prescriptive analytics seeks to select the best decision moving forward.18

MANAGING RISK IN THE DEFININGGLOBAL SUPPLY THE TERMS CHAIN 13 Challenging Your Mindset about the Future JARED NICHOLS Futurist and faculty for graduate and executive education in the Haslam College of Business

o take an active role in creating the future if you want to think about the future in a more Tof your organization, your industry, or your powerful and productive way. Understanding that community, you must be willing to challenge the future as it stands today is an entire spectrum three key areas: of possibility is absolutely essential. This may INDUSTRY INSIGHT INDUSTRY 1. Traditional ideas about the future seem intimidating and overwhelming, but it is also 2. Your own bias and assumptions about freeing and empowering. While you should take the future note of predictions and projections and use these 3. Traditional models of decision making ideas to boost your creative power, you must go beyond what has been predicted and consider Challenging Traditional Ideas what else might be possible. Most of all, you have about the Future to recognize the power you currently possess to shape a future that is not set in stone. Many people see the future as a predetermined inevitability. It is a destination that has been mapped out by experts; therefore, there is little You Must be Willing to Challenge they can do, except prepare. The problem with Your Own Bias and Assumptions this line of thinking is that inevitability assumes about the Future predictability, which has never been very Bias exists in every industry and must be reliable, even in times of greater stability. You recognized and challenged on a regular basis would probably be hard pressed to find anyone in order to stay viable in a quickly changing in today’s world that would openly attest to the world. The way in which technology and idea of stability and the efficacy of a business- industry intersect today means that disruption as-usual approach. However, we still see these can come from any direction. Leadership must faulty ideas play out in the decisions people be aware of how their industry, training, or make and the actions they take. background might limit their scope of strategy The next line of thinking is that the future is and prevent them from seeing and seizing vital inevitable, but unknown, so we must strive to opportunities. One of the most well-known determine exactly what it might look like. These examples of this is Blockbuster and Netflix. We thinkers recognize that predictions are precarious all know that Blockbuster missed the boat in in today’s world; however, they still see the future terms of entertainment and video distribution, as a set destination, and their job to map out a and now Netflix is king. But, you have to look a precise route to get there. This line of thinking little more closely to see how it all went wrong. is very limiting and debilitating. Decisions are In 1994, shortly after Viacom acquired delayed until more data can be analyzed while Blockbuster, they decided to bring in senior analysts try to make the unknown known. Risks executives from Walmart to help them use go untaken and opportunities missed. the massive amount of data they had on their The future is not a set destination and there is customers in order to make better decisions nothing inevitable about it. Some people avoid about inventory. They were using big data before this line of thinking because it is too frightening anyone was calling it that. So, what went wrong? and take comfort in the predictions of experts. Blockbuster’s success was tied to the This is where the shift in mindset must occur success of Hollywood Box Office receipts. In

14 MANAGINGCATEGORIESINDUSTRY RISK INSIGHT: OF IN INNOVATIONTHE CHALLENGING GLOBAL SUPPLY YOUR CHAIN MINDSET ABOUT THE FUTURE 1994 those receipts began to drop around 2 This worked as a simple model in the past, but percent, creating a dip in demand for video not anymore. Consider the following: rentals. Keep in mind that this was three years In the past we had limited or delayed access before Netflix was even established. Senior INDUSTRY INSIGHT INDUSTRY to information. This allowed for a narrower leadership at Viacom made a critical decision interpretation of that information, thereby that would set in motion the eventual demise of making it easier to integrate information into a Blockbuster. Rather than taking the opportunity cohesive narrative or worldview. Just think back to completely reinvent themselves in a way that to the late 20th century and the clash between loosened the ties to the box office and created western capitalism and eastern communism. unique customer experiences (they had all the The dominant cultural narratives about each data they needed to do so), they decided to do economic system were very clear and easily what they do best and stocked the shelves with integrated into one’s worldview. This is where the candy, toys, and other disposable items. Their past and present part ways. bias and assumptions were rooted in the world of big box retail (i.e. Walmart). Today, we have immediate access to an unprecedented amount of information, which When that didn’t work out, 7-Eleven executives leads to multiple interpretations of that were brought in and rebranded the stores like information, thereby preventing the formation of a convenience stores. This was still two years coherent or useful narrative. It is clear why decision before Netflix. Again, their biases were rooted in making has become so difficult and daunting the world they knew, and they failed to consider for so many. The good news is that information, the assumptions they were making about the while it may have many interpretations, can still future could be any different. be used for effective decision making. But, your The future is being influenced each and every relationship to it must change. day and our ideas about it must be continually Decision-making models today need to have updated and revised. This is not a one-off revision built into the process. This is the only process. It must be ongoing. way to leverage that vast amount of information, rather than letting it become a liability. Heavily You Must Be Willing to Challenge bureaucratic processes will not be able to compete the Traditional Models of Decision with more adaptive and fluid systems. Strategic Making planning is no longer a one-off process for The following three steps outline the process by corporate retreats and board meetings. Members which we form an understanding of our world, of an organization should be trained to have which in turn influence the decisions that we make: greater autonomy in making decisions that adapt to an increasingly fluid environment. A strong 1. Our access to information narrative and culture within organizations can 2. Our interpretation of that information ensure that this kind of fluid decision making will 3. Our integration of that information into a be aligned with its values and overall objectives. narrative or worldview that informs our decisions Jared Nichols is founder of The Jared Nichols Group, thenewfuturist.com

INDUSTRY INSIGHT: CHALLENGINGMANAGING YOUR RISK MINDSETCATEGORIES IN THE GLOBAL ABOUT OF INNOVATIONSUPPLYTHE FUTURE CHAIN 15 Research Findings

or this white paper, we conducted field research interviews with senior supply chain and technology experts from 17 companies across multiple industries. The group was composed of Fortune F100 companies in automotive, heavy equipment, consumer package goods, consumer electronics, global logistics, and mass merchant retail as well as thought leaders from information and consulting firms and professional organizations. They revealed seven primary themes regarding the future of supply chain digitalization. These themes range from rethinking how firms manage their data to societal implications.

Rethinking Data At the dawn of the computing era, carbon copies of typewriter-generated invoices, shipping declarations, and other standard paperwork was the norm, and these copies were dispersed to intra- and inter-organizational entities. This mindset transcended into how we developed MRP and ERP systems, and ensuring every entity that required the information had access to the single, paper document was a key success factor. During rise of the internet, the same document-centric view of information emerged as pages were linked to one another. Today’s internet and most computer software programs continue this tradition.

Today’s informational needs are often at the datum-level, and technologies

16 MANAGINGRESEARCH RISK FINDINGS IN THE GLOBAL SUPPLY CHAIN allow us to disaggregate information and documents to such a level. Unfortunately, most of our information infrastructure remains focused on the document level. The future of supply chain digitization will be enveloped by this data-centric view, and companies need to prepare for managing (collecting, storing, retrieving, analyzing, and leveraging) information at a data level.

Instead of competing on protocol, technology providers and large organizations benefit from establishing standardization across platforms. For connectivity to work, we need to talk in the same language, which requires some degree of standardization. Perhaps technology providers and organizations can then compete on implementation, adding value by way of Dark Data: developing and sustaining data-driven processes and best practices. Data that firms cannot or New forms of data acquisition, storage, and management are already being do not know how to use in implemented by some top companies. Using different types of data lakes to create industrial dataspaces is replacing the traditional relational databases a meaningful way. (See that dominate current data storage applications. This will allow for increased Glossary) standardization and transferability of data across a supply chain. As we Data Lake: transition to this new perspective, the mountains of dark data collected by companies should become more useful than today as this information can be A means to store data in more easily shared across platforms, systems, and siloes. their native format until To this end, one technology expert we interviewed described the idea of a needed. (See Glossary) “data billboard,” where a firm would publish real-time access to all of its data that it wished to share on a web-based billboard. Supply chain partners can then obtain tokens to access data relevant to their particular relationship. In this way, companies can easily share and control access to their data in a way that enhances transparency and visibility in a secure manner.

These changes to the way that companies store and manage data will likely not be met with much enthusiasm from technology professionals who have spent the last 20 years assembling high quality and integrated master data stores in traditional relational databases. The good news is that new technology should be much more flexible in how it ingests and uses data, which will hopefully deprioritize restrictive and costly traditional IT practices. For example, artificial intelligence can be used to connect existing systems and solutions, eliminating boundaries and limits across organizational and technology siloes.

MANAGING RISK IN THE GLOBALRESEARCH SUPPLY FINDINGS CHAIN 17 Technology Stacking Individual technologies can be powerful, but the experts we interviewed are convinced that companies need to find the right technology ‘stacks’ in order to reap maximum benefits. A stack refers to the use of two or more technologies to achieve a certain desired capability. Managers can pick best aspects of each technology a-la-carte to develop new and unique capabilities.

For instance, one technology developer we interviewed is using a combination of nanotechnology ink, new scanning technology, and blockchain to provide more security and visibility across the supply chain. The process begins by printing nearly invisible QR codes onto products that cannot be easily altered or duplicated. They then use advanced scanning technology to read these unique Machine codes and track products across the supply chain. Each scan automatically Learning: updates a blockchain ledger, where all authorized users can acquire full visibility and traceability of products in a secure manner. This is of course just Use of AI to provide one proposed stack, and the right stack is certainly company and situation computers the ability to dependent. learn from past experience Given the number of existing and emerging technologies, there are several without programming. stacking options available. There will be some best-in-breed stacks that many (See Glossary) companies will adopt. This might include stacking machine learning with big data, stacking advanced tracking equipment with blockchain ledgers, and other complimentary and more obvious pairings. AI-based systems facilitate this stacking concept as they can draw from many types of data and serve as an aggregating system that can pull useful insights across many systems. Such stacks will add standard sets of capabilities that many companies will over time need to employ to remain competitive. These will be akin to how companies currently combine existing complementary technologies to achieve enhanced effects, such as RFID combined with warehouse management systems to enable more efficient and visible operations.

Although some stacks will be obvious and focus more on continuous improvement, others will be unique and/or simply difficult to achieve, thus becoming a source of sustained competitive advantage. Gartner differentiates these two (bimodal) methods of transformation, suggesting that companies should focus on both. Mode 1 is used in areas that are more predictable and focuses on exploiting the known, updating legacy environments to digitals ones. Mode 2 is exploratory and experiments to solve new problems. Firms that can master mode 2 transformation have an opportunity to leverage new capabilities that catapult them well past their rivals.

18 MANAGINGRESEARCH RISK FINDINGS IN THE GLOBAL SUPPLY CHAIN For instance, Amazon currently has a unique ability to pair their robust technology infrastructure with physical distribution capabilities to produce what is being called the ‘Amazon effect.’ As with Amazon, the big win will be when digitalization can differentiate value, increase revenue and cash flow, and reduce capital investment. Indeed, digitalization today is being used to drive incremental change, generally to improve transactional efficiency and reduce cost. However, today’s technological breakthrough can provide those with the technological savvy and business foresight to devise some truly groundbreaking and unique capabilities. What will be the unique combination of technological capabilities that will enable your organization to achieve a sustainable competitive advantage?

Internal Collaboration and Realignment Like many innovations, a company’s first digitalization initiatives tend to be consumer-focused. Companies tend to use new groundbreaking technologies to support marketing and sales functions, whereas these technologies are often slowly adopted into supply chain management functions. For instance, companies most notably use big data and predictive analytics to predict consumer purchasing behavior. Now, they are leveraging these technologies along with AI to improve supply chain functions via forecasting supplier shortages, optimizing logistics and transportation processes, and keeping ahead of regulatory issues to name a few.

Although supply chain functions are often seen as technology laggards when compared to other organizational functions, this need not be the case any longer. As more technologies develop that have more visible and significant improvements to critical supply chain functions, an organization’s supply chain managers can be out in front to adopt new-to-the-business technologies that can make a large impact on the bottom line. The transition to a digital supply chain will likely create new forms of value and undoubtedly give supply chain managers a new platform upon which to enable additional intra-organizational collaboration. In some cases, it might even thrust the SCM team into the driver’s seat for the overall organization.

Supply chain professionals seek optimized supply chain performance, ensuring that their configuration balances customer service levels and return on investment. The cadence of how often a organization’s optimization reviews take place has changed markedly over the past decade. These reviews used to occur every few years through major initiatives, but many companies have adopted a faster drumbeat and optimize on a regular, ongoing basis.

MANAGING RISK IN THE GLOBALRESEARCH SUPPLY FINDINGS CHAIN 19 Digitalization of the supply chain will allow us to think about optimization in new and different ways. We are already seeing the beginning of this in the transportation function where best practice is now to optimize the network at the transaction level. As digitalization pushes us to collect more data and, as improvements occur in the analytical capabilities through cognitive systems, it is possible to envision a time when technology will allow both the visibility and the analytical capabilities to optimize and fine-tune our supply chains on a continual basis.

Companies have used internal collaboration initiatives like S&OP for years to align intra-organizational functions. The experts we interviewed suggest that digitalization technologies like artificial intelligence will continue to improve Digital Twin: cross-functional collaboration, as data becomes more easily acquired and shared across the organization. Not only can technology help to automate and enhance A digital version of a specific demand and supply planning and financial integration efforts, but advanced physical asset, allowing for digitalization technologies like data lakes, digital twins, and the Internet of dynamic, 3D modeling. Things will enable companies to create and sustain higher-level forms of intra- (See Glossary) organizational collaboration. A company’s custom collaborative environment and processes might be used as a form of sustained competitive advantage when it enables organizations to leverage cross-functional resources to better respond to the demands of trading partners and ultimately end consumers.

Successful collaboration within the organization has long been a goal for supply chain teams. Incomplete and incorrect information traditionally hampers internal collaboration. If everyone on the team could easily and quickly access data, historical decisions, and guidance for who should be involved in a decision, the chances would be much higher that internal collaboration would be successful.

Advances in technology could make that a reality soon. For example, AI systems can recommend the right internal supply chain and other team members to help resolve specific events or disruptions. Implemented correctly these systems can then provide that team with the relevant information, updates and insights needed to manage the event. In this way, AI-based systems can help to drive greater collaboration and augment a team’s knowledge, speeding response. Over time, as each event and resolution are captured, AI can even develop digital playbooks—a body of knowledge of how specific issues were resolved. These learned best practices enable even greater speed and accuracy in responding to future events.

20 MANAGINGRESEARCH RISK FINDINGS IN THE GLOBAL SUPPLY CHAIN External Collaboration Digitization will help to improve how we collaborate with trading partners in a traditional sense, especially in terms of facilitating supply chain visibility and traceability. For instance, blockchain can be used to support smart contracts, where digital and trustworthy records can be updated in near-real time and visible to all applicable parties. Track-and-trace tools supported by blockchain can also allow trading partners to determine a product’s point of origin and can maintain a travel history. Such tools will help to solve timeless supply chain management problems such as trust and allow firms to integrate at deeper levels.

Beyond solving more traditional problems, digitalization will also change the 5G Mobile nature of both inter- and intra-organizational teamwork. As organizations flatten Network: in concert with improvements in technology, ad-hoc teams are becoming the norm for completing operational, tactical, and even strategic-level tasks. For An improved system of example, when a company like AT&T works with Ericson to develop a 5G network wireless networks that will in a new metropolitan area, it needs to establish a short-term yet robust business enhance signal and data ecosystem to stand up the network. Not only are these two large companies rates to meet the demands involved, but so are many small and mid-size entities, such as local authorities, for hundreds of thousands of real-estate agents, crane rental and operators, electricians, and others. simultaneous connections. Today, although some collaboration software exists, most of these short-term projects are managed with Excel and email. This can be difficult and will often (See Glossary) lead to sub-optimal results in terms of meeting time and costing targets. Open Source: A more comprehensive EDI or other type of network using an open source Freely available software approach would be ideal; yet the time and cost to develop such a network is unrealistic to support this one project. Instead, we should think about how developed, updated, and to leverage supply chain digitalization to create more of a social network extended by users. (See Glossary)

MANAGING RISK IN THE GLOBALRESEARCH SUPPLY FINDINGS CHAIN 21 approach, akin to a Google hangout or Facebook event but with more powerful project and supply chain management tools embedded within, and perhaps facilitated via artificial intelligence.

Efforts to enable standardized yet ad-hoc methods toward facilitating collaboration will be one of the main benefits digitalization, and should be considered when making decisions regarding the capabilities you need at your organization. To develop a new collaborative partnership, supply chain managers should no longer have to involve the IT department, but instead be able to pick and operationalize the capabilities that they need to manage their partnerships. Supply chain professionals can learn from the use of APIs in the IT world as an example of these standardized yet ad-hoc methods that will Application emerge to help us thrive in the digital supply chain era. Programming Advances in technology, combined with a better understanding of integrated supply chain management, can combine to deliver a transformational change Interface: to how companies work with others in the supply chain. A combination of IoT, A set of defined methods blockchain, and cognitive systems offers the promise that we will have the data for communication necessary to actually achieve visibility in our supply chain, can trust it through the use of blockchain, and can quickly draw insights through the support of between various software cognitive or AI systems. We remain in the early stages of this transition, but components. (See Glossary) it is possible to see a picture of how supply chains could be transformed by entering the digital age.

New Capabilities, Businesses, and Processes Technologies that enable automation and drive efficiencies will continue to have a profound impact on SCM. Technologies that support digitalization hold even greater potential. In today’s digitized economy, imagination and technical competence are an organization’s only limiting factors to creating game- changing new capabilities, businesses, and processes. Companies are gaining new opportunities to create new value for themselves and their supply chain.

Businesses at every level of the supply chain will, more so than today, compete on data management, analysis, and insight implementation dimensions. These dimensions will drive new business models and processes to support them. Indeed, there will be several new mediums of competition, which will require new competencies upon which to compete.

22 MANAGINGRESEARCH RISK FINDINGS IN THE GLOBAL SUPPLY CHAIN Data Science: The confluence of computer science, data analytics, Sustaining Human Capital information systems and With technology rapidly changing how, where, and why people work, companies technology management, need to take close inventory of their human capital to understand precisely and business management how their employees add value (both current and potential value). Often times, organizations will find that the value of their human capital transcends the work skillsets. (See Glossary) captured in job descriptions. Indeed, human capital considerations go hand- in-hand with changes driven by digitalization. This will require digital-savvy managers that can match the right talent to the right emerging requirements.

The battle for top talent is already raging and experts suggest that an organization’s capability to find, recruit, and sustain these high performers is already a form of competitive advantage. Indeed, those who have advanced and unique technical and analytical capabilities are heavily recruited out of top undergraduate and graduate programs. Savvy managers with a track record of developing and sustaining businesses leveraging advanced digital capabilities are also in high demand. As seasoned businesses seek to build new data-related capabilities and new businesses seek to remain positioned at the cusp of high technology, those with advanced skillsets in any one or more data science competency will remain coveted.

Technological advances can also aid the quest to develop high-performing employees and teams. Some organizations are using artificial intelligence to provide employees with the time and information to think more strategically. For instance, IBM is using Watson to learn supply chain information to create playbooks capable of providing situational intelligence to employees. New employees can access years of supply chain experience whenever they need it. Current employees can also benefit from market and customer intelligence to help inform real time decision making. In these ways, technology can help to create competitive advantages in the form of captured knowledge and institutional intelligence.26

MANAGING RISK IN THE GLOBALRESEARCH SUPPLY FINDINGS CHAIN 23 Chatbots: Computer programs used in live chats as an interface to The Nature of Work carry out tasks for customers. The future business environment will be markedly different from today’s, and (See Glossary) businesses and society need to start thinking about the future nature of work. Today’s workers, and especially the top talent that every organization vies for, are more concerned than ever with finding purpose and value in their work. We will need to find better ways of building this meaning into human-centric job functions, and ensuring that these human tasks are able to interface with emerging technology.

As digitalization changes the nature of how, why, and where people work, there is a need to consider what a society’s members will do to add value to the digital economy at large, and supply chain functions in particular. Experts like MIT’s Erik Brynjolfsson suggest that advances in computer technology are a driving factor behind sluggish employment growth and economic inequality.28 A McKinsey research study recently confirmed this notion, estimating that upwards of 800 million of today’s jobs across the globe will be automated by 2030.29 A visible example of this is the increasing prevalence of chatbots in routine online customer service tasks. We believe that these kinds of societal repercussions will continue, unless we begin to rethink the nature of work and citizenry. There is no reason why advances in digitalization cannot result in win-win for all stakeholders.

A global research initiative conducted by ManpowerGroup of 18,000 employers in 43 countries across six industry sectors asked respondents to predict how they expect technology to impact their business by 2020, and how they are ensuring their workforce has the right skills and is ready to

24 MANAGINGRESEARCH RISK FINDINGS IN THE GLOBAL SUPPLY CHAIN adapt.30 The results of this research confirm that the future of work is bright, as most employers expect automation and digitalization to bring a net gain in employment. Eighty-three percent of the respondents reported that they intend to maintain or increase their headcount, although employees will require new skills during this timeframe. Skills such as creativity, emotional intelligence, and cognitive flexibility will tap human potential and allow people to augment, rather than be replaced by, technology.

We propose that businesses can and should be at the forefront of driving positive change in this regard. This does not entail providing social welfare, but instead seeking ways to leverage uniquely human capabilities—whether skilled or unskilled. This is where sharing economy functions could play a Crowdsourcing: larger role in transportation, shipping, and even warehousing functions. If operationalized to satisfy both human and organizational needs, paid A sourcing model in which crowdsourcing functions and networks similar to Amazon Mechanical Turk individuals or organizations can be used as mechanisms to employ many. If paid a livable wage and use contributions from made to feel part of the larger business team, these employees might offer internet users to obtain a refreshing improvement. needed services. So, what will society look like? Supply chain functions are at the heart of (See Glossary) this debate, and can play leading roles in getting it right.

MANAGING RISK IN THE GLOBALRESEARCH SUPPLY FINDINGS CHAIN 25 Automation, AI, and the Future of Work: More Good Than Bad KEVIN O’MARAH Chief content officer at SCM World and Distinguished Faculty Fellow of the Global Supply Chain Institute

olumnist for the New York Times driving vehicles, and much more. In fact, CEduardo Porter says that the recent AI is more like electricity than it is like surge of populism will continue, but may mechanical looms, automobiles, and shift leftward in response to the job- airplanes. Electricity changed nearly all INDUSTRY INSIGHT INDUSTRY destroying power of artificial intelligence. jobs by applying light, power, and heat He may be right about the politics, but is essentially anywhere. AI is doing this now. he missing something about the changes we might see in the nature of work itself? The scary news is that everyone is subject I think so. to the changes that AI will bring. This is also the good news. Extensive economic analysis, both historic and forward-looking, points Free Your Mind and the Rest to a consistent pattern of technology Will Follow displacing workers only to create entirely new categories of work. Cottage industry McKinsey has done some excellent weavers were replaced by millworkers. research on the topic of automation Blacksmiths were replaced by auto and the future of work. In it, they have mechanics and service station attendants. taken the time to break down jobs into Railroad workers were replaced by activities, isolating work that is routine airline crews. Consumer choice widens, and susceptible to automation from that economies grow, and populations thrive. which is not. Overall they conclude that 45 percent of the activities people get So why do we worry about artificial paid to do can be automated away with intelligence (AI)? existing technologies. Also, since the original analysis was published in late More Pervasive Than 2015, it is safe to say that this number is Electricity probably conservative.

Technology as a job eliminator is typically Mass unemployment might be something concentrated in a specific industry or we should expect given these numbers, function. Containerization, for instance, which could help realize the worst of eliminated nearly all of the work done by Porter’s political predictions. And yet, longshoremen. Absorbing these workers if one looks in detail at the breakdowns was not too hard since many worked in McKinsey offers behind their assertions, big port cities where plenty of other jobs it looks like we have more to celebrate could be found. than to fear. Consider the picture in manufacturing, for instance (Fig. I-1a). AI is different. It is already in use by customer service applications, Physical work, both predictable and e-commerce ordering systems, self- unpredictable, comprises only 11 percent

26 INDUSTRY INSIGHT: AUTOMATION, AI, AND THE FUTURE OF WORK: MORE GOOD THAN BAD INDUSTRY INSIGHT is physical andsignificantlymore ofit different. Nearlyaquarter ofthe work (Fig. I-1b onpage28)thepicture looks In retail, trade, andtransportation eliminate it. more ready to helpwithwork thanto AI andotherforms ofautomation seem of itcanbeautomated. Inotherwords, manufacturing companies, andvery little represent nearlyhalfofthework donein along withstakeholder interactions, eating up26percent ofallhours.This, problem solving)isthetop useoftime, applying expertise (alsoknown as of allwork timespent.Insharpcontrast, Figure I-1a manufacturing, of which28%hasthepotential to beautomated. Sales andrelated workers performing stakeholder interactions activitiescomprise 11%of total work in Computer and Computer and Business and Business and Maintenance, Architecture Mathematical Engineering Installation, and Repair Operations Sales and Financial Financial Related Family Other Job and Hours spent

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27 28 INDUSTRY INSIGHT INDUSTRY INSIGHT: AUTOMATION, AI,AND THEFUTURE OFWORK: MOREGOOD THANBAD things are ripefor AI. information. These typesof price commodity developing route plans,andcrunching forecast data, runningsupplieraudits, from dutieslike gathering andnormalizing we shouldmove asquicklypossible away The lesson for supplychainleadersisthat Redesign theWork Figure I-1b WORK ACTIVITY AUTOMATION DETAIL: RETAIL, TRADE,AND TRANSPORTATION in retail, trade, andtransportation, of which75% hasthepotential to beautomated. Office andadministrative support workers performing data processing activities comprise7% of total work Arts, Design, Entertainment, Arts, Design,Entertainment, Personal Care andService Installation, Maintenance, Administrative Support Food Preparation and Transportation and Sales andRelated Sports, andMedia Hours spent Material Moving Serving Related Computer and Mathematical Management Production Job Family and Repair Office and Office and

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100% Stakeholder Interactions Source: McKinsey GlobalInstitute analysis

Expertise Applying Applying

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The SAVVY Framework

iven the potential scale and complexity that digitalization brings for supply chain leaders, it is advantageous to develop a mental framework for this changing landscape. All great leaders have a clear picture of Gwhere their organization is today and where it should be going to prepare for tomorrow. As with any disruptive movement, digitalization tends to blur the picture quality that most leaders have for their organizations. In order to assist supply chain managers with sharpening their vision of their current and future supply chain The SAVVY environment, we have developed the SAVVY framework. This framework can be used by supply chain leaders to help inform decisions about supply chain framework digitalization technology and to accelerate what Gartner has called the slope of leads SCM executives enlightenment leading toward a plateau of productivity. through a process The SAVVY framework leads SCM executives through a process of assessing of assessing where where digitalization might be successfully employed within their organizations. digitalization might be successfully employed Sources of Data within their organizations. Digitalization revolves around data. Companies typically rely on structured data to drive decisions even though over 80 percent of the data available to them is unstructured—weather reports, political developments, supplier news, and financials—that can significantly impact operations. Fortunately, with advanced sensor and communications technology collecting data is remarkably easier that it was even just a few years ago.

Electronic sensing capabilities are advancing in terms of size (smaller), durability (enduring abnormal temperatures and shock), and frequency (number of data points captured per second). In addition, companies continue to find new and

MANAGING RISK IN THETHE GLOBAL SAVVY FRAMEWORK SUPPLY CHAIN 29 Figure 2 THE SAVVY FRAMEWORK

A Analytical capabilities V Value provided to the S organization Sources of data V Y Variety Your of applications across changing the supply chain role

innovative ways to collect data from customers, suppliers, and the marketplace. Examples include data derived from the IoT, crowdsourcing, and the myriad new technologies that are able to sense and transmit data. Where these data ultimately come from are limited only to your imagination. However, there are some common considerations that apply when examining any data source.

First, we need to consider how we are to collect and store our data. Does your company use a data lake approach? Regardless of your answer, data governance and management should enable the collection of and fast access to both structured and unstructured data. The type of data will help to determine the appropriate analysis strategy.

Structured data are familiar to most supply chain managers and take the form of sales data, demand data, manufacturing specifications, and other typically numerical data that you would find in a relational database (i.e., Excel, Access, etc.). Structured data have common elements and fields and relate to other elements and fields in a predetermined way.

Unstructured data elements are typically not pre-defined and do not necessarily relate to other elements in a consistent way. These data often take the form of text (i.e., internet blogs, customer feedback survey responses, etc.), images, video, and even some more complex forms of numerical data. Whereas structured data is well suited for most of today’s software programs and analytical tools, unstructured data can provide significant value with more contextual insights.

Next, business rules should dictate the value of data. Although easier and less expensive than before, data collection and management still come at a price. Even freely available data needs to be captured and curated in some form. Valuing resources and making investments that drive profits is a long-standing

30 MANAGINGTHE SAVVY RISK FRAMEWORK IN THE GLOBAL SUPPLY CHAIN business competency. However, applying existing valuation techniques is not always as straightforward as valuing, say, a new piece of equipment for manufacturing or distribution.

Current value is difficult to assess, but future value is even more so. Even top firms find it difficult if not impossible to assess data value, and the trend today is to gather and store anything that is not cost-prohibitive and that has any potential to add value. Of course, this method of determining which data should be collected is highly subjective. However, this can also be seen as a kind of firm competency in that firms that are better able to acquire and leverage the right kinds of data to support the right kinds of initiatives are more likely to achieve higher ROI on their data collection efforts. But what returns do we expect? To this end, the problem now becomes one of deciding how data are to be used once acquired. This leads to the next consideration regarding choosing the type of analytical approach to employ.

There are two approaches to data-driven decision making. The first is to begin with a question or desired outcome. In this question/answer approach, you begin with questions such as: What decision do you want to make? What information would you need to be able to make a perfectly informed decision? In answering these questions, you then determine what data are required and develop a subsequent data collection plan. This approach follows the basic analytics cycle outlined in Figure 3.

Figure 3 QUESTION/ANSWER APPROACH

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MANAGING RISK IN THETHE GLOBAL SAVVY FRAMEWORK SUPPLY CHAIN 31 The second approach—opportunity seizing—is more open-ended and concerned with uncovering the art of the possible. Although untraditional in some regards, this method has gained significant prominence in the era of big data. Initial questions typically resemble: What data are available? What can these data tell us that we don’t already know? What new opportunities can we leverage with these data?

The process is not as structured as the question/answer approach but for good reason. This exploratory approach allows companies to better compete with their data analytics, as they find new opportunities and seize upon them. Figure 4 outlines one potential sequence using this approach, but it should be noted that strategies are quickly evolving and firms compete on operationalizing these strategies.

In any case, successful entry into the world of a digital supply chain requires that organizations have a solid strategy for collecting and managing data that can provide valuable insights to their business. The Internet-of-Things, crowdsourced collection of data by everyone including customers and

Figure 4 OPPORTUNITY SEIZING APPROACH

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32 MANAGINGTHE SAVVY RISK FRAMEWORK IN THE GLOBAL SUPPLY CHAIN Figure 5 PREDICTIVE THROUGH COGNITIVE ANALYTICS

HOW DO WE OPTIMIZE A DYNAMIC, BIG DATA ENVIRONMENT? Cognitive Deeply analytical computing systems that learn & interact naturally with people Veracity (DATA IN DOUBT)

WHAT SHOULD WE DO ABOUT IT? Velocity Prescriptive Collaborate for maximum business value, informed by advanced analytics (DATA IN MOTION)

WHAT WILL HAPPEN? Predictive Understand the Variety most likely future scenario, and (MANY FORMS OF DATA) its business implications

WHAT HAPPENED? Volume Descriptive Get in (DATA AT REST) touch with reality, a single source of truth, visibility

suppliers, and the growing use of unstructured data make it much more difficult to plot a course.

Gone are the days where we were only concerned about clean inventory and sales data for our ERP systems. Now we must consider what type of data can be used to gain insight and potentially competitive advantage. The supply chain function is also often finding itself in a position to provide data to other areas of the business such as sales and marketing given how much customer interaction happens in many supply chains.

Analytic Capabilities As shown in Figure 5, organizations continue to leverage descriptive analytics to obtain visibility into their supply chains. They use other analytics capabilities to predict future scenarios and the implications thereof and even prescribe solutions to future problems and opportunities. Some suggest that these more traditional analytic capabilities will be overshadowed by systems using artificial intelligence that not only enable descriptive, predictive, and prescriptive insights simultaneously, but also learn from and interact naturally with humans. In this way, companies can obtain more unique, timely, and accurate insights to inform a host of decisions, from when to release a new product to determining value loss or gain from the adopting certain sustainable supply chain practices.

MANAGING RISK IN THETHE GLOBAL SAVVY FRAMEWORK SUPPLY CHAIN 33 Organizations embark on this journey in order to gain superior insights into decisions regarding procurement, customer and supplier relationship management, product demand patterns, new product development, and other important factors. There is no one path to take on this journey as, just like with all the technologies discussed in this white paper, businesses will choose and leverage the right tools and techniques to meet their own unique needs.

For instance, some firms can find great success in their predictive analytics program, which might motivate advances toward prescriptive and cognitive approaches, or simply motivate investment in predictive capabilities. Ultimately, success is measured in terms of value derived from analytical insights—whether these are derived from descriptive, predictive, prescriptive, cognitive, or even other means.

Ultimately, We often jump to extremes when we think about AI systems, envisioning a success central computer that makes all the decisions and acts like a human. While this may someday come to a reality, the truth is that the systems of today and the is measured in terms near future, tend to be best employed in more focused ways. of value derived from Using cognitive or AI systems can best be thought of as a journey. Many areas analytical insights—whether of the supply chain such as inventory management, planning, and transportation these are derived from are already ripe for AI technology. Organizations that embark on this journey descriptive, predictive, by implementing AI-based systems in these areas are finding deeper and prescriptive, cognitive, or more impactful ways to employ technology to support and guide decision making in their supply chains. Early adopters will enjoy competitive advantage even other means. and encourage others to follow suit. Virtually all supply chains will eventually make this move into the digital age. It is advisable for every company to make intentional decisions to prepare and progress on this journey of transformation.

AI systems, such as IBM’s Watson, have the potential to help supply chain teams achieve resolution to supply chain challenges more quickly and more robustly by suggesting options based upon past behavior or underlying data. This likely will not eliminate supply chain professionals from ultimately making the decision, but AI systems can arm them with valuable insights and data that they would not otherwise have.

In a similar vein, AI technology might also be used to easily bring diagnosis tools or work instructions to manufacturing floor personnel or immediately answer complex and ever-changing international trade questions for shipping personnel. AI technology can aid supply chain strategy teams in understanding data patterns from unstructured sources such as social media, and it can better alert supply chain planners to potential shortages from suppliers.

Variety of Applications Across the Supply Chain Many limit the potential of digitalization to marketing and customer-facing applications. Indeed, companies typically first employ new technologies in a

34 MANAGINGTHE SAVVY RISK FRAMEWORK IN THE GLOBAL SUPPLY CHAIN way that they can easily map to the bottom line, which often results in driving sales and other sources of revenue. However, top-performing firms have realized for some time that innovating with the supply chain allows them to compete and win in entirely new dimensions.

These early adaptors of digitalization initiatives find novel ways to employ technologies that might have first been used elsewhere. As outlined in this white paper, there are many new technologies available, and many new applications of existing technologies that directly apply to the supply chain. These technologies can be stacked to perform a wide variety of applications that support most supply chain functions.

For instance, blockchain was initially used for cryptocurrencies like Bitcoin, and the extant applications of blockchain often center around other financial functions. Today, companies are considering blockchain for a variety of other purposes, the most prominent of which is to facilitate SCM.

As shown in Figure 6, transparency can be achieved across a supply chain using blockchain and other enabling technologies. At the origin, for example a

Figure 6 BLOCKCHAIN IMPLEMENTATIONS

PROCESSING TRANSPORTATION SUPPLIER FACILITY PROVIDER STORE CUSTOMER

• When and • Automatically • Is informed about • Adds potential • Scans QR code Where fish receives origin and recipes and wine via app was caught, notification about destination of fish suggestions to the • Gets insights boat name, receipt of fish and • Reviews data record into fish origin, etc. adds time stamp instructions how • Provides app for date caught, • Fish is tagged • Chooses to ship the end customers etc., and suited with barcode transportation products • Has full wines provider based • Flexibility transparency on • Earns points in on fully available optimizes delivery time cross-company data on customer network flows • Adapts orders, loyalty program deliver date, etc. promos, etc. accordingly

MANAGING RISK IN THETHE GLOBAL SAVVY FRAMEWORK SUPPLY CHAIN 35 fishing boat off the coast of North Carolina, fish can be tagged with a barcode that includes information such as the time and date it was caught, the boat name, etc., that goes into a blockchain entry. After the fish is prepared at the processing facility, a barcode is also included on packaging that matches the information from the fish as well as new information about the processing facility, for example a time stamp. The blockchain entry is updated to reflect this new information. This routine of gathering and recording information is repeated across the tiers of the supply chain such that all stakeholders can verify the provenance of the product at any time. Such information can be extremely marketable to a chef at a restaurant or even to the consumer of the fish as she prepares to eat her meal.

Autonomous vehicles are another technology that has several supply chain implications. As with blockchain, autonomous technology was initially developed for other non-supply chain applications. The military first used remotely piloted aircraft for intelligence purposes, and later armed these vehicles with weapons for combat support. It is only recently that this technology is being used to solve transportation problems.

On the commercial side, autonomous vehicle technology is being developed with both personnel and material transport in mind. For instance, Tesla, Uber, Google, Daimler, and others are working toward advancing current semi- autonomous tractor trucks to create fully autonomous fleets of trucks, which they hope will increase safety and efficiency of over-the-road transport.

In the air, distribution centers are using drones to fly the aisles obtaining accurate inventory counts. Manufacturing facilities use them to fly the line to measure inventory levels and play the role of a modern-day Kanban. These autonomous vehicles have several applications across a supply chain, and as a maturing technology are able to add near-immediate value for adopting companies. Of course, autonomous vehicles are a physical supply chain innovation, but they also relate closely to digital supply chains in that they produce significant amounts of new data that can potentially be used to improve supply chain performance.

Digitalization has opportunities and consequences across the entirety of the end-to-end supply chain—strategy, planning, sourcing, manufacturing, distribution, collaboration, and customer service. Digitalization technologies can be implemented within these functions to improve the efficiency and effectiveness of operations, and they can be implemented across functions to improve both internal and external collaboration.

Finally, we are seeing that the idea of trying to keep up with technology

36 MANAGINGTHE SAVVY RISK FRAMEWORK IN THE GLOBAL SUPPLY CHAIN is becoming more untenable as a management strategy because of the large amount of newer and more advanced technologies being released at an increasing pace. Although managers need to continuously scan the environment for new technologies, there is no longer any meaningful way for any organization to try to adopt every technology relevant to their industry. Instead, tools such as enterprise architecture can be used to identify and operationalize opportunities for existing and new technologies to support many dimensions of supply chain performance.

Value to the Organization Digitalization is not simply about using technology for its own sake. As There is no described throughout this white paper, digitalization is about generating longer any new forms of value, which requires careful coordination between technology meaningful way for any resources and experts, supply chain process experts, senior management, and other stakeholders. As we’ve seen with the myriad implementation failures in organization to try to adopt areas such as ERP, technology can fast become a cost center if not leveraged every technology relevant to appropriately. We cannot state the importance of knowing that the technology their industry. is just one piece of a much larger digitalization program strongly enough.

So how exactly will your digitalization efforts add value? Value can be assessed in terms of opportunities discovered, threats allayed, and gains in measures of supply chain efficiency, visibility, security, and any host of firm-desired outcomes. As described earlier, digitization will not just improve hard metrics, but the greatest value might be found in improvements in trust, customization, authenticity, accessibility, patronage, and other factors that are known to be critical to enabling SCM.

In the digital economy, value is unique to each organization and its supply chain. Some common technologies will likely become standard across most organizations (just like EDI and SRM/CRM software is today). For instance, most companies will use predictive, prescriptive, and eventually cognitive analytics capabilities to harness the potential of big data. However, as we discussed about technology stacking, digitalization enables a new space in which firms can compete. This is especially true for supply chain functions, where digitalization is used to enhance intra- and inter-organizational outcomes.

At its core, supply chain management is about managing inventory. To this end, it is estimated that there is $10 trillion in inventory across the globe. Supply chain managers have employed a host of technologies in an effort to better

MANAGING RISK IN THETHE GLOBAL SAVVY FRAMEWORK SUPPLY CHAIN 37 manage inventory, and digitalization will continue to reduce and right-size inventory levels in dramatic new ways.

Consider IBM’s recent supply chain transparency initiative, where it sought to reduce inventory and working capital. The initiative leveraged AI technology to extend and connect existing inventory systems and processes, as well as more than 30 ERP systems in an attempt to improve demand variability by correlating all structured supply chain data related to inventory. They also supplemented that structured data with third-party, dark, and unstructured data sources. IBM used AI technology to read, understand, and correlate data across disparate sources both internal and external to the company in order to generate real-time inventory insights. In doing so, the company realized greater data integration that provided near-complete visibility. This enabled more accurate demand and inventory analyses and predictions, and increased speed and agility across many supply chain functions, thereby lowering inventory levels.

This is just one example of how an organization might realize several supply chain-related improvements via digitalization. Note that success was driven just as much by people and process changes as it was by the technology. Also note how the company used a cognitive approach to integrate and enhance the use of a suite of existing technologies. This ability to link, build, and integrate is one of the most promising features of new technology.

Your Changing Role As described in the research findings, the role of business leaders, supply chain managers, consumers, and even society at large is going to change as a result of supply chain digitalization. Here, we will focus on changes to your role as a supply chain leader, whether you are located in the C-suite or rolling up your sleeves to execute on a daily basis.

First, the way talent is acquired and managed is going to change. As one of the leaders we interviewed noted, the trend toward just-in-time training and education continues to flourish. Organizations can send workers of all skill and education levels to learn anything from business writing to advanced data science techniques. In this way, workers can learn the right skills or sets of skills to complete new tasks in support of digitalization efforts. If managed correctly, leveraging these courses and development opportunities can provide organizations with a sustained competitive advantage based on their uniquely trained human capital.

38 MANAGINGTHE SAVVY RISK FRAMEWORK IN THE GLOBAL SUPPLY CHAIN Next, you will need to become savvy with respect to applicable technological capabilities and how they can add value. While you might not need to know everything about every technology or even understand the inner-workings of them, you should be aware of technological capabilities that might have a potential application in processes under your purview.

Do you manage a warehouse? If so, then gaining knowledge on automated vehicles and material handling equipment is a must. Are you the vice president for supply chain? If so, then your team should update you regularly on new capabilities relevant to their area that might have a potential to add value to or disrupt your existing processes. Keeping abreast of new technologies via your favorite website, blogger, twitter feed, or what have you will keep your senses sharp and your mind thinking about the myriad new ways that you can manage your supply chain. In this quickly changing world, it is imperative that you somehow get a regular feed of advances in this area.

MANAGING RISK IN THETHE GLOBAL SAVVY FRAMEWORK SUPPLY CHAIN 39 Mastering the Matrix: A Roadmap to Mastering Digital Transition KEVIN O’MARAH Chief content officer at SCM World and Distinguished Faculty Fellow of the Global Supply Chain Institute

n July of 2017 SCM World hosted 100 technology as applied to supply chain. This IC-level supply chain executives in London included TED-style talks on blockchain, for our 7th annual Leaders Forum. Our Uberization, and a sociologist’s view on the theme, “Mastering the Matrix,” was all about future of work, plus a deep dive on artificial INDUSTRY INSIGHT INDUSTRY empowering supply chain professionals to intelligence. take point on developing the roadmap for digitization. The takeaway for attendees was a heady mix of exciting possibility, genuine threat, and We heard from companies as diverse as budding confidence that supply chain leaders General Electric, TomTom, Heineken, Bayer, are ready to build and own the roadmap for Ford, Land O’Lakes, and Kimberly-Clark about taming digital disruption in operations. how they are approaching the massive wave of digital innovation that confronts us right now. It is becoming increasingly obvious that this We also took an insider’s look at issues beyond revolution in supply chain is part of a society- the traditional boundaries of information wide transformation in the way we live, work,

Figure I-3a

GARTNER’S HYPE CYCLE FOR EMERGING TECHNOLOGIES, 2017

2 to 5 years 5 to 10 years More than years

Deep Learning Connected Home Machine Learning Virtual Assistants Autonomous Vehicles Nanotube Electronics IOT Platform Cognitive Computing Smart Robots Blockchain Edge Computing Augmented Data Discovery Commercial UAVs (Drones) Smart Workspace Conversational User Interfaces Cognitive Expert Advisors Brain—Computer Interface Volumetric Displays Quantum Computing Digital Twin Serverless PaaS 5G Human Augmentation Neuromorphic Hardware Deep Reinforcement Learning Virtual Reality Enterprise Taxonomy and Ontology Management Artificial General Intelligence Security-Defined Security 4D Printing Augmented Reality

Smart Dust

Innovation Peak of Inflated Trough of Slope of Plateau of Trigger Expectation Disillusionment Enlightenment Productivity

40 INDUSTRY INSIGHT: MASTERING THE MATRIX and play. Digitalization makes possible new Gartner’s Hype Cycle for Emerging levels of efficiency, speed, and precision, and in Technologies (Fig. I-3a) covers crazy-sounding the process sets the foundation for an entirely stuff like quantum computing, smart dust, and new type of economic activity. Science fiction is human augmentation. All of it is real and much INDUSTRY INSIGHT INDUSTRY becoming reality and supply chain will lay many of it is moving faster than most people realize. of the foundation stones for that future. Supply chain, which encompasses nearly all material movement, conversion, and delivery The Matrix is Real is suddenly faced with a digital technology landscape that is far more than just software for Almost everyone in supply chain circles process enablement and transaction tracking. remembers the scene from 1999s classic film The Matrix, in which Morpheus offers Neo a choice: The rabbit hole goes deep into a future world take the blue pill and remain blind to the truth, built around connected homes and smart or take the red pill and “see how deep the rabbit infrastructure, artificial intelligence, and nano- hole goes.” The challenge in this metaphor for material assembly. In this world competition supply chain leaders is to ask whether familiar will no longer be based on long production tactics like process-heavy S&OP or single- runs feeding full truckload logistics to fill instance ERP is lulling us into a false sense retail store shelves. It will more likely be based of security, while new realities like machine on intellectual property platforms, pure learning, molecular-level material design, and content consumer experiences, and extreme brain/computer interfaces give birth to a world personalization. that will obsolete what were once best practices.

Figure I-3b

THE BIGGEST ROADBLOCK I AM FACING OR HAVE FACED IN BEGINNING A DIGITAL PILOT IS . . .

Lack of direction from leadership 6.5

Lack of a clear use case 9.7

Lack of budget 9.7

Lack of overall roadmap 45.2

Lack of a priority based on other initiatives 19.4

Other 17.7 % of respondents n=67 Source: SCM World Leaders Forum 2017

INDUSTRY INSIGHT: MASTERING THE MATRIX 41 How will you navigate the transition? One of the panelists on stage in London, Yone Dewberry, CSCO of Land O’Lakes expressed Write Your Roadmap, Start surprise at how many felt the lack of an overall roadmap was a problem. His message was INDUSTRY INSIGHT INDUSTRY Your Journey simple and powerful: “We pretty much know Digitalization is transforming processes, where we’re going, so we’re just getting started.” products, and even people. As such, it demands much more than just a project plan (Fig. I-3b Mastering the Matrix on previous page). For many, this remains a challenge. Live polling of the Leaders Forum Approaching digital with a bias for action audience in London on the question of barriers is correct. And yet, it’s so big that chunking to digitization found a huge portion hindered it down is necessary to avoid starting too by the lack of an overall roadmap. This result many random initiatives. To make this easier was surprising and may be underselling supply and provide a single page view of everything chain’s qualifications as architect of change. from supplier to customer, and from sense to respond we have developed a framework that Confusion in the face of so much technology we call the Matrix (Fig. I-3d). change is understandable, but no one is better qualified to cut through it all than supply Its purpose is to ground work on everything chain leaders. Supply chain’s scope includes digital in one or more practical business areas— everything from drones and 3D printing to knowing what customers need, knowing what is cloud and AI. It also naturally spans nearly all possible to supply, deciding what is profitable existing ERP and related systems. Plus, supply to promise, making it happen, and finally chain has clear objectives on cost containment delivering what customers want. The roadmap in internal operations and service excellence in is simply a layered, time-sequenced view of customer experience that provide a north star where digital technologies can be deployed in for direction setting. these areas for better business performance.

Figure I-3c

WHERE WOULD YOU FOCUS YOUR DIGITALIZATION EFFORTS FIRST?

Digitize customer experience 47

New digital business model 8

Digitization of operations 51

% of respondents n=56 Source: SCM World Leaders Forum 2017

42 INDUSTRY INSIGHT: MASTERING THE MATRIX Science fiction may well become reality as expect intensively personalized, information- demand sense reaches into our lives and dependent businesses that arise to serve the brains, supply sense scours the earth for risk digitally empowered consumer. and opportunity many tiers up the supply INDUSTRY INSIGHT INDUSTRY chain, and response operations customize Mastering this transition will not be a matter of every molecule and line of code that we luck. The truth runs very deep and only those ultimately experience. For good or ill, this will with a roadmap can expect to survive and tilt business away from mass production, mass thrive. marketing, and a fundamentally materialist www.scmworld.com/beyond-supply-chain view of economics. In its place, we should

Figure I-3d

THE MATRIX: THE ONE-PAGE DIGITAL ROADMAP FOR THE COO

Sense ‘Learning’

Know what Know what Supply Demand a customer is possible Sense Sense needs

Supply Decide & Demand Commit

Profitable to promise

Give Make it Supply Demand customers happen Response Response what they want

Respond ‘Doing’

Source: SCM World Leaders Forum 2017

INDUSTRY INSIGHT: MASTERING THE MATRIX 43 Conclusion

ur research suggests that supply chains, and the organizations which support them, are in the early stages of a digital transformation that will likely represent the biggest change in Othe integrated supply chain era. At this point, the picture of exactly how things will look is still understandably blurry, but supply chain leaders must begin to incorporate this new paradigm into their strategies, plans, and organizations or risk being quickly and irreversibly left behind by competitors who do so. Our research A common criticism of supply chain leaders is our tendency to focus on suggests that supply chains incremental improvements. On one hand, this is how we have consistently delivered 5 percent year-over-year cost reductions, continual improvements are in the early stages of in product availability, and regular reductions in working capital. And, as we a digital transformation discussed earlier, the transition to a digital supply chain is sometimes in neat that will likely represent alignment with this incrementalist approach.

the biggest change in the On the other hand, we must sometimes step out of our comfort zone of integrated supply chain era. incremental improvements and think transformatively about how the confluence of new technologies and capabilities might transform our field. We have seen colleagues accomplish this with the digitization of books and music, and the Amazon effect and Uberization are now part of our vocabularies due to their transformative impact on supply chain management. The opportunity afforded by digitalization could serve as a mechanism for supply chain leaders to be better accepted as critical players in developing and executing corporate strategy.

44 MANAGINGCONCLUSION RISK IN THE GLOBAL SUPPLY CHAIN If we are to respond to this change and the opportunity it affords, we must possess a solid shared understanding of digitalization. Many aspects of supply chain digitalization can seem very futuristic—and they are! However, ever- increasing technological clock speed dictates that future dominant technologies are being developed today. So, what do you need to do to ensure that you adopt the right technologies at the right time? It can start with something as simple as beginning to formally have these discussions as a part of leadership team meetings. If digitalization has already worked its way into conversations in your organization, then take the time to develop a strategy and digital roadmap for your organization.

We have developed a short diagnostic tool that follows to help you gauge your company’s current positioning, and we hope that this tool can serve as a starting point for discussion amongst your supply chain team.

Investing in new technologies can be scary. The fortunate thing about some of these technologies is that up-front costs and risks of adoption are being reduced in comparison to, say, ERP systems of the past. Top companies are finding ways to fail fast. This involves jumping in head first, and empowering employees and teams to take calculated risks. The impending digital transformation in supply chain and society in general is both exciting and terrifying, but it looks to be an epic journey. Let’s get on the road.

MANAGING RISK IN THE GLOBALCONCLUSION SUPPLY CHAIN 45 Digital Supply Chain Maturity Assessment How far has your organization transformed its supply chain to meet the challenges of the digital economy?

or each of the five dimensions of the SAVVY framework, assign a score from 1 to 7, according to your organization’s current level of maturity in adopting digital supply chain technologies and Fpractices, with 7 being the highest possible maturity rating and 1 the lowest. The statements below the first and third columns represent the scenario for a 1 and 7 score, respectively. Use these to help you decide where your organization falls on the digital supply chain maturity spectrum.

46 MANAGINGDIGITAL SUPPLY RISK IN CHAINTHE GLOBAL MATURITY SUPPLY ASSESSMENT CHAIN YOUR SCORE 1 7 (1 – 7)

My organization uses advanced sensor and Data in my organization communications technology, gathering is principally derived from data from customers, suppliers, and the structured master data stored Sources marketplace, including both structured in our ERP systems and shared of data and unstructured data derived from the with select customers and IoT environment, crowdsourcing, and new suppliers. technologies that are able to sense and transmit data.

My organization leverages descriptive analytics to obtain My organization utilizes AI computing visibility into its supply chains. capabilities that not only enable Other analytics capabilities descriptive, predictive, and prescriptive are used to predict future Analytical insights simultaneously, but also learn scenarios and the implications capabilities from and interact naturally with humans to thereof, and even prescribe generate timely and accurate insights to solutions to future problems and inform decisions. opportunities.

My organization innovatively utilizes supply Digitalization in my organization chain digitalization across the entirety of is limited mainly to marketing Variety of the end-to-end supply chain, including and customer-facing applications strategy, planning, sourcing, manufacturing, applications that can easily map across the distribution, collaboration, and customer to the bottom line to drive sales supply chain service to improve the efficiency and and other sources of revenue. effectiveness of operations.

My organization uses the digital supply In my organization, digital Value chain to generate efficiency, visibility, supply chain improvements security, and other traditional supply chain are being explored to improve provided outputs as well as improvements in trust, supply chain efficiency and to the customization, authenticity, accessibility, effectiveness. organization patronage and other factors that improve both customer and financial performance.

My organization utilizes just-in-time training courses and other developmental There are no formal training or opportunities to ensure that critical educational processes in my Your personnel learn the right skills or sets of organization to develop skills skills to complete new tasks in support of or skillset for key employees to role will digitalization efforts and become aware of better utilize the digital supply change technological capabilities that might have chain to create value. a potential application across end-to-end supply chain processes.

FINAL SCORE

DIGITALMANAGING SUPPLY RISK CHAINCATEGORIES IN THE MATURITY GLOBAL OF ASSESSMENT INNOVATIONSUPPLY CHAIN 47 Glossary

DARK DATA Dark data refers to data that are collected, processed, and stored during the course of regular business activities that firms cannot or do not know how to use in a more meaningful way (i.e., business analytics, monetizing). Owing its name to the concept of dark matter in physics, an organization’s universe of data is often comprised mainly of dark data. Organizations often retain dark data for compliance purposes or as a byproduct of other perceivably more important data. Storing and securing these data is often seen as more risky and cost prohibitive given their perceived value; however, some firms are seeking ways to leverage even dark data to gain business insights.19

DATA LAKE A means to store data in their native format until they are needed. Each element is tagged with relevant metadata and given a unique identifier. Queries can be run to call on relevant data, which can then be formatted and analyzed as desired. The idea of data lake is to have a single store of all data in the enterprise ranging from raw data to transformed data which is used for various tasks including reporting, visualization, analytics, and machine learning. The data lake can include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs), and even binary data (images, audio, video) thus creating a centralized data store accommodating all forms of data. A data swamp is a deteriorated data lake that is inaccessible to its intended users and provides little value.20

MACHINE LEARNING Machine learning is an application of artificial intelligence seeking to provide computers the ability to learn and make continuous improvements from past experience without being programmed. In machine learning, computer systems access data, look for patterns, and attempt to make better decisions in the future based on those experiences. Algorithms for machine learning range from supervised to unsupervised and everything in between, but the ultimate goal is to have computers learning and making decisions with little or no human input and interaction. In application, machine learning allows for large quantities of data to be analyzed faster and results to be more accurate.21

48 MANAGINGGLOSSARY RISK IN THE GLOBAL SUPPLY CHAIN DIGITAL TWIN/DIGITAL THREAD A digital twin literally implies a digital version of a specific physical asset. Digital twins are created using the same engineering specifications, allowing for dynamic 3D modeling that can be used to support operations and maintenance. This technology is typically used to model larger and higher-value assets, such as motor vehicles and aircraft, and although digital twins might be duplicated to some degree at product manufacture, each product (i.e., vehicle identification number) has its own digital twin. As the technology matures, it is anticipated that more physical products will have digital twins, which will enable a host of capabilities.

A digital thread refers to the information ecosystem that enables real-time or near-real-time data transmission between the digital twin and the physical product. The digital thread consists of sensors, communication devises, and software that enables data access, integration, and analysis.22

NEXT GENERATION (5G) MOBILE NETWORKS As we think about employing new technologies, we sometimes take for granted adequate internet capabilities. Within manufacturing, warehouse, and other hard facilities, we can often purchase the required internet bandwidth and speed from an ISP, and optimize wireless networks accordingly. However, these capabilities are diminished outside of our facilities and today’s wireless networks are overwhelmed with bandwidth demands driven by both individuals and businesses. 5G telecommunications standards will improve upon existing 4G standards to provide a more robust ecosystem that reduces latency, enhances signal efficiency and coverage, increases data rates, and supports hundreds of thousands of simultaneous connections that are needed to support today’s wireless sensors and other systems.23

OPEN SOURCE Open source refers to freely available software that is voluntarily developed, updated, and extended upon by users or freelance developers (i.e., a user innovation approach). As advances in computing and information technologies are developed and diffused, the collaborative model offered by the open source ecosystem can potentially change the cooperative nature of organizations across a supply chain.24

APPLICATION PROGRAMMING INTERFACE In computer programming, an application-programming interface (API) is a set of subroutine definitions, protocols, and tools for building application

MANAGING RISK IN THE GLOBAL SUPPLYGLOSSARY CHAIN 49 software. In general terms, it is a set of clearly defined methods of communication between various software components. A good API makes it easier to develop a computer program by providing all the building blocks, which are then put together by the programmer. An API may be for a web- based system, operating system, database system, computer hardware, or software library. An API specification can take many forms, but often includes specifications for routines, data structures, object classes, variables, or remote calls. POSIX, Microsoft Windows API, the C++ Standard Template Library and Java APIs are examples of different forms of APIs. Documentation for the API is usually provided to facilitate usage.25

DATA SCIENCE Data science is the confluence of computer science, data analytics, information systems and technology management, and business management skillsets. Simply put, the purpose of data science is to extract actionable insights from data that can be used to inform business decisions. Given the scope, data scientists come in many forms, from economists to computer programmers, database managers to disciplinary experts with domain knowledge, and statisticians to supply chain managers who know the right questions to ask. Although the term data science is relatively new, the underlying skillsets and processes are timeless. That said, today’s datasets are much larger and more complex (i.e., unstructured), and new methods are being developed in consideration of new technologies and analytic techniques.27

CHATBOTS Chatbots are computer programs that typically use text-based live chat as an interface to carry out tasks for customers on behalf of the business. Instead of real human interaction, such programs are often designed to convincingly simulate how a human would behave as a conversational partner with the customer. For this reason, chatbots are emerging as an inexpensive way to introduce artificial intelligence (AI) into industries such as banking.31

CROWDSOURCING/SHARING ECONOMY Crowdsourcing is a specific sourcing model in which individuals or organizations use contributions from internet users to obtain needed services or ideas. The idea behind crowdsourcing is to achieve an end goal cumulatively through the financial efforts of many participants. One key concept in crowdsourcing is that the contributors are rarely defined. A related concept, the sharing economy, is an economic model in which individuals are able to borrow

50 MANAGINGGLOSSARY RISK IN THE GLOBAL SUPPLY CHAIN or rent assets owned by someone else. This model is most often used when the price of a particular asset is high and the asset is not fully utilized all the time. One example of this can be seen is the popular Uber and Airbnb applications in which the companies own very little, if any, actual assets in the business. Instead, they profit on the resources of their user base. Because of this, a large criticism of this concept involves regulatory uncertainty.32, 33

MANAGING RISK IN THE GLOBAL SUPPLYGLOSSARY CHAIN 51 Physical Supply Chain Technology Glossary

ADDITIVE MANUFACTURING/3D PRINTING 3D printing, or additive manufacturing, refers to processes used to create a three-dimensional object where layers of material are formed under computer control. These printed objects can be of almost any shape or geometry and typically are produced using digital data from a 3D model or another electronic data source such as an additive manufacturing file (AMF) or stereolithography (STL). Unlike traditional reductive manufacturing processes where a larger piece of raw material is somehow reduced to the desired specification, additive manufacturing gets its name from its ability to use only the materials required to build the desired product.34

AUTOMATED GUIDED VEHICLES/AUTONOMOUS VEHICLES Automated guided vehicles are portable robots that follow markers or wires in the floor, or use vision, magnets, or lasers for navigation. They are most often used in industrial applications to move materials around a manufacturing facility, warehouse, or port. Autonomous vehicles, or self-driving cars, are those that can safely guide themselves without any real-time human input. Firms across both technology and automotive industries are making significant advancements toward creating safe and reliable self-driving cars. Automated warehouses are adopting autonomous vehicles that can move and fetch materials and products.35, 36

AUGMENTED REALITY Augmented reality is a live direct or indirect view of a physical, real-world environment whose elements are augmented by computer-generated or extracted real-world sensory input such as sound, video, graphics, haptics, or GPS data. In fundamental terms, the expression augmented reality, or AR, refers to a simple combination of real and virtual (computer-generated) worlds. Given a real subject, captured on video or camera, the technology augments that real- world image with extra layers of digital information. Handheld devices such as smartphones and iPads are other ways to use augmented reality. They contain software, sensors, a compass, and small digital projectors which display images

52 MANAGINGPHYSICAL RISK SUPPLY IN THE CHAIN GLOBAL TECHNOLOGY SUPPLY CHAIN GLOSSARY onto real world objects. Another option is a head mounted display (HMD) which is often used in virtual reality applications. This type of device allows users to simultaneously look at data presented on a screen while maintaining their current line of sight.37

PHYSICAL INTERNET In logistics, the physical internet is an open global logistics system founded on physical, digital, and operational interconnectivity, through encapsulation, interfaces, and protocols. The physical internet is intended to replace current logistical models and interconnect global logistics networks much like the digital internet did with computer networks. Just as the digital internet transmits data in packets of information, the physical internet seeks to contain physical objects in modularized packets or containers allowing for much greater standardization in logistics. It seeks to generalize the ocean container as we know it now into one that better supports globalization in the supply chain. These modular containers can be further augmented through connection to the IoT, allowing containers to be continuously monitored throughout their journey.38

ROBOTIC PROCESS AUTOMATION (RPA) Robotic process automation (RPA) is the application of technology that allows employees in a company to configure computer software or a robot to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses, and communicating with other digital systems. In practice, RPAs are extremely beneficial in situations where tasks are routine and uninteresting. An RPA application can be implemented to do such repetitive work more quickly, accurately, and tirelessly that a human, while freeing that operator up to do tasks that require human reasoning, judgement, or customer interaction.39, 40

VIRTUAL REALITY (VR) Virtual reality (VR) is a computer technology that uses headsets or projected environments to generate realistic images, sounds, and other sensations that simulate a user’s physical presence in a virtual world. A person using virtual reality equipment is able to look around the artificial world and interact with virtual features or items. Users have the ability to place themselves into any virtual world that can be created digitally. The effect is commonly created by VR headsets consisting of head-mounted goggles with a screen in front of the eyes, but can also be created through specially designed spaces with multiple large screens. VR turns experiences into data that can be tied to deep analytics and machine learning tools.41

PHYSICALMANAGING SUPPLY RISK CHAIN IN THETECHNOLOGY GLOBAL SUPPLY GLOSSARY CHAIN 53 Endnotes

1 IBM, IBV Global Chief Supply Chain Officer Study, February 2018, https://www-01. ibm.com/marketing/iwm/iwm/web/signup.do?source=swg-smartercommerce&S_ PKG=500007132

2 IBM, IDC, The Thinking Supply Chain, March 2017, https://www-01.ibm.com/marketing/ iwm/dre/signup?source=urx-16898&S_PKG=ov59296

3 Geriant John, The Changing Face of Supply Chain Risk Management (SCM World: October 2015)

4 Kevin O’Marah and Xiao Chen, Report: Future of Supply Chain (SCM World: November 2016).

5 Kevin O’Marah, Your Roadmap to a Digital Supply Chain (SCM World: July 2017).

6 Paul Dittmann, “New Supply Chain Technology Best Practices” (white paper, University of Tennessee’s Haslam College of Business, 2017).

7 Lora Cecere, Driving Digital Supply Chain Transformation – A Handbook for Action, May 2017, https://www.slideshare.net/loracecere/driving-digital-supply-chain- transformation-a-handbook-23-may-2017

8 Russell, Stuart J. and Peter Norvig, Artificial Intelligence: A Modern Approach (Upper Saddle River, New Jersey: Prentice Hall, 2003)

9 Christine Taylor, “Artificial Intelligence and Logistics is Transforming Business,” Big Data (blog), Datamation, October 25, 2017, https://www.datamation.com/big-data/artificial- intelligence-and-logistics-is-transforming-business.html

10 John Kelly III, Computing, cognition and the future of knowing, (white paper, IBM Research: Cognitive Computing, 2015)

11 D. Ferrucci et al., “Building Watson: An Overview of the DeepQA Project,” AI Magazine,Fall 2010, 59–79.

12 Stephen Deanfelis, “Will 2014 Be the Year You Fall in Love With Cognitive Computing?” Wired, April 21, 2014, https://www.wired.com/insights/2014/04/will-2014- year-fall-love-cognitive-computing/

13 M. Hilbert & P. Lopez, “The World’s Technological Capacity to Store, Communicate, and Compute Information,” Science, April 2011.

14 Ameer Rosic, “What is Blockchain Technology? A Step-by-Step Guide for Beginners,” Blockgeeks, February 2017, https://blockgeeks.com/guides/what-is-Blockchain- technology/

15 “What is Cloud Computing?” Amazon Web Services, Amazon, updated March 19, 2018, https://aws.amazon.com/what-is-cloud-computing/.

54 MANAGINGENDNOTES RISK IN THE GLOBAL SUPPLY CHAIN 16 Verizon, “Internet of Things: Science Fiction or Business Fact?” Harvard Business Review, April 24, 2016..

17 Eric Siegel, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die (Hoboken, New Jersey: John Wiley & Sons, 2016).

18 James R. Evans & Carl H. Lindner, “Business Analytics: The Next Frontier for Decision Sciences,” Decision Line, March 2012.

19 “Dark Data,” IT Glossary, Gartner, accessed March 2018, https://www.gartner.com/it- glossary/dark-data

20 Chris Campbell, “Top Five Differences between Data Warehouses and Data Lakes,” Blue Granite, Jan. 26, 2015, https://www.blue-granite.com/blog/bid/402596/top-five- differences-between-data-lakes-and-data-warehouses.

21 “What is Machine Learning? A Definition,” Blog, Expert System, accessed March 2018, http://www.expertsystem.com/machine-learning-definition/

22 Bernard Marr, “What Is Digital Twin Technology – And Why Is It So Important?” Tech (blog), Forbes, March 7, 2017, https://www.forbes.com/sites/bernardmarr/2017/03/06/ what-is-digital-twin-technology-and-why-is-it-so-important/#45b0c0752e2a

23 NGMN, 5G White Paper, March 2015, https://www.ngmn.org/5g-white-paper/5g-white- paper.html

24 Yochai Benkler, The Wealth of Networks: How Social Production Transforms Markets and Freedom, (Princeton, New Jersey: Yale University Press, 2006).

25 Steven Clarke, “Measuring API Usability,” M-Dev (blog), Dr. Dobb’s, updated May 1, 2004, http://www.drdobbs.com/windows/measuring-api-usability/184405654.

26 Rob Handfield, The Evolution of the Transparent and Cognitive Supply Chain, (white paper, IBM and North Carolina State University, 2017).

27 V. Dhar, “Data science and prediction,” Communications of the ACM, 2013, https:// cacm.acm.org/magazines/2013/12/169933-data-science-and-prediction/abstract

28 David Rotman, “How Technology is Destroying Jobs,” Business Impact (blog), MIT Technology Review, June 12, 2013, https://www.technologyreview.com/s/515926/how- technology-is-destroying-jobs/.

29 James Manyika et. all, Jobs Gained: Workforce Transitions in a Time of Automation, (white paper McKinsey Global Institute, November 2017).

30 John Prising, The Skills Revolution, (white paper, ManpowerGroup, January 2017).

31 “What is a chatbot?” TechTarget, updated September 2005, http://searchdomino. techtarget.com/definition/IM-bot.

32 “Sharing Economy,” Investopedia, accessed March 2018, https://www.investopedia. com/terms/s/sharing-economy.asp#ixzz4xmrn08Yp.

ENDNOTES 55 33 Jonathan Webb, “Will the Shared Economy Kill the Global Supply Chain as We Know It?” Business (blog), Forbes, July 31, 2017, https://www.forbes.com/sites/ jwebb/2017/07/31/will-the-shared-economy-kill-the-global-supply-chain-as-we-know- it/#76245fda72a6.

34 Kate Cummins, “The rise of additive manufacturing,” The Engineer, May 24, 2010, https://www.theengineer.co.uk/issues/24-may-2010/the-rise-of-additive- manufacturing/.

35 “The Basics of Automated Guided Vehicles” AGV Systems, March 5, 2006, http://www. agvsystems.com/agvs-basics/.

36 Sebastian Thrun, “Toward Robotic Cars,” Communications of the ACM, April 2010.

37 Patrick Schuettel, The Concise Fintech Compendium, (white paper Fribourg: School of Management Fribourg/Switzerland, 2017).

38 Andrew Palmer, “The Physical Internet,” The Blog, Global Business Intelligence, Jn. 19, 2016, http://gbievents.com/blog/The-Physical-Internet

39 ‘What is Robotic Process Automation?” Institute for Robotic Process Automation & Artificial Intelligence, last updated 2014, http://irpaai.com/what-is-robotic-process- automation/.

40 Xavier Lhuer, “The next acronym you need to know about: RPA (robotic process automation),” Digital McKinsey, McKinsey&Company, December 2016, https://www. mckinsey.com/business-functions/digital-mckinsey/our-insights/the-next-acronym- you-need-to-know-about-rpa.

41 Myron Krueger, Artificial Reality 2 (Boston, Massachusetts: Addison-Wesley Professional 1991).

56 MANAGINGENDNOTES RISK IN THE GLOBAL SUPPLY CHAIN

IN THE SUPPLY CHAIN

A FINAL NOTE We hope you have found the material in this white paper helpful and useful. We at the University of Tennessee’s Global Supply Chain Institute are committed to industry impact. This white paper is an example of how we translate research from our no. 1 ranked research faculty into useful information for supply chain professionals. We believe the real world of industry is our laboratory, and this is why we are so committed to engaging with practitioners. It fuels our research. It defines our courses. It prepares our students. This deep connection with industry has allowed us to build the largest university-sponsored Supply Chain Forum with over sixty sponsoring companies. Our relationship with industry also drove the creation of our unique Executive MBA for Global Supply Chain and a robust portfolio of other SCM executive programs to help you be prepared to succeed now and into the future. We have more than 1,000 undergraduate students studying supply chain management, and our graduate students study around the world in innovative programs such as our first-of-its-kind Tri-Continent MS-SCM. In the end, we are only as good as our network. If your organization is passionate about being a leader in the SCM field, please consider joining us. Shay D. Scott, PhD Managing Director, Global Supply Chain Institute The University of Tennessee Haslam College of Business [email protected] 865.974.6110

gsci.utk.edu MANAGING RISKCATEGORIES IN THE GLOBAL OF INNOVATIONSUPPLY CHAIN 57 310 STOKELY MANAGEMENT CENTER KNOXVILLE, TN 37996 865.974.9413

GLOBALSUPPLYCHAININSTITUTE.UTK.EDU

58 MANAGINGCATEGORIES RISK OF IN INNOVATIONTHE GLOBAL SUPPLY CHAIN