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In our highly competitive industrialized world of lean production TT and fast innovation, it comes as no surprise that customers demand O CASA Yves Coze instantaneous delivery of individualized products at the best price- , > performance ratio. For manufacturing companies, vast product REAL PROFIT ranges of high quality and complexity mean that ﬂexible develop- Simulation and Manufacturing Digital with VIRTUAL CONCEPT ment and ramp-up across supply chain networks is crucial to survive with Digital Manufacturing and Simulation and thrive. Global competition, economic pressure, environmental and energy issues demand state-of-the-art capabilities and, above all, timely action. Such formidable challenges can only be met by Nicolas Kawski tightly interwoven lifecycle-oriented engineering and manufacturing technologies and processes. To date more than ever, the ongoing development and integration of digital manufacturing and simulation is critical to eliminate the waste of time and money in the physical world, and to ensure product success as much and as early as possible.

Torsten Kulka Digital manufacturing and simulation clearly constitute contemporary extensions of the train of thought and practice that Frederick

Winslow Taylor started a century ago. The evolution from “Taylor- > made” to “tailor-made” is in perfect concert with the ongoing REAL PROFIT customization that customers have learned not only to demand but to even co-create. Apart from lowering cost and improving time- to-market, digital manufacturing and simulation are targeted at Pascal Sire intensifying the intimacy, efﬁciency and effectiveness of co-creation feedback loops, fostering the collaboration of manufacturers, customer communities, independent R&D institutes and individuals. This emerging democratization of design, engineering, production, maintenance, repair, overhaul and recycling marks the impending impact of digital manufacturing and simulation.

Philippe Sottocasa In five chapters this book discusses the various topics and issues that are central to the implementation and development of digital manufacturing and simulation. The first “Welcome” chapter presents key concepts, needs and issues. These are further explored in four other chapters: “A Crash Course,” “Challenges,” “Benefits” and “The Future.” Each chapter starts off with an introductory snapshot and YVES COZE concludes with a Bookmark section that relates the chapter to the NICOLAS KAWSKI other parts and the message of the book. Jaap Bloem TORSTEN KULKA Readers who would benefit from this book belong to various cat- PASCAL S IRE See “About the Authors” egories, ranging from decision makers and business developers to PHILIPPE S OTTOCASA on page 160-161. engineers, technical managers and researchers. JAAP BLOEM [ed.]

ISBN 978-90-75414-25-7

I 9 789075 4 14257

Virtual Concept > Real Profit

Virtual Concept > Real Profit with Digital Manufacturing and Simulation

Production LINE UP Book & Media, The Netherlands Editing Susan MacFarlane Editorial supervision Minke Sikkema Cover illustration Jennifer Hoarau Book design Jan Faber Printing Bariet, The Netherlands

ISBN 978 90 75414 25 7

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the publisher’s prior consent. Contents

Foreword by Philippe Charlès, CEO DELMIA– Dassault Systemes Group 7 Foreword by Luc François Salvador, Chairman and CEO of Sogeti 9 About this book 11 Acknowledgement 11

1 Welcome to the Reality of Digital Manufacturing and Simulation 12 Introduction From Taylor-made to Tailor-made 14 1.1 Why Digital Manufacturing and What about Simulation 15 interview Michel Vrinat, independent consultant and Product Lifecycle research director 16 1.2 Proven Benefits of Simulation and Digital Manufacturing 21 interview Fernando Mas, CAD/CAM & Digital Mock Up Manager at EADS/ CASA 26 Bookmark Chapter 1 29

2 A Crash Course in Digital Manufacturing and Simulation 30 Introduction ManuFuture 2020 32 2.1 Simulation Through the Ages 34 2.2 Computer Simulation in Everyday Life 39 2.3 The History of Computer Simulation 43 2.4 Types of Computer Simulation 50 2.5 Definition and Development of Digital Manufacturing 53 2.6 Digital Manufacturing Projects 61 interview Frédéric Bertaud, responsible for the Airbus A350 DM Project 63 2.7 Beneficial Use of Simulation in Manufacturing 66 Bookmark Chapter 2 70

3 Challenges for Digital Manufacturing and Simulation 72 Introduction Identifying the Traps 74 3.1 Simulation in Manufacturing Systems 77 3.2 Six Grand Challenges for 2020 82 3.3 Changing Undesirable Social Behavior 86 3.4 Confronting Today’s Limits 91

5 interview Fulvio Rusino, Head Advanced Engineering at Comau 96 3.5 Automotive 2020 98 3.6 The Digital Factory Challenge 103 Bookmark Chapter 3 106

4 Benefits in Real-World Examples Introduction Current and Future Benefits 110 4.1 “Manufacturing Ready” for Maximum Profitability 112 interview Philippe Hamon, R&D Manager at LEONI Wiring Systems 113 4.2 Benefits in Perspective for Automotive 115 4.3 Benefits in Perspective for Aerospace 122 4.4 Benefits in Perspective for Shipbuilding 127 4.5 Benefits in Perspective for Consumer Goods 129 4.6 Benefits in Perspective for Energy 130 4.7 Digital Manufacturing as a Communications Platform 131 Bookmark Chapter 4 135

5 The Future is Open and Personal Introduction Towards Crowdengineering 138 5.1 From Mass Production to Mass Customization 140 5.2 Customization and Virtual Reality in 2015 142 5.3 Engineering for the Masses 145 5.4 Critical Assessment of the New Industrial Revolution 146 Bookmark Chapter 5 154

References 155 About the Authors 160 Index 162

6 Foreword by Philippe Charlès, CEO DELMIA – Dassault Systemes Group

For manufacturers, global competition has become increasingly fierce due to recent economic declines. Companies should embrace this situation as an opportunity and even a directive to rethink the way that products are developed and manufactured. Research carried out by the Aberdeen Group shows that over 85 percent of product costs are incurred in the pre-manufacturing phase, product design being the single greatest expense. Another finding is that there are three main reasons for development failure: • a product cannot be manufactured • components are not ready for production ramp-up • competition got in first.

This means that innovation is imperative; not only at the technology level, but in every aspect of the product development cycle. Further research by Proudfoot Consult- ing demonstrates that 37 percent of time spent at work is unproductive, 75 percent of this part being due to the many difficulties companies experience around their operational management system. The major barrier to improving productivity is related to efficient planning and controls, which have declined over the last 4 years to arrive at 46 percent. Both surveys lead to the conclusion that companies should focus more on effective means to reduce manufacturing costs and to better plan their activities. Digital manufacturing and simulation are the main enablers for obtaining such benefits. Digital manufacturing is the capability to define and simulate exactly how a product will be built in a global collaborative environment. Digital manufacturing allows production engineering staff access to product design at an early stage and also provides a clear view of the production environment. This results in better planning and validation of manufacture processes before a product is built. Simulation is key to many business operations, and its application is still developing at a rapid pace. Today simulation ranks amongst the highest valued business techniques, and is supported by modern mathematics, by information technology and by computer graphics, to create a 100 percent lifelike experience. By being able to reliably plan, define and simulate the events of any conceivable scenario – from materials and weather conditions to complex manufacturing and business processes – collaborative production solutions can be worked out at a speed

7 that was hitherto unknown. The combination of traditional computer-aided design simulations with business process flows has now begun to merge into a new form of digital manufacturing. The book you are about to read covers these exciting possibilities and their accompanying challenges. Modern digital manufacturing and simulation offer new opportunities to efficiently and effectively conduct profitable business in ways that, even a few years ago, could only have been dreamt of. In the near future, lifelike experience and extended digital manufacturing will be at the very heart of any state-of-the-art manufacturing industry.

Philippe Charlès CEO DELMIA– Dassault Systemes Group

8 Foreword by Luc-François Salvador, Chairman and CEO of Sogeti

The book in front of you is important for several reasons. Obviously the book is an artifact of Sogeti’s relentless drive to work “smarter” and to enhance productivity. The High Tech activity of Sogeti is passionately involved in the digital support and guidance of engineering and manufacturing activities. We are privileged partners in a number of key processes and innovations in many industries. The combination of simulation and digital manufacturing is the unique way chosen by Sogeti to address the new PLM challenges that companies are continually facing. At Sogeti High Tech, some 3,000 highly qualified engineers combine their expertise and experience to help clients improve their product and production quality, improve time-to-market and resolve environmental issues, so that they can meet their operational targets and innovation programs. As such, this book is a testament to Sogeti’s passion for technology, which drives our efforts to help clients get the best out of their technology investments. This is especially important in these times of economic turmoil, globalization and fierce competition. The second reason why this book is worth reading is because it addresses the new wave of optimization that will drive a lot of change in the years to come. Every industry is required to constantly enhance productivity. That is what has driven economic growth for the last century and created the tremendous increase in the standard of living in western society. However, global competition is fiercer than ever. Those who are able to reduce costs by structural means and improve productivity, win. Those who are not, lose. It is that simple. Working “smarter” is the solution. This book discusses digital manufacturing and simulation as two key approaches for working “smarter” that have the potential to significantly enhance productivity and thereby strengthen the competitive position of an organization. This book offers you the possibility of increasing your understanding of the change that is underway, so that you will be better able to anticipate and decide how this change will impact your organization. The third reason why you should take the time to read this book is that it explains how the next wave of manufacturing optimization is bridging the chasm between the physical “real” world and the “virtual” world that only exists in our computers. Being able to design, test, simulate, redesign and change a product in the virtual world, before it physically exists, means that huge “real” savings are possible, as this book will demonstrate. By virtually designing and analyzing the process by which a complex

9 product, like an automobile or aircraft, is manufactured, further gains are possible. Product and process are designed concurrently and can be constantly adjusted to find the right optimum. All before even a first prototype has been physically created! The fourth and final reason why this book will inspire you is that it identifies the potential for a global, digital model of your product, existing only in virtual space, to generate new ways of collaboration between engineers or between supplier and customer. Creating a fully digital model of a product is now within reach, enhancing the collaboration between engineers and making it possible to test and simulate that model in a truly integrated way. This will undoubtedly make these engineers much more productive and enable them to develop better products in a significantly shorter timeframe. But that is not where it ends! The same model can be used to communicate with clients, to evaluate their response to products that have not yet been manufactured. Subsequently, products can be adjusted before they have been manufactured, to meet client preferences, thus taking some of the risk out of innovation and involving the consumer in the development of the product. Sophisticated 3-D models are emerging as a very promising platform for this new type of collaboration and open innovation. This book will undoubtedly open your organization’s eyes and ears to the potential of digital manufacturing, simulation and open innovation. However, for this to happen you must set aside some time to read the book. What I can promise you is that it will be well worth your investment.

Luc-François Salvador Chairman and CEO of Sogeti

10 About this Book

In these economically, competitively and environmentally challenging times it is imperative for manufacturers large and small to innovate further towards a seamlessly integrated fabric of information technology, digital manufacturing, simulation, robotics and physical production. The title of this book,Virtual Concept > Real Profit with Digital Manufacturing and Simulation, expresses this need. In five chapters this book discusses the various topics and issues that are central to the implementation and development of digital manufacturing and simulation. The first “Welcome” chapter presents key concepts, needs and issues. These are further explored in four other chapters: “A Crash Course,” “Challenges,” “Benefits” and “The Future.” Each chapter starts off with an introductory snapshot and concludes with a Bookmark section that relates the chapter to the other parts and the message of the book. The collaborative and innovative style of today’s digital manufacturing and simulation practices is mirrored in the many different sources and references from reports, books and the Web that are presented throughout the chapters. The authors give a broad overview of digital manufacturing and simulation and delve into many specific issues, developments and perspectives. Interviews with renowned professionals in the field Michel Vrinat (independent consultant specialized in Product Lifecycle Management), Fernando Mas (EADS-CASA), Frédéric Bertaud (Airbus), Fulvio Ru- sina (Comau) and Philippe Hamon (LEONI Wiring Systems) illuminate the current state of affairs in modern organizations. Readers who would benefit from this book belong to various categories, ranging from decision makers and business developers to engineers, technical managers and researchers.

Acknowledgement In addition to all the people mentioned in this book, especially the above mentioned intervie- wees and Wolfgang Wohlers at Airbus in Hamburg, the authors would like to express their gratitude to the following individuals at Dassault and Sogeti for their expertise, guidance and support: Jochen Bauer, Michiel Boreel, Philippe Charlès, David Depret, Annabelle Ducel- lier, Claire Giraudin, Christian Gleyo, Cyrille Fronssart, Thomas Hallier, Jennifer Hoarau, Michael Hoarau, Frank Jankowiak, Dominique Lafond, Antoine Merval, Daniel Margerit, Patrick Michel, Thomas Müller, Laurent Peultier, Luc-François Salvador, Therese Sinter and Jutta Treutlein. Last but not least, we would like to thank our families for their continuous support and understanding during this project.

11 Virtual concept > real profit 1

12 Welcome to the Reality of Digital Manufacturing and Simulation

Introduction From Taylor-Made to Tailor-Made 14 1.1 Why Digital Manufacturing and What about Simulation? 15 interview Michel Vrinat, independent consultant and Product Lifecycle research director 16 1.2 Proven Benefits of Simulation and Digital Manufacturing 21 interview Fernando Mas, CAD/CAM & Digital Mock Up Manager at EADS/CASA 26 Bookmark Chapter 1 29

13 Introduction From Taylor-Made to Tailor-Made

A century ago Frederick Winslow Taylor’s monograph The Principles of Scientific Management (1911)1 was published. His insights into modern rational manufacturing remain valid today. In Taylor’s own words: “Analyzing the manufacturing work on elementary processes with scientific based methodologies gives benefits to the economic efficiency of companies and their workers.” Basically, Taylorism is still a dominant paradigm, although methods and tools have dramatically evolved and in nearly all processes computers facilitate and shape the development, manufacture and retirement of products. The great difference is that “Taylor-made” has been captured by and extended to “tailor-made” in unprecedented ways: the BMW x3 type, for instance, now comes in some 90,000 possible different varieties. Moreover, in our economically, competitively and environmentally challenging times, manufacturers must become even more efficient in order to survive and thrive. The old adage, “When the going gets tough, the tough get going” indicates that now is the time for manufacturers large and small to innovate further towards a seamlessly integrated and “scientifically managed” fabric of information technology, digital manufacturing, simulation and robotics. Today, the design, simulation, validation, manufacture and retirement of innovative products requires real-time, global collaboration among people and processes in R&D, product planning, sourcing, development and launch. Nowadays, the need for “Scientific Management” as proposed by Taylor has been extended to smart Product Lifecycle Management. A robust PLM system is driven by an intimately related range of key factors: the economic downturn, cost pressure from customers and sales channels, demand for shorter product lifecycles, increased competition, more demanding end users, globalized markets and supply chains, more complex products, faster commoditization, environmental and energy issues, and last but not least the vast volume of regulatory compliance involved. PLM must be an enterprise strategy built on the common access to a single repository of knowledge, data and processes related to products and markets. It captures best practices for re-use and provides visibility into workflows and dependencies critical to management decision-making at all stages of the product lifecycle. Even in the product design phase PLM pays off, since “designers spend about 60 percent of their time searching for the right information, which is rated as the most frustrating of engineers’ activities.” This observation was made by Karthik Ramani, a professor of mechanical engineering and director of the Purdue Research and Education Center for Information Systems in Engineering. Ramani: “The whole power of computers is lost if you are not able to retrieve and re-use what you have created in the past.”2 As said previously, now is the time. During an economic downturn, businesses can take the slack time as an opportunity to redirect resources, adjust their business models and to make changes to core business practices. This will better position them for the future upturn in the market without risking customer disappointment. Smart companies are taking this opportunity right now.

14 Virtual concept > real profit 1.1 Why Digital Manufacturing and What about Simulation?

Since the 1980s traditional production methods and tooling have gone digital at an ever faster pace. Evolving from 2-D representation via 3-D and so-called digital mockup we now have entered the stage of digital manufacturing. Digital manufacturing involves an integrated suite of tools to define and simulate all manufacturing operations and resources in the context of a product and a production plant. Modern digital simulation allows engineers to validate and optimize manufacturing processes (see Chapter 4 on benefits). At the end of this chapter Fernando Mas, CAD/CAM & Digital Mock Up Manager at the EADS-CASA aerospace enterprise, elaborates on his experiences with digital manufacturing and simulation. To date digital manufacturing developments are an integrated part of the total lifecycle of products, including virtual concept, clients and recycling. Product Lifecycle Management (PLM) extends across the design, manufacture, support and maintenance domains to ensure a short time-to-market and optimal profit and adoption. State of the art PLM nowadays even provides a collaborative online environment in which all production stakeholders can participate seamlessly across borders and time zones.

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Figure 1.1 The evolution of the design-to-build process from the 1970s until today’s knowledge capture in modern PLM. Source: ARC Advisory Group.

1 Welcome to the Reality of Digital Manufacturing and Simulation 15 INTERVIEW – Simulating the Stages in a Product Lifecycle Michel Vrinat is a renowned industry expert with over 30 years of experience. Nowadays he acts as an independent consultant and research director specialized in Product Lifecycle Management (PLM). Answering the following questions Mr Vri- nat elaborates on his experiences with PLM, digital manufacturing and simulation: • What do digital manufacturing and simulation mean to you? • What are the main development drivers? • Where are companies in the adoption cycle? • What are the main barriers to adoption? • What are the benefits, and how can they be measured? • What is the future of digital manufacturing and simulation?

Mr Vrinat, what do digital manufacturing and simulation mean to you? Initially, simulation was utilized for parts machining in order to assess a tool trajectory. This was the automatic digital control generation. The second phase involved simulation of the assembly phase with robots. Today, we have moved on to a third phase, in which all simulation functions are integrated. The entire product manufacturing process is now simulated, from the transformation of raw materials to machining, and beyond. However, PLM goes even further. On the one hand, it includes the demands set by marketing, without which the design and launch of a product would be meaningless. On the other hand, it takes into account the reg ulatory constraints of product manufacturing. Many business sectors – and the automotive and aeronautical industries to begin with – are taking an interest in PLM, which can simulate all of the different stages of a product lifecycle. Digital manufacturing falls within the scope of this global process, and should not be a silo in the product lifecycle.

Quality & validation Manufacturing Manufacturing production engineering Launch

ollabo Sales & e and c rate nag Product Ma distribution engineering Owner’s experience Sourcing Maintenance & repair Concept engineering Disposal & recycling

Requirements & planning

Figure 1.2 360-degree PLM has evolved as the next step in digital manufacturing and simulation to adequately cope with 21st century challenges and needs.

16 Virtual concept > real profit What are the main development drivers? The aim is to cut down the time required to design a product, and to avoid problems at the manufacturing, maintenance or final disassembly stages. By no longer separating design and manufacturing, end products become more reliable as manufacturing constraints are integrated upstream – that is, they are being identified at a much earlier stage, which makes changes easier. However, as each stage is simulated and feeds into the subsequent stage, powerful computer systems and consistent ongoing management of the data models, corresponding to the various stages of design and manufacturing, are required. This is the role of digital manufacturing.

Where are companies in the adoption cycle? Reliable solutions are now available. However, I do not expect companies to be adopting them in the very near future, due to organizational and managerial issues. By using digital manufacturing solutions the production manager integrates the design constraints, and vice versa. However, the manager is assessed by his line management on the basis of the particular area under his responsibility. The profit-sharing system applicable to each employee will therefore have to be revised in light of the overall objective. Silo management is no longer the order of the day and governance should be adjusted to global programs. Companies will also have to train their engineers on the new tools and working methodology. This takes time.

What are the main barriers to adoption? There are two. The main barrier is cultural. To date, engineers and production managers are not really used to operating within a fully integrated context. They know perfectly well how to optimize in their own area but are still exploring to broaden their scope, since they have not been trained to function in this way. Companies are therefore faced with the difficult task of ensuring that this technology is used efficiently, which leads us to the second barrier. The new systems of development now available enable companies to capitalize on this technology as best as possible. Within the aeronautical industry, the design process for a new plane covers a 10- to 15-year cycle. In the meantime, the current situation has to be managed. To my mind, the automotive sector, with its shorter development cycles, should therefore be able to utilize this technology to its full advantage much sooner.

What are the benefits, and how can they be measured? The benefits should not be measured only in terms of production or engineering efficiency. This wouldn’t make any sense. They should be measured on a wider and global scale, like the type of product recall system used in the automotive industry. In aeronautics, the number of modifications required after the first flight is taken as a benchmark. If no changes are required, then design and manufac-

1 Welcome to the Reality of Digital Manufacturing and Simulation 17 turing have worked effectively, in an integrated manner. The number of exclusions processed by production generally serves as a benchmark – i.e. the number of modifications made to the model way downstream in the production cycle. Oth- erwise, the benefits of PLM or digital manufacturing compared with a particular task are difficult to quantify. The investment is strategic, so it is better to take the time to produce a reliable virtual model in order to guarantee the quality of the end product.

What is the future of digital manufacturing and simulation? This question can be considered from two angles. From a technological viewpoint, there are still many areas of R&D to be covered for simulation, especially in terms of composite materials. Industry invents new materials; products and technologies are mixed without simulating performance or wear and tear. New mathematical models have yet to be developed, which also implies a huge potential. In business terms, digital manufacturing and simulation are opening up to other sectors. For instance, manufacturers of mass consumer products such as mobile phones use this type of technology.

Highlights Simulation, digital manufacturing and PLM hold many promises for increasing efficiency and effectiveness, including the innovations that are necessary to keep companies competitive, delivering products that customers favor. How- ever, the advanced level of integration that underlies the benefits of modern simulation, digital manufacturing and PLM, is a departure from how companies are organized and what people are used to. This means the next step towards the Digital Factory will be challenging (see Chapter 3).

Towards the Digital Factory

With new digital manufacturing methods and tooling, manufacturers pursue the optimization of production processes and the reduction of time to product launch and cost of production systems. Everywhere companies are increasingly transforming their manufacturing processes by integrating physical factories with virtual design and validation, or virtual factories, to one so-called Digital Factory. The term “digital manufacturing” stresses the digital integration of processes, methods and solutions, whereas “Digital Factory” denotes the specific environment where digital manufacturing and simulation are deployed, ideally throughout product lifecycles.

18 Virtual concept > real profit Digital Factory

Virtual Factory

Models Planning Simulation

Data

Real Factory

Figure 1.3 The role of planning, models, simulation and data in the Digital Factory, which spans both the virtual and the real factory. “The Digital Factory […] offers methods and software solutions for product and portfolio planning, digital product development, digital manufacturing, sales and support that deliver faster time-to-value. [… However,] the lack of open standards within a Digital Factory environment creates significant integration and implementation effort for customers trying to deploy digital manufacturing.”3

By merging virtual simulation with all kinds of information systems in an elegantly visualized or lifelike PLM environment, manufacturers are moving towards total control of product lifecycles – beyond Product Data Management (PDM), Enter- prise Resource Planning (ERP), Computer-Aided Design (CAD), Computer-Aided Manufacturing (CAM), Computer-Aided Engineering (CAE), Computer-Integrated Manufacturing (CIM), Manufacturing Execution System (MES) and the like. Traditional adopters of digital manufacturing solutions are the following industries: aerospace & defense, automotive, shipbuilding, machinery, industrial equipment, high tech, electronics and telecom. Today, new markets include consumer product goods (packaging), food & beverage (packaging), power & utilities, chemical plants and oil & gas refining. Some of the mainbenefits of digital manufacturing solutions are better planning, better product quality, a shorter time-to-market, a faster production ramp-up, meeting time & manufacturing cost targets, innovation, less changes and errors, and a lower total product cost.

1 Welcome to the Reality of Digital Manufacturing and Simulation 19 Product development Production planning Conventional approach Commissioning Product development Production planning Simulation Digital Factory Comm. Time beneﬁt Beneﬁt

Planning Production 0 Time Eﬀort Error handling Time to market (DF) Time to market (conventional)

Figure 1.4 Compared to the traditional manufacturing approach, which does not include simulation, the Digital Factory reduces effort and yields substantial benefit in terms of time-to-market, lower effort and the elimination of error handling afterwards. As can be seen, good and properly embedded simulation practices are making the difference.4

Enterprise Simulation Management (ESM) The central role of simulation in the context of digital manufacturing, highlighted in the interview with Mr Vrinat and in Figure 1.4, is articulated clearly by CIM data’s overarching ESM concept. ESM captures primary analysis input data like loads, constraints, mesh definitions, and results so that these can be used and reused to perform repeatable analyses. It avoids the costly and potentially error- prone re-entry of data and setups, providing more assurance that the results are valid. ESM transforms product-related simulation into a visible and accessible component of the product development process, across the full product lifecycle and across extended enterprises. As product analysis and simulation continues to become capable of supporting full systems simulation, combining control systems, electronics, motion, structural analysis, and more, a managed simulation environment becomes even more critical. ESM enables companies to sustain growth and maintain a knowledgeable workforce, ensuring high productivity and quality.

Bottom line, the fundamental relevance of simulation lies in replacing physical product validation with virtual validation. This approach not only advances validation itself, but in parallel drives down modification cost and increases modification opportuni-

20 Virtual concept > real profit ties. Being able to move from physical to virtual product validation means time gain, lower cost and better experimentation.

Cumulative modiﬁcation cost Digital Traditional Factory method

Validation Validation in a in a virtual physical world world

Modiﬁcation possibilities

Cumulative modiﬁcation cost Digital Factory

Validation in a Cumulative virtual modiﬁcation cost world

Modiﬁcation possibilities \ Figure 1.5 Product validation in a physical world versus validation in a virtual world in terms of time, modification cost and modification possibilities. Source: Dassault Systèmes.

1.2 Proven Benefits of Simulation and Digital Manufacturing

Digital simulation is crucial to the development of today’s manufacturing. Accord- ing to Benjamin Rauch-Gebbensleben, who lectures at the University of Magdeburg, simulation enjoys 20 percent growth per year and is very productive. The German guideline for “Simulation of systems in materials handling, logistics and production” (VDI 3633) states that in this field 2 to 4 percent cost reduction can be achieved with 0.5 to 1 percent investment. In digital manufacturing environments simulation can save billions, says Rauch.5 Compared to industry laggards, the best-in-class organizations that succeed in implementing and integrating modern digital manufacturing methods and techniques – from philosophy and concepts to software tools, plant

1 Welcome to the Reality of Digital Manufacturing and Simulation 21 equipment and workforce – are 36 percent more likely to meet target launch dates, are 30 percent more likely to meet development cost targets, are 28 percent more likely to meet product cost targets, are 27 percent more likely to meet revenue targets, and are 19 percent more likely to meet quality targets.6 Simulation is used to test new constructs and products, to optimize systems, to increase productivity, quality and customer satisfaction, and to make predictions and reliable decisions. Traditional application areas for simulation are crash test simulation, robotics, logistics, material flow and process management. However, each year 20 percent more areas come under the scrutiny and exploration of digital simulation. The growth of customer demand regarding the noise, vibration and harshness (NVH) of vehicles presented in Figure 1.6 is a good example. Besides social aspects, legislation and the environment play an increasing role.

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Figure 1.6 Growth of customer demand regarding the noise, vibration and harshness (NVH) of vehicles in the past 40 years. Source: Audi.

The fundamental value of simulation in modern manufacturing is explicitly expressed in the German Digital Factory guideline VDI 4499. It states the following: “Digital Factory is a generic term for a comprehensive network of digital models and methods especially for simulation and 3-D visualization. Their purpose is the holistic planning, realization, control and current improvement of all substantial factory processes and resources in connection with the product.”

22 Virtual concept > real profit Process Layout CAD planning planning

MES Material ﬂow material ﬂow simulation Digital Factory

Ergonomics Robot Process simulation simulation simulation

Figure 1.7 Various forms of simulation in digital manufacturing (not exhaustive). MES: Manufacturing Execution System.

The various software tools of advanced Digital Factory approaches are networked through a central data management system which constitutes the core of the integrated solutions incorporated in the product spectrum. This is to ensure that all planning results are always completely up to date and available to the authorized users at all times. A virtual reality system facilitates visualization of the planning results and thus interdisciplinary communication among various experts, despite differences in terminology. According to consulting firm CIMdata, organizations using digital manufacturing technologies can, on average, reduce time-to-market by 30 percent, reduce design changes by 65 percent, and reduce time spent in the manufacturing planning process by 40 percent. Production output can be increased by 15 percent with overall production costs reduced by 13 percent. Many companies state they are achieving or exceeding both initial objectives and benefits expected from digital manufacturing. The main benefits are in the domains of product and tool design, process planning, and operations planning and production. Together with the overall benefits of digital manufacturing the key findings are presented in Figures 1.8 to 1.11.7

1 Welcome to the Reality of Digital Manufacturing and Simulation 23 Reduction in overall production design time

Savings in tool design

Greater eﬀectiveness in communication & collaboration

Reduction in the number of design changes

Average improvement % Figure 1.8 Benefits achieved in product and tool design.

Savings due to improved quality from validation of processes prior to production Time savings by shortening the manufacturing planning process Average improvement % Figure 1.9 Benefits achieved in process planning.

All general benefits of digital manufacturing and simulation are relevant to today’s specific challenges. For instance, they can take the guesswork out of “green” product design. Being green not only demonstrates social responsibility, it also reduces the energy required, avoids wasted resources, and allows companies to run much leaner production operations. “Greening” manufacturing processes has become a mandate. Time is money, and we need to keep moving and keep operations flowing smoothly, uninterrupted, especially in precarious economic times. Retooling, rebuilding, and rethinking business practices means exploiting integrated computer control throughout the company, reconfiguring assembly lines,

24 Virtual concept > real profit Cost savings due to a reduction in inventory

Facility savings from better plant layout

Cost savings due to improved labor utilization in product manufacturing

Cost savings due to optimized material ﬂow

Reduced number of machines, work centers and tools

Average improvement % Figure 1.10 Benefits achieved in operations planning and production.

Reduction in overall production cost

Increased production throughput

Reduction in overall time-to-market

Average improvement %

Figure 1.11 Overall benefits from digital manufacturing.

starting new factories (for instance, using robotics), and considering better digital manufacturing solutions as well as intense collaboration and partnerships. Therefore integrated digital concepts are in high demand from large manufacturers as well as from small and medium-sized enterprises. All recognize the importance of digital manufacturing as not only a fundamental and essential asset but often as a major competitive differentiator. In the following interviewEADS-CASA’s Fernando Mas, who is involved in the development of the Airbus A400M transport and tanker, elaborates on his experiences with digital manufacturing and simulation.

1 Welcome to the Reality of Digital Manufacturing and Simulation 25 INTERVIEW – Solving Problems Before They Appear Fernando Mas, CAD/CAM & Digital Mock Up Manager at EADS-CASA, answers the following questions: • What do digital manufacturing and simulation mean to you? • What are the main development drivers? • Where are you in the adoption cycle? • What are the main barriers to adoption? • What are the benefits, and how can they be measured? • What is the future of digital manufacturing and simulation?

Mr Mas, what do digital manufacturing and simulation mean to you? Digital manufacturing or Digital Factory methodology enables us to validate and optimize the manufacturing process in a virtual world before moving into the real world. It has three basic characteristics. The first is the integration of product, processes and resources into a mDMU (manufacturing Digital Mock-Up). The mDMU is a cDMU (configured DMU) in which processes and resources are included. The second characteristic is the improvement of the “design to manufacture” concept. The third and final characteristic is the production of mDMU documentation. As for simulation, this is one of the most advanced tools, improving the performance of the digital manufacturing functions.

What are the main development drivers? Digital manufacturing has now become a reality as it is possible to represent product, processes and resources in 3-D (three dimensions). Over the last two decades, time and effort have mainly been focused on product design, consequently reinforcing the use of digital tools. We are currently in the industrialization or manufacturing engineering phase. As products are represented in 3-D, processes and resources are incorporated in order to build a full mDMU to fit all manufacturing requirements. 3-D then becomes the standard.

Where are you in the adoption cycle? EADS-CASA MTAD (Military Transport Aircraft Division) has been using computer- aided design and 3-D simulation tools for many years now. Our own developments together with the use of CATIA, a Dassault Systèmes solution, enabled us to build our digital manufacturing strategy. Our team is responsible for the development and implementation of these tools. We are also stakeholders in a concurrent engineering group with dedicated resources for tools that require highly specialized training. We harnessed these processes to the development phase of the Airbus A400M until the physical tools and technology were operational for installation of the FAL, the Final Assembly Line.

26 Virtual concept > real profit Figure 1.12 The Airbus A400M is a four-engine turboprop military transport and tanker.8 Assembly at the Seville plant of EADS Spain started in 2007.

What are the main barriers to adoption? I can see several. Firstly, those relating to the concept itself: it is essential to rely on a single definition of product functions and product industrialization. Digital manufacturing is often mistaken for the paperless factory or full automation. The second barrier concerns responsibility within the company. Digital manufacturing should not fall under the technical responsibility of IT departments but under that of the users (i.e. the engineers). The barrier I would next identify concerns management, which should understand that return on investment will only be achieved in the medium term. Training is also a barrier. This concept is not yet an innate part of academic educational culture, and engineers do not consider this methodology as standard procedure. And finally there is cultural change: Make sure to solve problems before they appear, rather than letting time pass and wait- ing for a problem to solve itself once it appears.

What are the benefits, and how can they be measured? There are many benefits to be derived from digital manufacturing. Above all, this tool improves production through cost effectiveness and enhanced collaboration between concurrent teams. Digital manufacturing minimizes the number of process, resource and product changes required, which can be measured in terms

1 Welcome to the Reality of Digital Manufacturing and Simulation 27 of time-to-market or in reduction in changes due to design errors, for example. Another asset is the digital mock-up, replacing the costly physical mock-up and providing a better guarantee of design quality at the same time. Finally, program risks are shown to decrease and production start-up is made more secure.

What is the future of digital manufacturing and simulation? First of all, we are about to witness a period of market consolidation, in which the use of digital manufacturing will be extended outside its traditional sphere, to low-cost products and/or small batches. The mDMU concept will then become a 3-D standard, signaling the automatic production of documentation. In addition, we are about to witness the development of specific methodology and tools for the first stages of the product development cycle: feasibility and design. Thanks to simulation, we will be able to prepare and test various scenarios in order to select the best option.

Highlights Digital manufacturing allows companies to validate and optimize the manufacturing process in a virtual world before moving to the real world. However, it is important to realize that return on investment will only be achieved in the medium term. Also cultural change and extra training have to be anticipated. Advanced simulation practices improve the performance of digital manufacturing functions. In representation, 3-D is now the standard. According to the Aberdeen Group, users of 3-D CAD report product profit margins that are 21 percent higher than those of 2-D CAD users.9

28 Virtual concept > real profit Bookmark Chapter 1

In our highly competitive industrialized world of lean production and fast innovation, it comes as no surprise that customers demand the instantaneous delivery of individualized products at the best price-performance ratio. For manufacturing companies, vast product ranges of high quality and complexity mean that flexible development and ramp-up across supply chain networks is crucial to survive and thrive. Global competition, economic pressure, environmental and energy issues demand state-of-the-art capabilities and, above all, timely action. Such formidable challenges can only be met by tightly interwoven lifecycle-oriented engineering and manufacturing technologies and processes. To date more than ever, the ongoing development and integration of digital manufacturing and simulation is critical to eliminate the waste of time and money in the physical world, and to ensure product success as much, and as early, as possible. In this first “Welcome” chapter, several credible sources have argued in favor of digital manufacturing, the Digital Factory, and of simulation as a key component in both. In the dynamic world of 21st-century manufacturing, accurate 3-D simulation is the big differentiator. From product to factory design, from testing to training and throughout lifecycles, simulation is rapidly evolving. This chapter also addressed and explained complexities and problems of digital manufacturing and simulation. This combination of developed strengths together with persistent weaknesses faithfully mirrors the current maturity of digital manufacturing and simulation. All in all, a picture that is both challenging and promising has emerged, which will be further discussed and detailed in the next chapters. Digital manufacturing and simulation clearly constitute contemporary extensions of the train of thought and practice that Frederick Winslow Taylor started a century ago. The evolution from “Taylor-made” to “tailor-made” is in perfect concert with the ongoing customization that customers have learned not only to demand but to even co-create. Apart from lowering cost and improving time-to-market, digital manufacturing and simulation are targeted at intensifying the intimacy, efficiency and effectiveness of co-creation feedback loops, fostering the collaboration of manufacturers, customer communities, independent R&D institutes and individuals. This emerging democratization of design, engineering, production, maintenance, repair, overhaul and recycling marks the impending impact of digital manufacturing and simulation. Chapter 5 discusses how digital manufacturing and simulation ideally will take the form of streamlined online 3-D communication, cooperation and coordination, involving expertise and experience from both inside and outside enterprise networks. Before explicitly discussing challenges and benefits, in Chapters 3 and 4 respectively, the next chapter provides a crash course in simulation and digital manufacturing, which will bring them to life and put them in perspective functionally and histori- cally, as both tools and targets.

1 Welcome to the Reality of Digital Manufacturing and Simulation 29 Virtual concept > real profit 2

30 A Crash Course in Digital Manufacturing and Simulation

Introduction ManuFuture 2020 32 2.1 Simulation Through the Ages 34 2.2 Computer Simulation in Everyday Life 39 2.3 The History of Computer Simulation 43 2.4 Types of Computer Simulation 50 2.5 Definition and Development of Digital Manufacturing 53 2.6 Digital Manufacturing Projects 61 interview Frédéric Bertaud, responsible for the Airbus A350 DM Project 63 2.7 Beneficial Use of Simulation in Manufacturing 66 Bookmark Chapter 2 70

31 Introduction ManuFuture 2020

At the start of the report ManuFuture. A Vision for 20201 Heinrich Flegel, the chairman of the EU High-Level Working Group involved, writes: “Standing still means moving backwards. This is particularly true for manufacturing and production. The production sector must continually confront new challenges in order to survive in competition. An active and foresighted technology development and a quick response to social and economic change are indispensable for this. Special R&D efforts are required for production to react quickly or, better still, to anticipate what is necessary.” The three main objectives for manufacturing industries in the years leading up to 2020 are competitiveness to thrive in the turbulent economic environment, leadership to lead manufacturing with global standards and environmentally friendly products, and production to reduce the environmental losses, to change the consumption of limited resources and to maximize the benefits of each product in its lifecycle. Before the extensive use of computers and dedicated software, the development of a product was cumbersome and sometimes dangerous. Typically, development involved a technical dossier of hand-drawn plans delineating all the sizes of the different pieces of the product and aiding the fabrication process. Development could be very costly, too. All conditions had to be checked continuously and physical sample testing was the only proof positive. Testing samples would range from small parts to the product itself: for instance, a car with crash-test dummies made of plastic and steel. Apart from its destructive nature, this particular example also illustrates the limit to those situations that can be simulated. In 2006 an international group of automakers and suppliers formed the Global Human Body Models Consortium to develop digital dummies. These models are highly detailed reproductions of the human body, including internal organs, ligaments, blood vessels, skin, and bones. By the end of 2008 crash-test dummies could welcome their virtual relatives; the consortium was ready to start modeling. Through state-of-the-art computing and simulation we are gradually moving towards the integration of the real manufacturing world and its intimately related virtual world aspects. Simulation over eventually complete product lifecycles, from design to site planning, production itself, marketing, repair, overhaul and recycling – all determined as early as possible – is a huge promise and priority these days for manufacturers struggling to survive and thrive in fierce globally competitive and economically challenging times. It is fascinating to see how simulation has developed in at least the past four millennia and more specifically in the last four hundred years, starting with the progress in mathematics, astronomy, statistics and the advent of the first computational hardware in the 17th and 18th centuries. In this period the story of building a trustworthy time simulator, a marine clock that could easily and accurately determine a vessel’s position at sea, stands out (see Section 2.3). In the last century it was the rapid progress in computer graphics from the 1950s onward.

32 Virtual concept > real profit Figure 2.1 Digital crash-test dummies faithfully simulate humans of flesh and blood. Shown are the benefits of the new virtual “Smart Dummy” as opposed to the old physical “Dumb Dummy.”2

All this and more will be discussed in this chapter. At the end of the chapter, benefits and issues of 21st-century computer simulation will be addressed, after a discussion of a few typical digital manufacturing projects, the last in an interview with Mr Frédéric Bertaud who at Airbus is responsible for the A350 Digital Manufacturing/ Digital Factory project. No one familiar with these matters would question the central importance of digital manufacturing and computer simulation techniques. Digital manufacturing involves the use of a wide range of planning tools, logistics tools and simulation tools to integrate all elements necessary for the design and operation of manufacturing processes. Ultimately a scalable 3-D virtual representation of entire factories will emerge, including buildings, materials, assembly lines, information systems, people and equipment. Planners and designers can use the information from such Digital Factories to realize dramatic time and cost savings in implementing new facilities.

2 A Crash Course in Digital Manufacturing and Simulation 33 However true and inspiring these developments and ambitions may be, many interested stakeholders will welcome further insight and background through concept discussion, examples and history so as to obtain a better and more thorough understanding of what simulation and digital manufacturing are all about. 2.1 Simulation Through the Ages

The general public understands the scope of simulation as ranging from faking so- matic and psychic states to assessing scenarios via experiment. A good example of the latter is car-crash tests with dummies instead of real people. By simulating accidents this way, various changes can be made to the car body, the seats, the seatbelts, the airbags, and so on, to minimize human injury. The word “simulation” entered the English language in the 14th century, however, it was not until the Enlightenment in the 17th and 18th centuries when advances in mathematics and the first computational “hardware” laid the foundations for the benefits of contemporary simulation. Maxims like “simulation-driven engineering helps you get it right the first time,” “eliminate doomed concepts early,” “empower more users,” and “boost productivity” would not have been possible today without the stunning scientific and engineering progress that was made a few centuries ago. The Enlightenment paved the way for 21st-century simulation to develop, especially in the efforts to faithfully model and simulate the movement of heavenly bodies and earthly phenomena: namely the work of Johannes Kepler (Laws of Planetary Motion), Wilhelm Schickard (Calculating Clock, the first mechanical computer), Isaac Newton (reconciling Kepler’s Laws with the Theory of Gravitation), Isaac Newton together with Gottfried Wilhelm Leibniz (Differential and Integral Calculus), again Leibniz but now by himself (Binary Numeral System) and later Carl Friedrich Gauss (Number Theory, Statistics). Of course these scholars themselves stood on the shoulders of and worked with many other giants like René Descartes, Blaise Pascal and Pierre de Fermat.

In the context of digital manufacturing, simulation is now being touted as the proper solution to real problems, saving costs and time, building knowledge, fostering collaboration, boosting quality, and facilitating change and innovation. Over time, even more concepts will be simulated by reusing data and models in powerful computer environments.

Through the ages, simulations have served to improve understanding and explanation of real-life phenomena. As pointed out in a manifesto of the International Mediter- ranean Society for Computer Simulation, construing simulations is a basic act of human intelligence since “evidence has shown that simulation issues were being addressed even in the ancient past.” From ancient Egypt, the pharaoh’s “Weaver’s Workshop” and “Carpenter’s Workshop” are among the scenes that provide con-

34 Virtual concept > real profit Figure 2.2 In 1623 Wilhelm Schickard completed his “Calculating Clock” which was reconstructed in 1957.3

vincing evidence of simulation practice as early as 2100 B.C. In Greece 1800 years later, Aristotle defined the “Aristotelian Coreia” in the Tragedy, creating rules and regulations to construct a representational model to generate effective “imitations” of reality. During the Roman Empire, naval battles with special ships sailing in an enormous pool were “simulated” in the Circus Maximus.

Figure 2.3 “Simulation Ante Litteram:” ancient 3-D simulation of manufacturing: a “Weaver’s Workshop” and a “Carpenter’s Workshop,” Egypt, 2100 B.C.4

2 A Crash Course in Digital Manufacturing and Simulation 35 Today, the computer simulation of complex systems is a reality. This represents the end of an age that could be called “simulation as an unorthodox solution,” and also a new challenge that could be summarized as “simulation as the proper solution to real problems.” All in all, simulation can be characterized as insightful abstraction to describe or depict and understand situations and processes, which depends upon observation, analysis and realization through language, writing, drawing and artifact. Notably, use of the formal language of mathematics, combined with computational tooling, ranging from Wilhelm Schickard’s “Calculating Clock” in 1623 to specialized contemporary computing applications like Intel’s Virtual Wind Tunnel, has enabled mankind to integrate real-time experimentation and lifelike experience based on the intimate interplay between reality, abstraction and language – eventually computer programming language and advanced mathematics.

Figure 2.4 At www.intel.com/tomorrow you can place objects in the Virtual Wind Tunnel to examine their aerodynamic behavior.

The first enclosed physical wind tunnel dates back to 1871. Virtual Wind Tunnels, which have been around since the 1990s, use a process called Computational Fluid Dynamics (CFD) which involves the prediction of processes involving fluid flow, heat and mass transfer, chemical reaction, and/or combustion. Anything that involves fluid flow can be simulated using these techniques. Over the past few years better hardware and software have improved Virtual Wind Tunnels significantly.

36 Virtual concept > real profit Figure 2.5 Outdoor wind simulation software applications allow accurate charting of wind flow through urban environments and quickly and easily interpret results (www. virtualwind.com).

Nowadays, advanced computer simulation increasingly enables us to avoid physical interference altogether. For instance, in difficult cases such as open heart surgery on infants, surgeons can virtually operate on a patient to optimize the procedure before- hand. The same goes for something quite different like nuclear plants. Throughout the world nuclear plants are being refurbished to reduce operation costs, extend plant life and optimize the energy output level. In the case of Hydro-Québec, simulation of the process has resulted in shortening the schedule by more than three years. Since one single day of closure costs a million euro, it is absolutely imperative to know how long nuclear plant refurbishments will take. In many optimizations time is of the essence, so it is not much of a surprise that the first mechanical computer or calculator, used to speed up astronomical science, was called a clock by its inventor. In 1623 the German mathematician Wilhelm Schickard completed his “Calculating Clock,” a device that could add, subtract, multiply and divide – a normal clock being a device that automatically and accurately keeps track

2 A Crash Course in Digital Manufacturing and Simulation 37 of time. The passage of time, displayed through hands on a scale can be seen as a faithful simulation of the rhythm in which physical phenomena occur – on earth, in the skies and within organisms, the so-called biological clocks of plants, animals and humans. A clock, extended with weekday display or planetary functions, makes this point even better. The “program” of the clock is its inner mechanism, which enables it to “compute” time automatically. The time simulators we know as (mechanical) portable clocks remained fragile until the 18th century. It wasn’t until John Harrison won the “Longitude Challenge” race and produced a reliable “marine clock” that it became possible to determine the longitude position of a ship at sea. If the clock showed that it was midnight in London while locally it was noon, as the sun stood on its highest, then the vessel would be at 180 degrees of longitude from London, thus halfway around the world.

Figure 2.6 With his innovative H4 marine chronometer John Harrison cracked the problem of determining the longitude while at sea. Watch the video “John Harrison, his clocks and the Longitude problem” on YouTube (search : Harrison longitude).

At the start of World War II the Hamilton Watch Company succeeded in mass- producing chronometers and made thousands of these time simulators for the allied navies. Today, biological clock rhythms get a lot of attention, for instance the “stochastic simulation of the mammalian circadian clock.”5 Of course, the inner workings of such simulations are no longer mechanical but contain virtual proteins that are manipu- lated in computer programs. Simulation nowadays is predominantly understood in this specific computer-related sense. The authoritative Merriam-Webster dictionary defines “simulation” in general as “the imitative representation of the functioning of one system or process by means of the functioning of another” while exemplifying this as “a computer simulation of an industrial process.” In the famous Encyclopædia Britannica, “computer simulation” is described as:

38 Virtual concept > real profit “the use of a computer to represent the dynamic responses of one system by the «behavior of another system modeled after it. A simulation uses a mathematical description, or model, of a real system in the form of a computer program. This model is composed of equations that duplicate the functional relationships within the real system. […] Computer simulations are used to study the dynamic behavior of objects or systems in response to conditions that cannot be easily or safely applied in real life. For example, a nuclear blast can be described by a mathematical model » that incorporates such variables as heat, velocity, and radioactive emissions.” 2.2 Computer Simulation in Everyday Life

Computer simulation is a mainstream phenomenon that we encounter daily. Potato chips with a regular shape, for example, are deliberately designed for optimal production and packaging. A completely different example concerns the nuclear detonations that simulation has made superfluous, thereby drastically reducing pollution and risk. Of course almost each component of a car was designed through simulation, among other things to test body thickness, noise-reduction characteristics of foam material, engine shape, pollutant emission, and so on. Virtual car design is tested with a sample group of drivers to get feedback before a prototype is built. Similarly, in the case of mobile phones, both heat and radiation may be assessed to meet industry standards.

Figure 2.7 Cell phone heat.6

2 A Crash Course in Digital Manufacturing and Simulation 39 Perhaps most familiar in everyday life are the computer simulation models of the atmosphere that meteorologists use to make weather forecasts for the next hours, weeks, or even months. Air quality prediction, hay fever conditions, noise maps, earthquake and tsunami watch all involve meticulous modeling and simulation. The butterfly effect, described by Edward Lorenz, shows that prediction still needs careful observation in order to calibrate modeling.

Figure 2.8 A weather map from www.ecmwf.int, the website of the European Centre for Medium-Range Weather Forecasts.

Another type of simulation becoming more and more common deals with road traffic. Online tools are available to inform drivers about the current state of the traffic and may give predictions for the coming days. Google Maps provides assessments based on past observations and will sometimes even take into account parameters like special events and road work. As in the standard simulation example mentioned in the Merriam-Webster dictionary – “a computer simulation of an industrial process” – industrial processes indeed are based upon our common experience in everyday life. This basic question of how a specific simulation relates to reality hits the nail on the head, and is probably the most essential part of what modern computer simulation is all about. Throughout history, most simulations have served to explore intensely experienced objects and behavior in the environment around us. Therefore accuracy is of the essence, meaning three things: freedom from mistake or error, as in “correctness”; conformity to truth or to a standard or model, as in “exactness”; and the degree of conformity of a measure to a standard or a true value, in the sense of “precision” (source: Merriam-Webster). Alas, the central notion of accuracy is not sufficiently

40 Virtual concept > real profit Figure 2.9 Traffic forecast in the city of Beijing on Google Maps.

Figure 2.10 Literally “lifelike” is the virtual ergonomics component in modern digital manufacturing simulation software (see also Chapter 4).

emphasized in many official explanations of (computer) simulation. Instead “imita- tion” and “real” are often used, which definitely diminish the fundamental importance of accuracy in terms of correctness, exactness and precision. In the context of simulation, accuracy, modeling, abstraction, system, process, mathematics and

2 A Crash Course in Digital Manufacturing and Simulation 41 lifelike go hand-in-hand. Together these seven factors clearly indicate the essential role of state-of-the-art computing in modern simulation.

“A simulation enacts, or implements, or instantiates, a model.” “A model is a description of some system that is to be simulated, and that model is often a mathematical one. A system contains objects of some sort that interact with each other. A model describes the system in such a way that it can be understood by anyone who can read the description and it describes a system at a particular level of abstraction to be used. To understand interactions in the game of billiards, for instance, one does not need to examine individual atoms or molecules. It is sufficient to model the balls, cue stick, and the table each as whole physical entities.”7

Early exploration of intensely experienced objects and behavior in the environment around us by means of mathematics and computation addressed the positions and movement of heavenly bodies relative to positions on earth. Essentially, it was a matter of accurate timekeeping and modeling orbits. Modern simulation started with time: the steady rhythmical patterns of progress and change in our universal environment, which we modeled, projected and put to further use in the abstraction of seconds, minutes and hours. These relate to the day and night rhythms throughout days, weeks, months and seasons from specific positions on earth, and are executed in ingenious clock machines. From there we were able to measure and test, extending system-to- system projection to other objects, which has culminated today in lifelike computer simulations of all kinds, ranging from weather forecasts to crash tests, flight simulators, virtual factories and social-networking environments like Second Life.

The Earth Impact Effects Program Let us examine the modeling and simulation of actual comets and meteors that enter Earth’s atmosphere, as shown in Figure 2.11. Centuries of astronomical observations and experiments with artificial projectiles have developed an enormous and consistent body of knowledge that provides a firm basis (data base) for comet and meteor modeling and simulation. The detailed predictions generated by the Earth Impact Effects simulation program, as described under Figure 2.11, are infinitely more satisfactory than the dry wit of Pulitzer Prize-winning humorist Dave Barry, who once said, “What happens if a big asteroid hits Earth? Judging from realistic simulations involving a sledge hammer and a common laboratory frog, we can assume it will be pretty bad.”8 Nevertheless, we persist in improving accuracy, correctness, exactness and precision in this field, as demonstrated by articles like “Modeling the Structure and Activity of Comet Nuclei.”9

42 Virtual concept > real profit Figure 2.11 The Earth Impact Effects Program allows the simulation of a comet or meteor strike.

Input parameters of the simulation model are: projectile diameter (L0), projectile velocity (v0), projectile density (ri), target density (rt), impact angle (Q), distance from the impact site (r), epicenter angle (D) and Earth’s radius (RE). Input values are needed to run the simulation and to predict several effects, including the energy of the projectile before atmospheric entry, projectile breakup in the atmosphere, the residual velocity after a burst into fragments, the energy of the airburst, crater shape, air blast delay and impact at a given point on earth, lost Earth mass and a change in Earth’s rotation period or the tilt of its axis.10

2.3 The History of Computer Simulation

Computing and simulation almost came together as early as 1623. By that year the German mathematician, astronomer and engineer Wilhelm Schickard had final- ized his so-called “Calculating Clock” or “Speeding Clock.” It was twenty-two years before Blaise Pascal invented his Pascaline calculator and seventy-one years before Gottfried Wilhelm Leibniz’s ingenious Stepped Reckoner design proved to be more or less reliable. In “Explication de l’Arithmétique Binaire,” published 1703, Leibniz described the binary numeral system, used in modern computers. Had it not been destroyed in a fire, Schickard’s first mechanical calculator could have helped in the tedious astronomical calculations of that time. Schickard is recognized as “Father of the Computer Age” for having built his Calculating Clock, which was successfully reconstructed in 1957 from discovered notes.

2 A Crash Course in Digital Manufacturing and Simulation 43 In September 1623 Schickard wrote to his famous friend and colleague Johannes Kepler: “What you have done by calculation I have just tried to do by way of mechanics. I have conceived a machine consisting of eleven complete and six incomplete sprocket wheels. It calculates instantaneously and automatically from given numbers, as it adds, subtracts, multiplies and divides. You would enjoy seeing how the machine accumulates and transports spontaneously ...”11 Kepler devised three mathematical laws of planetary motion, which were codified by later astronomers. In modern times, Kepler’s laws are still used to calculate approximate orbits. They apply when a smaller heavenly body is orbiting a larger one, though the effects of atmospheric drag, relativity, and other nearby bodies can make the results insufficiently accurate for a specific purpose. Apparently Kepler’s models – his laws – are not refined enough to handle all simulation cases.

Figure 2.12 Stellar map made by Jakob Bartsch, Kepler’s son-in-law, in 1624.12

44 Virtual concept > real profit The 3-D Virtual Reality of Planetariums

Besides representing the results of astronomical observations and calculations in tables and charts, the second half of the 17th century produced the first modern planetarium. Coincidentally, Wilhelm Schickard’s famous portrait was the first painting to show a handheld planetarium. Then in 1664 the Gottorp Globe was completed in Germany: a hollow sphere covered on the outside by a world map and turned by water power, it gave persons entering this magnificent simulator a 360-degree view of the heavenly bodies on the inside curved wall. The Gottorp Globe was literally a three-dimensional virtual world to step into and enjoy (and study) the splendor of the constellations without the distraction of precipitation, wind, cold and clouds.

Figure 2.13 The Gottorp Globe (1664) now resides at the St. Petersburg Kunstkammer and is an early example of 3-D simulation based on mathematics and observation.13

From the 18th century on, many planetariums of varying complexity were crafted as extensions of larger and smaller clocks and even watches. This underscores the fact that throughout history most simulations serve as an exploration of intense real-life experiences. Today, critical aspects of reality are magnified in simulations ranging from car-crash tests, virtual, physical and hybrid simulators, to socio-economic behavior in virtual worlds like EVE Online.

2 A Crash Course in Digital Manufacturing and Simulation 45 At PSA Peugeot Citroën as well as Volkswagen, for instance, car cabins can be studied by simulation in a so-called CAVE (CAVE Automatic Virtual Environment), which provides a state-of-the-art platform for lifelike collaborative design and testing of different in-car installation and layout solutions. The virtual vehicle can be positioned in line with the vision of the user, whose movements in time and space are tracked by sensors and also by the flystick (flying joystick).

Figure 2.14 Driving in PSA Peugeot Citroën’s CAVE.14.

Towards Lifelike 3-D

Computer simulation really took off after its first large-scale deployment in the Man- hattan Project to model the process of nuclear detonation during World War II. The common feature shared by all types of computer simulation is the attempt to generate a sample of representative scenarios for a model in which a complete enumeration of all possible outcomes would be prohibitive or impossible.

46 Virtual concept > real profit Images began to be used as part of computer simulation at the beginning of the 1950s at MIT in the context of improving military air-traffic control. The system, designed and set up by Ivan Sutherland, included a cathode screen and an optic pencil, and could manipulate two-dimensional technical diagrams.

Figure 2.15 Ivan Sutherland demonstrating Sketchpad, his “man-machine graphical communication system.”15

In 1970, Xerox founded its famous Palo Alto Research Center (PARC) where many innovations have been made in 3-D representation. In 1975 one of the most famous images was created, the teapot, which has since become a standard test object for 3-D applications.

Figure 2.16 The archetypal 3-D teapot.

2 A Crash Course in Digital Manufacturing and Simulation 47 Until the 1980s 3-D representation remained costly. But the appearance of high- and low-end personal computers, such as the Xerox Star and the IBM PC in 1981, and the Apple Macintosh in 1984, brought about a tremendous advance in the use of 3-D, notably in the production field. Pong, the first commercially successful but very crude tennis video game, had been launched in 1972. Five years later the first Flight Simulatorprogram was introduced, which became a PC evergreen as it entered Microsoft’s applications portfolio in 1982. After the famous Commodore 64 (1982) and later the Commodore Amiga, there was an explosion in graphics from the 1990s on with the development of advanced video game consoles by Sony, Nintendo and Microsoft, among others. In 1993 DOOM was the first realistic 3-D PC game.

Figure 2.17 First-person shooter multiplayer game DOOM (1993) was the first realistic 3-D computer game.

Next it would fall to the movies to embrace computer graphics in delivering a hyper- real movie experience combined with cross-media development. Tomb Raider for instance, featuring Lara Croft, was originally an adventure role-playing game released in 1996; the movies date from 2001 and 2003. Conversely, in 1995 Toy Story was the first fully computer-animated feature-length film. In 1999 Toy Story 2 was released

48 Virtual concept > real profit with an accompanying video game for several platforms. Ten years later Toy Story 2 was re-released in Disney Digital 3-D. 2009 also saw the release of the Toy Story Ma- nia game for the Nintendo Wii. The Toy Story 3 movie is scheduled for 2010. Other examples of impressive computer-enhanced and game-accompanied movie series are Star Trek, Star Wars, The Matrix, Lord of the Rings and Harry Potter.

Figure 2.18 Toy Story (1995) was the first fully computer-animated feature-length film.

Of course “lifelike” does not mean limited to everyday life; it also describes a realistic experience in general, including imaginary fun and games. So-called “serious play” and “serious gaming” is used for educational and training purposes that are relevant to real-life situations: from flight and combat simulators to simulations of human body parts, celestial bodies, management skills in role-playing games and, last but not least, digital manufacturing. Remember the very famous simulation games like Simcity (1989) or The SIMs (2000), the business simulation Railway Tycoon (1990), and many more. Nowadays, computer simulation has expanded to virtual reality settings where testing, training, education and entertainment go hand-in-hand in serious game environments of all kinds. Modern Digital Factory simulation offers “lifelike experience” in a virtual world to foster and improve 24/7 global collaboration in 360-degree product lifecycles.

2 A Crash Course in Digital Manufacturing and Simulation 49 2.4 Types of Computer Simulation

Many simulation methods have been and will be developed to model, chart and test physical behaviors for which closed analytical solutions are not possible. Engineers and researchers today have a large set of methods at their disposal to model the physical or functional behavior of a system or a group of subsystems. It is beyond the scope of this book to explore all the simulation types presently available. To give a general impression without going into any detail, we mention FDM (Finite Difference Method) used for solving all types of differential equations, FEM (Finite Element Method) used mainly in structural analysis and in acoustics, FVM (Finite Volume Method) used for fluid and flux analyses, as well as LES (Large Eddy Simulation) or DNS (Direct Numerical Simulation). BEM (Boundary Element Method) and RTC (Ray-Tracing Code) are used for solving different types of acoustic problems while MoM (Method of Moments) or MLFMM (Multilevel Fast Multipole Method) are computed to assess electromagnetic behavior. Simulation methods may be specific in that they may concern a dedicated area of physics or be most efficient for one industrial sector. However, every method mentioned above fits within the structure of Figure 2.19.

Simulation type

Deterministic Stochastic

Static Dynamic Static Dynamic

Continuous Discrete Continuous Discrete

'JOJUF&MFNFOU"OBMZTJT

Figure 2.19 Common types of computer simulation.

A deterministic model is by definition perfect, meaning that its characteristics are fixed and equal an exact value. However, reality is anything but exact. In order to model reality with the highest accuracy, imperfections have to be considered. As nature is random, probabilistic approaches are a developing field. Monte Carlo simulation is a means of solving problems using random numbers and probability. It involves us-

50 Virtual concept > real profit ing a large number of more or less random inputs to a model or equation to produce output that is then analyzed. Static simulation consists of representing events at a point in time, while dynamic simulation considers the evolution of systems in space or time. Discrete simulation means that a succession of events impacts the system. Basic mathematics only offers direct solutions to simple academic problems. More complex system models have been formed through mathematical modeling, which attempts to find approximate solutions to problems and predict system behavior from a set of parameters and initial conditions. For instance, properties of electromagnetic fields are symbolized by Maxwell’s partial differential equations, and Navier-Stokes partial differential equations are used to model the drag force of an aircraft. Finite Element Analysis or Finite Element Method originated from the need to solve complex elasticity and structural analysis problems in civil and aeronautical engineering. Finite element methods share one essential characteristic: they resolve a continuous domain into a set of discrete sub-domains called elements. At the end of the 1950s, the key concepts of stiffness matrix and element assembly existed as they are used today. In 1965 NASA issued a RFP for the development of the finite element software NASTRAN. Its mathematical foundation was published in 1973: Strang and Fix, An Analysis of the Finite Element Method.16 Technically, Finite Element Analysis is a numerical technique for finding approximate solutions using partial differential equations and integral equations.

Figure 2.20 Mesh plot of a Porsche 911 model imported from a NASTRAN bulk data file.

The Finite ElementMethod is a good choice for resolving systems composed of partial differential equations. It is possible to take into consideration complex systems inside complex environments, like a bumpy car ride or a landing aircraft. In a frontal crash

2 A Crash Course in Digital Manufacturing and Simulation 51 simulation it is possible to increase the accuracy of predictions affecting crucial areas, like the front of the car, and reduce specifications for the rear of the vehicle, thereby reducing the cost of the simulation. Other examples would be the simulation of the weather pattern on Earth (where it is more important to have accurate predictions over land than over the oceans), and also the analysis of stress, vibration, structural failure, structural durability, heat transfer, noise, acoustics, flutter and aero-elasticity. The growing importance of digital simulation today is in step with spectacular advances in computer hardware and with the increasing accuracy of mathematical models. The size and proliferation of models keeps on growing. There is a logical desire to scale computations to several hundred processors, since dynamic crash computations are already successfully executed this way. However, simulation still remains an approximation of reality, even with the error level tending towards zero. The tension between simulation and reality remains a hot topic, especially in modern digital manufacturing. This comes as no surprise, since “computation is the art of carefully throwing away information,” as Guy Steele once famously said (while forgetting the original source).17

Zero Prototype Engineering

The nirvana of the manufacturing industry may be said to be Zero Prototype En- gineering. Currently we perceive this to be the elimination of the cruder vehicles built up-front to see if the components all fit together and work. Later generation prototypes in the design phase are still created, and the challenge is to implement a design and manufacturing process that eliminates the use of prototypes altogether.18

The essential point can be summarized in this question: Is simulation able to fully replace physical testing? At first glance, there are many reasons why simulation is currently replacing testing: • Tests can be expensive because of their quantity or because they involve a complete prototype. In some cases tests have a huge impact on the environment and involve a (prohibitive) risk. Using a virtual wind tunnel instead of a physical prototype, for instance, reduces costs by a factor of 10. • Tests can take a long time to execute, because building a complete prototype of an aircraft, or even a factory on a reduced scale, is a lengthy process that cuts into the critical production lead time. • Tests come late in product development. They cannot be performed until the final product or part is designed and a prototype is produced. • Tests may not be possible or may not model the expected outcome. The clearest example is obviously the modeling of conventional and nuclear weapons, where testing is either impractical or inexact, although product reliability must still be determined.

52 Virtual concept > real profit • Accuracy of simulation is improving. For certain branches of physics, such as structural analysis, kinematics, or system simulation, models are very precise and can be used by certification authorities.

These arguments support the complete rejection of physical testing, but major companies in all industrial sectors will persist with physical testing. This also can be explained: • Most models still need to be validated, improved or at least calibrated by values obtained through physical or functional tests. For some acoustic simulations in high frequencies, models need to bear material characteristics that can be obtained only through tests. • In certain cases simulation is simply impossible. All domains of physics can be simulated as long as they have been idealized or simplified. But some behaviors are still too complex to reproduce in simulation. For instance, it is still difficult to couple aerodynamics and aero-acoustics for industrial cases. It is also difficult to obtain results for micro-electromechanical systems that combine electronics, electromagnetism, structural, thermal and fluid analyses • Tests remain mandatory around the world for certification of products or systems. Authorities demand a complete set of validated tests for physical applications, especially in the case of cars or aircraft.

Simulation has reached a state of maturity, although there are still limits to what can be simulated. However, companies still need to reduce costs, risks, time-to-market (meaning the entire time from design to development and production), and to dramatically reduce the numbers of prototypes. Therefore it is important to look at the current general trends in simulation. • Standardization. Companies try to develop standard simulation models and practices that can be re-used, ideally involving changeable parameters. • Optimization. Methods and tools are now efficient in optimization but still need to be implemented in industrial processes. • Multi-disciplinary. Simulation only now begins to propose numerical solutions for multi-physics problems, but companies have to change their development standards to allow functional performance. • Robust design. Simulation now can take into account manufacturing dispersions in symbolic models while Finite Element Analysis enables late engineering changes in development. 2.5 Definition and Development of Digital Manufacturing

The term “digital manufacturing” is relatively new. It has evolved from Computer Integrated Manufacturing (CIM), which was developed in the 1980s when the cost

2 A Crash Course in Digital Manufacturing and Simulation 53 of computing went down to a level at which computers could be used extensively in manufacturing for machine and automation control, planning and scheduling. The definition of digital manufacturing was formally established in the well known German VDI 4499 guideline. However, there is a long history of manufacturing enterprises seeking to reduce costs and improve the effectiveness of business processes with information technology. It is notable that the basic analysis and terms of digital manufacturing and Product Data Management (PDM) were already known in the 1990s – in, for example, an editorial published in The International Journal of Advanced Manufacturing. Reading this text, where “CIM” has simply been replaced by “digital manufacturing” three times, one discovers a lot of insight that could have been set down only yesterday. It is astonishing how closely this 1994 editorial describes the digital manufacturing/Digital Factory practices and vision of fifteen years later.

“The increasing role of computers, linked computer systems and electronic data «processing systems in all aspects of manufacturing systems including design, marketing, assembly, production planning and control, logistics, sales and distribution, has been an important first step in the development of [digital manufacturing]. Because of current industrial conditions, and, in particular, strong competition, a second step has been better monitoring of costs, times (time to market), environmental conditions, etc. To make improvements, the proposal for a lean production system has developed quite rapidly, with the objective of achieving lower costs through the reduction of waste of material. The introduction of factory automation (FA), database technology, computer-aided- design (CAD), computer-aided engineering (CAE), computer-aided manufacturing (CAM) and flexible manufacturing systems (FMS) was considered an important step towards [digital manufacturing]. The next step combined [digital manufacturing] with another important emerging philosophy: concurrent engineering (or simultaneous engineering). This philosophy has its roots in the much older concepts of manufacturing as a system (Marchant 1960) and of the computer integration of that system, but it involves additional demands on technologies and human relations beyond those necessary for the above mentioned systems. The definition of concurrent engineering (CE) is related to the “complex of -ac tivities dealing with the design and manufacturing of products in an industrial environment. The basic objective of CE is the installation, organization and control of the manufacturing process as a whole, and in such a way that all decisions to be taken in the course of the product-realization process can be executed in coherence with each other yielding the best possible solutions for design and manufacturing and regarding life-cycle aspects such as maintenance and disposal at the end of the product life” (Kals). In other words, CE is a strategic concept, leading to the systematic approach of the integration of design, production and related processes dealing with all aspects of the product life cycle (included manufacturability, assemblability and repairability considered at the earlier phases of the design process).

54 Virtual concept > real profit Concurrent engineering adopts a parallel procedure instead of a sequential one in a concurrent environment: this is an important change from conventional engineering which uses sequential, iterative and distributed steps. Consequently, CE requires a parallel, iterative and cooperative team approach. It seems today that the concept of CE will accelerate simultaneous and parallel development of the entire production process from marketing and design to the product itself, including the »manufacturing process and the manufacturing system and sales system.”19 This much is clear: CIM, the Computer Integrated Manufacturing of the 1990s, and the current digital manufacturing/Digital Factory movement are close, at least con- ceptually. Taking a broader perspective, the 30-year journey in engineering and planning towards the Digital Vehicle and digital manufacturing at DaimlerChrysler, set out in Figure 2.21, is typical of the development of digital manufacturing techniques and planning in the automotive industry. This slide was used byWolf-Peter Seuffert in 2003 when he headed the Digital Factory project. At the moment, the focus is on manufacturing and production of operations management software solutions, namely for advanced and highly regulated industries. Companies like Intercim – what’s in a name – enable users on the shop floor and across the supply chains to better collaborate on process definition, manufacturing execution, and quality management to reduce manufacturing costs, improve cycle times, and accelerate time-to-market in industries like automotive, aerospace and defense as well as semiconductors and pharmaceuticals.

Figure 2.21 The 30-year journey in engineering and planning towards the Digital Vehicle and digital manufacturing at DaimlerChrysler.20

2 A Crash Course in Digital Manufacturing and Simulation 55 From Physical to Virtu-Real Manufacturing

The history ofmanufacturing is a concatenation of developments around standardization, innovation and integration on the product side, and developments on the process side in engineering, planning, marketing and sales over supply chains and throughout the product lifecycle. In the 19th century the Industrial Revolution brought about some fundamental changes in product development. As simple production in workshops moved to industrial production in factories, technical offices began to emerge and development was separated from production, which meant a widening gap between office work and production work. Economic issues were decided in the office sphere. The design as well as the optimization of the product should always take place in the technical development stage. It is here that the “scientific” methods of Frederick Winslow Taylor first emerge. Taylor held the view that divided labor and targeted specialization of individual areas would result in higher productivity levels. However valid these principles are for certain products and their manufacture today, one should not dismiss the disadvantages, such as the loss of information transfer between development and production, the waste of unused abilities, and the rise of monotonous activities.

Workshop- Standard- Methodical Computer- Computer- Virtual oriented ization design supported oriented product design oriented construction product development design modeling Evolution level

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    Year Figure 2.22 Evolutionary stages in product development.21

56 Virtual concept > real profit Until some forty years ago the total exchange between development and production was done on paper. Technical drawings, descriptions and lists contained all of the information needed for the manufacturing process. When information technologies began to be gradually introduced into the product development and product planning stages, the exchange of data and the spread of information became digitized. Drawings and inventories could then be saved and, if needed, easily altered. On the production side, so-called NC (Numerically Controlled) factory machines were becoming more popular. The introduction ofPPC (Production & Planning Control) software solutions helped to define, manage and optimize the data and data flow of the manufacturing machines concerned. Despite some huge steps having been taken in the 1980s to develop CAD systems, NC and PPC systems remained more prevalent. One reason for the slow adoption of CAD tools was, among others, a steep learning curve for effective use. Then, with the combined use of CAD and CAM, the concept of CIM (Computer Integrated Manufacturing) began to take hold. Even in our age of virtual product development, technical drafting is still fundamental to design and production. Graphs are represented digitally on the computer screen where they can be viewed from different angles and printed when required. Drafting remains an important means of depicting construction, from the first concepts to the finished product. The focus of modern digital product development is on process chains to support and embody the total virtual development and manufacturing process. Central to these chains is a common database. For any industry, modern digital manufacturing can be explained in terms of four steps: scenario, strategy, process and system. Based on a structured scenario and technical analysis, multiple prospective developments will be found which provide the necessary information for strategizing. At this step the general action plan of a company can be defined (see also Figure 3.8). The main issue is in the optimal cor- respondence of scenario and strategy by means of a phase and milestone plan, to be translated into a set of structured business processes. In the last step these processes are enhanced with information systems. A well-defined PLM/PDM system is the backbone for digital manufacturing and simulation.

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Figure 2.23 Four-step model of digital manufacturing.22

Product Lifecycle Management

In gradually moving from merely physical to virtu-real industries the development of products based on customer need has for too long been characterized by so-called “over-the-wall processes.” Initially, these processes were ad hoc and highly paper or document driven all the way through engineering and manufacturing to production. Implementing changes originating from customer requirements to fix problems at the point of manufacture used to be difficult. Over the years the major issue has been to effectively tear down these walls. Eventually the concept of end-to-end Product Lifecycle Management emerged to include processes from design to service, as well as customers and suppliers, all linked to an adaptable core product manufacture and assembly. An extension of PDM and the next step in the common database philosophy, PLM represents the missing link between CAD, digital manufacturing and simulation. PLM has a stronger “lifelike” focus on the virtual world, and interfaces with the En- terprise Resource Planning (ERP) system that supports the physical side of modern manufacturing along the supply chain.

58 Virtual concept > real profit Suppliers

Materials & components acquisition

Product Manufacture Materials & Product Service Service parts innovation, & assembly component manufacture process and design & process speciﬁcation Service/MRO and development materials engineering development assembly planning

Product delivery / distribution

Customers

Figure 2.24 End-to-end Product Lifecycle Management.

“During the Production and Aftermarket Support phases of a product’s lifecycle, «systems for managing Process Engineering and Planning, Process Execution, Process Quality, and Maintenance, Repair, and Overhaul ensure that product designs are properly implemented according to design specifications and procedures, and that all required production information is captured in the process.” »Greg Gorbach, ARC Advisory Group As addressed in Chapter 1, digital manufacturing, its extension the Digital Factory, and computer simulation are central to any form of PLM. However, it comes at a price; for instance, “the lack of open standards within a Digital Factory environment creates significant integration and implementation effort for customers trying to deploy Digital Manufacturing”.23 These and other challenges will be discussed in Chapter 3. The ideal PLM design-to-build process can be explained as in Figure 2.25 by relating the quadrants of design, manufacturing engineering, ERP and production.

2 A Crash Course in Digital Manufacturing and Simulation 59 Design ERP

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Model-based definition: Part / equipment ordering, OUT OUT features, annotations, tooling definition work scheduling Model-based definition: Build aircraft, document IN OUT as-designed requirements as-built record

Manufacturing engineering Production

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Figure 2.25 The ideal PLM design-to-build process. Source: Dassault Systèmes.

The traditional vertical links between design and engineering, and between ERP and production, must be integrated. The model-based planning output from the manufacturing engineering quadrant must be related to the modular bill of materials (including standard parts) on the ERP side as well as to the build plans and working instructions on the production side. This can be realized via, for instance, the integration of the Dassault solutions CATIA, ENOVIA, DELMIA, 3DVIA on the design/ engineering side and SAP, Intercim and 3DVIA on the ERP/production side, where workers will be using tablet PCs instead of printed lists and drawings.

60 Virtual concept > real profit Engineering Engineering Production Production

Figure 2.26 In the 1950s communication between engineering and production was completely paper-based. Today engineering has gone digital but on the shop floor printed documents are still common. Modern digital manufacturing should be used to break the chains and eventually to eliminate paperwork altogether.

2.6 Digital Manufacturing Projects

Digital manufacturing sets the criteria by which manufacturing planning and execution are being integrated. Rich data, associative to product and process information, drives all processes. Furthermore, integrated simulation and validation tools ensure the quality and reliability of products as well as throughput and operational efficiencies. Digital manufacturing has been known by many names, including Manufactur- ing Process Management, Collaborative Manufacturing Process Management, and Computer-Aided Process Planning. According to the consulting firm CIMdata, digitalmanufacturing comprises “solutions that support manufacturing process planning collaboration among engineering disciplines, from product design to manufacturing. The solutions use best practice processes and allow access to the full digital product definition, including tooling and manufacturing process designs. Digital manufacturing is, in practice, an integrated suite of tools that work with product definition data to support tool design, manu-

2 A Crash Course in Digital Manufacturing and Simulation 61 facturing process design, visualization, simulation, and other analyses necessary to optimize the manufacturing process.”24 This way digital manufacturing and its extension the DigitalFactory knit together manufacturing process design, process simulation and engineering and production management. Definitions of products, processes, practices, plants, tools and resources are consistently integrated to successfully reach beyond production operations, to profitable product design investments and supply chain partnerships. Because of the many different flavors and the ever-expanding range of possibilities the digitization of manufacturing cannot be exhaustively described. To provide an impression of the numerous options in various manufacturing contexts, three representative digital manufacturing projects are offered – a plant upgrade, a new manufacturing facility and assembly-line optimization – to further illustrate what has been discussed in the previous section. An interview with Frédéric Bertaud, who at Airbus is responsible for the A350 digital manufacturing project, follows the project descriptions. Intelligently planning the manufacturing process and predicting and resolving issues before production begins belong to the core deliverables of digital manufacturing. The short-term benefits of the three case description below include advanced virtual simulation, improved production plans for space and resource utilization, improved production speed due to better planning and processes, streamlined material flow, optimized robotics for reduced idle time, shortened cycle time and smoother robot motion, balanced workload, and enhanced human ergonomics. Their main long- term benefit is the ability to successfully initiate new manufacturing projects since processes, technology and facilities are configured for optimal performance.

Digital Manufacturing Project: Plant Upgrade Rather than building a new plant, manufacturers want to use their existing facilities and resources for the production of new vehicle models. Assessing the feasibility of re-use requires an in-depth analysis of plant layout, human ergonomics, robotics, manufacturing processes and material flow. Thanks to modern digital manufacturing techniques all these factors can be virtually simulated. The digital 3-D model and the corresponding meta model make it possible to optimize and validate plant layout and storage space, to simulate and streamline material flow along the trim, chassis and final line, to simulate and optimize the existing robotic line, and to dynamically simulate the cockpit fitment process to improve human ergonomics.

Digital Manufacturing Project: New Manufacturing Facility Prior to building another wing manufacturing plant an aerospace manufacturer needs to analyze the assembly line, storage space, factory layout and material handling system as well as to evaluate and reduce the number of trips made by planes between the existing and planned facilities. Therefore the layout of complex equipment and process configurations must be studied and developed.

62 Virtual concept > real profit The plant layout must be optimized for better floor space utilization; a safe and ergonomically sound material handling system must be developed, and processes must be streamlined to improve cycle time, loading time, unloading time, setup time and batch sizes.

Digital Manufacturing Project: Assembly Line Optimization Key challenges of improving the efficiency of an assembly line are factory flow simulation, process planning, auto line balancing, station validation and logistics planning. Therefore benchmarks must be created for the number of stops and the number of operators, validating processes must be developed to determine the necessary design changes to tooling and equipment and various domains, and software and logistics planning requirements must be explored for current and future use.

INTERVIEW – Reconciling the Virtual and the Real World Frédéric Bertaud, responsible for the Airbus A350 digital manufacturing project, answers the following questions: • What do digital manufacturing and simulation mean to you? • What competitive edge can a group like Airbus gain here? • Where are you in the adoption cycle? • What are the main barriers to adoption? • What are the benefits, and how can they be measured? • What is the future of digital manufacturing and simulation?

Mr Bertaud, what do digital manufacturing and simulation mean to you? Digital manufacturing lies within the scope of Concurrent Engineering (a process whereby tasks are carried out simultaneously with multi-functional teams), applied not only to the plane itself but in defining all production resources required for manufacture, from the inception of the project up to the point of assembly. This principle is implemented in the manufacture of the A350. We will therefore keep the simulation environment available throughout the production stage. For previous programs, we utilized simulation tools during design; however they were no longer used in the production stage. The aim is to simulate changes and to measure their impact on the product and tooling. Simulation helps to enhance visualization of problems, to analyze their impact and to identify appropriate solutions. Previously, we did not have the graphic resources required to analyze the impact of a missing part, which could hinder a subsequently performed operation. We would have a printed copy of all missing parts, an expert would analyze the consequences and we were able to ask for a mock-up to be prepared manu- ally. This was a complicated process, implemented on an ad hoc basis. Our aim was to automate these tasks, to make them systematic and to simplify the shop floor mock-ups.

2 A Crash Course in Digital Manufacturing and Simulation 63 Figure 2.27 Airbus A350-900 is a long range, mid-sized, wide-body family of airliners currently under construction. The A350 will be the first Airbus with fuselage and wing structures made primarily of carbon-fiber-reinforced plastic. The A350 is scheduled to enter into airline service in 2013.

Doing away with physical mock-ups (in metal and wood) in addition to development time savings of 20 to 30 percent has obliged us to intensify the use of CAD models and digital mock-ups. Investing in simulation means that assembly problems can be anticipated, often years ahead of time, and that resources can be defined and optimized (for instance the number of assembly stations before they are physically installed), thus generating cost savings. Simulation bears visible impact by reducing the development cycle as well as improving cost-effectiveness and quality.

What competitive edge can a group like Airbus gain here? In order to build a new plane within the current time constraints, we can no longer be without simulation. Competitive edge is not acquired from the solution itself, as we use tools that are readily available on the market (DELMIA from Dassault Systèmes) and widely used by our competitors. It is more a question of quality of integration, particularly with the existing IT system, i.e. simulation at the production stage with connection to our SAP Enterprise Resource Planning software.

Where are you in the adoption cycle? We began using simulation in 1997 for the main section of the A340. Process planning activities began on the A380 in France and in Germany in the early 2000s. For the A400M (see the end of Chapter 1), we extended process planning to England for all pre-assembly lines in 2003. We now have a comprehensive

64 Virtual concept > real profit portfolio for robotics, process planning, 3-D and flow simulations. In 2009, we will integrate them into SAP as support for the production stage. For the A350, we rolled out the DELMIA Process Engineer software solution in September 2008.

What are the main barriers to adoption? The first hurdle is the difficulty in projecting ahead. Examining problems upstream does not come naturally. You have to be convincing, especially in companies like ours that work on multiple programs. Long-term considerations are complex in terms of implementation, and they require resources. Then, the barriers between engineering, manufacturing engineering and the shop floor have to be broken down. It is important to encourage the circulation of information, both to and from the plane engineer and the planner. For example, the shop floor must send information back for reviewing the design work. I don’t think that the technical barriers are insurmountable. Insofar as considering the technical issues and the potential organizational difficulties – which are the longest to deal with – you’ve almost reached the finish line. Despite the fact that tools communicate more, people have not evolved at the same pace.

What are the benefits, and how can they be measured? Digital manufacturing enables us to take more ambitious, less traditional, approaches. We can simulate all sorts of scenarios and test many possible variants, such as for assembly stations, and scenarios that we were unable to test before as they were too far removed from our routine practice. The technology break is helping us to overcome some major hurdles. We are also able to innovate in security. You can’t just jump in at the deep end with a completely new assembly concept. You have to proceed carefully and make sure that new processes are reliable, and that the design and manufacturing time frames and the resources stay the distance. Although we can simulate far more than we were able to do in the past, we still have to be realistic and cannot work haphazardly. Digital manufacturing also enables us to improve quality by succeeding with our first attempt. The other benefit is training. For example, training on the A400M – which is subject to a great number of simulations – was programmed ahead and colleagues were shown what they were going to do several months later.

What is the future of digital manufacturing and simulation? I can already sense what we intend to put in place at Airbus, that is to make the digital manufacturing and simulation worlds become as real as possible. Today, we are defining a theoretical environment, but tomorrow we will have to be capable of defining it in the real world. It is the real world in which unexpected events occur, so we have to be able to inject, into a virtual world, the unforeseen events occurring in the real one. Today, digital mock-ups represent the plane that is being assembled and that will fly. However, they do not strictly represent reality, so we still have to highlight the differences and identify possible rectifications.

2 A Crash Course in Digital Manufacturing and Simulation 65 The second major trend is the replacement of paper and designs by nomad computers, enabling our people to visualize the operations they will perform on tablet PCs. We are working on these projects for the A350. The aim is to consoli- date all information within 3-D to generate digital instructions rather than paper designs. In time, we will be working in a fully electronic environment – a paperless Digital Factory. This movement is occurring sector by sector, starting with areas such as the cabins where personalization is important for the customer. Docu- ments here are evolving rapidly and a fully electronic environment as opposed to the paper-based approach will save time and increase quality.

Highlights Simulation and related digital manufacturing practices predominantly help to enhance visualization of problems, to analyze their impact and to identify appropriate solutions. Organizational difficulties are the most difficult to deal with. Tools communicate better and better, but, as always, people remain the bottle-neck. Humans never evolve at the same pace.

2.7 Beneficial Use of Simulation in Manufacturing

Since the correlation between those companies rated as most innovative and their market value is strong, simulation has been, and will continue to be, a major force in product and process innovation. Some thirty years ago, Finite Element Analysis was very expensive and only the largest companies could afford computer hardware and dedicated staff. Now, simulation is accessible to every engineer. Simulation is a control feedback loop that will lower the cost of development and innovation and increase opportunities for growth. The following are helpful guidelines to increase innovation and thus benefit business by making better use ofsimulation in Computer-Aided or even Computer-Driven Engineering: • For a start, you have to develop the ability to run simulations quickly. Traditionally, simulation was often out of step with the design schedule and results came out too late to be useful. To speed things up, simulation processes must be aligned. Models with details commensurate with the desired accuracy must be available. • Not being afraid to fail would be the second guideline for increasing innovation. If the simulation process is fast enough, even absurd ideas will reveal useful information and increase insight into a design. Early in the cycle, there is a phenomenon called “positive failure.” • Practice “front-loaded development” that is: put as much effort as you can into a project as early as you can. 80 percent of a product’s cost is tied up in design decisions made during the very first stage of development. It costs millions to fix problems in terms of money and time-to-market after a design has been released for production.

66 Virtual concept > real profit • Putting emphasis on simulation early helps in deciding which parameters are important and what can be approximated. This allows rapid progress and a better finished product. For instance, Toyota identified 80 percent of problems with a new design before the first prototype, which helped eliminate the need for a second prototype altogether. Moreover, early simulation can help in keeping better contact with customers and can foster a positive product reception in the market.

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New methods can usually approach the same level of performance as traditional methods faster and for less cost. This is shown in Figure 2.28. The downside is that new methods are not usually as robust as the long-established methods that they replace. By combining new methods with old ways, it is possible to avoid the gaps that new methods alone will introduce, and still enjoy their cost and time savings. Traditional methods (shown by the dark blue line) are robust and reliable, but they can be expensive. They obviously require physical prototypes to test, and they can take up considerable precious development time. However, there are lots of issues that affect the feasibility of using the new virtual methods of simulation. Three will be discussed to end this section on the beneficial

2 A Crash Course in Digital Manufacturing and Simulation 67 use of simulation in manufacturing: parametric exploration, sensitivity analysis in rigorous optimization and the solution of reducing calculating costs through meta- models.

Parametric Exploration Instead of meticulously varying variables little by little, engineers could choose to conduct more bold exploration. This approach cannot estimate the effect on the initial configuration of modifying a variable – it will not, in effect, capture local effects of the modification – but rather aims at an exploration of all the possibilities. An advantage of such a parametric study as opposed to elaborate sensitivity analysis is that it can supply concepts totally different and possibly better. However, a number of simulations will be necessary in order to cover the product capacity and to give a good idea of the variations of the different indicators in the domain. In particular, if the number of variables to modify increases, the volume of digital experiments will quickly become too large to be feasible.

Sensitivity Analysis in Rigorous Optimization Sensitivity analysis is an important weapon in facilitating decisions. But its most interesting application is being integrated into a rigorous optimization procedure. A number of optimization algorithms, said to be “gradient,” use this method to modify the values of a variable in the right direction: minimizing weight, cost, waste from manufacture, waste from the consumption of energy, and so on. Such algorithms of optimization are very effective and nearly always lead to improvements in the existing concept. The computational cost of such a procedure is high and will be even higher when the number of variables implicated is large. When this number is too high, or where the cost in time associated with each calculation is large, such a study is not feasible.

Reducing Calculating Costs through Meta-Models The use of gradient or other – for instance, genetic – optimization algorithms brings up the issue of the required calculation time for each digital simulation. It may be reduced by working on the effectiveness of computational code or by improving the calculating capacity of the computer. Computational codes can be made more effective according to two axes: either by working on the computer code itself, or by simplifying the calculating model. Calculating the value of the quantity governed by an analytic formula linked to the flexibility of decision variables will always be shorter than using Finite Element or Finite Volume Analysis. However, it is not always possible to model a phenomenon by analytic formulas – in particular, phenomena depending largely on the weather or on other variables which cannot be controlled, since they are strongly risk-related. Another way of reducing calculation costs consists of replacing the cost of representing the model of a phenomenon with a digital approximation, a meta-model. A number of techniques have been developed in the past few years in order to obtain

68 Virtual concept > real profit functions that are realistic. The linear or poly-nominal regression which could quickly give a rough estimate of the range of variation within the functions is relatively well known. But many other more effective methods have been developed, in particular those based on neural networks, Support Vector Machines and so-called “kriging.” They all work on the same general principle: from all the available data, construction algorithms look to regulate the parameters of the meta-model in finding the most reliable relationship between the known entrances and exits. If this “apprenticeship process” is achieved correctly, then the model obtained could be used to estimate the value of the function aimed at with regard to a point unknown. Such methods are totally defined by an analytic formula and therefore produce estimates much quicker than through executing multiple calculating routines. These techniques can be implemented today in your process and make it possible to dramatically save time by simulating the simulation.

Coupling Simulation and Reality

Coupling simulation and real tests is already a reality for electrical sensors and ac- tuators. The principle is to build a complete chain of simulation modeling several systems. And when the chain is completed, the equivalent modulus is replaced by the system being tested. This means the hardware is in the loop, running both simulation and system in real time, which explains the term “HIL simulation.” HIL simulation platforms are developed to model and test automotive anti-lock braking systems, representing vehicle dynamics such as suspension, wheels and tires, road characteristics, and the brake system’s hydraulic components. As simulations can be run quicker and quicker, especially using meta-models giving immediate results, these techniques have started to be developed for new fields of applications, especially in the automotive and aerospace industries. Human-in-the-loop simulation is used also to include human interaction (such as decision, judgments or mistakes) within a simulation chain.

2 A Crash Course in Digital Manufacturing and Simulation 69 Bookmark Chapter 2

Especially in challenging times, visions are important. Therefore throughout this book we present a number of 2020 projections. This chapter starts with the European Union report ManuFuture. A Vision for 2020, which calls for competitiveness, global standards, environmentally friendly products and production, and a quick response to social and economic change. Although the report dates back to November 2004, this could have been written just yesterday. Notably the projected economic change today strikes us as visionary. We have the same experience in reading the editorial “Roots, Performance and Future of ‘Concurrent Engineering’” in the January 1994 issue of The International Journal of Advanced Manufacturing (see Section 2.5). As Chapter 5 points out, advanced concurrent engineering and crowdengineering are among the collaborative targets that likely will be realized in the near future. To date, the Springer book series on the subject of advanced concurrent engineering has two titles: Collaborative Prod- uct and Service Life Cycle Management for a Sustainable World (2008) and Global Perspective for Competitive Enterprise, Economy and Ecology (2009). As far back as the 1990s the American company Sandia National Laboratories was reporting on the topic of advanced concurrent engineering in a so-called “Visionarium Environment”: “Sandia demonstrated large-scale visualization in a conference room environment. Project focused on the installation of hardware for visualization and display, and the integration of software tools for design and animation of 3-dimensional parts. Using a high-end visualization server, 3-dimensional modeling and animation software, and leading edge World Wide Web technology an advanced concurrent engineering environment was simulated where a design team was able to work collectively, rather than as solely disjoint individual efforts”.26 This remarkable achievement was a first step towards the advanced online concurrent engineering environments under development today. Essentially, the ultimate goal of simulation always has been to improve understanding and explanation of real-life phenomena. Developing simulations is a basic act of human intelligence: even during the Roman Empire naval battles with special ships sailing in an enormous pool were “simulated” in the Circus Maximus (see Section 2.1). For four hundred years now mathematical modeling and simulation have been around, involving the observation of occurring phenomena, mathematical models, accurate timekeeping and representation in drawings and machines like planetariums. The application of timekeeping for determining the longitude at sea in the 18th century serves as a brilliant practical application of executing a model in order to predict something, which is the essence of simulation. Computer simulation, which took off at the end of World War II in the Manhattan Project, was enriched in the 1950s with simple 2-D drawings and in the 1970s with 3-D imagery. With the advance of personal computers the gaming industry emerged, followed in the 1990s by the full computerization of the film industry (see Section 2.3). It was all a matter of “Reconciling the virtual and the real world,” as Airbus’s Frédéric Bertaud puts it in

70 Virtual concept > real profit the interview above. This indeed is the common denominator for simulations from astronomy to weather forecasts to crash tests, flight simulators, virtual factories and virtual worlds. Lifelike simulation and interfaces to reality lie at the heart of digital manufacturing in a context of distributed data management, process engineering, collaborative, networked design, engineering and production, all the way up to maintenance, repair, overhaul and recycling. We have been in a global era of manufacturing for some time now, and the growth of manufacturing is accelerating due to new economic conditions. No doubt the tools of future engineering, management and manufacturing are digital, distributed and integrated, spanning product lifecycles and continents. Industries are being reshaped globally while digital manufacturing and simulation gain ever more ground as core production strategies and practices. Today the environment of manufacturing is turbulent and requires ongoing adaptation of manufacturing systems, from networks to processes and from real-time to long-term operations. The challenges involved are being addressed in the next chapter. Not surprisingly, another report from a decade ago sets the stage, identifying and analyzing Visionary Manufacturing Challenges for 2020.

2 A Crash Course in Digital Manufacturing and Simulation 71 Virtual concept > real profit 3

72 Challenges for Digital Manufacturing and Simulation

Introduction Identifying the Traps 74 3.1 Simulation in Manufacturing Systems 77 3.2 Six Grand Challenges for 2020 82 3.3 Changing Undesirable Social Behavior 86 3.4 Confronting Today’s Limits 91 interview Fulvio Rusino, Head Advanced Engineering at Comau 96 3.5 Automotive 2020 98 3.6 The Digital Factory Challenge 103 Bookmark Chapter 3 106

73 Introduction Identifying the Traps

Benefits and challenges, opposite as they may seem, cannot be addressed sepa- rately, because they are opposite ends of the same spectrum. The same goes for related polarities like “opportunities and threats,” “chances and risks,” “strengths and weaknesses,” “success and failure,” and ultimately, of course, “profit and loss.” Their common denominator is that all hold promises and caveats to be tested against reality and to be continuously improved. In the previous chapters several compelling reasons for investing in digital manufacturing and simulation have been explored and assessed. However, a deliberate focus on challenges and benefits as presented in this and the next chapter is essential to get a clear picture of why enthusiasm but also disappointment reigns in digital manufacturing and simulation. Many of the international manufacturing roadmaps that have been drawn up over the past decade identify digital manufacturing and simulation not only as core enabling practices and targets but as key targets in achieving an economically and environmentally sustainable future. The “Manufuture Strategic Research Agenda” of 2006, for instance, promotes Virtual Factory digital manufacturing as a tool for knowledge-based engineering, which enables manufacturers to operate with unprecedented speed, quality, precision, efficiency, responsiveness, and cost-effectiveness. In 2009 we can safely skip the research aspect of this agenda since more than ever “the production sector must continually confront new challenges in order to survive in competition. An active and foresighted technology development and a quick response to social and economic change are indispensable for this.” This clear warning might have been written only yesterday, but it stems from the 2004 report ManuFuture. A Vision for 2020.1 However promising and bright a full-fledged digital manufacturing and simulation future eventually will be, there are many hurdles to overcome in terms of hardware, software, networks, parallel processing, data exchange, algorithms, virtual reality, social media, concurrent engineering, product data management, enterprise resource planning, supply chain management, business processes, factory layout, factory processes, lifecycle management and so on. All these domains ultimately coincide in the vision of a Digital Factory, where real and virtual manufacturing, production, planning and coordination components will be combined into one meaningful and easily reconfigurable entity. The principles and structure of this factory of the future are described in the German VDI guideline 4499 (Verein Deutscher Ingenieure). Figure 3.1 shows the organization or planning workflow of the Digital Factory. It consists of eight interfacing sections, ranging from CAD and Digital Mockup to Virtual Reality/Augmented Reality and Material Flow Simulation. The Digital Factory is only feasible when these eight sections can seamlessly communicate via a computerized integration platform that is firmly rooted in a structured common data repository. The challenges in realizing this vision, particularly given the state of today’s manufacturing practice, are discussed in the Bookmark section at the end of this chapter, which puts into perspective all the challenging angles and

74 Virtual concept > real profit issues presented in this chapter. Also in the concluding Bookmark section we see how challenges for the Digital Factory change gear to become the benefits of digital manufacturing and simulation as laid out in Chapter 4.

Organization

Material ﬂow simulation CAD

VR/AR DMU

Structured database

Data CAPP analysis Integration platform

3-D Kinematics visualization simulation

Planning workﬂow

Figure 3.1 High-level structure of interfacing Digital Factory domains (Fraunhofer IPA, Stuttgart, Germany).2

Some ten years ago, U.S. industry experts and analysts gathered together to determine the manufacturing challenges we would face today and in the decade to come. In hindsight, this most interesting piece of work by the Committee on Visionary Manufacturing Challenges, the Board on Manufacturing and Engineering Design, the Commission on Engineering and Technical Systems and the National Research Council offers a remarkably accurate account of facts, forces, developments and implications that are currently at issue. Indeed, the title of the report, Visionary Manufacturing Challenges for 2020,3 published in November 1998, hits the nail on

3 Challenges for Digital Manufacturing and Simulation 75 the head. It is considered a seminal and truly visionary study, an evergreen still being quoted and referenced: for instance, recently in the article “Facing Manufacturing Challenges.”4 For a start, Visionary Manufacturing Challenges distinguishes seven critical technical, political, and economic forces and then it pictures a realistic future vision for today and the next decade. After that the central theme of six “Grand Challenges” is presented, followed by ten strategic technology areas of development to meet those challenges. These are combined into a coherent foundation with several specific actions to be taken. Finally, the importance of human psychology and social behavior is stressed. This conglomerate of issues serves to identify and explain the challenging nature of contemporary and future manufacturing environments. Clearly, such a nature cannot be reduced to an absolute set of challenges, since promises, visions, even “neutral” trends and facts upon examination entail a certain amount of risk. Therefore they must be taken into account and analyzed, although superficially their appearance may not reveal any cause for concern. Moreover, in complex settings with many stakeholders (like the manufacture of goods) it is the interaction between factors, which often bear disguised risk, that fosters or mitigates challenges. The presentation of this landmark study will be continued in Section 3.2. In their article “Grand Challenges in Modeling and Simulation of Complex Manu- facturing Systems” (2004) John Fowler and Oliver Rose5 actually say that apart from cost and organization speed, integration and acceptance are the main hurdles in the path towards a bright digital manufacturing and simulation future. The authors distinguish four main challenges: one “grandest”, two “emerging grand challenges” and a final “big” one: • Grandest Challenge: A significant reduction in problem-solving cycles regarding the simulation process for manufacturing systems analysis, model design, model development and deployment. • Emerging Grand Challenge 1: Developing real-time simulation-based problem- solving capability. Permanent, always on, synchronized factory models, on-demand, automatically built factory models. • Emerging Grand Challenge 2: Plug-and-play interoperability of simulations and supporting software within a specific application domain. • Big Challenge: Greater acceptance of modeling and simulation within the industry.

To conclude this introduction, it must be stressed that, although we are able to discern and even pinpoint challenges, the concept of “challenge” itself is not simple, distinct and clear-cut. Rather, it is a characteristic derived through the analysis of complex interaction within a chosen time frame. The same goes for benefits and profit. It is common knowledge that by focusing on short-term benefit and profit, organizations can easily do themselves more harm than good. Unforeseen and sudden “surprises” are inherent within complex systems.

76 Virtual concept > real profit 3.1 Simulation in Manufacturing Systems

To provide a clear, practice-oriented and detailed picture of the challenges facing digital manufacturing and simulation as a whole, we present the following summary of the ideas of Juhani Heilala.6 Over time manufacturing systems, processes and data have grown more and more complex. Since changes occur at ever shorter intervals, production systems need to outlive the product they were originally designed for. Simulation and modularity bear the solution. Designing and building systems should be as easy as selecting modules from a catalogue and placing them in the right order for process flow and system layout. In practice however, simulation is mainly used in system design evaluation, in the analysis of system concepts and in specification testing. Today, simulation seldom is used in the system operation planning phase as decision support tool for production managers, although the benefits would be huge. In manufacturing simulation, different organizational levels can be identified like product, process, production system (work cells, line system) and plant/enterprise. At the enterprise or supply chain level, and also at the factory or production line level, engineers are studying material flow, work in process, and resource utilization, i.e. factory or production system design issues or logistic planning. A more detailed level of manufacturing simulation studies the behavior of automated machines or manufacturing processes. Another more detailed level of production simulation focuses on kinematics or dynamics of machines and robots. Machine tools or robot simulators are used for system design or off-line programming, for example in cases of path and process planning for welding robots. This frees the production units to carry out manufacturing and programming in parallel. Man and robot models are used for task and sequence planning collision checking, ergonomics and safety design. Eventually, the use of simulation will cover lifecycle phases from strategic design and concept creation to original design, system reconfiguration, and operation planning (Figures 3.2 and 3.3). Some of the benefits of implementing an operative simulation scheduling system are reduced planning effort for day-to-day scheduling, customer order due date conformance, synchronization of flow through the plant; minimization of set-ups/changeovers, early warning of potential problems, check of critical resources and materials, and, naturally, what-if scenario analysis for capacity planning. In general, ever-increasing complexity means that model building is time- consuming. The focus is on speeding up this core process.

3 Challenges for Digital Manufacturing and Simulation 77 System lifecycle phase What & how Why

Concept creation t Visualization 2-D and 3-D, Selling and development of Layout planning of communication, animation, ideas and concepts, fast cells, lines, factories easy and fast modeling elimination of unsuitable ones needed Production simulation t Analysis of control principles, Investment insurance, strategic Detail level development routing, buﬀer sizes, capacity, decisions and detailed of the system utilization, throughput-time, evaluation of alternatives; bottlenecks, etc simulation model is an t Data analysis, reports, intelligent document of the multiple runs, stochastic system

Control software t Emulation, integration of Debugging and testing against development with validated simulation model virtual model of the system, virtual model and control software shortening the development time Training of operators t Emulation, integration of Experience for operators, faster with virtual model and validated simulation model ramp-up control software and control software, planning of ramp-up Operational use t Data integration, embedded Decision support system for with virtual model and simulation with graphical user daily operations, scheduling control software interface to results and production planning

Figure 3.2 Simulation in production systems.

Manufacturing system modeling and simulation strongly needs further development, for instance regarding the integration of different simulation and modeling methods and the use of multi-disciplinary simulation with different abstraction levels. Simulation should be combined with manufacturing lifecycles, resulting in a virtual, concurrently evolving digital image of the real system through real-time coupling with data from the factory floor. Standardized interfaces are needed to aid integration and development of reusable simulation models and objects. These interfaces would make modeling easier and reduce the cost of model construction and data exchange. This would help make simulation more affordable and accessible to a wider range of users, even to non-simulation experts, preferably on-line and in real time on the factory floor. Often, the high cost of simulation in terms of software, hardware, and expertise limits its use.

78 Virtual concept > real profit Decision Assignment of order to Assignment of order each line/machine, scheduling of orders, re-scheduling Determination of an listorder XPSLMPBE Scheduling of orderbook and forecasts production or buy, Make planning, mix, budget scenarios, plans game Production planning Data and information Production line data Production line data FHQSPDFTTEBUB FRVJQ NFOUVUJMJ[BUJPO NBUFSJBM BWBJMBCJMJUZ Summarized from data cell/line level FHQSPEVDUJPO TVC DPOUSBDUJOH NBUFSJBMT Summarized from data business unit/plant level mining data with Supply chain data FHPSEFSEVFEBUFT Minutes Hours Shifts Days Weeks Months Days Weeks Months Shifts Days Weeks Time frame Supervisor Process engineer Production planners management Upper OEMs Partners Plant manager Production teams Customer service User t t t t t t t t t Organizational and Organizational operational level Manufacturing cell/ line Enterprise Suppliers Business unit/ plant

Figure 3.3 Operative decisions and planning data at different levels.

3 Challenges for Digital Manufacturing and Simulation 79 The manufacturing simulation tools available can be divided into three basic classes: general-purpose simulation languages, simulation front-ends and simulators. The general-purpose simulation languages have a high abstraction level, can model any system and scenario, but require substantial knowledge and experience on the programming side as well as on the simulation side. The simulation front-ends are interface programs between the user and the simulation language. Simulators use manufacturing specific constructs and terminology, and offer graphical presentation and animation. These simulators have a medium abstraction level, and can model any system using similar processes, but may exceed the capabilities of the average engineer. For specialized situations and scenarios outside the available set of modeling constructs or objects, programming is often required to complete the model. Outside of these three basic classes lies fixed process simulation, which consists of simulation runtime models. Thanks to the fact that they are simple and easy to use they enable professionals to perform iterations and evolutions.

Manufacturing simulation should support discrete events analysis for at least material flow, machine utilization and robotics simulation. Sophisticated systems offer 3-D visualization, menu-driven applications, and integration with other product design and enterprise data systems. These comprehensive solutions, likeDELMIA/CATIA, are commonly used in the automobile and aviation industries. Cost-effective new tools are available for SMEs (small and medium enterprises). The use of simulation in manufacturing control is also increasing where the control system communicates with a virtual system instead of a real factory. Large savings can be obtained when evaluating and validating control systems like Programmable Logic Controllers offline. Since production lines are in a constant state of flux due to demand, design changes, and the introduction of new processing technologies, the rapid development of simulation is necessary. The ever-changing design process requires frequent evaluation of system changes that take only a few minutes or hours instead of days or weeks. They range from simple parameter modifications like new cycle times to total line configuration. There are four main tools to speed upsimulation: reference models, a module library, Application Solution Templates and through integration with other software tools. • Reference models are a complete set of model structures together with a description of how they apply and how they can be adapted to a given problem. A reference model is not an ideal solution used as a measure, but a typical structure for a specific problem type. • A module library. Hierarchical modeling allows the user to save whole models as clusters – groups that can be deleted, moved, or scaled as a single object. Groups or clusters can make up sections of a larger, complex model. The components are pre-engineered, standardized and reusable.

80 Virtual concept > real profit • Application Solution Templates (AST). Using industry-specific templates the user can start up with icon libraries, functions, element terminology, element types, and other settings. AST can offer a highly graphical, easy-to-use simulation system that allows rapid modeling of manufacturing systems. • Integration with other software tools like CAD, spreadsheets and databases. Using existing information is important, while the problem lies in data interfaces. The biggest challenges are in data integration, automatic universal data mapping from different sources, and how to present the results to users. A lot of work still needs to be done in the development of user friendliness. A special case is automated model building based on ERP data. High-end visualization is the latest development, before there were only Gantt and bar charts showing scheduling of customer orders and resource utilization. These solutions were quite easy to use, scheduling changes were easy to make and test, and model modifications were automatic. Data integration was partially generic using custom-built proprietary interfaces.

The use of standardized structured manufacturing data in a neutral format like XML clearly increases interoperability between manufacturing information systems and simulation, and also speeds up modeling. Steps are being taken by SISO, the Simulation Interoperability Standards Organization and CMSD Product Development Group to create a Core Manufacturing Simulation Data Information Model (CMSDIM). The data elements of the current data model are organizations, calendars, resources, skill definitions, setup definitions, operation definitions, maintenance definitions, layout, parts, bills of materials, inventory, procurements, process plans, work, schedules, revisions, time sheets, probability distributions, references, and units of measurement. The costs of integrating simulation systems with other manufacturing applications are high, and there is always a need to transfer and share data between simulation and other manufacturing software applications. Custom-built proprietary interfaces are costly, which prohibits the proliferation of simulation technology. Applications need a lot of customization and, due to the lack of standardization, system integration is challenging. The IMTI (Integrated Manufacturing Technology Initiative) Modeling and Simula- tion for Affordable ManufacturingRoadmap defines 75 top-level goals and 250 supporting requirements for research, development, and implementation of modeling and simulation technologies and capabilities. Subsequent processing distilled these into four high-level goals: Automated Model Generation, Automated Model-Based Process Planning, Interoperable Unit Process Models and Scalable Lifecycle Models. • Automated Model Generation: develop techniques to enable automated generation and management of models at various levels of abstraction for multiple domains. • Automated Model-Based Process Planning: provide the capability to automatically generate manufacturing process plans based on product, process, and enterprise models, with integrated tools to evaluate producibility of features, resources, and iterability.

3 Challenges for Digital Manufacturing and Simulation 81 • Interoperable Unit Process Models: develop a shared base of robust, validated models for all materials and manufacturing processes to enable fast and accurate simulation of any combination of processing steps. • Scalable Lifecycle Models: provide the capability to create and apply scalable product lifecycle models in every phase of the lifecycle and across all tiers of the supply chain. 3.2 Six Grand Challenges for 2020

The core of this section is adapted from Visionary Manufacturing Challenges for 2020, which is being used throughout this chapter to highlight and mirror specific key challenges for digital manufacturing and simulation.7 The report Visionary Manufacturing Challenges for 2020 (1998) anticipates the profitable and productive potential of manufacturing for today and the next decade. The study takes an international view of future manufacturing that considers the leaps and bounds of technological innovation and the blurring of the lines between the manufacturing and service industries. Ten strategic technology areas are identified as well as six “Grand Challenges” to be overcome. A host of issues are discussed that push and pull at manufacturing: the changing workforce, the changing consumer, the rise of bio- and nanotechnology, the prospects for waste-free processing, simulation and modeling as design tools, shifts in global competition, and more.

Technical, Political, and Economic Forces

According to the report, the seven most important technical, political, and economic forces for the development of manufacturing can be characterized as follows: • The competitive climate, enhanced by communication and knowledge sharing, will require rapid responses to market forces. • Sophisticated customers, many in newly developed countries, will demand products that are customized to meet their needs. • The basis of competition will be creativity and innovation in all aspects of the manufacturing enterprise. • The development of innovative process technologies will change both the scope and scale of manufacturing. • Environmental protection will be essential as the global ecosystem is strained by growing populations and the emergence of new high-technology economies. • Information and knowledge on all aspects of manufacturing enterprises and the marketplace will be instantly available in a form that can be effectively assimilated and used for decision making.

82 Virtual concept > real profit • The global distribution of highly competitive production resources, including skilled workforces, will be a critical factor in the organization of manufacturing enterprises.

Vision 2020

As the Visionary Manufacturing Challenges study confidently sketches out, in 2020, manufacturing enterprises will bring new ideas and innovations to the marketplace rapidly and effectively. Individuals and teams will learn new skills rapidly because of advanced network-based learning, computer-based communication across extended enterprises, enhanced communications between people and machines, and improvements in the transaction and alliance infrastructure. Collaborative partnerships will be developed quickly by assembling the necessary resources from a highly distributed manufacturing capability in response to market opportunities and just as quickly dissolved when the opportunities dissipate. However realistic this may be, such a vision also entails the profound challenge of accommodating the potential. The same goes for the following. Although in 2020 manufacturing will continue to be a human enterprise that converts ideas for products into reality from raw and recycled materials, research and development, design engineering, manufacturing, marketing, and customer support will be so highly integrated that they will function concurrently as virtually one entity that links customers to innovators of new products. The form and identity of companies will be radically changed to encompass virtual structures that will coalesce and vanish in response to a dynamic marketplace.

Slowly But Surely We Are Getting There According to Rod Martin of AMR Research, the most important trend driving production automation is our evolving ability to holistically model and simulate the complete product value chain. Although we are not yet able to model value chains as broadly as we would like, we slowly but surely are getting there. Then we will be able to optimize manufacturing processes so that they can be modified in response to actual external demand. From there, the next capability is the integration of manufacturing operations into the supply chain. In most environments the cost of design could be cut in half if design and portions of execution were accomplished virtually. Today, however, most companies still produce as much as they can and rely on sales and marketing. Only the most advanced organizations are starting to implement outside-in-driven manufacturing. This amounts to a strategic joint value- creation relationship between the manufacturer, suppliers, and the customer. In 10 to 15 years modeling will include not only mechanical components but human and network components, and even the behavioral aspects associated with operations. We will be in a position to simulate opportunities to a very late stage, right up to the point that virtually all of the questions have been answered.

3 Challenges for Digital Manufacturing and Simulation 83 To accomplish this, we will conduct at least 80 percent of development in the virtual world. Today it’s just the opposite.8

Six Grand Challenges

In this new dynamic there are six “Grand Challenges” to be met: • Grand Challenge 1. Achieve concurrency in all operations. • Grand Challenge 2. Integrate human and technical resources to enhance workforce performance and satisfaction. • Grand Challenge 3. “Instantaneously” transform information gathered from a vast array of diverse sources into useful knowledge for making effective decisions. • Grand Challenge 4. Reduce production waste and product environmental impact to “near zero.” • Grand Challenge 5. Reconfigure manufacturing enterprises rapidly in response to changing needs and opportunities. • Grand Challenge 6. Develop innovative manufacturing processes and products with a focus on decreasing dimensional scale.

Ten Strategic Technology Areas

The following ten strategic technology development areas – no particular order im- plied – are considered as the most important for meeting these Grand Challenges: • Adaptable, integrated equipment, processes, and systems that can be readily re- configured. • Manufacturing processes that minimize waste and energy consumption. • Innovative processes for designing and manufacturing new materials and components. • Introduction of biotechnology in manufacturing processes. • System synthesis, modeling, and simulation for all manufacturing operations. • Methods and technologies to convert information into knowledge for effective decision making. • Product and process design methods that address a broad range of product requirements. • Enhanced human-machine interfaces. • New educational and training methods that enable the rapid assimilation of knowledge. • Software for intelligent collaboration systems.

84 Virtual concept > real profit Foundation and Actions

In this context, adaptable and reconfigurable manufacturing systems, information and communication technologies, (enterprise) modeling and simulation, analytical tools for modeling and assessment, processes for capturing and using knowledge for manufacturing, and intelligent processes are especially important because they are key to manufacturing capabilities. Because manufacturing is inherently multidisciplinary and involves a complicated mix of people, systems, processes, and equipment, the most effective approach will be multidisciplinary and grounded in knowledge of manufacturing strategies, planning, and operations. Special attention should be given to: • adapting and reconfiguring manufacturing processes rapidly for the production of diverse, customized products. • adapting and reconfiguring manufacturing enterprises to enable the formation of complex alliances with other organizations. • developing concurrent engineering tools that facilitate cross-disciplinary and enterprise-wide involvement in the conceptualization, design, and production of products and services to reduce time-to-market and improve quality. • developing educational and training technologies based on learning theory and the cognitive and linguistic sciences to enhance interactive distance learning. • optimizing the use of human intelligence to complement the application and implementation of new technology. • understanding the effects of new technologies on the manufacturing workforce, work environment, and the surrounding community. • developing business and engineering tools that are transparent to differences in skills, education, status, language, and culture to bridge international and organizational boundaries. • managing and using information to make intelligent decisions from amongst a vast array of alternatives.

Psychology and Culture

As for the last truly fundamental issue, manufacturing-specific methods for people to make decisions, individually and as part of a group, must be devised and implemented in collaboration systems and models. Therefore the effect of human psychology and social behavior on decision-making processes in the design, planning, and operation of manufacturing processes must be properly understood. It is this fundamental factor that the next section deals with. It focuses on the undesirable practice of herd- mentality (also known as “groupthink”) and on managing and promoting teams with innovative missions, as is the case regarding digital manufacturing and simulation.

3 Challenges for Digital Manufacturing and Simulation 85 3.3 Changing Undesirable Social Behavior

Manufacturing naturally deals with human factors on the product side, as well as on the client side. Challenges on the product side are manifold and actually constitute the more important of the two. Most attention to human factors is being spent in the production area on ergonomics and collaborative process flow. However, in the organizational value chains many departments and stakeholders must constantly make decisions, determine strategic directions and synchronize actions, which have far-ranging consequences for the competitive positions of companies in the short, mid and long term. Since the timeliness of measures to be taken is of the essence, often, there is little opportunity to revise or reconsider proposals that are in a final phase. Yet practice has shown that agreements arrived at too easily or too early may have disastrous consequences. While argument, criticism and even conflict are normal in any dynamic environment, the opposite,a herd mentality called “groupthink,” can be more dangerous. Two YouTube videos are especially illustrative of the groupthink phenomenon, which can take several forms, as we will discuss in a moment. The first video shows how groups naturally look for consensus and will often come up with a false consensus, even when individual members disagree. The video uses the space shuttle Challenger disaster to dissect this phenomenon and show how you can avoid it. The second video goes into more detail in analyzing the group pressure towards uniformity by the mechanisms of self censorship, direct pressure, mind guards, and the illusion of unanimity. The group norm is one of agreement, not of discord, and that central rule often leads groups into making decisions against their better judgment. Even when skilled, well trained and respected people are involved, a group’s high regard for agreement can override the realistic consideration of alternatives. The leader can foster a positive decision-making climate through openness, through avoiding insulation of the group by bringing in critical evaluators, and through avoiding being extremely directive. No matter how sophisticated our technology, attaining our collective goals will always depend on our individual courage to confront, to reason and to make decisions together.

86 Virtual concept > real profit Groupthink

Watch groupthink videos on YouTube.

The American sociologist and journalist William H. Whyte coined the term “groupthink” in 1952, in Fortune magazine. Twenty years later, psychologist Irving Janis, in his book Victims of Groupthink; a Psychological Study of Foreign- Policy Decisions and Fiascoes, defined groupthink as “a mode of thinking that people engage in when they are deeply involved in a cohesive in-group, when the members’ strivings for unanimity override their motivation to realistically ap praise alternative courses of action.”9 Consensus-driven groupthink decisions are the result of the following practices: an incomplete survey of alternatives, an incomplete survey of objectives, failure to examine the risks of the preferred choice, failure to reevaluate previously rejected alternatives, poor information research, selection bias in collecting information, and failure to work out contingency plans. Figure 3.4 presents a practical and more elaborate scheme, listing three antecedent categories that lead to undesirable groupthink, as well as two categories of observable consequences of groupthink and defective decision making.

3 Challenges for Digital Manufacturing and Simulation 87 ANTECEDENTS OBSERVABLE CONSEQUENCES [A] Decision makers [C] [D] Cohesive group Symptoms of Symptoms of + groupthink defective digital manufacturing [B-1] Structural faults 1. Overestimation of group 1. Incomplete survey of alternatives 1. Insulation of group *MMVTJPOPG JOWVMOFSBCJMJUZ 2. Incomplete survey 2. Lack of tradition of of objectives impartial leadership Concurrence- #FMJFGJONPSBMJUZ seeking PGHSPVQ 3. Failure to examine 3. Lack of norms for risks of preferred methodical procedures tendency 2. Closed-mindedness $PMMFDUJWF choice 4. Homogeneity of GROUPTHINK 4. Failure to reappraise group SBUJPOBMJ[BUJPO 4UFSFPUZQFTPG 5. Poor information + PVUHSPVQT search 3. Uniformity pressures 6. Selective information [B-2] 4FMGDFOTPSTIJQ bias Provocative context *MMVTJPOPG 7. Failure to VOBOJNJUZ contingency plan 1. High stress from %JSFDUQSFTTVSF external threats .JOEHVBSET 2. Low self-esteem, Recent failures, Excessive complexity, Moral dilemmas, etc.

Figure 3.4 Antecedents of groupthink and symptoms of groupthink and defective decision making.9

The Abilene Paradox

The groupthink article in the EnglishWikipedia provides a good introduction to the subject and lists over thirty related Wikipedia lemmas. The first is The Abilene Para- dox, a dangerous manifestation of groupthink studied in 1974. The Abilene Paradox creates situations in which a group decides on a course of action that is counter to the preferences of all of the individuals: we may call it “pleasing behavior.” This situation can occur when groups continue with misguided activities, which no group member desires, because no member is willing to raise objections or displease the others. The term was coined by Jerry B. Harvey in his 1988 book The Abilene Paradox and Other Meditations on Management,11 where he uses the following anecdote to clarify the paradox, a video excerpt of which can be seen on YouTube:

88 Virtual concept > real profit Figure 3.5 The Abilene Paradox. In our work and personal life nothing is more satisfying and rewarding than agreement with our friends and co-workers. Or is it? What if our problems do not stem from conflict at all, but from agreement? Agreement can easily become a problem if we don’t communicate it with each other.12

On a hot afternoon visiting in Coleman, Texas, the family is comfortably playing «dominoes on a porch, until the father-in-law suggests that they take a trip to Abilene for dinner. The wife says, “Sounds like a great idea.” The husband, despite having reservations because the drive is long and hot, thinks that his preferences must be out-of-step with the group and says, “Sounds good to me. I just hope your mother wants to go.” The mother-in-law then says, “Of course I want to go. I haven’t been to Abilene in a long time.” The drive is hot, dusty, and long. When they arrive at the cafeteria, the food is just as bad. They arrive back home four hours later, exhausted. One of them dishonestly says, “It was a great trip, wasn’t it.” The mother-in-law says that, actually, she would rather have stayed home, but went along as the other three were so enthusiastic. The husband says, “I wasn’t delighted to be doing what we were doing. I only went to satisfy the rest of you.” The wife says, “I just went along to keep you happy. I would have had to be crazy to want to go out in the heat like that.” The father-in-law then says that he only suggested it because he thought the others might be bored. The group sits back, perplexed that they decided together to take a trip that none of them wanted. They each would have preferred to sit comfortably, but did not »admit to it when they still had time to enjoy the afternoon.

3 Challenges for Digital Manufacturing and Simulation 89 Here are six characteristics emblematic of a group failing to manage agreement effectively: 1. Members individually, but privately, agree about their current situation. 2. Members agree, again in private, about what it would take to deal with the situation. 3. Members fail to communicate their desires and/ or beliefs to one another and, most importantly, sometimes even communicate the very opposite of their wishes based on what they assume are the desires and opinions of others. People make incorrect assumptions about consensus. 4. Based on inaccurate perceptions and assumptions, members make a collective decision that leads to action. 5. Members experience frustration, anger, and dissatisfaction with the organization. 6. Members are destined to repeat this unsatisfying and dysfunctional behavior if they do not begin to understand the genesis of mismanaged agreement.

A structured way to prevent groupthink mistakes and The Abilene Paradox in particular is to address all of these actions together: focus on results, not efforts, always favor straight talking, understand what is really at stake, listen to the devil’s advocate and develop a fact-based culture, because even great individuals win or lose in teams. Therefore we will conclude this section with some fundamental advice on managing team-based innovation.

Managing Team-Based Innovation

Whatever the model of product lifecycle in digital manufacturing and simulation, it usually comes a priori from a demand and some constraints rooted in particular criteria, such as technical, commercial, financial and legal. One of the most important issues is how to build your employee teams when your company starts to integrate these innovative methods. Firstly, put people on the team who are not like you, then work to empathize with them. Secondly, brand your team and make it valuable to other groups within your organization. Thirdly, anticipate obstacles, enemies and other challenges to your innovation and develop options and creative responses. To successfully reach desired goals, the following methods and practices can be used: 1. Education and communication: Where there is a lack of information or inaccurate information and analysis, one of the best ways to overcome resistance to change is to inform and educate people about the change effort ahead of time. This reduces unfounded and inaccurate rumors concerning the effects of change within the organization. 2. Participation and involvement: Where the initiators do not have all of the necessary information to design the change, and where others have considerable power to

90 Virtual concept > real profit resist, promote involvement. When employees are involved in the change effort they are more likely to want change rather than resist it. This approach is likely to decrease resistance in those who merely acquiesce in the face of change. 3. Facilitation and support: Where people are resisting change due to adjustment problems, offer support. Managerial support helps employees to deal with their fear and anxiety during a transitional period. The resistance to change is likely caused by the perception that there will be some form of detrimental effect oc- casioned by the change in the organization. 4. Negotiation and agreement: Where someone or some group may lose out because of a change, and where that individual or group has considerable power to resist, negotiate. Managers can combat resistance by offering incentives to employees not to resist change, to veto certain elements of change that are threatening, or to leave the company through early buyouts or through retirement. 5. Manipulation and co-option: Where other tactics will not work or are too expensive, try co-option. Co-option involves bringing a person into a change management planning group for the sake of appearances rather than for substantive contribution. This often involves selecting individuals to participate in the change effort who are prominent among those people who are resisting the change. 6. Explicit and implicit coercion: Where speed is essential coercion may be necessary, although only as a last resort. Managers can explicitly or implicitly force employees into accepting change, by making clear that resistance to change can lead to job losses, dismissals, employee transfers, or non-promotion of employees.

Last but not least, systematically develop your ability to listen and facilitate in new contexts. Actively create positive relationships between team members and recognize synergistic results. For innovation teams, these tips can make the difference between success and failure. Teams working for innovation live in more extreme environments. They have fewer margins of error and more potential for catastrophe. 3.4 Confronting Today’s Limits

Three industry experts’ points of view are presented in this section. For Daimler- Chrysler’s Wolf-Peter Seuffert the issue of Integrated Data Management is among the main challenges of digital manufacturing. From the Aberdeen Group’s performance taxonomy we learn that only 20 percent of organizations can be classified as Best- In-Class, which underscores the challenging nature of digital manufacturing and simulation. At the end of this section Comau’s Fulvio Rusina stresses the problem of expert skills needed to roll out and operate the current versions of software tools. Rusina frankly mentions the various stages of adoption processes at Comau, and

3 Challenges for Digital Manufacturing and Simulation 91 addresses the fundamental risk against which digital manufacturing benefits are measured: “we are answerable – financially – for any defect.” The journey towards digital manufacturing and the Digital Factory has been under way for some 30 years. Optimism reigned, especially in the last decade, culminating in the period before the 2008 economic downturn. Bold and inevitable new DM/DF realities were prophesized, often in just a few sentences, like this one:

“Digital manufacturing, also referred to as ‘the Digital Factory,’ is the ability to « integrate CAD, PLM, simulation software, analytical applications, and control technologies. Together, they create a virtual world in which a product can be built and validated prior to commissioning any of the equipment used to build it or producing 13 »physical prototypes.” Accelerated digital manufacturing progress had already been envisioned and sketched out in the 1990s, as shown in Figure 2.22 (Evolutionary stages in product development). At present, however, we tend to be more cautious and realistic, as the following quotation demonstrates:

“Only twenty years ago experts dreamed of fully automated factories, almost with- « out human beings. Today machines still play a central role in their future visions as engineers are working on new computer-aided quality measurement methods and ever smarter manufacturing robots. However, the more sophisticated technology becomes, the more humans are needed to keep complex production processes 14 »flexible.” Integrated Data Management

Considering all constituents one lesson clearly stands out: “The challenges for Digital Manufacturing are multifarious and complex.” This observation is from Wolf-Peter Seuffert, who since 2000 has been leading the digital manufacturing transformation at DaimlerChrysler in Germany (see Figure 2.21). Central to such development is Integrated Data Management, also known as the necessity for one common database to support and knit together all process domains that comprise complete design-to- build cycles.

92 Virtual concept > real profit Cooperation and communication platform

Simulation

Planning Common Factories and suppliers database around the world CAD Interface

Input Output

DMU Integrated Data Management Tools X

Digital manufacturing & simulation Real factory with existing with digital tools, processes and methods structures, processes and products

Figure 3.6 Digital manufacturing requires Integrated Data Management to support and knit together the domains that comprise complete design-to-build cycles.

Again it is important to see that such a vision may be easy to formulate but is definitely hard to achieve and maintain. This is mainly because of the many process domains, data formats and flows, and the expertise required: people must understand and be able to easily work with software tools and the data involved. Integrated Data Manage- ment requires a full lifecycle approach to managing application data, with common standards, models, and policies for each lifecycle stage. Moreover, compliance and governance standards must be built in to control the data throughout movements from stage to stage. Such Integrated Data Management poses huge challenges and is in sync with the following general assessment by IBM: In 1999, worldwide twelve exabytes (1014) of digital data were created. Three years later that figure had doubled. From 2011 on, the amount of digitally created data, as opposed to data converted to digital, will be growing by sixteen exabytes each year. This excessive explosion urges us to improve not only the management of data itself but also of data’s lifecycle, that is, from its creation to its use, reuse, and finally its disposal.15

SAP’s Future

The further development of SAP’s PLM software suite is a good example of what In- tegrated Data Management in a digital manufacturing environment exactly means. In 2010, SAP will integrate its systems for tracking design information with tools

3 Challenges for Digital Manufacturing and Simulation 93 for managing and simulating digital design and manufacturing processes. Specifi- cally, SAP plans to integrate its PLM suite with digital manufacturing tools, such as Dassault’s DELMIA. SAP also plans to enable manufacturers to track product usage and performance information via radio frequency identification (RFID) technologies and to allow that information to be fed back into the product design process. This further development and integration follows upon the introduction in 2009 of a package that broadened the scope of SAP’s PLM suite to address the management of all product-specific information, including idea generation, product design, customer requirements, variant configuration, and maintenance. This solution already allowed users to manage the design of services as well as manufactured products. On the client side, state-of-the-art digital manufacturing/Digital Factory implementation takes years of hard work in several phases. All the while, additional functionalities are being added and adequate integration must be assured. One huge problem is the lack of awareness as to what the proper interaction of tools, methods and skills can deliver. People may understand isolated areas like the flow within a plant or a factory layout without understanding how it all comes together in a truly integrated digital product model. Other problems for digital manufacturing include cost, the learning curve, cultural clashes between departments, a lack of standards, and unreliable telecommunication infrastructures limiting the ability to share data globally.

Digital manufacturing Implementation roadmap

3-D Product lifecycle Supplier management integration Tool planning t.BDIJOFSZFRVJQNFOU Logistics BiW planning t'BDUPSZMBZPVU t4UBOEBSEFMFNFOUT tTVQFSPSEJOBUF Assembly SFTPVSDFT(BOUU planning t8FMEJOHBOE t"TTUBTLT 3-D Factory adhesive seams t"TTQSJPSJUZ t6OJGPSNQMBOOJOH "EEJUJPOBMGVODUJPOBMJUJFTBSFTQFDJáFE planning t4FRVFODJOH JOUFSGBDF JOUFHSBUJPODPOUJOVFTDPOTJTUBOUMZ Ramp-up management Synchronization & integration management Maturity management Project management

Figure 3.7 The digital manufacturing implementation roadmap leads from several planning phases via supplier integration to PLM and should rest on a firm and interactive basis of project, maturity, synchronization-integration, and ramp-up management.16

94 Virtual concept > real profit Aberdeen’s Performance Taxonomy

In the report Digital Manufacturing Planning: Concurrent Development of Product and Process17 the Aberdeen Group defines manufacturing enterprises as falling into one of three levels of practice and performance: Best-in-Class, Industry Average, and Laggards. Only 20 percent of companies belong to the Best-in-Class category, which clearly indicates the huge challenges accompanying digital manufacturing. In terms of process, organization, knowledge, technology and performance measurement, the Best-in-Class 20 percent employ practices that are significantly superior to the Industry Average, which results in top industry performance. The 50 percent of companies who are the Industry Average employ practices that represent the norm, resulting in average industry performance. The Laggards are the 30 percent of companies who employ practices that significantly lag behind industry norms, resulting in below-average performance. Best-in-Class companies are more likely to leverage engineering assets and deliverables to create manufacturing deliverables. For example, generating the manufacturing Bill of Materials (mBOM) from the engineering Bill of Materials (eBOM), and work instructions from 3-D design data. 3-D visualization makes it easier for manufacturing personnel to complete their tasks. A person trying to machine an extremely complex part can get a much more accurate idea of what the part should look like from a 3-D model than from a thousand drawings. 3-D allows you keep in mind design for manufacturability, so you can better determine how to fixture the part and what tools are needed. The Best-in-Class are also more likely to create mBOMs, lay out facilities, create work instructions and program robotics prior to design release in a true concurrent product development fashion. Progressive approaches such as 3-D plant layouts, robotic virtual commissioning, and engineering-to-manufacturing system integrations will enable companies to pursue truly concurrent design.

Pressures, Actions, Capabilities, and Enablers (PACE)

The level of competitive performance that a company achieves is strongly determined by the so-called “PACE” choices that they make and how well they execute those decisions. PACE stands for business Pressures, Actions, Capabilities, and Enablers that indicate corporate behavior in specific business processes. • Pressures are external forces that impact an organization’s market position, competitiveness, or business operation. For instance, pressures may be economic, political, regulatory, technological, or related to changing customer preferences and competition • Actions are the strategic approaches that an organization takes in response to industry pressures. For instance, actions may align the corporate business model to leverage industry opportunities, including product / service strategy, target markets, financial strategy, go-to-market, and sales strategy.

3 Challenges for Digital Manufacturing and Simulation 95 • Capabilities are the business process competencies required to execute corporate strategy. For instance, capabilities may include skilled people, brand, market posi- tioning, viable products/services, ecosystem partners, and financing. • Enablers are the key functionalities of technology solutions required to support the organization’s business practices. For instance, enablers may include development platform, applications, network connectivity, user interface, training and support, partner interfaces, data cleansing, and management.18

The discussion in this section of the current state of the Digital Factory is brought to life even more by the following straightforward testimonial of Mr Fulvio Rusina, who heads Comau’s Advanced Engineering department.

INTERVIEW – A Critical Mix of Benefits and Challenges In this interview, Fulvio Rusina, Head of Advanced Engineer- ing at Comau answers the following questions: • What do digital manufacturing and simulation mean to you? • What competitive edge can a group like Comau gain here? • Where are you in the adoption cycle? • What are the main barriers to adoption? • What are the benefits, and how can they be measured? • What is the future of digital manufacturing and simulation?

What do digital manufacturing and simulation mean to you? Simulation and digital manufacturing are an integral part of our activity. Comau has been a huge consumer of simulation technologies for almost 15 years. We also work on research programs – financed by the European Union – and with software companies to define specific environments and updates. Our software contacts interact with us just as we interact with our own clients. We have to work very closely with the automotive industry for the purpose of common production programs. This is why we have to simulate as many functions as possible before the final production process. Delmia is used both for analysis, design and mock- up, and for virtual manufacturing operations. All the assembly line systems are simulated and then the virtual part of the production line is tested. These highly complex systems are comprised of 150 to 300 robots spread over hundreds of meters, with different manufacturing cycles and times for each robot, thereby complicating the management functions for software control. The same is true of engine tooling. We therefore have to test the software and wiring and carry out the appropriate developments and corresponding tests at the same time.

What competitive edge can a group like Comau gain here? Firstly, we can be more reactive – and proactive sometimes – in meeting our customers’ expectations. Digital manufacturing also generates customer loy- alty over time, and also helps to project an excellent company image within the

96 Virtual concept > real profit market. Thanks to our technological environment, we are able to create and analyze the various production line components, thereby identifying any problems between the software, wiring and mechanical parts. Hence the need for testing prior to the integration of the various assembly line components. Lastly, through simulation, we can show our customers prototypes and mock-ups with an almost cinematographic quality that we have conceptualized and contextualized. The visual image provided by the software packages sways the customer’s decision more easily in our favor.

Where are you in the adoption cycle? Our company has a large number of service lines. At Comau digital manufacturing has therefore developed several levels of use and more-or-less advanced applications. Bodywork, for example, is an area in which the company is strongly involved. For engine, tooling, and machine tool systems, on the other hand, we only undertake part of the assembly process. We therefore keep close track of digital manufacturing developments thanks to our software partnerships.

What are the main barriers to adoption? These tools are still tough to master due to the complex digital processing. This causes problems for users who are not IT specialists. Rolling out this type of software involves a lot of training. The software must help people to perform their assignments by reducing the problems. In the future, software packages should become less complex, perform better, be easier to handle and monitor the project from A to Z – from engineering to installation. The software currently marketed is too project-oriented.

What are the benefits, and how can they be measured? The benefit is measured mainly in terms of development time frames. Whenever a customer places an order, he defines when production should begin and the costs measured. By anticipating and cutting down on errors, systems development and installation is quicker and cheaper. Quite apart from the time and cost aspects, the benefits also extend to the quality of the finished product. Manufacturers define stringent contract objectives, which have to be followed very strictly. If not, we are answerable – financially – for any defect. Digital manufacturing lowers this risk.

What is the future of digital manufacturing and simulation? Solutions will become more powerful and more efficient, with more user-friendly and dynamic graphical interfaces. Digital manufacturing should spread into other sectors such as retail via the logistics functions, including people-flow analysis. I think that digital manufacturing will also make its way into human resources, with software capable of simulating the operator’s workload and job strenuous- ness. This means recreating a common playing field, analyzing the involvement

3 Challenges for Digital Manufacturing and Simulation 97 of robot and human being alike. Robotics failures can be disastrous, even for individuals. The human being is still an essential part of the production line. By analyzing physical and psychological fatigue, human resources and productivity can be planned more effectively and working conditions distinctly improved.

Highlights As an integral part of manufacturing, digital design, engineering, planning and production enable the benefits outlined in the next chapter but at the same time pose some major challenges and risks which make these efforts failure-prone. All in all, and despite the extra complexities which unavoidably occur, digital manufacturing definitely lowers liability risk.

3.5 Automotive 2020

Product models, lean manufacturing, environment, innovation, quality, skills and partner selection are just seven out of numerous challenges industries must address these days. The global era of manufacturing is upon us. Digital manufacturing is one of the core strategies behind the agenda of knowledge-based production. It is driven by the application and standardization of information and communication technologies and by the demand for increasing efficiency of operations in global networks. Particularly in the automotive sector, the manufacturing environment is turbulent and requires both economic and environmental adaptation. The economic downturn has put even more pressure on top of the demands from the vast number of parties that play a role in the lifecycles of products and services. Among complex manufacturing industries, the automotive sector undoubtedly is subject to severe strain, which is not likely to change in these transformative times. The report Automotive 2020: Clarity Beyond the Chaos19 sets out many of the fundamental changes affecting modern manufacturing practices, although assessments these days will differ due to financial and economic fluctuations.

98 Virtual concept > real profit Migration Energy price Global context Economic structure Technology adoption Environmental Demographic developments Labor policy issues Society Te mics chnology Econo En viron tics Economic Data security men Poli Biotechnology t development Political Catastrophes stability Laws, rules & regulations C lien rs ts lie /m pp a u rk S e t s

Industry Company

Co p m ro ple ers du men ppli cts tary Su & se rvices

Sector context

Figure 3.8 Elaborate model of external factors in a global and sector context, affecting enterprises in their Industries.20

When looking at the external forces, as presented in Figure 3.8, that impact manufacturing industries – automotive in particular – the following categories stand out: technology, globalization, economies and markets, consumers, governments, sus- tainability, corporate social responsibility, global labor force and personal mobility. According to the IBM report Automotive 2020: Clarity Beyond the Chaos intelligent vehicles, sophisticated consumers and dynamic business operations are at the heart of extended and integrated automotive enterprises, which operate in ecosystems governed by the external factors mentioned above. In general, taking “intelligent vehicle” to mean “smart product,” companies can excel in their relationship to these five dimensions, thus differentiating themselves from their competition.

3 Challenges for Digital Manufacturing and Simulation 99 Interdependent ecosystem Integrated enterprise

Intelligent vehicle

Sophisticated consumer

Dynamic operations

Figure 3.9 There are five dimensions of differentiation for automotive and, by extension, for manufacturing industries in general.21

The envisioned “Connected Vehicle” of 2020 (or sooner) will be a communications wonder. As another node on the internet, it will connect with other vehicles (V2V connectivity), to the transportation infrastructure (V2I) and to homes, businesses and other sources (V2x). Many related applications under development today will gradually be implemented in new vehicle models. Safety, driver assistance and service belong to the main areas of interest with features like intersection control, lane/ road departure, road surface monitoring, 360/distance vision, active suspension and stability, dynamic route guidance, information (on incidents, special events, weather and work zones), data downloads (entertainment, media, home network, personal preferences), recovery of stolen vehicles, electronic payments (including toll, drive- through, parking and road pricing), remote vehicle diagnostics (also prognostics and automatic repairs), transfer of vehicle data based on warranty, customer relations management (including vehicle use profiles and dealer use data) and driving-based behavior service. Needless to say, these benefits and opportunities entail new challenges for design, engineering, simulation, production and maintenance. Over the next years the level of innovation in various aspects of the vehicle will be dominated by a combination of software, electrical systems, engine and auxiliary systems, power train, body structure, interior, chassis and body exterior or skin. At the same time economic and environmental challenges are behind innovations like the Vectrix VX-1 Personal Electric Vehicle (see www.vectrix.com/fleet) andTesla Motors’ Model S, the world’s first mass-produced electric vehicle, capable of carrying five adults and two children, which will be available in 2011.

100 Virtual concept > real profit As the groundbreaking study Visionary Manufacturing Challenges for 2020 stated back in 1998, during the first two decades of this century “the basis of competition will be creativity and innovation in all aspects of the manufacturing enterprise.” This goes beyond the aforementioned developments. An exciting innovative example of car part design is TRIS, which Fioravanti re- introduced in 2009 at the Geneva Motor Show as a role model for a new type of three-door car. Measuring 3,850 mm in length on a wheelbase of 2,550 mm, TRIS is intrinsically economical to consumers and manufacturers thanks to a symmetrical design that reduces the number of components to one type of door for left, right and rear, one bumper type, one type of lamp instead of four, and two types of window instead of five. Founder Leonardo Fioravanti spent almost a quarter century at Pininfarina creating models such as the Ferrari 308 GTB and the 288 GTO. The cutting-edge automaker bets that in today’s troubled times the TRIS concept might be just what the automotive industry needs. Costs of engineering, tooling, production, and assembly are all said to be drastically reduced. Relying on basic essentials seems necessary both in developing countries and in developed countries, where the need for simplicity is felt even in sophisticated new designs. At least a unique exercise in sense and simplicity, TRIS-like endeavors may help deliver the promise of bringing clarity beyond the chaos of these challenging times.

The Fioravanti Design Process • The design process harnesses both traditional methods and the most recent generation of computerized tools. • The traditional side includes early project development through the use of conceptual drawings setting out the vehicle’s architecture. • Renderings and the surface drawings are executed during the final phases to build the actual model. • The high-tech side uses the latest generation of software systems such as CAS for surface modeling, CAD for feasibility studies and CFD to analyze predictive aerodynamics in the project phase. • The final phase includes the building of models and prototypes, which then undergo aerodynamic trials and verification in the wind-tunnel.

3 Challenges for Digital Manufacturing and Simulation 101 Figure 3.10 The use of symmetrical components strongly contributes to Fioravanti’s vision of an intrinsically economical vehicle (see www.fioravanti.it).

All external factors, differentiation dimensions and smart information mentioned in this section must be satisfied in innovative product models through a variety of integrated critical industry skills, including engineering, management, product planning, software development, design, marketing, service, finance, procurement and, last but not least, manufacturing. The challenge of adequately addressing the complexities of automotive lifecycle management in its typical stakeholder-rich setting can serve as a standard for reflection on comparable issues in other manufacturing industries.

102 Virtual concept > real profit 3.6 The Digital Factory Challenge

In its ideal guise, the Digital Factory must be viewed as a superior concept for smoothly integrating all digital manufacturing and simulation efforts and developments in supply-chain and production networks – around the globe and around the clock. Digital Factories are built upon integrated databases for product, process and factory modeling, and they include advanced visualization, simulation and documentation in order to improve the quality and flexibility of products and production processes. The first phase of the Digital Factory, for which many tools are available, focuses on integrated product engineering. Phase number two, for which special-purpose tools are available, includes plant design and optimization in a collaborative environment concurrently with product engineering. However, there is still a huge need for open and standardized integration. The third phase of the Digital Factory focuses on operative production planning and control right on down to the factory floor. Establishing this ideal situation is extremely demanding for manufacturing companies and their partners. Ideally PLM software designs and simulates products and their manufacture, while ERP software sends data to the machines, informing them of changes regarding customers, orders and supplies. MES software controls both PLM and ERP information, and production is automatically altered when necessary. However, at the moment immature integration is still blurring this vision of design, production, the enterprise, the supply chain, and wireless communication between machine sensors together driving continuous improvement. Such an advanced type of Digital Factory is still many years away. The technology may be there, but it is complex, expensive and there is a steep learning curve, while standardized integration currently is at some 20 percent. Moreover, few companies build new factories, so the factory of the future has to work with the imperfect factory of today, and everyone will have to integrate ERP, MES, PLM, and other software in their own environment and among supply chain partners – not to speak of unreliable wireless connections. Leaving the “primordial soup” of simple CAD designs, as ARC’s Dick Slansky likes to put it, and moving towards PLM has cost automakers billions plus license costs. Although the automotive industry has readily adapted PLM, it has not done a good job of feeding after-sales information about a car’s performance and maintenance back into the PLM system. If PLM knew about deficiencies and failure, designs and manufacturing instructions for SCADA (Supervisory Control and Data Acquisi- tion) and MES systems could be adjusted, which of course is vital to a holistic PLM approach. As for the adoption of PLM, at the moment all large automotive and aerospace manufacturers have deployed this Digital Factory foundation to shorten development time and to stay competitive. Also some consumer electronics makers, as well as consumer packaged goods giant Procter & Gamble, have started implementing PLM. Slowly but surely PLM is starting to really catch on, while more affordable

3 Challenges for Digital Manufacturing and Simulation 103 solutions are targeted at mid-market players. PLM is not so much an IT challenge as it is a business challenge. PLM represents a transformational business strategy built on common access to a single repository of all knowledge, data and processes that are related to a product.

Figure 3.11 Robotic motion planning, simulation, and offline programming for a spot welding process (DELMIA).

Central to the Digital Factory are plant design and optimization. This involves the sensible integration of several essential domains, ranging from the resource database to factory design and layout, factory flow optimization, plant, line and process simulation, dynamic line balancing, part manufacturing, robotic work cell simulation, model-based PLC offline programming and human resource simulation. During production, operative control and optimization should take place. The corresponding feedback loop is sketched out in Figure 3.12. In order to deliver accurate results always start from the actual work in process and the actual resource status.

104 Virtual concept > real profit Interferences

Production Production parameters, Production target control strategies result Production Production process planning and Actual Real Factory control For tests Current status

Simulation model

Estimated production result

Actual production result

Figure 3.12 Operative control and optimization feedback loop.

Operative production planning requires the sequencing, scheduling and routing of orders to production resources. Allocating orders to the factory floor on single lines, parallel lines, multiple lines, as well as splitting and merging lines requires detailed information and complex rules and strategies. Production management in the Digital Factory requires a complete and scalable shop floor environment to improve agility, capture operational knowledge and to increase efficiency. The integration connects process planning with the control level from manufacturing execution with MES to real-time process monitoring and control with SCADA.

Based on four pillars the German Fraunhofer Institute defines the intimate relationship between the next-generation virtual factory and physical production facilities in its Virtual Factory Framework. Ramp-up time will be reduced by 30 percent while design, reconfiguration and re-engineering time will be cut in half.22

3 Challenges for Digital Manufacturing and Simulation 105 Bookmark Chapter 3

Putting challenges before benefits, as in placing Chapter 3 before Chapter 4, is a proven way of tempering excessive optimism. Also, however, challenges and benefits belong together like the bull and the bear in the economy, like risk and return, like profit and loss, like opportunities and threats. As opposed to “problem,” the term “challenge” expresses confidence and therefore is closer to benefit. The counterpart of “problem” is “solution,” the kind of ad hoc patch that will be valid only temporarily. Together each set of challenges and benefits constitutes a promising step towards maturity. That is why Chapters 3, 4 and 5 are closely related, if not intimately intertwined. Chapter 5 presents the immediate future of advanced concurrent engineering and crowdengineering, a mature benefit that already has taken shape in open-source software development. From there it has made its way into mainstream software engineering and has inspired other disciplines and economic sectors to foster product success and innovation. Crowdmanagement, or at least the active involvement of larger communities from inside and outside corporate networks, may well be the permanent solution to the challenge of reaping sustainable benefit from digital manufacturing and simulation. In Chapter 2 we saw that simulation – not meaning faking a physical or mental state – is a general human inclination. Simulating real-world situations has been a successful and fun strategy that for millennia has been used to learn, explain and predict things about reality. On the other hand, it may be the same all-too-human herd mentality that poses a major threat to the successful adoption of digital manufacturing. In Section 3.3 the undesirable social behavior of groupthink was addressed as a main barrier to achieving successful business practices. As the report Visionary Manufacturing Challenges for 2020 stressed in 1998, manufacturing-specific methods for people to make decisions, individually and as part of a group, must be devised and implemented in collaborative systems and models. Therefore the effect of human psychology and social behavior on decision-making processes in the design, planning, and operation of manufacturing processes must be properly understood. On the more structural side of processes and technologies, the necessity of fully integrated data management spanning information systems, departments, suppliers and partners is among the top-priority challenges. At the start of this chapter, Fig- ure 3.1 shows a high-level structure of interfacing Digital Factory domains, consisting of eight interfacing sections, ranging from CAD and Digital Mockup to Virtual Reality/Augmented Reality and Material Flow Simulation. The Digital Factory is only feasible when seamless real-time communication between these components is guaranteed. Considering all factors, one lesson is clear: “The challenges for Digital Manufacturing are multifarious and complex.” These are the words of Wolf-Peter Seuffert, who since 2000 has been leading the digital manufacturing transformation at DaimlerChrysler in Germany. Specifically, modeling and simulation of manufacturing systems needs better development (for instance regarding the integration of different simulation and modeling

106 Virtual concept > real profit methods and the use of multi-disciplinary simulation with different abstraction levels). Simulation should be combined with manufacturing lifecycles, resulting in a virtual, concurrently evolving digital image of the real system through real-time inclusion of data from the factory floor. These simulations could adapt existing specifications to deliver benefits as laid out next in Chapter 4.

3 Challenges for Digital Manufacturing and Simulation 107 Virtual concept > real profit 4

108 Benefits in Real-World Examples

Introduction Current and Future Benefits 110 4.1 “Manufacturing Ready” for Maximum Profitability 112 interview Philippe Hamon, R&D Manager at LEONI Wiring Systems 113 4.2 Benefits in Perspective for Automotive 115 4.3 Benefits in Perspective for Aerospace 122 4.4 Benefits in Perspective for Shipbuilding 127 4.5 Benefits in Perspective for Consumer Goods 129 4.6 Benefits in Perspective for Energy 130 4.7 Digital Manufacturing as a Communications Platform 131 Bookmark Chapter 4 135

109 Introduction Current and Future Benefits

Now you have arrived at the last two chapters of this book about how virtual control through digital manufacturing and simulation can generate sustainable profit for customers, companies, economic partners and the environment. After the introduction in the “Welcome” chapter we offered more detail and background in the “A Crash Course” chapter. After that we turned to challenges and benefits. In the previous chapter we dwelt on basic challenges posed by digital manufacturing and simulation, and in this chapter we turn to a discussion of clear and recognizable benefits for several key economic sectors. The next and final chapter extends the discussion of complementary challenges and benefits into the foreseeable future. It starts from the well known concept of concurrent engineering, which dates back to the early 1990s, and muses about taking this practice even further, involving customers and other stakeholders who take an interest in the development and manufacture of durable goods, both small and large. This new concept of “crowdengineering” has its roots in “crowdsourcing,” the term that Wired magazine editor Jeff Howe coined in 2006, and is closely related to so-called open innovation and open source software development. A central feature of crowdsourcing and crowdengineering is collaboration on electronic network platforms. Eventually, it may be this crowdengineering democratization that constitutes the ultimate benefit of digital manufacturing and simulation. But before speculating about these future possibilities in the last chapter, we focus systematically in the next sections on well documented current examples of benefits for the automotive, aerospace, shipbuilding, consumer goods and energy sectors, in that order. First, however, we examine the desirability of 100 percent “manufacturing ready” as a standard design and engineering deliverable. At the end of that section R&D manager Philippe Hamon will elaborate on the importance, background and future of digital manufacturing and simulation at LEONI Wiring Systems. LEONI is a global supplier of wires, cables, wiring systems with integrated electronics, and related services, mainly to the automotive industry. Before this chapter arrives at its concluding “Bookmark,” it explores the basic role of digital manufacturing as a communications platform. This subject functions as a bridge to the final epilogue on crowdsourced development and open innovation.

Impressive Figures

The first “Welcome” chapter of this book presented many of the promising findings from the CIMdata report The Benefits of Digital Manufacturing.1 Essentially the report finds the following: with digital manufacturing, time-to-market can be reduced by 30 percent and manufacturing process planning by 40 percent; also, production throughput increases by 15 percent while overall production cost decreases by 13 percent and equipment costs by 40 percent. These truly impressive figures have

110 Virtual concept > real profit been checked and validated time and again since the publication of the report in 2002. Surely the promise of such impressive ROI sparks curiosity. The same CIMdata report offers some general estimates. Small implementations ($200K initial and annual) have a 5:1 return on annual investment. For medium-size implementations ($1M initial and annual) this ratio is 8:1 and for large projects ($5-10M initial and annual) it is as high as 10:1.

Implementation size

Small Medium Large

Initial investments $200K $1M $5M- $10M $5M- Annual investment $200K $1M $10M

Annual savings $1M $8M $50M- $100M

Annual return on annual investment 5 to 1 8 to 1 10 to 1

FTUJNBUFEJO64% Estimated return on investment

Figure 4.1 Digital manufacturing ROI. Source: CIMdata.

Patrick Michel, VP DELMIA Industry Solutions and Marketing, comments:

“With these kinds of numbers, adopting digital manufacturing isn’t just a good idea – it’s a necessity for any organization committed to retaining (or creating) «competitive advantage within the highly crowded global manufacturing industry. Organizations who deployed digital manufacturing realized an exceptional return on investment in a matter of months. Undoubtedly digital manufacturing is a step in the right direction for saving our planet but it didn’t start out as an eco-social initiative and that’s still not its main focus today. Companies who deploy digital manufacturing solutions enterprise-wide do so because they want to take virtual plant tours, mitigate the risks inherent in design planning, identify how plant designs will impact the workers, reduce the need to redesign equipment, utilize resources more efficiently and eliminate prototypes. By supporting visualization, process planning, factory modeling, simulation, collaboration and taking into account human reaction and comfort, digital manufacturing optimizes the design process. It is becoming a major element of product lifecycle management. It’s the process by which companies can de-

4 Benefits in Real-World Examples 111 fine and optimize manufacturing processes, manage manufacturing data, and encourage collaboration between different types of engineers by incorporating both digital and plant product definitions. Digital manufacturing presents a view of product and process design holistically, as part of the product lifecycle, and allows products to be designed in a way that adjusts for process capabilities or limitations. The practical reasons for adopting digital manufacturing are fairly obvious – no paper revisions, no prototypes, more collaboration, and more output. However, consider the effects that it can have on the scale and reach of projects. With digital manufacturing designers are no longer constrained by space or volume limitations. They can build greener, smaller plants and experiment with all types of new materials, layouts and equipment. What would’ve seemed extremely tedious and time-consuming a year ago (such as designing a production line) can now be done in a fraction of the time. Being able to simulate the designs »online, collaboratively, provides unlimited potential.”2 4.1 “Manufacturing Ready” for Maximum Profitability

The goal of any company is to produce a product that meets market demand with exceptional quality and with a profitable return on investment. Even the most innovative product is not going to remain in production unless it is “manufacturing ready” and produced effectively and efficiently. Improved quality, higher returns, and shorter time-to-market for new programs constitute an increasing executive mandate for many of today’s manufacturers. At the same time, continued market pressures have dictated that companies must continue to improve efficiency, drive down cost and increase time-to-volume. However, ensuring this level of product manufacturability and profitability is extremely difficult in a mainstream environment of expensive physical prototypes and delicate machinery. A production line itself can be a constant source of hold-ups, quality errors, and human performance constraints. The ultimate goal, then, is to ensure that 100 percent “manufacturing ready” becomes a standard design and engineering deliverable. ThePLM approach to the entire design-manufacture-market strategy offers a radical solution to the quality, cost and time pressures of modern production applications and ensures a solid business model for any enterprise. With PLM, digital products exist as rich information circulating in a highly collaborative work environment. Virtual design and engineering mockups can be used not just to craft a physical object, but to actually define, plan, and validate the way products are manufactured. Empowering companies with digital manufacturing as part of an overall PLM strategy can revolutionize the product lifecycle by creating new value and innovation at each stage of the process.

112 Virtual concept > real profit With digital manufacturing, organizations have the right technology in place to interact with factory processes early in the design stage and miles before actual production commences. Engineers, management and various other stakeholders can have a 3-D visualization of the real world with the ability to make changes, identify and eliminate costly errors and design mistakes, facilitate higher quality, and foster innovation. Once physical production is deployed, flexibility decreases rapidly while cost of change increases. By previewing many of the decisions about how the product will be produced with the associated manufacturing process and resources, the digital manufacturing environment allows validation of these decisions earlier in the product lifecycle and provides the ability to make changes much more readily, providing for optimized manufacturing processes and a significant reduction in costly engineering changes after physical deployment. For an open-minded real-life account of the role, background and future of digital manufacturing and simulation, let’s now turn to Mr Philippe Hamon who is responsible for R&D at LEONI Wiring Systems.

INTERVIEW – Process Quality as the Key to Success Philippe Hamon, R&D Manager at LEONI Wiring Systems, answers the following questions: • What do digital manufacturing and simulation mean to you? • What are the main development drivers? • Where are you in the adoption cycle? • What are the main barriers to adoption? • What are the benefits, and how can they be measured? • What is the future of digital manufacturing and simulation?

Mr Hamon, what do digital manufacturing and simulation mean to you? These tools enable a product’s behavior to be anticipated throughout its lifecycle. Meaning that, for example, corrective action can be applied early on at the design stage, in order to guarantee the quality of a product. However, product and process are intrinsically linked in the automotive industry. You can design a very good product but if you do not take into account the manufacturing constraints, you may end up with a substandard product in terms of operation and quality. From simulation of product diversity to workstation ergonomics, technologies have progressed over time. However, simulation of assembly is still in the early stages, having only been used for three or four years.

What are the main development drivers? The main issue, I believe, is the optimization of the overall product, time frames and costs. As far as costs are concerned, efficient production is a key issue. How many people should be allocated to a given number of products? How can workstations be best organized? We supply manufacturers with a wide range of

4 Benefits in Real-World Examples 113 wiring systems for connecting the various electrical and electronic components of a motor vehicle. In fact, manufacturing this type of product involves many manual operations and very few automated tasks. Inefficient processes can therefore generate differentials of 10 to 20 percent from business objectives. In addition, as our production centers are located worldwide, a product that may be well designed in the Paris area but without full integration of Moroccan, Russian or Chinese production constraints can quickly cause serious difficulties. In our increasingly complex environments, the quality of the process is the deciding factor.

Where are you in the adoption cycle? Product simulation was initiated some twenty years ago, and we have now extended our scope of activity in accordance with the developments referred to above. Three years ago we went into assembly simulation because our clients now have products with short lifecycles and a wide range of requirements. Further- more, the development of a new motor vehicle, which takes two years, requires many modifications to the wiring system, and we have had to adapt to the increasingly complex automotive equipment industry. Therefore we have assessed and selected the appropriate line-balancing software and calculated the return on investment. A pilot scheme was run until 2007, but Dassault’s DELMIA software subsequently became the standard software for all units manufacturing complex wiring systems, which is the bulk of our production. During the development stage, three highly skilled people were employed full time in addition to some ten people locally. A core team, situated in the Paris area, is in charge of standardization of working methods, training, and technical support, while the operational teams at the production sites are responsible for the simulation of new products.

What are the main barriers to adoption? The main difficulty is cultural. Implementation of this type of tool requires changes to working methods for design and, even more, for production. Produc- tion and design must be completely integrated so as to sustain each other, which requires precise and well structured databases. We described complex assembly designs in great detail and intensely trained our people on the new software. These changes mean that we have increased the proficiency of our teams, who now work with complex equipment. We should emphasize the fact that assembly simulation tools not only include core calculation but require major developments in order to obtain solutions, including the appropriate product specificities and full integration within a CAD chain. Therefore this type of development has been applied to the automated links between product design tools and production databases, providing an integrated method for calculating industrial processes.

What are the benefits, and how can they be measured? We were lucky in that we had several identical production lines, meaning that we could conduct comparative tests. The pilot factory was used as a benchmark for

114 Virtual concept > real profit return on investment, which is threefold. Firstly, production ramp-up has accelerated since assembly design has been better prepared. Where, previously, several weeks were required to reach a production rate on a given product, the simulation process has enabled us to cut this time down. Secondly, our overall industrial efficiency has improved, leading to better assembly line balancing. Thirdly, we can now generate instructions automatically at each workstation, thereby making the operator training process easier.

What is the future of digital manufacturing and simulation? We are looking into two new evolutions. The first concerns the ergonomics and design of the assembly station. The second concerns the simulation of flows within the factory itself, due to the numerous components distributed. These flows can be optimized. At the same time, we hope that digital manufacturing in general, which is only in the early stages, will generate software with broader functions, as there is considerable scope for progress. In the digital manufacturing development stages, technical support from the software company is imperative. Sector- specific criteria should be taken into account so that simulation tools are adapted to individual manufacturing requirements.

Highlights From simulation of product diversity to workstation ergonomics, technologies have progressed over time. However, simulation of assembly is still in the early stages, having been used for just three or four years. In the digital manufacturing development stages, technical support from the software company is imperative. Sector-specific criteria should be taken into account so that simulation tools are adapted to individual manufacturing requirements. Although the promised benefits are compelling, implementing this type of tool requires a change in working methods for design and, even more so, for production. Production and design must be completely integrated and sustain each other, which requires precise and well structured databases, as well as a considerable training effort.

4.2 Benefits in Perspective for Automotive

Whilst digital manufacturing and simulation bring benefits to every manufacturing company, the source of these benefits may vary depending on the type of industry. The first industry to deploy digital manufacturing in the late 1990s was automotive, quickly followed by aerospace. Nowadays, several others such as shipbuilding, high tech and energy have joined the pioneers. In the following sections we will present the benefits of digital manufacturing and simulation, related to specific industry challenges in automotive, aerospace, shipbuilding and energy.

4 Benefits in Real-World Examples 115 The automotive business has become increasingly globalized with stronger competition from emerging markets. In addition to this rising competition, automotive suppliers face ascending pressure from the market and government to increase safety, pollution control and product recycling. In order to remain competitive, OEMs and suppliers not only have to introduce innovative products to the market (e.g. hybrid engines), but must also reduce time-to-market. Being the first to introduce new products on the market is a key competitive edge.

Develop- ment time (years)









   

Figure 4.2 Time-to-market evolution in automotive. In only four years this period was reduced by over fifty percent.

Cost reduction is also critical due to competitive pressure and the need to develop emerging markets. Costs must be reduced while quality must be perceived as improving, since customer expectations are greater than ever. In addition, companies must manage increased product complexity – more options, more variants, more mixed technologies, with electronics and systems becoming increasingly important components.

116 Virtual concept > real profit Pickup Pickup Oﬀ-road Oﬀ-road Sports utility vehicle Multipurpose vehicle Sports utility vehicle Multipurpose vehicle Hatchback Hatchback Multipurpose vehicle Hatchback Station wagon Station wagon Hatchback Station wagon Limousine Limousine Limousine Station wagon Limousine Sports car Compact car Compact car Limousine Compact car Spyder Sports car Sports car Compact car Sports car Coupé Coupé Sports car Coupé Cabrio Coupé Cabrio Cabrio Roadster Roadster Hybrid car Altern. power trains Crossover s s s s >

Figure 4.3 The number of car programs and associated variants to manage simultaneously has significantly increased (courtesy of Audi).

In order to satisfy local markets, companies must also build manufacturing plants around the world, with the objective of maintaining the same standard of quality wherever the product is manufactured. These challenges are a departure from the traditional way of working in the automotive industry. Companies can no longer afford to build many physical prototypes and they cannot waste time and energy. Innovation and global collaboration is crucial, both internally between people from different disciplines,e.g. engineering and manufacturing, and externally, between an OEM, its partners and suppliers.

BiW

Painting Stamping Final assembly

Logistics Powertrain machining Part/module supplier Powertrain assembly

Figure 4.4 Digital manufacturing can apply to every subsequent domain of expertise: body in white, final assembly, power train, etc.

4 Benefits in Real-World Examples 117 Digital manufacturing and simulation can help to overcome or at least mitigate these challenges in several ways. Individually and in conjunction they directly reduce development time and manufacturing costs. Solutions to the following types of problems are common: • Body in white, including stamping, painting • Final assembly, including manual and automated operations • Power train, including machining, assembly planning and simulation • Logistics

Because of its virtual character, digital manufacturing constitutes a global collaborative platform where contributors from various disciplines around the globe can work together (see also Chapter 5). Engineering can get early access to product information and from there start developing the production plan, while optimizing resources and processes and integrating partners and suppliers. This global digital collaboration enables manufacturers to meet their time reduction, cost reduction and productivity targets. Digital manufacturing lets companies define and simulate processes and resources to perform any conceivable operation. Manufacturers can test and validate different scenarios to decide on the best without building a prototype, which significantly accelerates production ramp-up.

Digital Manufacturing Examples and Benefits The automotive examples below follow the classical organization of a factory assembly line in the automotive industry.

1 Plan and Validate a Stamping Line for a New Car Body

By defining and validating a method and the path planning for a stamping line, both throughput and productivity will increase. A complete stamping line can be defined and simulated using 3-D visualization. Standard process templates are available. Line layout and behavior as well as I/O connection can be defined in

118 Virtual concept > real profit the same environment in order to avoid collisions. Advanced methods and simulation result in significant time reduction.

2 Plan and Simulate a Body-in-White Assembly Line

This can be achieved by defining and validating the assembly planning and resources for one work cell or a complete line, enabling offline programming. In this case digital manufacturing will dramatically improve the ramp-up and the throughput of a line. Resources such as welding guns can be simulated to verify accessibility and detect interference.

3 Simulate, Validate and Program Spraying or Painting Cell Robots

The benefits of digital manufacturing for the automotive industry include the definition of a work cell layout, performing feasibility studies, optimizing paint tra- jectories and creating offline programs for the painting robots. Digital manufacturing and simulation dramatically improve the time to program and set up such

4 Benefits in Real-World Examples 119 robots. Programming and simulation can be done off-line in a virtual mode. Once the set-up is complete, programs can be transferred to the robot, thus increasing ramp-up and quality.

4 Define and Simulate a Final Assembly Line

Manufacturers can define assembly operations and study process graphs in order to determine the best assembly scenario. This application dramatically improves production ramp-up, assembly time and productivity. A global digital manufacturing collaboration platform allows people having different expertise (such as process planners, efficiency specialists, ergonomics specialists, and so on) to access product and other related information at an early stage. This way the assembly operations sequences and work load capacity can be optimized along the assembly line. Such line balancing (see also below) reduces costs and time, while improving quality and productivity.

5 3-D Assembly Simulation, Power Train Included: the So-Called “Marriage” of Body and Power Train

3-D assembly simulation gives early feedback to the product engineering department so that these people can analyze and manage changes and identify bottle-

120 Virtual concept > real profit necks in the production, the tooling and the resources in the manufacturing plant. 3-D simulation combining the information from the product, the tooling and other resources in the plant assists in anticipating and reducing assembly issues early in the product design phase. Using 3-D manikins (see below), human constraints can be considered.

6 Line Balancing and Workstation Simulation

Manufacturers can define and optimize the number of stations and workers with respect to a specified cycle time, and also the logistics to serve the cells along the line. Once operations have been defined, they can be allocated to various workstations along the assembly line. Digital manufacturing can then perform workload and line balancing to minimize idle time on the assembly line and to maximize worker engagement, as well as to optimize the placement of the part bins and tooling, then sequence the logistics for delivering the parts at the right time at the right place along the line.

7 Human Simulation and Ergonomics Analysis

Human tasks, postures and vision can be defined and validated using virtual human beings or manikins that represent common body types. Such analysis

4 Benefits in Real-World Examples 121 improves security and ergonomics. This way important savings in training and supporting complex manual operations can be achieved. Also see Chapter 2, Figure 2.10.

Sumitomo: “DELMIA Solutions Help Sumitomo Wiring Systems Slash Product « Development Time by 67 percent… Cuts Product Development Cycles, Improves Internal Collaboration.” Audi: “No More Physical Cars Without Digital Validation… Audi will use DELMIA software for digital vehicle assembly planning into reality.” Daimler: “In total, the cost for all the planned Mercedes passenger cars construc- 3 »tion projects has been reduced by 20 percent.” 4.3 Benefits in Perspective for Aerospace

While time-to-market pressure in the aerospace industry does not compare to that in, for instance, automotive or consumer goods, aerospace OEMs face intense pressure on price, particularly in Europe where the exchange rate between the euro and the dollar is not favorable. With increased competition and a need to manufacture greener products, OEMs have to introduce new products to the market that meet four main challenges: • Introduce innovative products at lower prices, products that respect the environment in terms of reduced fuel consumption, reduced noise, reduced pollution, and so on. • Collaborate with risk partners and suppliers to share economic responsibility. For example, OEMs need to sub-contract more aircraft components, since time-to- market must be reduced. Risk must be shared with key partners, and both internal and external collaboration is crucial. • Reduce development time significantly and deliver programs on time and on budget. Since a program lasts many years after initial launch, costs must be controlled throughout the development cycle. • Face increased competition in the aerospace market, particularly from multiple emerging vendors producing regional aircraft who are based mainly in countries with low labor costs.

These challenges mean a departure from the traditional way of working in the aerospace industry. Innovation and global collaboration is crucial, both internally between people from different disciplines,e.g. engineering and manufacturing, and externally, between an OEM, its partners and suppliers. Digital manufacturing and simulation can help to overcome or at least mitigate these challenges. They allow manufactur-

122 Virtual concept > real profit ing engineers and process planners to define, simulate, validate, manage, and deliver to the shop floor the content needed to manufacture air vehicles such as aircraft or spacecraft, according to the following “wish list,” which will be discussed in detail below: • Reducing risk, time-to-market, and overall cost of manufacturing • Supporting business transformation • Enabling a new business paradigm through pervasive 3-D • Creating a continuum from engineering, manufacturing, operations, and in-service domains • Being global, lean, efficient and flexible • Generating detailed maintenance manuals in 3-D

Reducing Risk, Time-to-Market, and Overall Cost of Manufacturing Manufacturing plans are pre-validated in a 3-D environment to avoid unexpected problems on the shop floor. Conditions under which it is impossible to build are discovered early on in the design cycle when the cost of change is minimal. Global collaboration (especially between engineering and manufacturing, as well as between the OEM and its risk partners and suppliers) becomes critical to drive down costs and reduce risks. Quality targets are met sooner due to the reduction or even elimination of rework and engineering change orders driven from the shop floor.

Supporting Business Transformation Global collaboration provides a new platform for people having different expertise and from different locations to work together in synch around the globe. Production analysis can directly influence design, thus providing a true “design for manufacturing” environment. The enterprise is able to capture and reuse manufacturing best practices in a formal way. Redundant business systems, personal reference files, and individual Excel spreadsheets are eliminated in favor of a common shared database, which results in major reductions in costs and overhead.

Enabling a New Business Paradigm Through Pervasive 3-D 3-D is now leveraged not only for design but also for manufacturing planning, simulation-based validation, work instruction authoring and delivery to the shop-floor workforce. Intuitive 3-D work instructions, combined with authoring engineering data and attributes, empower shop-floor workers to perform their jobs faster and with fewer mistakes. In this process, the learning curve is dramatically reduced. In strict contrast to traditional Product Data Management systems, digital manufacturing provides the means to explicitly manage product and process and resource objects, as well as the relationships between them, all within the context of configuration and effectiveness. As such relationships are explicitly defined and managed within the database, it is possible to directly see the impact of changes of one class of object on any other, e.g. if one part is changed, which manufacturing plans will be affected?

4 Benefits in Real-World Examples 123 Product Process Resource

Figure 4.5 Product, process and resource data are linked together to understand the impact of a change.

The global collaborative environment (see also Chapter 5) allows users to capture, manage, and reuse best practices including standard manufacturing processes and specifications, design rules, resource constraints, standard time analysis, and perhaps most notably, the ability to define and manage arbitrary relationships between such knowledge-based objects to support specific corporate business rules.

Creating a Continuum from Engineering, Manufacturing, Operations, and In-Service Domains Traditionally, the engineering and manufacturing domains operate in a self-contained operating unit, separated from the operations community. As a result, it is difficult if not impossible to actually leverage engineering data downstream into the operations area where, typically, mBOMs are defined along with related procurement data and shop-floor work instructions. One major problem with this traditional scenario is that effectively managing engineering changes and reconciling the parallel worlds of engineering and operations is extremely difficult and expensive to carry out accurately. Digital manufacturing provides a means to integrate the two domains, as well as the in-service domain, through several core technologies and application layers. Digital manufacturing also provides the development of manufacturing plans in the context of an overall resource plan and facility layout including visualization and routing definition, and validates the entire scenario with analysis tools including discrete event simulation, all based on the common product / process / resources dataset.

124 Virtual concept > real profit Figure 4.6 shows an example in which once validation has been carried out in a virtual world, data is transferred and reused in a real-world production scenario.

Global, lean, eﬃcient, ﬂexible

Non tangible assets Tangible assets

Product & process knowledge Production

INTELLECTUAL PROPERTY REAL OPERATIONS

Figure 4.6 From intellectual property to real operations.

Being Global, Lean, Efficient and Flexible Intangible assets like product and process knowledge are defined, simulated and validated in a virtual world. Once validated, the information is transferred to real operations. In this scenario, the 3-D PLM virtual world is integrated with the physical ERP world through an intelligent interface that supports a bi-directional data transfer such as BOM, routings, cost, etc. The goal is to provide visibility within the 3-D PLM environment for such data managed within ERP that might have an impact on manufacturing planning and an impact on the evolving design, much earlier on in the overall product evolution cycle. Moreover, this interface supports the transfer of manufacturing planning content, which is authored further upstream within the 3-D PLM world. Redundant business systems are eliminated, providing major reductions in cost and overhead. An additional benefit of this type of integration is the ability to reduce the number of redundant business systems Moreover, costs are further reduced by eliminating both the need for data re-entry as well as the possibility of introducing incorrect data into the process. One of the key areas in which this integrated environment provides value is in the ability to define, evaluate, and document various manufacturing alternatives such as alternate routings or resource utilization, based on evolving conditions within operations.

4 Benefits in Real-World Examples 125 The process plans, resource allocations and precedence requirements can be further analyzed to balance work across the manufacturing facility and to ensure proper utilization of workers.

Generating Detailed Maintenance Manuals in 3-D During the product design and manufacturing process planning, digital manufacturing can be used to validate product maintenance as well as to develop technical manuals containing text, images, and videos derived directly from the 3-D-based process plans (see Figure 4.7).

Figure 4.7 Analysis of 3-D process plans for production is turned into web-based technical maintenance manuals.

3-D is now leveraged not only for design but also for manufacturing planning, simulation-based validation, work instruction authoring and delivery to the shop-floor workforce, thereby enabling a truly paperless manufacturing process. This is easily extended to 3-D maintenance and repair instructions. Operational and maintenance scenarios can be simulated using ergonomics analysis early on in the design cycle to provide efficiencies later in the product lifecycle. With a systematic methodology, a true “design for maintainability” business process can be supported. The virtual production mock-up eliminates the requirement for outsourcing mock- ups of production tooling and fixtures, reducing cost and time. Tooling orders can be placed at a much later stage in the development plan with the latest design revisions incorporated as they will work the first time, eliminating costly change orders to tools and part designs. Designs can be modified early on in the design cycle to accommodate manual assembly and maintenance tasks, therefore eliminating the requirement for special tools.

126 Virtual concept > real profit Airbus: “50 percent reduction in optimizing central fuselage assembly (from 14 to «7 months).” EADS-CASA: “DELMIA’s solutions allow us to control operations in manual as- »semblies without the need to create physical prototypes.” 4.4 Benefits in Perspective for Shipbuilding

Outside automotive and aerospace, digital manufacturing brings benefits to a vast range of other industries, for instance industrial equipment, consumer goods, shipbuilding, energy, life sciences and architecture & construction. Here we will discuss shipbuilding, consumer goods and energy, respectively. Yantai Raffles Shipyard (YRS) is a leading offshore and marine fabrication specialist. Based in Shandong, China, in close proximity to Korea and Japan, an area that accounts for 80 percent of the global shipbuilding capacity, YRS builds vessels for customers around the world. Also YRS is a rig builder with state-of-the-art building facilities, including Taisun, the world’s largest crane, which can carry up to 10,000 Mercedes Benz cars in one lift. The whole shipping industry is under pressure to deliver vessels faster and at a lower cost. To stay truly competitive, it needs to demonstrate consistency in innovation, product quality and customer service. In summary, it must • Streamline processes for greater efficiency and lower costs • Implement design innovations faster to maintain market competitiveness, • Reduce delivery time while improving product quality, and • Increase production capacity through smarter allocation of resources.

YRS realized that it needed to quickly move away from paper-based systems and 2-D tools and migrate to a 3-D-based system. This also means integrating Product Life- cycle Management solutions including a CAD system for offshore structural design, a simulation tool for stress analysis on parts and assembly, and a PLM system for collaborative product lifecycle management and decision making. There were immediate benefits: • A 30- to 40-percent increase in design efficiency; significant improvements in vessel design efficiency • 2,000,000 man hours of anticipated savings on each vessel built with YRS’s new crane designed with the PLM solution • 70 percent fewer engineering man hours • Over 30 stress analysis tests used to determine the strongest design configuration for Taisun using simulation tools

4 Benefits in Real-World Examples 127 Designing Engineering Feats The PLM solution has not only helped YRS to improve vessel design, but it has also made it possible for the company to build its own mega-structure – Taisun, the world’s largest crane. YRS was able to accelerate Taisun’s building process, to reduce costly design errors, and to significantly improve production efficiencies.

Increased Productivity and Capacity With Taisun earmarked for ten lifts over the next twenty months and with each lift estimated at 10,000 to 16,000 metric tons, YRS will be able to build offshore structures 70 percent faster and see increases in design productivity of up to 30 percent. This will allow the company to save over two million man hours on each semi-submersible built, as well as increase YRS’s overall building capacity.

Improved Collaboration A PLM solution transformed the way YRS did business with its third-party suppliers. By making information readily shared and easily updated, the PDM software opened the lines of communication between YRS and its partners and suppliers.

Shorter Manufacturing Cycles and Reduced Costs By shifting towards 3-D CAD software and the virtual design approach, YRS is now enjoying production cycles with limited rework. Previously, projects required up to 30 percent rework due to changes or problems in the design. With digital manufacturing YRS can eliminate manufacturing costs caused by unforeseen errors, streamline processes, optimize resources and shorten production time. 4.5 Benefits in Perspective for Consumer Goods

In this type of industry, Overall Equipment Effectiveness of plant machinery can sometimes be as low as 40 percent as a result of poor production sequencing (for example, parts are not available when the machine is ready for them). Product lifecycle is often shorter than in traditional industries, and manufacturing premises often need to be modified to accommodate quick changes in production type and volume. Digital manufacturing helps to visualize plant behavior for optimal Automated Guided Vehicle (AGV) parameters, study line stoppage due to parts shortages, and reroute during equipment failure to improve Overall Equipment Effective- ness (OEE).

128 Virtual concept > real profit Benefits expected from the use of digital manufacturing include optimizing AGV pick and drop points, rerouting the AGV path in the event of machine breakdown, redesigning batch schedules to maximize yield and OEE through product sequencing, improving mobility inside the plant by relocating battery recharge areas. Other benefits are optimized workload balancing along the assembly line, improved ergonomics station layout for improved worker productivity, and improved layout space and material flow.

Figure 4.8 Adapting the production plant for fast changes to product type and volume.

4.6 Benefits in Perspective for Energy

The focus of today’s nuclear power suppliers has shifted from decommissioning to re-licensing nuclear power facilities. With a large number of nuclear power plants looking to retrofit as an alternative to decommissioning, the Nuclear Life Extension Initiative is already addressing this clear market opportunity. Southern California Edison is an example of an owner/operator using Dassault Systèmes’ V5 PLM to simulate the entire retrofit and refurbishment of a nuclear facility. Southern California Edison’s San Onofre Nuclear Generating Station (SONGS) in California is 25 years old and needed to replace critical components, such as its

4 Benefits in Real-World Examples 129 four 65-foot-tall, 620-ton steam generators. The value of PLM was proven in simulating and streamlining the critically important $680 million 2009 Steam Generator Re- placement. Project SONGS used DELMIA to simulate a critical maintenance procedure in a highly radioactive environment of the SONGS nuclear reactor. The specific goal was to validate the procedure’s work process by using DELMIA to add the time dimension to the simulation. This assembly simulation allows for the validation and optimization of process plans with so-called 4-D simulation. Additionally, these simulations can be used to collaborate with design engineers as well as to communicate assembly concepts to planners, project managers, or even to shop-floor technicians. The addition of 4-D allowed SONGS to validate the work process through physical and virtual mock-ups. The successful proof-of-concept allowed SONGS and its suppliers to improve upcoming scheduled maintenance processes without using actual physical mock-ups or real people but simulation instead. Within the first five minutes of reviewing the simulation, SONGS discovered three alternate ways to perform a single task more efficiently. At $1 million per day of downtime/outage, using 3-D simulation to discover issues and possible problems that impact efficiency means money saved. A virtual simulation can ensure adherence to a tight schedule, budget, and accurate manpower estimates, and is flexible enough to address dynamic, real-time changes. The simulation of a diver cutting the thimble rods allowed SONGS to validate planning, procedures, schedules, and budgets. It also showed how it could potentially identify roadblocks; even in a complex operation being conducted for the first time.

130 Virtual concept > real profit “When the plant was originally designed, a retrofit or replacement of the genera- «tors was never considered,” said Ralph Miller, design group supervisor, San Onofre Nuclear Generating Station. “Physical mock-ups for a project of this magnitude are impossible, so a virtual simulation of parts, processes, and resources accurately communicates to the entire organization, both inside and outside of SONGS, how »the retrofit will proceed.” 4.7 Digital Manufacturing as a Communications Platform

This chapter has discussed in detail the benefits of digital manufacturing and simulation in the daily practice of industries around the world. Opportunities for companies were put into perspective by addressing potential benefits in the context of actual industry-specific challenges. Thus we compared similarities and differences. This section summarizes the basic role of digital manufacturing as a communications platform, laying the foundation for global collaboration in advanced concurrent engineering and crowdengineering (see Chapter 5). The potential advantages of virtual collaboration were recognized very early on. Initial experiments and investigations of potential benefits were conducted within the automobile industry in 1998 by the major car manufacturers and their service providers and suppliers. At the time, the technical environment was far less mature than it is today. Broadband internet connections, powerful computer systems and the appropriate software did not exist at that time. Calculations made at the time about potential benefits were based on the number of participants in a meeting, the length of meetings and the resulting costs. Decisive factors were communications costs (which some ten years ago were high), travel costs (which were low) and wages (which were moderate). These days, the relationships between individual cost factors are entirely different. Communications are fast and economical, travel costs are high and the related employee travel time leaves less time for development, where available time is often already limited. Then as now, additional factors had to be taken into consideration besides the purely economical aspects. There are a great many advantages to having a common communications platform, and many problems could be solved. Here we identify isolation created by division of labor, capacity limits of small companies, leveraged capacity, competition from larger companies, demands for general contracting services, smooth execution of orders, coordination of service delivery, standardized order acquisition and improved coordination between processes. • Isolation created by division of labor. Increased and necessary specialization within the company in response to changing market requirements can narrow the focus of some company sectors, resulting in silos isolated from the “big picture.”

4 Benefits in Real-World Examples 131 • Capacity limits of small companies. It often happens that customer orders cannot be promptly or properly executed as a small company’s capacity is reached very quickly. • Leveraged capacity. With collaboration and synchronization between companies, individual capacities in terms of personnel, software and hardware, for example, can be coordinated with other companies, resulting in reduced costs overall. • Competition from larger companies. Larger companies are increasingly expanding into business areas that have traditionally been the territory of smaller companies. The resulting competitive disadvantages to smaller companies can be offset, to an extent, by a common communications platform. • Demands for general contracting services. Customers and clients are increasingly demanding that orders and projects be executed from a single source, meaning that they need only deal with one representative on a project. • Smooth execution of orders. Existing collaborative arrangements that do not use a common communications platform frequently encounter the problem that individual steps in the overall order-execution process utilize different media, so data is thereby archived in an unstructured and inconsistent manner. • Coordination of service delivery. Individual services can be listed and consolidated within a uniform process so that comprehensive bids can be submitted from a single source. • Standardized order acquisition. The acquisition of orders can be facilitated by collaboration between several partners, which is sometimes the only way to win the order at all. Working together improves the quality of order acquisition and also distributes costs. • Improved coordination between processes. The coordination required in a collaborative arrangement can be significantly improved by means of a common communications platform. Everyone is able to view current status at any time by accessing a common database.

To realize these advantages and guarantee the efficient use of the common communications platform, several essential requirements must be met in terms of participants and expertise:

Participants • Participating companies within a communications platform must realize that they may have to fundamentally change their former attitude to work, which was characterized by competitive behavior. Specifically, they must adopt a cooperative and collaborative approach. • Participants must be aware that they bear a common responsibility and must present a united front. The “not my problem” attitude has to go. • The collaborative method of working, which is currently inevitable, requires a flat hierarchy. In other words, decision-making procedures that have often tradition-

132 Virtual concept > real profit ally been multi-layered must be revised from their foundation and replaced with project-related decision-making responsibilities. • Using a communications platform allows the work to become transparent to stakeholders outside the company. Specific steps must be taken to eliminate any concerns related to such transparency. This requirement becomes vital when collaboration involves developments in intellectual property that are not yet protected. A consistently applied PDM system manages information and the rights to it. • The disclosure of expertise, made necessary via the communications platform, should not be interpreted as a loss by an individual company, but as a gain for the growth of collaboration.

Expertise • Ideally, companies will already have practical experience in working with communications platforms. If this is not the case, then there is a risk that companies may expect the platform to do all the work for them. In this event, processes must be defined to clearly describe the communications paths and operating procedures. • Ultimately, collaboration thrives by helping people to work together; meaning that interpersonal relationships have to work. The climate must be constructive and partners must contribute enthusiasm and conviction. • Partners should be aware that a common communications platform is a tool for collaboration, just as the CAD system itself is an essential component of the de- veloper’s everyday toolkit. The practical operating procedures along the order- execution chain are often murky or not wholly defined; meaning that they must be made transparent. This can be accomplished by the process-oriented design of working methods when DMF is introduced. This process must be accompanied by a QM certification. The steps within the process must be documented visually and communicated to employees.

What are the benefits and advantages that can be realized based on the requirements cited above and the expertise implemented? Some of the basic features and their respective characteristics in the realms of communications, information and organization are as follows:

Communications It is important to ensure that no information gaps result from automatic reporting. A comprehensive overview of working processes and their development is impossible if the underlying information is absent or not readily available. Consequently, situations cannot be optimally assessed and corresponding opportunities to react will be missed. Direct responses to questions and requests from customers, project managers and other team members are impossible on account of a lack of transparency. The required information cannot be retrieved as contacts do not have the appropriate access, for example, or the necessary information may simply not exist. A common communications platform automatically notifies participating employees

4 Benefits in Real-World Examples 133 about new information. The system sends an e-mail to all employees in the workflow, for example, when new documents are available for processing. All information is exchanged within the team by the system on the basis of the communications rules, and deadlines are coordinated by means of common calendars.

Information Avoid information overload through consistent data management. Due to the quantity and variety of the data compiled it is sometimes difficult to decipher what information is actually required. Information from many different sources is often available as a response to a question or inquiry. With a common communications platform, all new and existing information is managed and communicated to the individual employees as appropriate. Access to databases by the relevant users is regulated to provide a continuous and comprehensive overview. The probability of achieving a satisfactory result is significantly higher. An essential feature of such a communications platform is that the system is not restricted to a single location. Access to data is easily and conveniently available at any time from any connected computer: office, home, or laptop.

Organization Effective communications must not be allowed to remain as a theoretical ideal; it must become an essential part of daily practice. The root cause of many misunder- standings and delays in communication exists long before the actual work begins, as the common communications groundrules are often only agreed to half-heartedly. There is a lack of clear and transparent communication pathways, as well as confu- sion as to who is responsible for providing and retrieving information. A common communications platform supports the flow of information among team members by defining agreed-upon rules of communication and monitoring compliance as necessary. A prerequisite is a clear definition of personal roles (such as developers, for example), and task-related processes (such as, for example, the clash analysis of components). Processes can be matched to specific addressees, procedures, processing times or responsibilities, including expediting follow-up with reminder functions. This clear, one-time process definition produces a significant reduction of routine tasks in everyday work. The quality of the work improves, as a result of which the team is free to concentrate on core tasks such as development itself.

134 Virtual concept > real profit Bookmark Chapter 4

At the end of Chapter 2 the main benefits of using simulation in manufacturing were pointed out. Simulation has been, and will continue to be, a major force in product and process innovation. Simulation is cheaper than building a system for real and it can be done during an early stage of design. Simulation allows what-if experiments, and it delivers proof of concept. However, a lot can and must be gained, as will become clear in Section 5.4, “Step 2 – Enhance Simulation Capabilities.” Today we can model the production line, change the equipment, and simulate how it reacts to that change. Modern manufacturing simulation supports material flow, machine utilization and robotics simulation. Sophisticated systems offer 3-D and 4-D visualization, menu-driven applications, and integration with other product design and enterprise data systems. These comprehensive solutions are commonly used in the automobile and aerospace industries. Cost-effective new tools are available for SMEs (small and medium enterprises). The use of simulation in manufacturing control is also increasing where the control system communicates with a virtual system instead of a real factory. Large savings can be obtained when evaluating and validating control systems like Programmable Logic Controllers off-line. Modern digital manufacturing solutions support the design of large assemblies, concurrent design practices, multi-disciplined engineering, knowledge-based design and design for manufacture. Ideally this functionality is contained within a selection of dedicated industry-based software modules that have been configured to provide a focused set of tools. The correct and informed use of digital manufacturing and simulation is indeed a way for companies to lower costs and make their products and processes more profitable and efficient. The proper, efficient and informed usage of digital manufacturing and simulation by qualified specialists can definitely bring significant savings. When manufacturing output is being reduced, there is a window of opportunity for companies to continue to invest in an integrated set of digital methods. Competitiveness will certainly increase, allowing companies to emerge from the economic downturn in a stronger state, and in a market which will probably see fewer competitors.

4 Benefits in Real-World Examples 135 Virtual concept > real profit 5

136 The Future Is Open and Personal

Introduction Towards Crowdengineering 138 5.1 From Mass Production to Mass Customization 140 5.2 Customization and Virtual Reality in 2015 142 5.3 Engineering for the Masses 145 5.4 Critical Assessment of the New Industrial Revolution 148 Bookmark Chapter 5 154

137 Introduction Towards Crowdengineering

It is a well established fact that simulation and digital manufacturing improve design quality and speed throughout the product lifecycle. These benefits occur early in the product development process, during conceptual and design engineering, but also during production, maintenance and service. Also, cost savings, business value and market value increase significantly when more people from various disciplines in company networks use digital manufacturing technologies or their results. The next step in improving collaboration and time-to-market is in getting experts and customers from outside the enterprise involved in digital engineering and manufacturing. This opening-up or democratization has been proven to boost innovation, improvement, productivity and revenue. Today, such “open innovation” practices are developing from the well known concept of “concurrent engineering” in the direction of “crowdsourcing” or, more specifically and actively put, “crowdengineering,” as we would suggest calling it. In the context of digital manufacturing, simulation and product lifecycle management have advanced to where concurrent engineering and crowdengineering promise to spark a new industrial revolution. In this chapter we will first argue in favor of this promise, proposing that it may be realized by 2015. Subsequently, we will critically assess its feasibility, based on the current state of development in digital manufacturing and simulation. Engineering for the masses is a promising challenge, however, the future of engineering and manufacturing certainly will become more open, personal and multifarious than it is today. The Concurrent Design Facility within the European Space Agency definesconcur - rent engineering as “a systematic approach to integrated product development that emphasizes the response to customer expectations. It embodies team values of cooperation, trust and sharing in such a manner that decision making is by consensus, involving all perspectives in parallel, from the beginning of the product life cycle.” In 2008, following the revolution around Web 2.0, Dassault Systèmes, one of the key commercial players in digital manufacturing, simulation and product lifecycle management, introduced the notion of PLM 2.0, which encompasses a web-based open innovation social community approach to PLM. The crowdengineering approach of PLM 2.0 focuses on online collaboration, collective intelligence and online communities.

Figure 5.1 Customers thinking up their ideal product is phase one of crowdengineering in a PLM 2.0 setting.1

138 Virtual concept > real profit To fully develop crowdengineering, business processes must be easy to activate, config- ure and use through online access. Perhaps in the near future truly “open-source” cars like Riversimple will be designed and manufactured, as Dassault Systèmes employee Jonathan Dutton suggested on the company’s Perspectives weblog in mid 2009:

Image a group of engineers who all have a passion for developing and producing a sustainable mobility solution, and just like Riversimple, this group is spread «out across the world. How can they possibly share ideas efficiently? Phone, Fax, email? What about an online platform where they can literarily co-design, instantly share, mark up each others’ work, package parts on the fly, . . . but the thing that’s critical in this sort of crowdsourcing environment, where ideas are abundant, is to know what to do with all these ideas, i.e. sort them out, accept them, refuse them, send them back for enhancements, and most importantly make sure that everybody is working on the same engineering requirements and has followed » the same validation processes.”2

Figure 5.2 The hydrogen-powered Riversimple urban car, built at Silverstone near Towcester, UK.3

The use of a sophisticated lifelike 3-D PLM 2.0 environment, as in Dassault Systèmes V62009 and V62010 software suites, would clearly boost the performance of “open- source” automotive initiatives like Oscar, Riversimple and eCars – Now!, the same as it does for established automotive enterprise networks and their collaborative innovation efforts. These developments were already envisioned some years ago by renowned innovative thinkers like Henry Chesbrough, executive director of the Center for Open Innovation at the University of California, and Eric von Hippel, who heads the Innova- tion and Entrepreneurship Group at the MIT Sloan School of Management. The Sogeti book Open for Business: Open Source Inspired Innovation describes the relationship between open source software development, open innovation, lead-user innovation, crowdsourcing and business success.4

5 The Future is Open and Personal 139 Crowdengineering In- and Outside Automotive • “Will 2009 Be The Year of Crowdsourcing?”: www.xconomy.com/ boston/2009/05/21/will-2009-be-the-year-of-crowdsourcing • “Local Motors: Crowdsourcing Automotive Engineering Competition”: www. collegemogul.com/content/local-motors-crowdsourcing-automotive-engineering- competition • “Crowd-Sourcing the Electric Car”: www.baselinemag.com/c/a/Automotive/ CrowdSourcing-the-Electric-Car • eCars – Now! Global Community: ecars-now.wikidot.com • “eCars.Now! in nutshell – short 3D presentation”: www.youtube.com/ watch?v=F6l0clzDhjA • “Severa To Sponsor Electric Car Venture eCars – Now”: www.emediawire.com/ releases/2009/6/emw2581844.htm • 3D Perspectives: perspectives.3ds.com/tag/crowdsourcing • Zazzle: www.zazzle.com • “Open arms: what prosthetic-arm engineering is learning from open source, crowdsourcing, and the video-game industry”: goliath.ecnext.com/coms2/ gi_0198-567627/Open-arms-what-prosthetic-arm.html • “Design futures”: memagazine.org/contents/current/features/desfut/desfut.html • “Design Engineers, I.T. and Crowdsourcing”: www.shubhspace.com/2008/09/ design-engineers-it-and-crowdsourcing.html • Crowdsourcing – University of Strathclyde: www.strath.ac.uk/dmem/research/ crowdsourcing

5.1 From Mass Production to Mass Customization

Henry Ford, one of the founding fathers of modern manufacturing, created the first success in the mass-market automotive industry. His Ford Model T was the first low-cost, mass-produced automobile. Fifteen million Model Ts were sold in the early 20th century. On an industrial level two structural factors made such an explosion possible: the mechanization of the assembly line and the specialization of tasks as- signed to each worker. The consequence of mass production for consumers was, as Henry Ford himself pointedly put it, “Any customer can have a car painted any color that he wants so long as it is black.” Today, some hundred years after the arrival of the Model T, and in spite of much wider choice, every car still remains a number in a series. This issue is addressed by regularly producing special editions. In general, mass production has two major disadvantages: • consumers have little influence on the design of products, • companies run major financial risks if the products they market are not successful.

140 Virtual concept > real profit Figure 5.3 “Some 38 dependable, economical Datsun models. One of which should be just like you designed it.”5

Consumers still depend for their choices on decisions made by product designers. In the showroom or the car dealer’s catalog products are completely finished, and cannot, except for minor variations, be adapted further. Consumers decide whether the product will be a success or a failure by buying it or not. For manufacturers, this presents a large financial risk, as the design of a product accounts for the greatest part of its cost and also determines when the product hits the market. For example, at PSA Peugeot Citroën the average time required to launch a new product is 217 weeks, plus an additional 156 weeks for a convertible and 144 for a station wagon. In civil aviation, it is estimated that it takes 7 years to bring a product to market, including 2 full years of pre-engineering. To overcome this problem and prevent total disaster when the products finally reach the consumer, manufacturers spend a lot of time and money on market research, consumer focus groups and other forms of customer testing during the different phases of product design. The resulting product is designed to please the largest

5 The Future is Open and Personal 141 number of customers in accordance with the requirements of industrial production. In this respect, the advent of 3-D simulation software is a real revolution as it allows consumers to suggest changes or even alternative designs throughout the development stage. Returning to the automobile example, dealer catalogs currently allow us to choose a vehicle model and make minor adjustments to the color and within a limited range of options, most of which are related to comfort (air conditioning, upholstery, etc.) and sometimes to more structural aspects like bumpers, spoilers, skirts, headlight assemblies, rims, engines, and so on. Nevertheless, the only way to significantly modify a mass-produced vehicle in its performance as well as its appearance is customization. Full modification of a production vehicle – whether automobile, motorcycle, motor- bike, motor scooter, etc. – by replacing or adding parts can be seen in, for instance, MTV’s “Pimp My Ride” show. Such serious modification can be made only after the car has been manufactured, and takes a skilled mechanic or dedicated DIYer. Simulation and virtualization technologies have a promising future since they unify three worlds that were previously distinct: product design, manufacturing and distribution. Digital simulation covers all areas of the virtual product, virtual production (digital factory) and even the means of distribution (virtual supermarkets). Simulation and virtualization accommodate the trend in consumer preferences for products that stand out. This includes the ability to adapt a product to end-user requirements in all their specificity and diversity. This way the manufacturer can genuinely employ individual marketing – one consumer, one unique product – while significantly shortening the design and marketing cycle. 5.2 Customization and Virtual Reality in 2015

Tired of the car you customized at great expense several years ago? It is time for something completely different. In 2015 the only thing you have to do is visit the online CarSim showroom. Not a traditional showroom but a “Virtual Design Studio” where you can indulge your fancies in building your own customized vehicle.

Figure 5.4 Designing a car in the MySims racing game already hints at the magnificent possibilities of crowdengineering in 2015.6

142 Virtual concept > real profit First you browse the catalogs that define the general appearance. The models on display range from sports cars, family sedans, utility vehicles and a vast number of creative commons vehicles that were designed and tested by companies and teams of individual engineers and enthusiasts. Specifications are up for grabs, and help you to play by the rules and laws that apply to your country. Also the highly sophisticated 3-D PLM software suite available for use on the CarSim website can advise you on every conceivable aspect and detail: from performance, wear and tear, cost and manufacturability to the “delivery due date” of your newborn car, together with the complete routing of unique or exclusive parts with cost- and time-optimized assembly. The predecessor of this type of portal, which contains templates and allows the user to create the product according to individual specifications, already exists today, for instance, in helping consumers customize laptop computers. Dassault Systèmes has also created a public demonstration site featuring a 3DVIA database of 3-D objects.

Figure 5.5 Visit, for instance, 3dvia.com/search/models.php and 3dvia.com/blog/ software/3dvia-virtools-learning-center.

In 2015, when you plan to build a highly exotic automobile online, you should accept all the online guidance and as many of the automatic recommendations as possible, otherwise at least some of the specifications may not be validated. If the specs fail to meet the threshold feasibility level, after months of hard work the software may even refuse to send your car specs to the manufacturing network. Of course, you pay a premium for this invaluable guidance via CarSim’s biometric registration system. Normally people choose to tweak and tune an existing car model that is well documented, tested and certified. Even then, its appearance and performance can be quite astonishing to your friends and neighbors. You can choose from so many validated examples, and, for an additional fee, CarSim can show exactly what model types already exist in your geographical area, how old they are, etc. This way you can always be the proud owner of a vehicle that is unusual in your area. Even with this abundance of choice, chances are that you will opt for a totally reliable low-maintenance family sedan type of electric vehicle controlled 24/7 by a software system monitoring every function on board. That same evening, you will proudly present your suggestion to your wife and kids so that each family member can make comments and propose changes. Your 7-year-old son wants a car he can finally climb into without help, while your teenage daughter complains that this time she must be

5 The Future is Open and Personal 143 able to recharge her cell phone from the back seat. Your wife wants a sun roof and a bigger trunk, not to mention heated seats. And everyone wants an environmentally friendly engine that gets good mileage, especially in the city. Straight away, and this time on the big screen in the living room, all changes are implemented. Oops, that extra-large trunk has some disadvantages. Not only would it put the car over budget, it looks bulky, and when it is fully packed the drivable distance radius drops below the distance you live from the seashore. That means two extra batteries or a solar-power enhancement, which would kill the sun roof. Luckily both trade-offs are in your wife’s court. Reluctant and somewhat unconvinced, she gives up the trunk idea, as you and the kids promise not to take all that unused fun gear on holidays anymore. With the basics in place and agreed upon, you can focus on some further traditional details like the shape of the sun roof, bumpers, lights, shaded glass, noise level and the comfort and usability of the interior.

Figure 5.6 Today’s CarSim Driving Simulator (carsim.com) with live audio and three screens.

That’s enough for tonight, so tomorrow morning, right after breakfast, the family will convene for a final 3-D drive via the standard game consoles you have at home. One will take you through the city where you live and the other takes you down that crowded road to the sea. You can program the test drive’s time and day, and choose favorable or harsh weather conditions to check the visibility through your windshield, rear window, and in the mirrors. In 2015 this procedure will be reduced to human sense control from the past, since the obligatory driving guidance system will communicate with other cars and objects on and next to the road to guarantee optimal speed and safety. Of course, the virtual drive also includes testing all functions of the central and personal media systems, notably the interruption settings for your headsets, for when the kids start a sentence with “Mom…” or “Dad….”

144 Virtual concept > real profit 5.3 Engineering for the Masses

The interfaces we use every day to work on computers were designed to enable non- specialists to benefit from advances in information technology by relegating programming language to the background and neutralizing technical aspects that can be off-putting for most people, whose interest is in knowing what their computer can do, not how the computer does it. Today, engineering and manufacturing have started to undergo the same revolution and place not the engineers but the “neophytes,” the consumers of the products that engineering enables, at the center. Just like computer technology thirty years ago, engineering and manufacturing are becoming available to a broader public and are now on the journey of digital commoditization. This will not only minimize the gap between the design of products by engineers and their manufacture in the factory, it will also create a closer connection between the end user and the product, as well as between the end user and the manufacturer. Simulation and digital manufacturing will not remain the exclusive ball game of industry professionals for long. The tendency towards increasingly realistic virtual worlds that has been accelerated by the success of realistic video games and other lifelike entertainment and serious game environments has not gone unnoticed among manufacturers, software developers, marketers and, last but not least, the public in general. In many virtual worlds it is already possible to create clothing, weapons, interior decoration, jewelry and property, thanks to computer models created by the gaming community or by a few enthusiasts. Of course, these virtual objects are not designed according to scientific or physical laws; they are simple 3-D animations. Nevertheless, within a few years, computer modules available to everyone for the purpose of creating and personalizing various objects will have a genuinely scientific basis. It was not unintentional that in the previous section the example of the automotive industry was chosen to picture the digital future of simulation and manufacturing. In this industry, which is subject to numerous constraints – operations closely linked to a complex and global supply chain, increasingly shorter development times, cost cutting, compliance with regulations, increasing passive safety systems, increase in the number of variants and the complexity of systems, etc. – the lifelike digital experience is swiftly becoming reality. One of its primary uses is as a tool to aid designers in the styling and appearance of future models in the form of photo-realistic renderings. In general, simulation is becoming industrialized, with the dynamic simulation of fluids, structural mechanics (see Figure 5.7) and aerodynamics, as well as applications in the field of crash testing that require enormous amounts of time and resources. Digital simulation is rapidly becoming an essential element in maintaining a company’s competitive edge. Therefore, it is highly likely that car manufacturers will be among the first to adopt this new mode of unified operation.

5 The Future is Open and Personal 145 Figure 5.7 Structural mechanics simulations of today are shifting into high gear. New multiphysics tools allow you to easily solve real-world problems.7

We are witnessing a veritable convergence of the concerns of the market and a shared vision of the future, as the major creators of simulation software emphasize similar concepts. The accent is on the ability of the simulation tools to enrich the virtual products with real feedback, to build a bridge between the consumer reality and the direct impact of this reality on the products they design, in an unlimited process of customization. In the near future “Engineering for the Masses” will emerge from these developments. 5.4 Critical Assessment of the New Industrial Revolution

More freedom of choice for the consumer, lean production for the manufacturer, access to remote teams working on the same models, and product lifecycle management for continuous improvement are among the impressive potential advantages of digital manufacturing and simulation. Such reality-enhancing virtualization holds the promise of a new industrial revolution that entails both advanced concurrent engineering and crowdengineering. However, its impact may not be felt until 2015, as described in the previous sections, without a number of technical and strategic improvements. Several roadblocks have to be removed in order to create and share digital models that can be re-used in a product lifecycle, consisting of the following five principal steps: functional design, design, calculation, manufacture and commercialization. • Functional design: defining all the products’ functionalities. • Design: the product is reproduced in three dimensions in the form of a digital model. • Calculation: validation via mathematical approaches (analytical/digital) confirming that the system designed meets the functional requirements (resistance to pressure or heat, electric consumption, etc.)

146 Virtual concept > real profit • Production or manufacture: Once the virtual component has been optimized, the production tools are adapted and put in place for fabrication. • Commercialization: the final user/consumer has access to the product.

An overall digital strategy is necessary to substantially improve interaction and uni- fication in the different phases of product design, manufacture, and adaptation to individual end-user requirements. Such a strategy involves maturing along the five steps in Figure 5.8, discussed below.

Conception Design Analysis Manufacturing Use

Step 1 Deﬁne a common data model and compatible methodologies

Enhance simulation Step 2 capabilities

Step 3 Capture knowledge, capitalize and re-use

Step 4 End-to-end requirements traceability & simulation security

Step 5 Advanced concurrent engineering and crowdengineering

Figure 5.8 These five necessary steps towards digital maturity must be taken to lay the foundation for a new industrial revolution.

Step 1 – Define a Common Data Model and Compatible Methodologies

Communications and the sharing of information between the design department and the engineering department, between the design department and the manufacturing department and between the different engineering disciplines remain complex. For example, when an engineer tries to optimize a system he will work with thousands of parameters, some of which include important variations in values. Theoretically, by including design constraints from the beginning the number of parameters could be reduced, perhaps to a hundred (or even to a single series if the manufacturing requirements were taken into account as well). Unfortunately, the imperfect real-time sharing of data and knowledge is an essential obstacle to reducing production times and can also be very damaging for the quality and operation of the end products. Failure to immediately notify the three disciplines involved (design, engineering and manufacturing) of modifications as they are made means that a certain number of tasks – often very time-consuming and labor-intensive – will fail, and also that all or part of the cycle will have to be repeated once the new component design is shared. Or

5 The Future is Open and Personal 147 worse still, teams may be working simultaneously on two parts of a single component which, because the modifications were not shared, will no longer fit together and may no longer work at all. This type of problem becomes more common and potentially damaging as companies work in the “extended enterprise” model, with a large number of subcontractors who may be spread over very wide geographical areas. Integrating design, engineering and manufacturing departments requires the sharing of information in real time. The idea is to make the CAD data accessible from the start of the design process right through to the manufacturing phase. When the manufacturing department identifies a problem in the original design of a component, or in one of the modifications, the design and engineering departments can be notified immediately. In this way the workflow, which is by definition sequential, can become a unified and simultaneous process. This step is about to hit the market, with the arrival of PLM 2.0 solutions, a new generation of interactive environment software providing intelligent access to all data involved. Each modification is therefore accessible to the design, engineering and manufacturing departments in real time, and the digital model incorporates a history of the changes, as well as lists of the various parties who have worked on each component. The first step clarifies the simulation process in its five principal phases (functional design, design, calculation, manufacturing and commercialization). Analyzing the working methods of everyone involved in the development of a product is a lengthy and painstaking process. It requires the documentation of each product lifecycle phase, of the practices, tools and resources used by all participants, and also the recording of all individual processes. Such comprehensive and detailed investigation is the foundation for a classification of methods and processes. Only when the workflow has been defined, documented and organized in detail can the standardization of methods be considered.

Step 2 – Enhance Simulation Capabilities

The second step in reaching a maturity level that can support the promise of advanced concurrent engineering and crowdengineering in a product lifecycle is lengthy and tedious. First, the engineering world must define a data standard. Today calculation still is the realm of specialists. It is also an extremely fragmented market, in which each company and sometimes even each engineer works within a very specific and self-reliant niche. For these reasons, each uses his own data model without considering overarching compatibility and makes calculations in the order he chooses, without any standard sequence. One of the major issues is therefore the standardization of working methods and processes. This standardization must follow three main paths: • It must define, document and adopt engineering protocols that will standardize fixed calculation sequences, which engineers must then execute in a certain order, with pre-defined checkpoints to verify the process.

148 Virtual concept > real profit • It must give engineers locked, validated components in order to prevent unap- proved modifications. • It must implement efficient data management in order to create a pedigree for calculation data.

Standardization of this type represents a core challenge, as it will make it possible in a second phase to define best practices and shared standards, which are absolutely necessary to accelerate the development of a competitive and productive digital simulation ecosystem. Simulation will also progress more rapidly when the resources – both hardware and software – are exploited to the limits of their capabilities. The maximization of computing power involves two types of initiatives: • computer networking to multiply their computing power: so-called “grid computing.” • efficient use,i.e. in this context the utilization of software tools to analyze the IT resources available and to perform calculations where they can be carried out at their fastest, considering the power of the computer, its properties and its compatibility with the calculation required, as well as the availability of networks to access it.

Defining the time/reliability optimum for models is imperative since models used to reproduce behaviors based on the laws of physics are subject to a dual limitation: • certain physical behaviors are still difficult to analyze mathematically, and • the existing models are extremely cumbersome, require large amounts of computer time and memory and are mathematically very complex. The more detailed and realistic the behavior reproduced, the more cumbersome the model will be. Global models which are more approximate and less precise are easier to use.

In the current stage of development, a balance is needed between the reliability of the behaviors simulated and the amount of design time available. Therefore the most common current strategy is to go back and forth between very complex local models, which are used with priority on certain components that are deemed critical and which will be defined in their entirety, and global models, which are less precise but can be used more rapidly. For instance only 30 percent of aircraft components are simulated in detail, while the rest are designed either by derivation from the previous version or by empirical methods (based on tests and experiments) or simplified analytical models. Currently there is no strategy that defines what needs to be simulated within a system and what does not. Once again, this choice is left to the discretion of the engineers, and the more users there are, the more responses there will be. Once it has been decided what must be simulated, the next choice is between universal or detailed simulations. At the moment, there is no method shared by all participants to determine when it is necessary to use a detailed model and when a general one

5 The Future is Open and Personal 149 will suffice. Each engineer on each project makes the ad hoc decisions that seem appropriate to him. The typical process used for a given simulation activity is to conduct a certain number of iterations between the use of detailed models and the use of universal models, although there are no rules or standards in this area. One of the principal benefits of standardization resides in the user’s ability to re- use existing building blocks or to create a new 3-D computer model. On each new project, the engineer decides whether to re-use the previous simulation and make extrapolations based on the results obtained, or to create a new one. Today re-use is rare. If models could be re-used, they would save significant amounts of time and resources as well as significantly reduce the data volume. The last issue to be considered in the context of enhancing simulation capabilities is robust analysis. Robustness is a concept that originated in statistics and describes the stability of a system despite minor changes in the underlying data or in the parameters of the model created to simulate the system. In digital simulation, robust analysis constitutes a bridge to reality.

A Simulation Reality Check In January 2009, on NAFEMS’ 25th anniversary, CEO Tim Morris looked at the past and the future of computer modeling and simulation in manufacturing. In 1983 the now international NAFEMS organization, which is increasingly recognized as a one-stop shop for all aspects of information on engineering analysis, was founded at the UK’s National Engineering Laboratory as the National Agen- cy for Finite Element Methods and Standards. In Morris’s opinion, a large part of the promise of computer modeling and simulation has not yet been realized, but certainly will be in future.

“Engineers rely on computer modeling and simulation methods and tools as vital components of the product development process. These methods develop at an «ever-increasing pace. Today, computers dominate every office, and simulation technology is mature and is an integral part of the mainstream design process. Or is it? Personally, I am of the firm opinion that FEA, CFD and other related technologies are still very much in their infancy: that engineers will look back in 25, 50 or even a hundred years from now and be amused at how crude and unreliable the methods of today are, when compared with the technology that is yet to come. The vision of a general engineer utilizing simulation with little or no training, and without any guidance from a specialist has, to date, only been realized for a tiny percentage of applications. The technology itself continues to be developed at an ever-increasing rate, and the complexity of the applications which industry would (in an ideal world) like to tackle still comfortably exceeds » the available capabilities.”8

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Figure 5.9 ASC Program: Verification and Validation.9 The U.S. Advanced Simulation and Computing program is a good example of the hard work needed to proceed from test-based to simulation-based. Under this program, computer simulation capabilities are developed to analyze and predict the performance, safety, and reliability of nuclear weapons and to certify their functionality. Based on the functional and operational requirements established by designers, analysts and code developers for greater fidelity of codes and models, the Verification & Validation subprogram establishes a technically rigorous foundation for the credibility of code results. Verification activities assess code precision in implementing numerical approximations and assess the accuracy of these numerical approximations. Validation activities aid in the understanding and assessment of a model’s accuracy by comparing model predictions with experimental data. Quantification methodologies provide measures of the uncertainties associated with the simulations.

Step 3 – Capture Knowledge, Capitalize and Re-use

Once each process has been linked to a well defined phase of the cycle, the challenge is to simplify and standardize the working methods and processes. As seen above in the case of the engineering calculations, this requires fixed and fully independent working protocols. This makes processes and data common to the lifecycle, regardless of the product. Standardization achieves great progress in terms of time saved and

5 The Future is Open and Personal 151 simplicity. This enables the re-use of processes and, even more important, re-use of the virtual objects created. Assembling and industrializing the skills and expertise of specialists transforms engineering into a lifecycle-oriented commodity. One of the essential consequences of this change is that an accessible library of virtual objects has been created, containing simple shapes that can be re-used by anyone and that are easier to handle than objects with complex parameters. These shapes can be used as building blocks in the construction of larger virtual objects and therefore serve as the basis of re-use strategies. (See for instance “Crowdsourcing Canonical Views of 3D Models” and “Validation of Purdue Engineering Shape Benchmark Clusters by Crowdsourcing” at www.strath.ac.uk/dmem/research/crowdsourcing.)

Step 4 – End-to-End Requirements, Traceability and Simulation Security

Some ten years ago, information technology underwent a transformation in its software development processes that is just beginning in systems development: namely, the formalization of requirements, which are translated into specifications. Thanks to this practice, automatic coding has become possible. The traceability of requirements has been part of information technology for years, and is currently gaining acceptance in engineering. It includes the ability to input the functional constraints of the simulated system (what the system is supposed to do) directly into the modeling software. This evolution will make it possible to advance from elementary simulation, which is what is currently available, to so-called contextual simulation. Returning to the example of the design of an automobile, certain verifications are currently only possible by means of expensive and time-consuming crash tests and tests under real conditions (under extreme conditions, road and track tests). Furthermore, these tests can only be performed when the vehicle is already in a very advanced stage of development. At this stage, any modification represents major costs. On the one hand there are costs in terms of time – as it is necessary to go back to the beginning of the design chain to make the necessary changes to the design. And on the other hand, there are financial costs – as, in addition to the supplementary development costs that have to be invested in the project, its introduction to the market is delayed by the extra time required. For instance, it is not unusual for vehicles to reach the phase where they are ready for test drive, only to find that they produce unexpected responses that are so dangerous that the car simply cannot hold the road in tight turns or slippery conditions. Thanks to digital simulation, in a few years it should be possible to conduct almost all of these tests in virtual mode. Lifelike simulation will eventually cover the entire product lifecycle, from the idea to the contextual use of the product by the user. Some companies already use virtual supermarkets, enabling them to place 3-D models of their new products on supermarket shelves to test the reactions of potential custom-

152 Virtual concept > real profit ers. For the time being, these contextual applications are still very “reactive,” as they primarily collect consumer opinions on the appearance of the product: color, shape, size, consumer perception of these elements, whether they can identify the brand or not, etc. Nevertheless, they establish the foundations for a genuinely collaborative development of the final product, by directly taking the improvements suggested by the user into account during the design. The next step is to promote broader use of this realistic contextual simulation. With the contribution of the end user of the product, a virtual circle of improvements can be created. The iterations and successive modifications will make it possible to verify not only that the product meets the general requirements that were the basis for its design, but also meets the individual specifications of the end user.

Step 5 – Advanced Concurrent Engineering and Crowdengineering

The development and expansion of an ecosystem integrating all stakeholders is absolutely critical for the harmonious and rapid development of digital manufacturing and simulation. Specifically, this means a stakeholder community with members inside and outside an enterprise network who have rights to review part of the content and comment on activities. In order to become involved a stakeholder must be keenly interested, but need not be a part of the enterprise process or be a professional involved in the technical issues under development. Certainly professionals participating from within the enterprise will be able to highlight details or make changes in design. Creating such an ecosystem is not without problems. Three requirements must be met for all parties to move forward together: the interoperability of PLM systems, digital production from idea to factory, and an open collaborative online innovation environment. An essential requirement is to ensure that each component can communicate with the others. To accomplish this everything, from the bolts used in the assembly of the final product to the robots in the factory in which the product will be manufactured, must have a virtual double. Only such virtual integration will make it possible to seamlessly connect to and operate in an open collaborative online innovation environment.

5 The Future is Open and Personal 153 Bookmark Chapter 5

Undoubtedly, computer modeling and simulation in the future can and will be significantly improved and better tooled, as AFESM CEO Tom Morris pointed out (see Section 5.4, Step 2). However, current deficiencies will not prohibit the rise of advanced concurrent engineering and crowdengineering. For example, the spectacular rise of mechatronics is imminent, which will drive advanced concurrent engineering. Whereas at the start of the millennium hardware contributed 80 percent to the revenue of a product, electronics 16 percent and software only 4, in 2020 the revenue contribution of hardware will be reduced to 50 percent while electronics and software together will account for the other half. Today a high-end automobile has 10 million lines of computer program code, 30,000 calibration points and 3 kilometers of wire. Looking at key factors that impact business priorities and practices through 2020, firstly, economic stress will stimulate innovation in product development and the use of simulation. Secondly, the demand for environmentally friendly practices will spur the adoption and development of simulation to achieve objectives. And thirdly, today’s students will demand their future workplace offer what we today tend to call an “open and collaborative multimedia social network” approach, which will be the norm in tomorrow’s businesses. Similarly, just as has been the case in (open source) software engineering and in product design and engineering in many other sectors, the current state of components and templates in current manufacturing environments already enables the opening- up and advance of R&D via broader online communities. As already demonstrated in many examples of so-called “crowdsource” and user-led innovation, collaborative business models will emerge that foster digital manufacturing and simulation beyond the boundaries of traditional industry networks. This will surely happen through so-called seeker-solver networks like InnoCentive, where a seeker makes a request, the online community proposes solutions, and eventually the seeker selects the most suitable solution. Taking inspiration from Gartner’s Marc Halpern we confidently conclude this book with the following three predictions and recommendations: 1. By 2020, emerging classes of products will necessarily drive the rapid advance of digital manufacturing and simulation, since today’s mainstream tools will have proven to be insufficient. 2. By 2020, C-level executives will be significantly more involved in collaborative PLM and product development activities, because they must provide additional value, revenue and profit to the business. 3. By 2020, the biggest challenge for companies will be to adapt to a reality where digital manufacturing and simulation are considered strategic, and large online communities are active inside and outside the corporate networks. This will require strong executive commitment and support, based on dependable ROI and risk analyses involving products, processes, people, physical resources, digital resources, partners, suppliers and, above all, end users.10

154 Virtual concept > real profit References

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2 A Crash Course in Digital Manufacturing and Sumulation 1 European Commission, ManuFuture. A Vision for 2020. Report of the High-Level Group, November 2004, ec.europa.eu/research/industrial_technologies/pdf/ manufuture_vision_en.pdf. See also: www.manufuture.org. 2 Image: images.forbes.com/media/magazines/forbes/2007/0813/Forbes_0813_p071_f1.gif. See also: “Virtual human crash model is no dummy,” news release, April 4, 2003, www. eng.wayne.edu/page.php?id=1414. 3 research.microsoft.com/en-us/um/people/gbell/cybermuseum_files/bell_artifacts_files/ images/B1626.01_Roubos_replica_of_Schickard’s_Calculating_Clock.JPG. 4 I_M_SCS International Mediterranean Society for Computer Simulation, “I_M_SCS Manifesto,” www.liophant.org/i_m_cs/i_m_scs_manifesto.pdf. 5 Forger, D.B. & C.S. Peskin, “Stochastic simulation of the mammalian circadian clock,” Proc Natl Acad Sci USA 102(2): 321-324 (2005), www.pubmedcentral.nih.gov/ articlerender.fcgi?artid=544301. 6 cell-phone-dangers.com. 7 Becker, K. & J.R. Parker, “A Simulation Primer”, in: D. Gibson & Y. Kyun Baek (eds.), Digital Simulations for Improving Education: Learning Through Artificial Teaching Environments (pp. 1-24). Hershey: Information Science Reference, 2009. 8 en.wikiquote.org/wiki/Dave_Barry.

155 9 Prialnik, D., J. Benkhoff & M. Podolak, “Modeling the Structure and Activity of Comet Nuclei”, in: M. Festou, H.U. Keller & H.A. Weaver (eds.), Comets II (pp. 359-387). University of Arizona Press and the Lunar and Planetary Institute, 2004, www.lpi.usra. edu/books/CometsII/7007.pdf. 10 Earth Impact Effects Program, www.lpl.arizona.edu/impacteffects. 11 www.thocp.net/biographies/schickard_wilhelm.html. 12 www.rathay-biographien.de/persoenlichkeiten/B/Bartsch_Jakob/bartsch_jakob.htm. 13 Foer, J., “A Minor History Of / Giant Spheres,” Cabinet, Issue 27, Mountains Fall 2007, www.cabinetmagazine.org/issues/27/foer.php. 14 “Systems of increasingly advanced technology,” PSA Peugeot Citroën Magazine, September 12, 2005, www.psa-peugeot-citroen.com/en/magazine/magazine_doss_ c1.php?id=244; image: frikingenieria.files.wordpress.com/2008/04/psa_cave_3.jpg. 15 Image from: www.youtube.com/watch?v=USyoT_Ha_bA. See also: Sutherland, I.E., Sketchpad: A man-machine graphical communication system, Technical Report Number 574. Cambridge: University of Cambridge Computer Laboratory, 2003, www. cl.cam.ac.uk/techreports/UCAM-CL-TR-574.pdf. 16 Strang, G. & G. Fix, An Analysis of the Finite Element Method. Prentice-Hall, 1973. 17 Steele, G.L. Jr., Comments on Hofstadter, in: Synthese 53(2): 224 (1982); www.cc.gatech. edu/computing/classes/cs2360/spring96/lec02.html. 18 MIRA, “New Technology 2006,” mira.co.uk/news/techbook%20stories/designedforlife. pdf. 19 “Roots, Performance and Future of ‘Concurrent Engineering’” (editorial), The International Journal of Advanced Manufacturing Technology (January 1994) 9: 1-2, www.springerlink.com/content/rg666160q01u3kl7/. 20 Seuffert, W.-P., “Digital Manufacturing at DaimlerChrysler – Challenges and Opportunities,” www.daratech.com/conferences/presentations/summit/2003/seuffert_ peter.pdf. 21 Spur, G. & F.L. Krause, Das virtuelle Produkt: Management der CAD-Technik. Fach buchverlag Leipzig, 1997. 22 Kuehn, W., “Digital Factory,” I.J. of SIMULATION Vol. 7 No 7, 2006, ducati.doc.ntu. ac.uk/uksim/journal/Vol-7/No-7/Paper3.pdf. 23 Gausemeier, J. & A. Fink, Führung im Wandel. Ein ganzheitliches Modell der zukunftsorientierten Unternehmensgestaltung. Carl Hanser Verlag, 1999.

156 24 CIMdata, Digital Manufacturing in PLM Environments, January 2006. 25 Curry, T., “The Business Benefits of Simulation,” NAFEMS World Congress 2003. 26 Jortner, J.N. & J.A. Friesen, Advanced Concurrent-Engineering Environmental Final Report, Sandia Report, 1997, www.osti.gov/bridge/servlets/purl/674701-byFYg2/ webviewable/674701.pdf.

3 Challenges for Digital Manufacture and Simulation 1 European Commission, ManuFuture. A Vision for 2020. Report of the High-Level Group, November 2004, ec.europa.eu/research/industrial_technologies/pdf/ manufuture_vision_en.pdf. 2 www.computerbase.de/lexicon/Digitale_Fabrik. 3 Committee on Visionary Manufacturing Challenges, Commission on Engineering and Technical Systems & National Research Council, Visionary Manufacturing Challenges for 2020. Washington, D.C.: National Academy Press, 1998. See books.nap.edu/ openbook.php?record_id=6314&page=R1. 4 Havas, D.W., “Facing Manufacturing Challenges,” The Bent of Tau Beta Pi, Spring 2009. 5 Fowler, J. & O. Rose, “Grand Challenges in Modeling and Simulation of Complex Manufacturing Systems,” Simulation, Vol. 80, No. 9: 469-476 (2004). 6 Heilala, J. et al., “Modelling and Simulation of Manufacturing Systems in Different Life Cycle Phases,” 2007, conference.iproms.org/sites/conference.iproms.org/files/ papers2007/55.pdf. 7 Committee on Visionary Manufacturing Challenges, Commission on Engineering and Technical Systems & National Research Council, Visionary Manufacturing Challenges for 2020. Washington, D.C.: National Academy Press, 1998. See books.nap.edu/ openbook.php?record_id=6314&page=R1. 8 Rod Martin, AMR Research, in “Interview with Roddy Martin,” w1.siemens.com/ innovation/en/publikationen/publications_pof/pof_fall_2007/factories_of_the_future/ interview_with_roddy_martin.htm. 9 Janis, I.L., Victims of Groupthink; a Psychological Study of Foreign-Policy Decisions and Fiascoes. Boston: Houghton Mifflin, 1972. 10 Chun Wei Choo, University of Toronto, 2008, http://choo.fis.utoronto.ca/FIS/Courses/ LIS2149/Groupthink.html.

References 157 11 Harvey, J.B., The Abilene Paradox and Other Meditations on Management. Lanham: Lexington Books, 1988. 12 www.youtube.com/watch?v=z_iGdiYO7gI. 13 Neil, S., “The Digital Factory” (editorial), Managing Automation, November 2007, www.managingautomation.com/maonline/magazine/read/view/The_Digital_ Factory_22839297. 14 Technology Review 05.2009, German edition. 15 Adapted from IBM Database Magazine, October 2008. 16 Seuffert, W.-P., “Digital Manufacturing at DaimlerChrysler – Challenges and Opportunities,” www.daratech.com/conferences/presentations/summit/2003/seuffert_ peter.pdf. 17 Aberdeen Group, Digital Manufacturing Planning: Concurrent Development of Product and Process, November 2007, www.3ds.com/fileadmin/WHITEPAPERS/PDF/Aberdeen- Digital-Manufacturing-Planning.pdf. 18 Ibid. 19 IBM Institute for Business Value, Automotive 2020: Clarity Beyond the Chaos, 2008. 20 Gausemeier, J. & A. Fink, Führung im Wandel. Ein ganzheitliches Modell der zukunfts orientierten Unternehmensgestaltung. Munich, Vienna: Hanser Verlag, 1999: 90. 21 IBM Institute for Business Value, Automotive 2020: Clarity Beyond the Chaos, 2008. 22 www.ipa.fraunhofer.de/index.php?id=235.

4 Benefits in Real-World Examples 1 CIMdata, The Benefits of Digital Manufacturing, 2003, me.emu.edu.tr/majid/IENG447/ IE%20447/PDF%20FILES/DELMIA%20CIMdata%20ROI%20Report.pdf. 2 Michel, P., “Digital manufacturing: The green path to growth,” www.reliableplant.com/ Article.aspx?articleid=18188. 3 CIMdata, The Benefits of Digital Manufacturing, 2003, me.emu.edu.tr/majid/IENG447/ IE%20447/PDF%20FILES/DELMIA%20CIMdata%20ROI%20Report.pdf.

5 The Future is Open and Personal 1 See www.youtube.com/watch?v=G37S4zv6B3g and www.youtube.com/ watch?v=Zy6fMPnzeM8.

158 2 Dutton, J., “Riversimple Urban Car: Simply a Revolution!” June 18, 2009, perspectives.3ds.com/2009/06/18/riversimple-urban-car-simply-a-revolution. 3 www.aboutmyarea.co.uk/Northamptonshire/Towcester/NN12/News/Local- News/133992-Hydrogen-Powered-River-Simple-Urban-Car-Built-in-Silverstone- Launched. 4 Bloem, J., M. van Doorn & E. van Ommeren, Open for Business: Open Source Inspired Innovation. VINT (Sogeti), 2007, www.methemedia.com/wp-content/uploads/2008/12/ openforbusiness.pdf). 5 www.garygraf.com/html/04_myads/p_datsuns.html. 6 www.youtube.com/watch?v=oXNN1lcM4Y8. 7 comsol.com/intro/structural. 8 Morris, T., “25 Years of NAFEMS,” BENCHmark January 2009, www.nafems.org/ publications/benchmark/archive/jan09. 9 ASC, poster “Verification and Validation”, www.sandia.gov/NNSA/ASC/pubs/sc05/ prog_posters/Poster-V&V-halfsize.pdf. 10 Halpern, M., “Product Performance Simulation in the Year 2020,” NA Regional Summit 2008 NAFEMS, www.nafems.org/publications/benchmark/archive/jan09/halpern.

References 159 About the Authors

Yves Coze is Vice President Sales & Marketing DELMIA EMEA South at Dassault Systèmes. An expert in Product Lifecycle Man- agement solutions as well as sales and marketing, Mr Coze oversaw the integration and merger of companies purchased by Dassault Systèmes to form DELMIA Corp. He set up the sales department of the new company, and has established DELMIA offices in Italy and Sweden. Before joining Dassault Systèmes, Yves Coze was Manag- ing Director for France at Engineering Animation Incorporated, a 2- and 3-D visualization software company. Prior to that, Mr Coze was Southern Europe Director for SHERPA, a Product Data Man- agement company.

Nicolas Kawski is a technical manager at the Sogeti High Tech’s Simulation Department in France. Having graduated as an engineer in fluid mechanics, Mr Kawski worked on the development of parametric standards and modulus integration for mechanics simulation as well as on the optimization of structural analyses in aeronautical projects. Nicolas Kawski currently oversees the R&D of Sogeti High Tech’s simulation offer, focusing on multi-disciplinary process ac- celeration.

Torsten Kulka is Business Development Manager of the Product Lifecycle Management and Digital Mock-up Department of Sogeti High Tech in Germany. Previously, Mr Kulka worked on several Digital Factory projects in the aerospace, automotive and supplier Industry. His focus is on digital manufacturing planning, virtual reality and methodical engineering data exchange, and on technical and business processes, comprising the product life cycle.

160 Pascal Sire is currently a Global Innovation Catalyst on assignment at Sogeti. His base is IBM Corp., where he teamed up with Strategic Alliances to enable Global Systems Integrators to use innovative technologies and social networking. Previously, Pascal was a technical expert in new technologies and a software architect at IBM, helping clients from aerospace, automotive, and other industries, progressing from a local to a European then global level. He was also an Internet entrepreneur before the dotcom bubble burst. Mr Sire is an engineer and holds an Advanced Master’s degree in Innovative Design (TRiZ expertise). He has now extended his business interest to strategies for intellectual capital and innovation.

Philippe Sottocasa heads the Simulation Department of Sogeti High Tech in France. An expert in Simulation solutions, Mr Sottocasa oversaw the development of the simulation offer within the Sogeti. He set up the environment (experts, software, hardware and partnerships) necessary to support the “Simulation Out of the Box” concept. Before this role, Philippe Sottocasa was managing a business unit dedicated to engineering services in aerospace, specialized in numerical simulation and composite structure.

Jaap Bloem is a Senior Analyst at VINT, Sogeti’s Research Institute. Mr Bloem’s expertise ranges from (open) innovation to IT governance, cloud computing, new media usage and development, and industrial automation. Before joining Sogeti, Jaap Bloem was a consultant at KPMG Consulting’s World Class IT Department, and a publisher and editor-in-chief at Wolters Kluwer. With its VINT Research Institute (Vision Inspiration Navigation Trends) Sogeti aims to guide organizations around the globe in their effort to successfully integrate technology and grow their business.

About the Authors 161 Index

3-D representation 46-49 3DVIA 60, 143 A Aberdeen Group Performance Taxonomy 95 Abilene Paradox 88-90 aerospace industry 62 benefits of digital manufacturing and simulation 122-127 Airbus A350 63 Application Solution Templates 80 assembly line optimization 63 Automated Model Generation 81 Automated Model-Based Process Planning 81 automotive industry 46, 55, 62, 69, 141-142, 145 benefits of digital manufacturing and simulation 115-122 vision for 2020 98-102 B Barry, Dave 42 Bertaud, Frédéric 63-66 Boundary Element Method (BEM) 50 C CAD 57 Calculating Clock 34, 37, 43 calculating costs, reducing 68-69 car design 39, 46 CarSim 142-144 CATIA 60, 80 CAVE 46 Challenges, Grand 82-85 chronometers 38 CIM 53, 57 CMSDIM 81

162 Comau 96 communication, role of digital manufacturing in 131-134 Computational Fluid Dynamics (CFD) 36 computer animation 48 computer games 48 Computer Integrated Manufacturing (CIM) 53, 57 computer simulation: definition 38 history of 43-49 in everyday life 39-42 types of 50-53 concurrent engineering: advanced 153 definition 138 Connected Vehicle 100 consumer goods, benefits of digital manufacturing and simulation 129 Core Manufacturing Simulation Data Information Model 81 crash-test dummies 32, 34 crowdengineering 110, 138-139 advanced 153 examples 140 crowdsourcing 110, 138 culture, role in manufacturing 85 customization: in 2015 142-144 mass 140-142 D DaimlerChrysler 55, 92 Dassault 60, 138 data model, defining common 147-148 Datsun 141 DELMIA 60, 80, 111 Digital Factory 18-21, 54-55, 62, 74 challenge 103-105 German guideline 22, 74 digital manufacturing: as communications platform 131-134 benefits 19, 21-25, 109-135 challenges 73-107 crash course 31-71 definition 53-61 development 53-60 four steps 57

Index 163 impressive figures 110 industries adopting 19 limits 91-96 projects 61-63 reality of 13-29 reasons for 15-21 digital strategy, five steps 146-153 Digital Vehicle 55 Direct Numerical Simulation (DNS) 50 DM, see digital manufacturing DOOM 48 Dutton, Jonathan 139 E EADS-CASA 25 Earth Impact Effects Program 42 Egypt 34 end-to-end requirements 152 energy sector, benefits of digital manufacturing and simulation 130-131 engineering for the masses 145-146 ENOVIA 60 Enterprise Resource Planning (ERP) 58 Enterprise Simulation Management (ESM) 20 F Finite Difference Method (FDM) 50 Finite Element Analysis 51, 66 Finite Element Method (FEM) 50-51 Finite Volume Method (FVM) 50 Fioravanti, design process 101 Flegel, Heinrich 32 Flight Simulator program 48 Ford, Henry 140 Ford Model T 140 G games 48 Gauss, Carl Friedrich 34 Global Human Body Models Consortium 32 Gottorp Globe 45 Grand Challenges 82-85 foundation and actions 85 psychology and culture 85 strategic technology areas 84

164 Greece 35 groupthink 85-90 H Halpern, Marc 154 Hamon, Philippe 113-115 Harrison, John 38 Harvey, Jerry B. 88 Heilala, Juhani 77 HIL simulation 69 I IMTI Modeling and Simulation for Affordable Manufacturing Roadmap, goals 81 industrial revolution, new 146-153 Industrial Revolution 56 Integrated Data Management 92-93 integration with software tools 80 Intercim 55, 60 Interoperable Unit Process Models 81 J Janis, Irving 87 K Kepler, Johannes 34, 44 knowledge, capturing, capitalizing and re-using 151 L Large Eddy Simulation (LES) 50 Leibniz, Gottfried Wilhelm 34, 43 LEONI Wiring Systems 113 M Manhattan Project 46 manufacturing: beneficial use of simulation 66-69 changing undesirable social behavior 86-91 Digital Mock-Up 26 from physical to virtu-real 56-57 Grand Challenges 82-85 history of 56 human factors 86 technical, political, and economic forces for development of 82-83 vision for 2020 83-84

Index 165 manufacturing facility 62 “manufacturing ready” 112-113 manufacturing simulation tools, basic classes of 80 manufacturing systems, simulation in 77-82 ManuFuture 2020 32-34 Martin, Rod 83 Mas, Fernando 26-28 mass customization 140-142 mass production 140 mDMU 26 meta-models, reducing calculating costs through 68-69 Method of Moments (MoM) 50 methodologies, defining compatible 147-148 Michel, Patrick 111 module library 80 Monte Carlo simulation 50 Morris, Tim 150 movies 48 Multilevel Fast Multipole Method (MLFMM) 50 N NAFEMS 150 NASTRAN 51 NC factory machines 57 Newton, Isaac 34 nuclear plants 37, 130 Numerically Controlled factory machines 57 O open innovation 138 Orbach, Greg 59 P PACE 95-96 parametric exploration 68 Pascal, Blaise 43 planetarium 45-46 plant upgrade 62 PLM, see Product Lifecycle Management PLM 2.0 138, 148 Pong 48 PPC software solutions 57 Pressures, Actions, Capabilities, and Enablers (PACE) 95-96 Product Data Management 54

166 Product Lifecycle Management (PLM) 14-16, 58-60, 112 Production & Planning Control software solutions 57 PSA Peugeot Citroën 46, 141 psychology, role in manufacturing 85 R Ramani, Karthik 14 Rauch-Gebbensleben, Benjamin 21 Ray-Tracing Code (RTC) 50 reference models 80 requirements, end-to-end 152 Riversimple urban car 139 Roman Empire 35 Rusina, Fulvio 96-98 S San Onofre Nuclear Generating Station (SONGS) 130-131 SAP 60 future of 93-94 SCADA 103 Scalable Lifecycle Models 81 Schickard, Wilhelm 34, 43 sensitivity analysis 68 Seuffert, Wolf-Peter 55, 92 shipbuilding, benefits of digital manufacturing and simulation 127-128 simulation, see also computer simulation 31 benefits 21-25, 66-69, 109-135 challenges 73-107 coupling simulation and reality 69 crash course 31-71 guidelines for better use of 66 history of 34-38 in everyday life 39-42 in manufacturing systems 77-82 Monte Carlo 50 reality of 13-29 reasons for 15-21 replacing testing 52 speed-up tools 80 static 51 trends 53 simulation capabilities, enhancing 148-150 simulation front-ends 80 simulation languages, general-purpose 80

Index 167 simulation security 152 simulators 80 SISO 81 Sketchpad 47 social behavior, changing undesirable 86-91 SONGS 130-131 Southern California Edison 130 standardization 148 Sutherland, Ivan 47 T Taylor, Frederick Winslow 14, 56 team-based innovation 90-91 Tesla Model S 100 testing: persistance of 53 replaced by simulation 52 traceability 152 traffic forecast 40 TRIS 101 V Vectrix VX-1 Personal Electric Vehicle 100 video games 48 virtual reality in 2015 142-144 Virtual Wind Tunnel 36 virtu-real manufacturing 56-57 Visionary Manufacturing Challenges for 2020 75, 82 Volkswagen 46 Vrinat, Michel 16-18 W weather forecast 40 Whyte, William H. 87 wind tunnel 36 X Xerox 47 Y Yantai Raffles Shipyard (YRS) 127-128 Z Zero Prototype Engineering 52-53

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ranges of high quality and complexity mean that ﬂexible develop- Simulation and Manufacturing Digital with VIRTUAL CONCEPT ment and ramp-up across supply chain networks is crucial to survive with Digital Manufacturing and Simulation and thrive. Global competition, economic pressure, environmental and energy issues demand state-of-the-art capabilities and, above all, timely action. Such formidable challenges can only be met by Nicolas Kawski tightly interwoven lifecycle-oriented engineering and manufacturing technologies and processes. To date more than ever, the ongoing development and integration of digital manufacturing and simulation is critical to eliminate the waste of time and money in the physical world, and to ensure product success as much and as early as possible.

Torsten Kulka Digital manufacturing and simulation clearly constitute contemporary extensions of the train of thought and practice that Frederick

Winslow Taylor started a century ago. The evolution from “Taylor- >

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ISBN 978-90-75414-25-7

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VIRTUAL CONCEPT &gt; REAL PROFIT

VIRTUAL CONCEPT > REAL PROFIT