Technology Roadmapping and Development: A Quantitative Approach to the Management of Technology 9783030883461, 3030883469

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Olivier L. de Weck

Technology Roadmapping and Development A Quantitative Approach to the Management of Technology

Technology Roadmapping and Development

Olivier L. de Weck

Technology Roadmapping and Development A Quantitative Approach to the Management of Technology

Olivier L. de Weck Department of Aeronautics and Astronautics Massachusetts Institute of Technology Cambridge, MA, USA

ISBN 978-3-030-88345-4    ISBN 978-3-030-88346-1 (eBook) https://doi.org/10.1007/978-3-030-88346-1 © Springer Nature Switzerland AG 2022 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

This book is dedicated to Lynn for her love and unending support

Foreword

If you want to spend a million dollars to develop a specific technology or system, you have a myriad of methodologies and tools at your disposal to help plan and execute your project. You might employ, for instance, design thinking, agile, waterfall, systems engineering, model-based design, TRIZ, axiomatic design, and any number of design and project management tools. If you want to spend a billion dollars on a portfolio of technologies, you are pretty much on your own. Not only is there a dearth of sound theoretical work on the subject of technology planning at scale, but the state of practice is remarkably primitive. If you want to spend a trillion dollars over the course of decades, you are in largely untrodden territory. Turns out, we, as a species, are not very good at technology planning. The most celebrated technological feats—the Manhattan Project, the Apollo Program, and the iPhone—are renowned for their rapid execution and narrow focus. There have been long-term projects too—the pyramids and the cathedrals—but these took place in times of minimal technological change. Long-term, diverse technology portfolios do not have a good track record. For instance, the U.S.  Department of Energy invested about as much as the Manhattan Project and Apollo Program  combined (adjusted for inflation) over 35 years into the decarbonization of the US economy with few visible results.1 NASA spent much of the decades of the 1980s, 1990s, and early 2000s with little to show for its sizable crewed space exploration budget largely due to poor planning.2 In my career, I had the opportunity to observe up close technology planning in the Pentagon and in the Silicon Valley venture ecosystem. I was also responsible for a $3 billion/year R&D portfolio at United Technologies and €1 billion in annual technology spending at Airbus (a journey on which this book’s author joined me). While at DARPA, I led an unusual (even for DARPA) initiative called the 100 Year Starship, in which we studied how to organize a multi-decade investment in the 1  The Manhattan Project, the Apollo Program, and Federal Energy Technology R&D Programs: A Comparative Analysis https://fas.org/sgp/crs/misc/RL34645.pdf 2  In 2012, this led NASA to undertake an ambitious technology roadmapping effort described in Chap. 8.

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broad set of technologies needed to travel to the nearest star. While interstellar travel may seem far-fetched and whimsical as a use case for technology planning, the resources and time scales involved are not so different from those needed to decarbonize the world economy, for instance. I had a few battle scars and takeaways from these experiences. First, the approach to technology planning is usually qualitative and lacking in rigor. This is especially apparent when you compare it to the increasingly sophisticated analysis, modeling, and experimentation used in actually executing technology projects and combining multiple technologies to build systems and products. Almost every organization professes to practice roadmapping to inform its technology planning. Most of these roadmaps are—in a term of art I learned from former DARPA head Regina Dugan—“swooshy.” They comprise a fat arrow (a “swoosh”), going from the lower left (bad) to the upper right (good), along an x-axis that loosely corresponds to the passage of time and a y-axis that vaguely represents some unitless measure of progress, with a series of projects enumerated along the swoosh. This kind of roadmap has minimal descriptive value (it is essentially a list of projects) and no prescriptive value whatsoever to help make decisions about which projects should be undertaken, when, and why. Instead, these decisions are made largely through a combination of intuition, opinion, politics, quid pro quos, and fads. What this conceals, of course, is the fact that every organization operates with constraints, including a finite R&D budget to invest in its technology portfolio. In whatever manner decisions are made, they represent a ranking of possible projects, with some getting funded and others cut. A real roadmap makes this process explicit, which can be uncomfortable. It exposes the tradeoffs being made. It pits near-term revenues versus long-term growth and risk versus returns. It forces the choice between low-risk, incremental improvements to existing products and high-risk technology bets with potentially revolutionary but uncertain outcomes. Second, time horizons for technology planning are typically very short: one or two years. This is a byproduct of annual budget cycles, which are ubiquitous both in industry and government. Each budget cycle provides an opportunity to re-plan, particularly as new stakeholders come with different opinions and new priorities. So even if there is a longer-term plan, there is frequent opportunity to deviate from it. While this can be helpful in adapting to lessons learned and changing circumstances, it is generally counterproductive to making progress toward long-term goals. The Pentagon attempts to counteract this through a 5-year planning process. Many companies likewise create multi-year plans. However, since both Congress and corporate boards typically approve budgets on an annual basis, the longer-term planning process is largely a pro forma exercise. Third, there is a frequent failure to recognize the exponential nature of technological progress. In part, this is because the planning intervals are so short that changes in technology look locally linear. It is also because humans are notoriously bad at conceptualizing exponentials. By the time the exponential becomes perceptible, it is usually too late. History is littered with carcasses of companies that failed to spot exponential technological change. Spotting it is no guarantee of success,

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however. Exponentials are notoriously sensitive to initial conditions, so it is important to recognize the limits and uncertainties in technology forecasting. In fact, there is an almost universal failure to take into account and plan for uncertainty in technology planning. This includes technological uncertainty—the risk that a technology may or may not pan out as planned—as well as volatility in budgets, requirements, and priorities. The conventional approach to dealing with uncertainty is with margins—adding reserves to account of lower performance, greater weight, or growth in schedule and budget that commonly plagues technology projects. But there are other potent tools that are seldom employed and almost never in a systematic manner across a technology portfolio. One such tool is diversity—pursuing multiple technological paths that are unlikely to suffer from the same failures. Another is optionality—investing in future flexibility to change course. Both require a quantitative framework for modeling uncertainty and its impact on the value and cost of a technology portfolio. The genesis of this book harks back to one late-summer day in 2016. Prof. de Weck and I met in a Silicon Valley café and I had a proposal. A few months earlier, I was asked by Airbus CEO, Tom Enders, to become the company’s Chief Technology Officer. Tom was just entering his second term as CEO and had an ambitious agenda. He wanted to streamline Airbus’ governance, undertake a digital transformation of the company’s operations and services, and be faster and bolder at technological innovation. Tom understood that the visibly exponential pace of development of digital, electronic, and electrical technologies was much faster than the aerospace industry was used to—and that Airbus had to catch up. I translated Tom’s mandate into three priorities for the Airbus technology organization. First, rationalize, streamline, and focus the roughly €1 billion in annual research and technology (R&T) spending. Second, introduce frequent and ambitious flight demonstrators as a way of bringing together clusters of technologies, accelerating their development, and providing early validation of their maturity. And third, to significantly accelerate the speed with which Airbus developed and manufactured new airplanes and other systems. The efficiencies from the first would also have to pay for the latter two! This was my proposal to Prof. de Weck that day in Silicon Valley—would he come to Toulouse,  France, the heart of Aerospace Valley, and help sort this out? More specifically, would he lead the creation of a rigorous technology planning and roadmapping capability for the company that would help deliver on future flight demonstrators and products? He was perfect for the role. We had known each other for over a decade, with Prof. de Weck providing valuable guidance to DARPA in the agency’s quest to improve the design process for complex military systems. He was an eminent academic who spent much of his MIT career thinking deeply about the interaction between technology and its surrounding social and societal systems. He cut his teeth in industry on the McDonnell Douglas F/A-18 program and knew how to navigate large, complex organizations. And he was originally Swiss, and therefore could plead neutrality between the French and German factions at Airbus, which, while much subsided since its early years as a government-owned consortium, still figured prominently in decision-making.

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Airbus presented an opportunity to take the latest theoretical work from multiple fields (strategic planning, portfolio theory, formal modeling, etc.), mold it into a technology planning and roadmapping process, and prove it out in the messy reality of corporate planning and budgeting at one of the world’s great aerospace companies. Prof. de Weck and I discussed at some length the features of a successful technology planning process and agreed that it should address the four major shortfalls I outlined above: • It should be objective, as well as both descriptive (where we are and where others are) and prescriptive (where we could go and where we should go). • It should explicitly link the technology portfolio to the company’s long-term product and service strategy, and one should inform the other. • It should accurately reflect the pace of technological progress with quantitative figures of merit both for internal projects as well as for the external technology ecosystem. • It should quantify uncertainty and capture the value, cost, and risk associated with each technology and the portfolio as a whole. In the two years that Prof. de Weck spent at Airbus as Senior Vice President of Technology Planning and Roadmapping, most (though not all) of the items on this list went from an aspiration to a pressure-tested methodology, enabled by a robust set of tools and processes, and operationalized by a well-trained and well-respected cadre of technology roadmap owners. And it has endured. Today, the methodology is well on its way to becoming part of Airbus’ cultural fabric. Nothing about this approach, however, is unique to aviation or aerospace. Any technologically driven field such as automotive, consumer electronics, energy, medical devices, and mining—just to name a few—can benefit from a similar journey. Ultimately, it was the freedom and encouragement to write a book based on the experience that convinced Prof. de Weck to come to Toulouse. It would become a book documenting what is certainly the most rigorous technology planning and roadmapping process ever implemented at scale and battle-tested in a complex, corporate environment. It would be a book to teach and inspire a generation of practitioners and theorists to improve the way in which we plan and manage technology development for the long term. This is that book. Los Angeles, CA, USA December 2021

Paul Eremenko

Preface

I am writing these words at the Massachusetts Institute of Technology (MIT), which has been my professional home for the last 25 years. In this book I focus on the last word in the name of our institution: Technology. We all know what it is. And yet, when asked to describe it succinctly, many of us struggle. This is a somewhat startling admission. When asking students, professionals, or the general public for a definition of what is “technology” (without using the word itself) we hear a bewildering variety of answers. This has been compounded in recent years by the use of the short form “tech” to refer among other things to a set of electronic devices we carry around with us. Sometimes “tech” simply seems to refer to all technologies as a collective. It may be useful to go back to the founding of MIT in 1861 to see what was meant by technology back then. The inscription inside Lobby 7, now the main entrance to MIT, has always held a special meaning for me. I see it nearly every day on the way to my office and I often crane my neck to read it again and again, even though I have seen it many times. The text reflects the original intent of William Barton Rogers, the founder of MIT, and it is also reflected in the Institute’s charter. Established for Advancement and Development of Science its Application to Industry the Arts Agriculture and Commerce. Charter MDCCCLXI

Thus, “tech” is about the development, advancement, and beneficial application of scientific principles in industry and in other domains such as the arts, agriculture, and commercial enterprises. We will take a similarly broad view here. Interestingly, MIT itself as an institution was referred to simply as “Tech” or “Technology” in its early years.

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Why This Book? Since my early childhood growing up in Switzerland I have always been fascinated with technology. I would look up at the sky in the Alps through my first telescope, and observe the Moon and planets at night, and I would follow the helicopters resupplying mountain huts and rescuing mountaineers during both day and night. I would disassemble my mechanical alarm clock to better understand how it worked. What material was this device made of? How did it work? What was its internal mechanism? Could it be made better? In the late 1980s, I studied engineering at ETH Zurich and decided to specialize in the area of production and technology management. Right after university I was fortunate to be asked to develop and implement a technology transfer plan for the Swiss F/A-18 aircraft program which is what brought me to the USA in 1994. Little did I know that over 25 years later I would still be living in the USA and that my profession would be to think about technological systems and how they evolve over time. This book was written over a period of three years in 2019–2021, but it is in reality the culmination of two decades of research and application of technology in a variety of sectors. The final impetus for it came when I took a leave of absence from MIT to serve as Senior Vice President for Technology Planning and Roadmapping at Airbus in Toulouse, France, as described in the foreword by Paul Eremenko. Much of what I learned during this time is in this book. The book provides a review of the principles, methods, and tools of technology management, including technology scouting, technology roadmapping, strategic planning, R&D project execution, intellectual property management, knowledge management, technology transfer, and financial technology valuation. In 22 chapters we explain the underlying theory and empirical evidence for technology evolution over time and present a rich set of examples and practical exercises from a number of domains such as transportation, communications, and medicine. The linkage between these topics is shown using what we call the Advanced Technology Roadmap Architecture (ATRA). Each chapter’s position in the ATRA framework is shown using a graphical map at the start of each chapter. Technology roadmapping is presented as the central process that holds everything together (Chap. 8). Readers of this book will learn how to develop a comprehensive technology roadmap on a topic of their own choice. This is also the foundation of my popular MIT class 16.887-EM.427 Technology Roadmapping and Development which was first offered in 2019, and an on-line version of the class available to practitioners via MIT Professional Education. Technology roadmapping is presented as the core activity in technology management. Every year my students develop a number of technology roadmaps which are subsequently published and are freely accessible over the Internet1. There are several reasons that make this book pertinent at this time:  To view these technology roadmaps, use the following link: http://roadmaps.mit.edu

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• Exponential progress of technology in many areas is now apparent. However, quantification of technological progress needs to be done carefully and with real data. Few texts address this issue head-on. • Roadmaps are a central boundary object in technology-based organizations. While there has been much emphasis on innovation in general, there is not a large literature on how to explicitly connect strategy, technology, and finance. The emphasis on roadmapping in this book explains how these concepts link together. • The impact of technologies and the products, missions, and systems in which they are infused on their surrounding ecosystems and industrial clusters is addressed in several chapters. To put it simply, firms should not reinvent the wheel by investing in technologies and intellectual property (IP) that already exist. Conversely, technologies themselves shape innovation ecosystems around the globe in ways that were unimaginable a century ago. The following individuals may find this book interesting and useful: • • • • • • • •

Chief technology officers and chief innovation officers Technology executives and engineering managers Students in engineering, management, and technology Researchers in technology and innovation management Educators Financial market analysts Technology enthusiast and historians of technology Venture capitalists

This book is organized into different parts and chapters within the ATRA framework as follows:

Descriptive Part (Chaps. 1, 2, 3, 4, 5,7, 19, 20, 21, 22) This part describes what we mean by technology, how technological progress can be quantified, and what are the key elements of a technology roadmap. We also look at the history of technology in broad strokes and consider the relationship between nature and human-made (artificial) technologies. This boundary was once considered to be very sharp, but is becoming increasingly blurred with advances in biotechnology.

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Prescriptive Part (Chaps. 8,10, 11, 12, 14, 15, 16, 17) This part develops a systematic approach and methodology for technology roadmapping specifically, and technology management more generally. We review different ways of implementing and linking to each other the most important technology management functions including technology scouting, technology roadmapping, and the management of intellectual property (IP).

Case Studies (Chaps. 6, 9, 13, 18) In this part of the book we take an in-depth look at several case studies of technology development over time. These cases look primarily at cyber-physical systems, that is, those containing complex hardware and software such as automobiles, aircraft, and deep space communications, but not exclusively so. One of our case studies looks at the progress in DNA sequencing, which is one of the foundations of modern biotechnology. These cases and the book overall show that technological progress is not smooth and “automatic.” Rather, it is a deliberate and stepwise continual process, driven by powerful forces such as the desire for human survival, scientific curiosity, as well as competition and collaboration between firms and nations. Technology must be carefully managed, since it may sow the seeds of our eventual destruction as a species, or it may propel humanity to new levels of capability and yet unimagined future possibilities. Cambridge, MA, USA February 2022

Olivier L. de Weck

Acknowledgments

There are many individuals to thank without whom this book would not have seen the light of day. First, my professors and colleagues who initially got me interested in the topic of technological systems in Switzerland in the late 1980s and early 1990s. These include Professors Pavel Hora, Hugo Tschirky, and Armin Seiler at ETH Zürich and Dr. Claus Utz and Dr. Elisabeth Stocker at F+W Emmen (which today is part of the company named RUAG). One of the foundations of thinking about technology in a rigorous way is systems architecture. I want to acknowledge the influence and mentorship I have received from Prof. Edward Crawley at MIT over the years on this subject. Prof. Dov Dori from the Technion introduced me to Object Process Methodology (OPM) – which is used extensively in this book – and our collaboration on applying OPM to technology management has grown into a real friendship. A significant portion of this book is based on a framework for technology management that was elaborated and put into practice at Airbus between 2016 and 2019. At Airbus, there are numerous individuals to thank for their support for what seemed initially to be an insurmountable task. These include Paul Eremenko, the Chief Technology Officer (CTO) who also contributed the foreword to this book, Tom Enders the CEO, members of the Engineering Technical Council (ETC), as well as members of the Research and Technology Council (RTC). My colleagues including Dr. Martin Latrille, Prof. Alessandro Golkar, Fabienne Robin, Jean-Claude Roussel, and Dr. Mathilde Pruvost worked with me to create a new organization called “Technology Planning and Roadmapping” (TPR) with about 60 technology roadmap owners and supporting staff. Specific technology thrusts were spearheaded by Thierry Chevalier in the area of digital design and manufacturing (DDM), Pascal Traverse in autonomy, the late Mark Rich in connectivity, as well as by Glenn Llewellyn in aircraft electrification. Matthieu Meaux and Sandro Salgueiro contributed to the details of the solar electric aircraft sample roadmap in Chap. 8. Marie Tricoire deserves mention for her outstanding administrative support. The passion for technology and planning for a better future were the fuel that carried us through many challenges and difficulties. Further thanks go to Grazia Vittadini, former CTO of Airbus, and Dr. Mark Bentall for continuing to implement the approach, even xv

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after my return to academia. Specific contributions to this book were made by Dr. Alistair Scott on the topic of intellectual property (Chap. 5), as well as Dr. Ardhendu Pathak in the chapters on technology scouting (Chap. 14) and knowledge management (Chap. 15). Once back at MIT, the idea of creating a book and a new class on Technology Roadmapping and Development was greeted with enthusiasm by my department head Prof. Daniel Hastings, as well as by Prof. Steven Eppinger at the Sloan School of Management. The work of Prof. Christopher Magee in tracking technological progress over time was an inspiration and is referenced extensively in several chapters. Prof. Magee also provided a critical and in-depth review of the manuscript. I want to further thank Dr. Maha Haji, former postdoctoral associate at MIT and now a Professor of Mechanical and Aerospace Engineering at Cornell University, as well as my teaching assistants Alejandro “Alex” Trujillo, Johannes Norheim, and George Lordos for supporting the three first offerings of the Technology Roadmapping and Development class at MIT in 2019 and 2021. Dr. Haji in particular contributed substantially to Chap. 19 on industrial ecosystems. Additionally, we had about 80 students, many of them affiliated with the MIT System Design and Management (SDM) program, give valuable feedback on the content of the chapters and the logic and workability of the approach. On specific topics I wish to acknowledge the contributions of Dr. Joe Coughlin and Dr. Chaiwoo Lee on the relationship between aging and technology (Chap. 21), as well as the specific situation of military intelligence and defense technologies that has been extensively studied by Dr. Tina Srivastava in her doctoral thesis and subsequent book (Chap. 20). Dr. Matt Silver, the CEO of Cambrian Innovation, had substantial inputs on Chap. 3 which discusses the relationship of technology with nature. The specific case studies were supported by experts in the field including Dr. Ernst Fricke, Vice President at BMW, on the automotive case (Chap. 6), Dr. Les Deutsch at the Jet Propulsion Laboratory (JPL) on the Deep Space Network (Chap. 13), and Dr. Rob Nicol at the Broad Institute on DNA sequencing (Chap. 18). Moreover, Chap. 12 on technology infusion analysis is largely based on a collaboration with Prof. Eun Suk Suh, formerly a system architect at Xerox Corporation, and now a full professor at Seoul National University (SNU). The work on technology portfolio optimization benefited from the contributions of Dr. Kaushik Sinha. My thanks also go to Dr. Robert Phaal at the University of Cambridge for his detailed review of the manuscript, and the inspiration that his impressive body of work on roadmapping provided to this author. Finally, my thanks go to the staff at Springer Nature for believing in this project and supporting its implementation. First and foremost, Michael Luby, who came to visit me at my MIT office in December of 2019 and is the senior editor for this book. Thanks also go to Brian Halm for excellent advice and coordination during the writing and editing process. I want to thank Cynthya Pushparaj and her team at Springer Nature for typesetting the manuscript and expertly producing this book in both physical and electronic format.

Contents

1 What Is Technology?��������������������������������������������������������������������������������    1 1.1 Definitions of Technology����������������������������������������������������������������    2 1.2 Conceptual Modeling of Technology������������������������������������������������   12 1.3 Taxonomy of Technology ����������������������������������������������������������������   19 1.4 Framework for Technology Management����������������������������������������   23 Appendix����������������������������������������������������������������������������������������������������   28 References��������������������������������������������������������������������������������������������������   29 2 Technological Milestones of Humanity��������������������������������������������������   31 2.1 Prehistoric and Early Inventions ������������������������������������������������������   32 2.2 The First Industrial Revolution ��������������������������������������������������������   37 2.3 Electrification������������������������������������������������������������������������������������   46 2.4 The Information Revolution��������������������������������������������������������������   49 2.5 National Perspectives������������������������������������������������������������������������   53 2.6 What Is the Next Technological Revolution? ����������������������������������   57 References��������������������������������������������������������������������������������������������������   60 3 Nature and Technology����������������������������������������������������������������������������   61 3.1 Examples of Technology in Nature��������������������������������������������������   62 3.2 Bio-Inspired Design and Biomimetics����������������������������������������������   67 3.3 Nature as Technology������������������������������������������������������������������������   74 3.4 Cyborgs ��������������������������������������������������������������������������������������������   79 References��������������������������������������������������������������������������������������������������   82 4 Quantifying Technological Progress������������������������������������������������������   83 4.1 Figures of Merit��������������������������������������������������������������������������������   84 4.2 Technology Trajectories��������������������������������������������������������������������   98 4.3 S-Curves and Fundamental Asymptotic Limits��������������������������������  101 4.4 Moore’s Law ������������������������������������������������������������������������������������  111 References��������������������������������������������������������������������������������������������������  118

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5 Patents and Intellectual Property����������������������������������������������������������  119 5.1 Patenting ������������������������������������������������������������������������������������������  120 5.2 Structure of a Patent – Famous Patents��������������������������������������������  126 5.3 U.S. Patent Office and WIPO������������������������������������������������������������  138 5.4 Patent Litigation��������������������������������������������������������������������������������  141 5.5 Trade Secrets and Other Forms of Intellectual Property������������������  143 5.6 Trends in Intellectual Property Management������������������������������������  148 References��������������������������������������������������������������������������������������������������  152 6 Case 1: The Automobile��������������������������������������������������������������������������  153 6.1 Evolution of the Automobile Starting in the Nineteenth Century����  154 6.2 The Ford Model T ����������������������������������������������������������������������������  157 6.3 Technological Innovations in Automobiles��������������������������������������  162 6.4 New Age of Architectural Competition��������������������������������������������  170 6.5 The Future of Automobiles ��������������������������������������������������������������  178 References��������������������������������������������������������������������������������������������������  181 7 Technological Diffusion and Disruption������������������������������������������������  183 7.1 Technology Adoption and Diffusion������������������������������������������������  184 7.2 Nonadoption of New Technologies��������������������������������������������������  195 7.3 Technological Change and Disruption����������������������������������������������  199 7.4 The Innovator’s Dilemma ����������������������������������������������������������������  204 7.5 Summary ������������������������������������������������������������������������������������������  211 Appendix����������������������������������������������������������������������������������������������������  212 Matlab Code for Agent-Based Simulation of Technology Diffusion����������������������������������������������������������������������������������������������   212 References��������������������������������������������������������������������������������������������������  213 8 Technology Roadmapping����������������������������������������������������������������������  215 8.1 What Is a Technology Roadmap? ����������������������������������������������������  216 8.2 Example of Technology Roadmap: Solar-Electric Aircraft��������������  222 8.2.1 2SEA – Solar-Electric Aircraft ��������������������������������������������  223 8.3 NASA’s Technology Roadmaps (TA1–15) ��������������������������������������  238 8.4 Advanced Technology Roadmap Architecture (ATRA) ������������������  242 8.5 Maturity Scale for Technology Roadmapping����������������������������������  247 Appendix����������������������������������������������������������������������������������������������������  249 References��������������������������������������������������������������������������������������������������  250 9 Case 2: The Aircraft��������������������������������������������������������������������������������  251 9.1 Principles of Flight����������������������������������������������������������������������������  252 9.2 Pioneers: From Lilienthal to the Wright Brothers to Amelia Earhart������������������������������������������������������������������������������  256 9.3 The Bréguet Range and Endurance Equation ����������������������������������  257 9.4 The DC-3 and the Beginning of Commercial Aviation��������������������  262 9.5 Technological Evolution of Aviation into the Early Twenty-First Century����������������������������������������������������������������������������������������������  264 9.6 Future Trends in Aviation������������������������������������������������������������������  270 References��������������������������������������������������������������������������������������������������  274

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10 Technology Strategy and Competition��������������������������������������������������  277 10.1 Competition as a Driver for Technology Development������������������  278 10.2 The Cold War and the Technological Arms Race ��������������������������  282 10.3 Competition and Duopolies������������������������������������������������������������  285 10.4 Game Theory and Technological Competition ������������������������������  290 10.5 Industry Standards and Technological Competition ����������������������  298 References��������������������������������������������������������������������������������������������������  300 11 Systems Modeling and Technology Sensitivity Analysis����������������������  301 11.1 Quantitative System Modeling of Technologies ����������������������������  302 11.2 Technology Sensitivity and Partial Derivatives������������������������������  311 11.3 Role of Constraints (Lagrange Multipliers)������������������������������������  316 11.4 Examples����������������������������������������������������������������������������������������  319 References��������������������������������������������������������������������������������������������������  327 12 Technology Infusion Analysis������������������������������������������������������������������  329 12.1 Introduction������������������������������������������������������������������������������������  330 12.2 Problem Statement��������������������������������������������������������������������������  332 12.3 Literature Review and Gap Analysis����������������������������������������������  333 12.4 Technology Infusion Framework����������������������������������������������������  337 12.5 Case Study: Technology Infusion in Printing System��������������������  344 12.6 Conclusions and Future Work��������������������������������������������������������  356 DSM of the Baseline Printing System ������������������������������������������������������  358 References��������������������������������������������������������������������������������������������������  359 13 Case 3: The Deep Space Network����������������������������������������������������������  361 13.1 History of the Creation of the Deep Space Network����������������������  362 13.1.1 Impetus for the Creation of the DSN����������������������������������  362 13.1.2 Designing the DSN ������������������������������������������������������������  364 13.1.3 JPL Versus STL������������������������������������������������������������������  368 13.1.4 JPL Versus NRL������������������������������������������������������������������  368 13.1.5 The Birth of the Deep Space Network��������������������������������  369 13.2 The Link Budget Equation��������������������������������������������������������������  370 13.3 Evolution of the DSN����������������������������������������������������������������������  373 13.3.1 Organizational Changes in the DSN ����������������������������������  374 13.3.2 The DSN Proceeded in Three Distinct Stages��������������������  374 13.3.3 Mission Complexity as a Driver ����������������������������������������  376 13.3.4 Physical Architecture Evolution ����������������������������������������  379 13.3.5 Technological Evolution of the DSN����������������������������������  382 13.4 Technology Roadmap of the DSN��������������������������������������������������  386 13.5 Summary of the DSN Case ������������������������������������������������������������  389 References��������������������������������������������������������������������������������������������������  392 14 Technology Scouting��������������������������������������������������������������������������������  395 14.1 Sources of Technological Knowledge��������������������������������������������  396 14.1.1 Private Inventors ����������������������������������������������������������������  396 14.1.2 Lead Users��������������������������������������������������������������������������  398

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14.1.3 Established Industrial Firms ����������������������������������������������  400 14.1.4 University Laboratories������������������������������������������������������  402 14.1.5 Startup Companies (Entrepreneurship)������������������������������  404 14.1.6 Government and Non-Profit Research Laboratories����������  405 14.2 Technology Clusters and Ecosystems��������������������������������������������  407 14.3 Technology Scouting����������������������������������������������������������������������  413 14.3.1 What Is Technology Scouting? ������������������������������������������  413 14.3.2 How to Set Up Technology Scouting?��������������������������������  413 14.3.3 What Makes a Good Technology Scout?����������������������������  417 14.4 Venture Capital and Due Diligence������������������������������������������������  418 14.5 Competitive Intelligence and Industrial Espionage������������������������  420 14.5.1 What Is Competitive Intelligence?��������������������������������������  420 14.5.2 What Is Industrial Espionage?��������������������������������������������  420 14.5.3 What Is Not Considered Industrial Espionage?������������������  421 14.5.4 What Are Famous Cases of Industrial Espionage? ������������  422 14.5.5 How to Protect against Industrial Espionage?��������������������  423 References��������������������������������������������������������������������������������������������������  424 15 Knowledge Management and Technology Transfer������������������������������  425 15.1 Technological Representations ������������������������������������������������������  426 15.1.1 Model-Based Systems Engineering (MBSE)���������������������  429 15.2 Knowledge Management����������������������������������������������������������������  430 15.3 Technology Transfer ����������������������������������������������������������������������  434 15.3.1 Internal Technology Transfer����������������������������������������������  437 15.3.2 External Technology Transfer��������������������������������������������  439 15.3.3 United States-Switzerland F/A-18 Example (1992–1997)������������������������������������������������������������������������  440 15.4 Reverse Engineering ����������������������������������������������������������������������  443 References��������������������������������������������������������������������������������������������������  446 16 Research and Development Project Definition and Portfolio Management ��������������������������������������������������������������������������������������������  447 16.1 Types of R&D Projects ������������������������������������������������������������������  448 16.2 R&D Individual Project Planning ��������������������������������������������������  450 16.2.1 Scope����������������������������������������������������������������������������������  451 16.2.2 Schedule������������������������������������������������������������������������������  452 16.2.3 Budget ��������������������������������������������������������������������������������  453 16.2.4 Plan Refinement and Risks ������������������������������������������������  455 16.2.5 Project Identity and Charter������������������������������������������������  457 16.3 R&D Project Execution������������������������������������������������������������������  460 16.4 R&D Portfolio Definition and Management����������������������������������  464 16.5 R&D Portfolio Optimization����������������������������������������������������������  470 16.5.1 Introduction������������������������������������������������������������������������  470 16.5.2 R&D Portfolio Optimization and Bi-objective Optimization ����������������������������������������������������������������������  473

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16.5.3 Investment Requirements for Technology Value Unlocking����������������������������������������������������������������������������  475 16.5.4 Technology Value Connectivity Matrix������������������������������  476 16.5.5 Illustrative Examples����������������������������������������������������������  477 16.5.6 Example 1 ��������������������������������������������������������������������������  477 16.5.7 Example 2 ��������������������������������������������������������������������������  479 16.5.8 The Future of R&D Portfolio Optimization�����������������������  481 References��������������������������������������������������������������������������������������������������  483 17 Technology Valuation and Finance��������������������������������������������������������  485 17.1 Total Factor Productivity and Technical Change����������������������������  486 17.2 Research and Development and Finance in Firms��������������������������  490 17.2.1 Balance Sheet (B/S)������������������������������������������������������������  490 17.2.2 Income Statement (Profit and Loss Statement: P/L)����������  491 17.2.3 Projects��������������������������������������������������������������������������������  491 17.3 Examples of Corporate R&D����������������������������������������������������������  496 17.4 Technology Valuation (TeVa)����������������������������������������������������������  500 17.4.1 What Is the Value of Technology?��������������������������������������  500 17.4.2 Net Present Value (NPV)����������������������������������������������������  502 17.4.3 Other Financial Figures of Merit����������������������������������������  504 17.4.4 Multi-Stakeholder View������������������������������������������������������  505 17.4.5 Example: Hypothetical Commuter Airline ������������������������  505 17.5 Summary of Technology Valuation Methodologies������������������������  515 17.5.1 Organization of Technology Valuation (TeVa) in Corporations�������������������������������������������������������������������  518 References��������������������������������������������������������������������������������������������������  519 18 Case 4: DNA Sequencing������������������������������������������������������������������������  521 18.1 What Is DNA?��������������������������������������������������������������������������������  522 18.2 Mendel and the Inheritance of Traits����������������������������������������������  523 18.3 Early Technologies for DNA Extraction and Sequencing��������������  524 18.4 Cost of DNA Sequencing and Technology Trends ������������������������  527 18.5 New Markets: Individual Testing and Gene Therapy ��������������������  531 References��������������������������������������������������������������������������������������������������  533 19 Impact of Technological Innovation on Industrial Ecosystems ����������  535 19.1 Interaction Between Technological Innovation and Industrial Structure������������������������������������������������������������������  536 19.2 Dynamics of Innovative Ecosystems and Industries����������������������  537 19.3 Proliferation and Consolidation������������������������������������������������������  543 19.4 System Dynamics Modeling of Technological Innovation ������������  545 19.5 Nuclear Power in France Post-WWII ��������������������������������������������  551 19.6 Electric Vehicles in France��������������������������������������������������������������  554 19.7 Comparative Analysis ��������������������������������������������������������������������  557 References��������������������������������������������������������������������������������������������������  559

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20 Military and Intelligence Technologies��������������������������������������������������  561 20.1 History of Military Technology������������������������������������������������������  562 20.2 Example: Progress in Artillery��������������������������������������������������������  568 20.3 Intelligence Technologies ��������������������������������������������������������������  577 20.4 Commercial Spinoffs from Military and Intelligence Technologies ����������������������������������������������������������������������������������  579 20.5 Secrecy and Open Innovation ��������������������������������������������������������  580 References��������������������������������������������������������������������������������������������������  585 21 Aging and Technology������������������������������������������������������������������������������  587 21.1 Changing Demographics����������������������������������������������������������������  588 21.2 Technology Adoption by Seniors����������������������������������������������������  591 21.3 Universal Design����������������������������������������������������������������������������  599 References��������������������������������������������������������������������������������������������������  601 22 The Singularity: Fiction or Reality?������������������������������������������������������  605 22.1 Ultimate Limits of Technology ������������������������������������������������������  606 22.2 The Singularity��������������������������������������������������������������������������������  613 22.3 Human Augmentation with Technology ����������������������������������������  619 22.4 Dystopia or Utopia?������������������������������������������������������������������������  622 22.5 Summary – Seven Key Messages ��������������������������������������������������  629 References��������������������������������������������������������������������������������������������������  630 Index������������������������������������������������������������������������������������������������������������������  631

List of Abbreviations and Symbols

Symbols ⇨ Exercises in chapters that are meant for self-study ➽ Questions as a prompt for group discussion [ ] Units of measurement ✦ Definition * Quote

Abbreviations and Acronyms ACH AGI AI AOA AR ASCII ASIP AUTOSAR BCE BEV BIT BLI BOF BOM BPR BPS bp B/S

Automated Clearing House Artificial General Intelligence Artificial Intelligence Angle of Attack Augmented Reality American Standard Code for Information Interchange Aircraft Structural Integrity Program AUTomotive Open System ARchitecture Before Common Era Battery Electric Vehicle Built-In Test Boundary Layer Ingestion Basic Oxygen Furnace (steel making) Bill of Materials Bypass Ratio Biomass Production System Base Pairs Balance Sheet xxiii

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CAFE Cal CAPEX CCS CD CDF CE CEMO CFRP CONOPS CLD CPI CPM CPU CRISPR CTO DARPA DDI DMMH/FH DNA DOC DOD DRB DSM DSOC DSN EAF EBIT ECU EDF EDL EEX EIS EML2 EMR EPA EPE EV EVM FAL FDI FFRDC FMA FMS FOM

List of Abbreviations and Symbols

Corporate Average Fuel Economy One kilocalorie of energy Capital Expenditures Carbon Capture and Storage Compact Disk Concurrent Design Facility Common Era Complex Electro-Mechanical-Optical Carbon Fiber Reinforced Polymer (material) Concept of Operations Causal Loop Diagrams Cost Performance Index Critical Path Method Central Processing Unit Clustered Regularly Interspaced Short Palindromic Repeats Chief Technology Officer Defense Advanced Research Projects Agency Digital Display Indicator Direct Man Maintenance Hours per Flight Hour Deoxyribonucleic acid Diesel Oxidation Catalyst Department of Defense Design Record Books Design Structure Matrix, or Dependency Structure Matrix Deep Space Optical Communications Deep Space Network Electric Arc Furnace Earnings Before Interest and Taxes Electronic Control Unit Electricité de France Entry Descent and Landing European Energy Exchange Entry Into Service Earth Moon Libration Point 2 Electronic Medical Records Environmental Protection Agency Enhanced Performance Engine Electric Vehicles Earned Value Management Final Assembly Line Foreign Direct Investment Federally Funded Research and Development Center First Mover Advantage Foreign Military Sales Figure of Merit

List of Abbreviations and Symbols

FPGA Field Programmable Gate Array FPM Functional Performance Metric FTP Federal Test Procedure GI Gastrointestinal GNP Gross National Product GPU Graphical Processing Unit GSE Ground Support Equipment GUI Graphical User Interface HAPS High Altitude Pseudo Satellites HEV Hybrid Electric Vehicle HPC High Performance Computing HR Human Resources HSR High Speed Rail (System) HSS High Strength Steel ICE Internal Combustion Engine ICU Intensive Care Unit IOT Internet of Things IP Intellectual Property IRL Integration Readiness Level ISRU In Situ Resource Utilization IT Information Technology ITAR International Traffic in Arms Regulations ITU International Telecommunications Union ISO International Organization for Standardization JPL Jet Propulsion Laboratory JV Joint Venture JWST James Webb Space Telescope KM Knowledge Management KPI Key Performance Indicator kya Thousands of years ago LAN Local Area Network LDP Low Drag Pylon LEX Leading Edge Extension Liquid Hydrogen LH2 LHS Left Hand Side LIB Lithium Ion Battery LIB Larger is Better LLO Low Lunar Orbit LOM Loss of Mission LSP Lunar South Pole MaaS Mobility as a Service MBSE Model-Based Systems Engineering MDM Multi-Domain Mapping Matrix MFC Microbial Fuel Cell MOSFET Metal–Oxide–Semiconductor Field-Effect Transistor

xxv

xxvi

MOT MRO mya M&A NAE NAICS NDA NE NEDC NIH NIH NIST NOx NPV NRC NRC NRE NREL NZF OEM OP OPD OPEX OPL OPM PCB PCT PDP PEV PHC PI PRC P/L PM PSTN PV RFID RHS RMO RNA ROI RT RVI R&D SAM

List of Abbreviations and Symbols

Management of Technology Maintenance Repair and Overhaul Millions of years ago Mergers and Acquisitions National Academy of Engineering North American Industry Classification System Non-Disclosure Agreement Nash Equilibrium New European Driving Cycle National Institutes of Health Not-Invented Here Effect National Institute for Standards and Technology Oxides of Nitrogen Net Present Value National Research Council Non-Recurring Cost Non-Recurring Engineering National Renewable Energy Laboratory Non-Zero Fraction Original Equipment Manufacturer Operational Program Object Process Diagram Operating Expenditures Object Process Language Object Process Methodology Printed Circuit Board Patent Cooperation Treaty Product Development Process Plug-in Electric Vehicle Patent Holding Company Program Increment People’s Republic of China Profit and Loss Statement Particulate Matter Public Switched Telephone Network Photovoltaics, also known as solar cells Radio Frequency Identification Right Hand Side Roadmap Owner Ribonucleic Acid Return on Investment Remote Terminal Relative Value Index Research and Development Surface to Air Missile

List of Abbreviations and Symbols

SARS SETI SI SLAM SME SOW SPI SPL SPO SSTO STEM SUV SWIFT SysML TAA TAM TCP/IP TDP TGV TIA TPS TRD TRIZ TRL TSTO UAV USPTO VFR VLSI VMT VR WBS WIPO WRU WWI WWII WWW

Severe Acute Respiratory Syndrome Search for Extraterrestrial Intelligence Système International (international unit system) Simultaneous Localization and Mapping Subject Matter Expert Statement of Work Schedule Performance Index Sound Pressure Level Single Pilot Operations Single Stage To Orbit Science Technology Engineering Mathematics Sports Utility Vehicle Society for Worldwide Interbank Financial Telecommunication Systems Modeling Language Technical Assistance Agreement Technology Acceptance Model Transmission Control Protocol/Internet Protocol Technical Data Package Train à Grande Vitesse Technology Infusion Analysis Toyota Production System Technology Roadmapping and Development Theory of the Resolution of Invention-Related Tasks Technology Readiness Level Two Stage to Orbit Unmanned Aerial Vehicle United States Patent and Trademark Office Visual Flight Rules Very Large-Scale Integration Vehicle Miles Traveled Virtual Reality Work Breakdown Structure World Intellectual Property Office Weapons Replaceable Unit World War I World War II World Wide Web

Mathematical Symbols B c C/N

Bandwidth [Hz] Speed of light in vacuum [m/s] Signal-to-Noise Ratio [-]

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D E E[ΔNPV] σ[ΔNPV] DT ,Di gC gI go h K l m N Ne NECΔDSM NECDSM N1 N2 Pi Rmax TIA TDSM v V, Vi Vo v(g) TDSM Ne Q QH K L t w 𝜎w

List of Abbreviations and Symbols

Diameter [m] Energy [J] Expected Marginal Net Present Value Standard Deviation of the Expected Marginal Net Present Value Total demand for the market segment, and demand for ith product Critical value for the attribute Ideal value for the attribute Market segment average value for the attribute Height [m] Market average price elasticity (units / $) Length [m] Mass [kg] Number of competitors in the market segment Number of elements in the DSM Number of non-empty cells in the ΔDSM Number of non-empty cells in the DSM Number of elements in the DSM Number of elements in the ΔDSM Price of the ith product Maximum data rate [bps] Technology Infusion Analysis Number of hours required to build a DSM model Velocity [m/s] Value of the product, Value of the ith product Average product value for the market segment Normalized value for attribute g Number of work hours required to build a DSM model Number of elements in the DSM Economic output measured as GNP (gross national product) in $ Heat [J] Capital actively in use in units of $ Labor force employed in units of man-hours1 Time in years Width [m] Yield strength [MPa]

1  Both capital K and labor L account for active workers and capital assets in use. This means that unemployment and idle machinery have to be corrected for.

Chapter 1

What Is Technology?

Advanced Technology Roadmap Architecture (ATRA) Inputs

Steps

Outputs

L1 Products and Missions

1. Where are we today?

FOMjj

Strategic Drivers for Technology

Technology State of the Art and Competitive Benchmarking

L2 Technologies

+5y Organization

2. Where could we go? Technology Systems Modeling and Trends over Time

Technology Projects

FOMi

Today

+10y FOMj

Dependency Structure Matrix

L1

Figures of Merit (FOM) Current State of the Art (SOA) Technology Trends dFOM/dt

Competitor 1 Competitor 2

Technology Systems Modeling Tech Pul Pull

Technology Roadmaps

+10y

Scenario A

+5y

Scenario B ?

3. Where should we go?

L2

Scenario Analysis and Technology Valuation E[NPV] - Return

Intellectual Property Analytics

4. Where we are going! Technology Portfolio Valuation, Optimization and Selection

Technology Investment Efficient Frontier Technology Portfolio Technology Projects σ[NPV] - Risk

Foundations

1

1

Definitions What is Technology?

History Milestones of Technology

Ecosystems Nature Technology, Nature Technology Diffusion, Infusion and Industry and Humans

The Future Is there a Singularity ?

© Springer Nature Switzerland AG 2022 O. L. de Weck, Technology Roadmapping and Development, https://doi.org/10.1007/978-3-030-88346-1_1

Design Reference Missions Future Scenarios Valuation T h l Technology V l ti Vector Charts

FOMi

Tech Push

Technology Scouting Knowledge Management

Scenario-based Technology Valuation

Case 1

Case 2

Automobiles

Aircraft

Pareto-optimal set of technology investment portfolios Recommended Technology Portfolio (Expected NPV and Risk)

C Cases Case 3 Deep Space Network

Case 4 DNA Sequencing

1

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1  What Is Technology?

1.1  Definitions of Technology Several definitions of what is meant by “technology” exist in the literature. Contrary to popular belief, the term is relatively recent. The first use of the word technology is generally traced back to the nineteenth century, and it became much more pervasive only in the first half of the twentieth century. ⇨ Exercise 1.11 What is your own personal definition of technology? Write it down. Do not look up a definition online or in a dictionary before answering this question.

The etymology2 of the word “technology” goes back to the Greek: Techne  – logia. It can be roughly translated to English as the “science of craft,” coming from the Greek τέχνη, techne, which means “art, skill, cunning of hand”; and the morpheme -λογία, −logia, which means “communication of divine origin.”3 This dual nature of technology is very important and will stay with us throughout this book. Technology can therefore be defined both as an ensemble of deliberately created processes and objects4 that together accomplish some function as well as the associated knowledge and skills used in the conception, design, implementation, and operation of such technological artifacts. A specific technology is then an instance of the application of said “science of craft” to solve a particular problem. Examples of this distinction between the underlying scientific knowledge and the embodiment of the technology itself, along with the problem it addresses, are given in Table 1.1. It is also important to distinguish between technologies and products. Technologies enable and are a part of products and larger systems (see Chap. 12) and are not usually the product itself. In Fig. 1.1, we look deeper at the first example, the electrically powered refrigerator. The left side shows the underlying thermodynamic cycle of a heat engine such as the one used in a refrigerator, and named after the French scientist Sadi Carnot (1796–1832). The refrigerator (right side) implements a heat engine according to the theory of the Carnot cycle (left side). The Carnot cycle defines the state changes of a working fluid (coolant) in terms of its pressure (p), temperature (T), and volume (V). By

1  Exercises are interspersed in each chapter to challenge the reader and help them explore more deeply their own mental models about key terms or concepts related to technology. However, readers may skip these exercises without loss of information or coherence. 2  Etymology is the science of the origins of words in human natural language. 3  See https://en.wikipedia.org/wiki/Technology, URL accessed June 30, 2020. 4  We will argue below that the deliberate creation of technology is a key element of understanding what it is. This means that objects and processes that occur spontaneously in nature, without the active involvement of an agent, are not “technology” as we understand it. Chap. 3 discusses the link of nature with technology in depth.

1.1  Definitions of Technology

3

Table 1.1  Distinction between technology as knowledge, technology as embodiment, and the specific problem solved by technology: four examples Scientific knowledge Technology embodiment Thermodynamics – the Carnot Electrically powered refrigerator cycle Microbiology – pasteurization High-temperature food processing using heat exchangers Fluid mechanics – Bernoulli’s Fixed-wing heavier-than-air aircraft principle Genetics – DNA double-helix molecule structure

Sanger’s method for DNA sequencing with the chain-­ termination method

Problem addressed Prolonging the shelf life of food and drink Preventing milk from carrying pathogens Rapidly transporting people over long distances Testing humans for genetically linked diseases

Fig. 1.1  Example of technology: refrigerator operating according to the Carnot cycle

going around the cycle counterclockwise, a low-pressure cold gas at point A is compressed adiabatically (without adding or removing heat) which raises its pressure and temperature to point B at which point it becomes a hot gas and is sent from the compressor to the condenser. The condenser is typically located at the back of the refrigerator which is the warmest part of the machine. The temperature of the condenser coils is hotter than the ambient air which implies a heat transfer from the hot working fluid to the surrounding air. The process of condensation B-C turns the hot gas into hot liquid-gas mix. The hot coolant is then sent through an expansion valve which allows it to cool from a high- to a low-temperature C-D. The cold fluid is then

4

1  What Is Technology?

sent to the evaporator inside the air chamber of the refrigerator. The evaporation is powered by extracting heat (QH) from the air inside and increases the volume of the fluid by allowing it to boil, that is, turn from a liquid back to a gaseous state. This process going from D-A extracts heat from within the air chamber and keeps food and drinks cold, thus prolonging their shelf life. The cold gas then returns to the ⇨ Exercise 1.2 What is an example of a technology you know and care about, and what are its underlying scientific knowledge and principles and the problem it solves?

compressor at A, after which the cycle is repeated as long as the temperature in the air chamber is above the temperature set on the thermostat. This example illustrates that in order to “master” the technology of refrigeration, both the theory of its operation (its underlying scientific principles) and its physical implementation have to be understood. This duality is something we call “mens et manus” at MIT, the working together of mind and hand. In the German language there is a distinction between the word “Technik” and “Technikwissenschaften.” The former refers primarily to the visible and tangible manifestation of technology, while the latter emphasizes the scientific and knowledge-­related aspects of technology. This distinction has largely disappeared, or never really existed in English. Schatzberg (2006) explains in detail how “technology” became a keyword only in the early twentieth century in the Anglo-Saxon world, whereas earlier a number of different expressions were used to describe the application of “arts and sciences” to industrial applications. Similar semantic subtleties with respect to technology exist in French, Chinese, and other languages. Despite these differences, most cultures agree that technology: • Does not occur spontaneously in nature, but is the result of a deliberate act of creation by one or more agents. As Thomas Hughes (2004) stated so well: “Technology is a creative process involving human ingenuity.” Here, we will argue that the agents may not always be humans, and that technology can also be invented accidentally (e.g., cooking food by using fire). • Results in the creation of one or more artifacts that are subject to inspection. In other words, the results of technological creation can be seen and used in the real world, such as in machines, software, tools, processes, etc. A mere idea is not (yet) a technology. • Requires specific knowledge and/or skills that must be acquired through study, apprenticeship, or copying from other agents. The technological knowledge can be based on planned scientific research and development or serendipitous discovery. • Solves a specific problem or challenge or creates a new capability. Technology does not exist merely for its own sake but it is or should be purpose-driven, usu-

1.1  Definitions of Technology

5

ally but not always, to improve the condition of those who invent, deploy, or use it. It has been suggested that the ability to invent new technologies is something that sets humans apart from other species on Earth. This topic has also been the subject of study for many philosophers who have reflected on the nature of humans and technology. One of them is the Scottish philosopher David Hume who wrote: The first and most considerable circumstance requisite to render truth agreeable, is the genius and capacity, which is employed in its invention and discovery. What is easy and obvious is never valued; and even what is in itself difficult, if we come to the knowledge of it without difficulty, and without any stretch of thought or judgment, is but little regarded. (David Hume (1739–1740), “A Treatise of Human Nature”, Book II, Part III, Sect. X) This quote speaks forcefully to the agency and effort required in making new scientific discoveries and rendering them useful to society. This should make us reflect more deeply on the relationship between science and engineering – the discipline generally credited with creating technology, art, and society. ➽ Discussion How does science create new knowledge? How is such knowledge rendered useful to society? What is the relationship between technology and engineering? How is technology different or similar to art5?

Technology is all around us. Unless you find yourself somewhere in the far northern latitudes of the Arctic or the sweltering heat of the Sahara or Gobi deserts, you cannot escape visible signs of technology and human civilization. Even in those remote places you will see satellites passing overhead at night reminding you that we have fundamentally reshaped life on this planet through technology. In Chap. 2, we will explore the technological milestones of humanity. The invention of the steam engine coupled with rotary motion in the eighteenth century began augmenting human and animal power with mechanical power and paved the way for the first industrial revolution. This included rapid transportation by ship, train, and later by air across continents and above the world’s oceans.

5  The reason we ask about art here is that in education the paradigm of STEM (science, technology, engineering, and mathematics) has become very prevalent, and is sometimes augmented as STEAM (science, technology, engineering, arts, and mathematics) to emphasize the importance of creativity. 6  We celebrated the 50th anniversary of the Apollo 11 mission in 2019. MIT’s Instrumentation Laboratory under Charles “Doc” Draper developed the guidance and navigation system for Apollo. 7  Some argue that Artificial Intelligence (AI) is the basis for a twenty-first-century technological revolution, but the roots of AI can in fact be traced back to the mid-twentieth century and are therefore not fundamentally new. This is not meant to diminish the tremendous impact that AI already has on many products and services, and society at large.

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1  What Is Technology?

Electrification helped light the night sky and led to the second industrial revolution in the late nineteenth century. The invention of the digital computer in the twentieth century enabled the lunar landings of program Apollo6 and the Internet revolution which has transformed how we as humans create, share, and consume information. This is often referred to as the third industrial revolution. More recently, the invention of genomic sequencing and gene editing is remaking the very nature of biology, which may well lead to the next technological revolution in the twenty-first century.7 The jury is still out as to what will be the largest driver of technological innovation in the twenty-first century. There are several candidates such as the sequencing and editing of DNA mentioned above (a strong candidate),8 the mastery of quantum effects as in quantum computing, the merging of hardware and software in large coupled networks as in cyber-physical systems, or the discovery of the exact nature of dark matter as we probe closer and closer to the Big Bang with a new generation of infrared space telescopes such as the James Webb Space Telescope (JWST). Or it may be something entirely different that no human has yet conceived of or understood. Every one of the abovementioned technologies and systems is the result of human ingenuity, determination, hard work, and transformation from a mere idea to physical reality. Many of these artifacts and capabilities are the outcome of multiyear research and development (R&D) projects executed by teams of people, consuming money, producing new technology and value, and overcoming failure. Everything man-made9 we see around us such as buildings, roads, bridges, automobiles, aircraft, spacecraft, hospitals, lights, computers, cleaning products, medications, and even some of the food we eat is the result of the following scientific, engineering, and design processes: • • • • • •

Inquiry and discovery Inspiration from nature (see Chap. 3) Invention including architecting and design Implementation and production Verification and replication Adoption and use (see Chap. 7)

 Chapter 18 will focus on the technological evolution of DNA sequencing.  When we say “man-made” we refer to inventors of all genders. The key distinction, which we probe deeper in Chap. 3, is that these products, systems, and services would not occur spontaneously in nature without human intervention or replication. This is also related to the notion of artificiality. We sometimes refer to human-made technology. 10  The aspect of deliberate continual improvement is a key feature of human-originated technology. We view the spontaneously occurring processes of evolution and natural selection in nature as distinct from this, as discussed in Chap. 3 on the relationship of nature and technology. A philosophical argument can be made that since humans (homo sapiens sapiens) are part of nature, that therefore technological evolution driven by humans is in itself simply an extension of natural evolution, including natural selection. The emergence of what has been called the Anthropocene, that is, a new age where human technology shapes our planet at a faster rate than the underlying natural processes that predate the industrial revolution, is generally recognized as new and important. Some of these anthropogenic effects turn out to be potentially undermining our long-term survival as a species on planet Earth. 8 9

1.1  Definitions of Technology

7

Fig. 1.2  Examples of technology in use today from upper left to lower right: Basic Open Furnace (BOF) in a steel mill, array of photovoltaic (PV) cells in a solar farm, graphical processing unit (GPU) for computing, large commercial aircraft, high-voltage electrical power transmission grid, the Deep Space Network (DSN), cryogenic hydrogen tank for the first stage of a large launch vehicle, grid-level lithium-ion electrical battery, optical compact disk technology (CD) for data storage

• Copying and technology transfer (see Chap. 15) • Continual improvement10 (see Chap. 4) Someone came up with the original idea. Some individual or group of individuals had the tenacity to prototype it. Someone had the courage to share it with others. Someone had the intellect and scientific acumen to perform experiments, derive equations, and uncover the working principles underpinning all of these artifacts, machines, and even life itself. This is the visible manifestation of technology. Figure 1.2 shows a collage of different technologies in use in the early twenty-­ first century. As we will see later in this chapter, the order in which these technologies are arranged in Fig. 1.2 is not random. For now, notice that the examples in the three columns relate to matter, energy, and information, respectively. It should also be noted that in each of these examples the technology does not exist alone, in

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1  What Is Technology?

isolation, but it is part of a larger system. Systems that contain technologies and are enabled by them are referred to as technological systems. For our purposes, we will now provide two definitions of technology, a longer one and a shorter one. No one can claim to have found the right definition of technology for all purposes and all audiences. Neither do we. However, we not only provide these definitions but also explain them in some detail. Long Version Technology is both knowledge and physical manifestation of objects and processes in systems deliberately created to enable functions that solve specific problems defined by its creators. This definition is intentionally abstract. It is similar and yet different from some of the common definitions of technology such as “Technology is the collection of techniques, skills, methods, and processes used in the production of goods or services or in the accomplishment of objectives.”11 ➽ Discussion Are humans the only ones capable of creating technology? Can technologies exist on their own or are they always part of a larger system such as an artifact, product, or system? Are technologies always created to generate value for some stakeholder? Does technology always have to be replicated and scaled up to have impact?

We see the following aspects as critical to understanding the essence of what is technology: • Technology is dual in the sense of knowledge of objects and associated processes, and their physical instantiation in the “real world” (as opposed to only in the mind of their creator). • Technology never exists in isolation. Technology is always part of a larger ensemble that we refer to as a “system” or a “system of systems.” In order for technology to have an effect on the real world, it must act on some objects, processes, or agents that are not part of the technology itself. Therefore, technology is always embedded in or infused in a parent system (see Chap. 12).

 See the source of this definition at: https://en.wikipedia.org/wiki/Technology. There are several points of debate that often come up with regard to a general definition of technology. These are summarized in the discussion point above and we encourage the reader to discuss these questions with a group of peers. 12  This will be explored more deeply in Chap. 3 on technology and nature. 13  It has been shown that homo neanderthalensis (ca. 400,000–40,000 BCE) also used fire, created tools, and was capable of inventing simple technologies. If humans, other animals with highly developed brains, and computers with AI can be potential originators of technology, we cannot preclude the existence of alien technology in or beyond our own solar system. In that case the beneficiary of technology will not be humans. 11

1.1  Definitions of Technology

9

• Technology does not arise spontaneously but is the result of a deliberate act of creation by one or more agents. Classically, we think of humans as agents and the sole creators of technology. However, recently it has been shown that other species (other than the subspecies Homo sapiens sapiens) can also create technology12 and that computers endowed with artificial intelligence (AI) may also create technology. Therefore, we use the rather unfamiliar and more general term “agent” as the potential originator of technology.13 • There is no such thing as “general technology.” Technology only exists in connection with a specific function or purpose. A specific technology may primarily help to solve the problem or class of problems of interest and may not represent the entirety of the solution space (see examples in Fig. 1.2). However, technologies may be repurposed from one use case to another. There may also exist multiple parallel and potentially competing technologies intended to solve the same problem. Usually, when “technology” is used as a general term, it refers to specific technologies as a collective. • Technologies are mostly created by humans with the intent to improve their own condition, as in providing clean drinking water, abundant food, safe transportation, the curing of diseases, rapid communications, etc. However, some technologies have known or emerging side effects that may be deleterious. An example would be technologies that rely on fossil fuels as a source of energy, thereby releasing carbon into the atmosphere which has been shown to be a major contributor to climate change on Earth. Some technologies, since the earliest days of humanity’s journey, exist specifically to harm or destroy some humans for the “benefit” of other humans, such as certain classes of weapons.14 While we do not take a position in promoting or favoring some technologies over others in this book, we emphasize the need to think through all major aspects of technologies when creating, deploying, or simply analyzing them. It should now be clear that understanding technology deeply is not a simple undertaking and that its creation and study requires a sustained effort over many years, both by individuals and by society as a whole. We now provide a shorter and more succinct definition of technology. Short Version Technology is both knowledge and deliberate creation of functional objects to solve specific problems. What is the relationship between technology, science, and engineering? The words technology, science, and engineering are often used interchangeably by the general public. They are related but not synonymous. Figure 1.3 shows the relationship between technology, science, and engineering in a societal context. The exact semantics of these words and their relationship is the subject of ongoing  The issues associated with technologies for military and intelligence purposes are explored in Chap. 20, where we cover technologies for offensive and defensive purposes including nuclear weapons and the emergence of cybersecurity-related technologies.

14

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1  What Is Technology?

Fig. 1.3  Relationship between technology, science, and engineering

research in the social sciences and in the field of Engineering Systems (de Weck et al. 2011), among others. The object process diagram (OPD) in Fig. 1.3 uses symbols that can be shortly summarized as follows: objects are represented by rectangles, whereas processes are ovals. The diagram in Fig. 1.3 is drawn using Object Process Methodology (OPM), a general conceptual systems modeling language that we will be using extensively in this book (Dori 2011). OPM became a standard in 2015 (ISO 19450) and helps clarify the semantics (meaning) and logical relationship between different entities. OPM produces both graphical representations and automatically also a formal Object Process Language (OPL) representation, thus appealing to multiple forms of cognitive processing and brain lateralization.15 We will use OPM to conceptually model technologies throughout this book. An OPL representation of Fig. 1.3 is shown below: Technology is physical and systemic. Society is physical and systemic. Nature On Earth is physical and systemic. Science is informatical and systemic. Engineering is informatical and systemic. Knowledge is informatical and systemic. Problems of Humans are physical and systemic. Solar System is physical and environmental. Humans are physical and systemic. Society relates to Nature on Earth. Solar System relates to Nature on Earth. 15

 According to Brain lateralization, language processing is often dominant in the left hemisphere.

1.1  Definitions of Technology

11

Humans are an instance of Society. Humans exhibit Problems. Discovering is informatical and systemic. Humans handle Discovering. Discovering requires Nature on Earth and Science. Discovering yields Knowledge. Creating is physical and systemic. Humans handle Creating. Creating requires Engineering and Knowledge. Creating yields Technology. Using is physical and systemic. Humans handle Using. Using requires Technology. Using affects Problems of Humans. Initially, this formal language may seem unfamiliar or even awkward to the uninitiated. However, these formal OPL statements, which are automatically generated from the corresponding graphical representation, help us better grasp the role of technology, which is the main subject of this book. Humanity is organized into different groups, tribes, or nations that we collectively refer to as “Society.” As such, society relates to “Nature” which includes our entire planet Earth including its geological mass, its biomass made up of plants and animals, the land, the oceans, the atmosphere, the Earth’s magnetic field, and all technological artifacts we have created. A recent approach by economists is to quantify the inclusive wealth of regions, countries, or the planet as a whole. This includes its natural capital (forests, minerals, animals, etc.), human capital (the population including its longevity, level of education, etc.), and produced capital (infrastructure, sovereign wealth, etc.), see Duraiappah and Munoz (2012). With the growth of the human population, especially over the last century, there has been a shift from natural capital to human capital and produced capital. Sustainability science is working to establish the carrying capacity of our planet and studies “problems” of society at different scales: individual, local, regional, national, and planetary.16 One problem which has been studied for centuries, for example, by Robert Malthus (1766–1834), is the relationship between food production and population growth. Agricultural technology, such as improved corn seeds, is a good example of the link between nature, society, science, engineering, and technology.17 Science studies nature to discover new principles and “laws.” This leads to new knowledge or confirms or modifies existing knowledge. Engineering applies this

 Eventually, humanity may become a multi-planetary species which may require expansion of these considerations. For the moment we focus mainly, but not exclusively, on technology located here on Earth. 17  The adoption and diffusion of new technology in agriculture will be discussed in Chap. 7. 16

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➽ Discussion Think of a societal problem that does not yet have a technological solution. What future technologies may change this? Can knowledge alone solve problems, without technology?

⇨ Exercise 1.3 Create a version of Fig. 1.3 for a specific example. This may be the same or different from the technology you had selected in Exercise 1.2.18 knowledge, combined with creativity (ingenuity), to create technology that helps solve or at least helps mitigate problems of society. In recent decades, this seemingly sharp boundary between science and engineering has become increasingly blurred. For example, in fields such as the fight against cancer, engineers and scientists work closely together in the areas of diagnosis (e.g., digital pathology enhanced by AI) and treatment (e.g., targeted chemotherapy, radiation, robotic surgery, and gene therapy).

1.2  Conceptual Modeling of Technology In order to better understand, describe, and transfer technology, humans have found and used different ways to describe it using a combination of human natural language (text), mathematics (equations), and graphics (drawings). Some of these descriptions are quite standardized, as in the structure of patents (see Chap. 5), while others vary widely depending on the application domain in science and engineering.19 There is evidence that the development of human language (Chomsky 2006) was a strong driver for the development of technology, and vice versa. Different fields of science and engineering have developed their own specialized way to describe technology which is not always easily applied across fields. There is consensus in the Systems Engineering community that the use of the full set of human natural  Readers can simply sketch the example by hand or on a computer. Later, we will use Object Process Cloud (OPCLOUD) to create such models. Anyone can quickly generate a model using the OPM Sandbox at: https://sandbox.opm.technion.ac.il/ Note that models cannot be saved, but screenshots can be captured. 19  Chapter 15 is dedicated to the topic of knowledge management and technology transfer. 20  This richness of human natural language is a big part of the beauty and inspiration of literary genres such as poetry. In science and engineering, however, the language needs to be limited and standardized in order to avoid unnecessary ambiguity. 18

1.2  Conceptual Modeling of Technology

13

language to describe technology, including the requirements for technology, has become an obstacle rather than an enabler of further progress. One of the reasons for this is that the same set of facts can be described in a large number of nonunique ways in natural language, which can lead to confusion, errors, and rework when it comes to technology.20 For this reason, we seek a more general, and yet precise, way of describing and analyzing technology. Despite the availability of several systems modeling languages that could describe technologies such as bond graphs (Montbrun-Di Filippo et  al. 1991) and SysML (Friedenthal et  al. 2014), we will use Object Process Methodology (OPM) as first defined by Dori (2011). The main advantages of OPM over other modeling languages are threefold: 1. OPM uses a subset of human natural language, Object Process Language (OPL), to define a clear ontology that describes technology. It is therefore easy to learn and apply. 2. OPM uses both the left and right hemispheres of our brain including the use of a single type of graphical diagram (OPD) to describe both natural and technological systems. 3. OPM became an international standard (ISO 19450) in 2015 and is easily accessible, without having to resort to proprietary software or licenses. We now provide a brief primer into Object Process Methodology (OPM). OPM is predicated on the fact that everything in the world can be described with either objects or processes, or a combination of both. Objects are things that can exist unconditionally. Objects can be “physical” things, such as galaxies, stars, planets, molecules, and organisms, or nonphysical things, such as concepts or ideas, which are generally referred to as “informatical.” Objects can also be attributes of other objects or processes. For example, in Fig. 1.3, “Humans” and “Technology” are physical entities that represent objects, whereas “Problems” are intangible or nonmaterial objects that are shown as rectangles, with and without shading, respectively. When objects are shown as solid rectangles, they are said to be “systemic,” meaning that they fall within the system boundary. Objects represented with dashed boxes fall outside the system boundary (e.g., our solar system not including planet Earth) and are said to therefore be “environmental.” Processes act on objects to create, modify, or destroy them. Processes cannot exist unconditionally but require at least a relationship to one object in order to exist. In OPM objects are shown as ovals and they can also be physical or informatical depending on whether they deal with informational objects or physical objects. An example of a process in Fig. 1.3 is “Creating,” which requires as inputs knowledge coming from the process of “Discovering” the methods of “Engineering” as well as an agent (in this case human) to drive the process. The resulting output of  Quantum technologies for computing, timekeeping, encryption, etc. have recently emerged and are at an early stage of maturity. Currently, OPM assumes that an object can only be in one state at a given point in time and we have not yet attempted to model quantum technologies using OPM, which does not mean that it cannot be done.

21

14

1  What Is Technology?

the process “Creating” is “Technology” which can then be used downstream to help solve or address society’s problems. In the case where processes modify objects, we introduce the notion of stateful objects. In order to describe the effect of a process on an object, we introduce the concept of “state” which is always attached to an object. In the macroscopic world that humans are able to perceive and influence, an object is only allowed to be in one particular state at any given moment in time. In quantum physics, on the other hand, it is possible for an object to occupy multiple states at once. Most technologies today exploit the fact that an object can only be in one defined state at once, or in a transition between states.21 Another important concept in OPM are the links. There are three classes of links. Links between objects are referred to as structural links. Links between objects and processes are referred to as procedural links. Links between processes are referred to as invocation links and they describe the links involving events and conditional actions. It is possible to develop an OPM model of a system or technology to the point where it can be simulated. Figure 1.4 shows a summary of the key concepts in OPM. The OPL (language) corresponding to the OPD (diagram) is shown in Fig. 1.4 along with a short description of what the symbols actually mean. Object is physical and systemic. Object can be in state1 or state2. Process is physical and systemic. Process changes Object from state1 to state2.

Fig. 1.4  OPM Primer, left: basic things in OPM are objects, processes, and states, center: object process links in OPM are known as procedural links, right: links between objects – without showing processes – are known as structural links

1.2  Conceptual Modeling of Technology

15

This is the most fundamental concept in OPM that we will use to describe technology. Imagine, for example, that this generic process represents “Transporting” and that the “Object” is you, a person. The process of “transporting” will change your state from being in location “origin” to being in location “destination.” Let us now move to the center column of Fig. 1.4. Object A is physical and systemic. Process A is physical and systemic. Process A affects Object A. This situation is shown at the middle top of Fig. 1.4 and represents the fact that Object A is being affected by Process A, but without showing the details. For example, in the case of “transporting,” the passenger or cargo object will be affected by the process, but we are not explicitly showing the state change. Here, we are simply hiding the states and using a double-headed arrow in OPM. This is known as a so-­ called “affectee” link. Object B is physical and systemic. Process B is physical and systemic. Process B yields Object B. This situation shows that Object B is created as a result of Process B occurring. In the example of our refrigerator in Fig. 1.1, a result of the process of refrigeration would be the waste heat that is convected from the condenser to the ambient air in the room. A one-sided arrow pointing from a process to an object is known as a “resultee” link. Object C is physical and systemic. Process C is physical and systemic. Process C consumes Object C. This is the opposite of the prior situation with the one-sided arrow pointing from the object into the process. This implies that the object is being consumed by the process. This is known in OPM as a “consumee” link, and an example in the case of our refrigerator example is the electrical energy that is used to power the process of compressing the cooling fluid. Object D is physical and systemic. Process D is physical and systemic. Object D handles Process D. Here, Object D, is neither a resultee nor consumee of Process D, but represents the agent that “drives” the process. Traditionally, in OPM an agent is a human agent. For example, in Fig. 1.1, the human agent is required to set the thermostat to the desired temperature. This is depicted with the so-called agent link. Some automated processes may be able to occur without a human agent, but in this case they would require an automated controller as an “instrument” of the process, see below. Object E is physical and systemic. Process E is physical and systemic. Process E requires Object E.

16

1  What Is Technology?

As described above, Process E cannot occur without the use of Object E, which is therefore linked to the object using an “instrument” link. In Fig. 1.1, we can think of the “Condenser” as the object required for allowing the process of “Condensing” to occur. In this case, the main instrument and the process conveniently have the same name. This is not always the case when it comes to describing technology. We now move on to the structural links on the right side of Fig. 1.4. Object F is physical and systemic. Object G is physical and systemic. Object H is physical and systemic. Object F consists of Object G and Object H. The dark filled-in triangle linking Object F, the uppermost object, to the subordinated Objects G and H indicates an “aggregation-participation” link which means that Object F is made up of or can be decomposed into Objects G and H. Another way to say this is that combining together Objects G and H will result in Object F. Finally, we explain the “exhibition-characterization” link which is shown as an empty triangle with a smaller inset filled-in triangle. Object I is physical and systemic. Object J is informatical and systemic. Object I exhibits Object J. Here Object J is an “informatical” object (its rectangular box is not shaded) that serves as an attribute to describe the physical Object I. An example in Fig. 1.1 would be the amount of interior volume filled with air, which is an attribute of the object “Refrigerator.” The things represented in Fig. 1.4 are not a complete set of all links defined in OPM; however, they are the main ingredients of what we will need to create OPM models of technology.22 OPM manages complexity by defining a System Diagram (SD) at the root level and allowing in-zooming and out-zooming and other processes for modeling systems and technologies at different levels of abstraction. We now have all the necessary elements to create a conceptual model of technologies, such as the refrigerator from Fig. 1.1. This is depicted in Fig. 1.5 as a two-level OPM model with (a) the SD diagram and with (b) the subordinated SD1 diagram which is obtained by zooming in on the main “Operating” process. The outline of the “Operating” process is shown using a thick line with shadow, indicating that a more detailed view (SD1) exists. What is interesting in this example is that only by zooming into one level of abstraction “down” from SD to SD1 do we expose the internal operating processes of the technology including the four processes corresponding to the four legs of the Carnot cycle (see Fig. 1.1). Most users of technology do not know or care about what is happening at SD1; they just want to have the refrigerator operate smoothly, set the temperature on the thermostat, and benefit from the cold temperature and associated shelf life extension of the food. This is typical of most beneficiaries of technology, where understanding the technology at the SD level is sufficient. For the

 Readers who are interested in further details are encouraged to consult (Dori 2011) and ISO standard 19450: https://www.iso.org/standard/62274.html

22

1.2  Conceptual Modeling of Technology

17

Fig. 1.5  Example of two-level OPM model of a refrigerator. (a) System diagram SD of refrigerator in OPM; (b) System diagram SD1 obtained by in-zooming to “Operating”

scientists, engineers, technologists, or technicians, however, the main focus is on the inner workings of the technology at SD1 or below, see Fig. 1.5b. The OPL for the refrigerator example is shown in the appendix at the end of this chapter. Is conceptual modeling only applicable to “modern” technologies? Definitely not. An example of an early technology in humanity’s evolution is the stone axe. Figure  1.6 shows a description of a stone axe as technology using OPM. One of the uses of a stone axe is to cut down a tree, that is, change the state of the tree from standing to fallen.

18

1  What Is Technology?

Fig. 1.6  Left: OPM of stone axe making and use for cutting a tree, right: sample axe (Stone tools are among the oldest known examples of human-made technologies. They were created and later refined to reduce the cutting force and therefore energy consumption for various tasks such as cutting and shaping wood, see Chap. 2.)

The OPL corresponding to our stone axe example is auto-generated as follows: Rock is physical. Handle is physical. String is physical. Energy is physical. Making requires Knowhow. Making is physical. Making consumes Energy, String, Handle, and Rock. Making yields Stone Axe. Stone Axe is physical. Human is physical. Human handles Cutting and Making. Tree is physical. Tree can be standing or fallen. standing is initial. fallen is final. Cutting is physical. Cutting requires Stone Axe. Cutting changes Tree from standing to fallen. Cutting consumes Energy. In order to understand the value of technology, it is important to quantify how it works and not only to describe it conceptually. For example, the stone axe is a way to amplify the cutting force that humans can develop. ⇨ Exercise 1.4 Consider the stone axe shown in Fig. 1.6 as a form of primitive technology. Derive a mathematical expression and estimate how much energy would be consumed and how many cuts (number of discrete chops) would be required for a human to cut down a pine tree with a trunk diameter of D = 10 [cm]. Use h = 0.5 [m] for the length of the handle, m = 0.5 [kg] for the mass of the rock, l = 0.1 [m] for the length of the blade (sharp edge of the rock), w = 2 [mm] for the width (thickness) of the blade, and v = 10 [m/s] for the axe head velocity at the end of the chopping motion. Assume that the ultimate lateral yield strength of pinewood is σw = 6 [MPa]. Which of the variables we have modeled here describe the “stone axe” technology? Given this result what are ways in which the stone axe could be improved?

1.3  Taxonomy of Technology

19

It is interesting that the axe is still used today in the twenty-first century, but usually it is implemented with more advanced materials and manufacturing methods.

1.3  Taxonomy of Technology A question that is often asked is how we can best group or classify technologies. As we have already seen, technologies can be grouped essentially by features of their form such as their material (metals, semiconductors, wood, etc.) or by their function, that is, their purpose. Given that several generations of technology (in terms of their implemented form) can fulfill the same function, we have found that grouping technologies and systems according to their function is the most effective and complete way to arrive at a taxonomy of technologies (de Weck et al. 2011). An additional point is that technology always involves at least one process such as the creation, transformation, or destruction of at least one object, which we will refer to as the operand. The operand is the thing that is being operated on, or acted upon by the technology. For simplicity, we can show this taxonomy as a matrix or grid, with the columns containing the operand(s) and the rows showing the processes. One of the most widely accepted versions of this is the 3x3 grid proposed by van Wyk (1988, 2017) and rendered in Table  1.2 with specific examples. Van Wyk refers to this as the “functionality grid.” The basic three operands are: • Matter, which can exist in different states (solid, liquid, gas, plasma) • Energy, which can take different forms (kinetic, potential, chemical, etc.) • Information, which also exists in different forms (analog, digital, intrinsic, explicit, etc.) The three canonical processes of technology are as follows: • Transforming – This is the process of changing one or more operands from one form or one state to another. Table 1.2  Technology matrix (3 × 3) for technology classification Technology matrix Transforming (1) Transporting (2) Storing (3)

Matter (M) Basic open furnace (BOF) in steel making Transport aircraft in civil aviation (see Chap. 9) Storage tank for cryogenic hydrogen (LH2)

Energy (E) Photovoltaic cells (PV) in a solar-electric farm High-voltage electric transmission lines

Information (I) Graphical processing unit (GPU) in computing Deep space network (DSN), see Chap. 13

Grid-level lithium-ion storage battery

Optical compact disk (CD) for data storage

20

1  What Is Technology?

• Transporting – This is the process of changing the physical location of one or more operands from one location to another. • Storing – This may appear at first to be surprising as a canonical process; however, many technologies exist to make sure that resources (such as matter, energy, or information) are available for use at a later time and at the same place. The examples provided in Table 1.2 were already shown as images in Fig. 1.2, and their selection makes more sense now when shown in the context of the 3x3 technology grid. They cover a range of instances of technological systems from rather simple to very complex. However, even technologies that appear to be “simple” often turn out to be rather complex, once we need to understand them at level SD1, SD2, etc. or even at the molecular or even atomic level. Lithium-ion batteries are a case in point. Thus, lithium-ion batteries can be classified as an E(3)-type technology whose purpose is to store electrical energy. Figure 1.7 shows how a Li-ion battery (LIB) works in principle, and its corresponding OPD is shown in Fig. 1.8. An electrical battery can store and release (discharge) energy obtained from a chemical reaction. It is composed of an anode (−), a cathode (+), the electrolyte, and a separator. The chemical reaction is a redox reaction caused by an electrical potential difference between the anode and the cathode. That is, electrons flow from the anode to the cathode via an external circuit and metal ions (e.g., Li+) in the electrolyte migrate from the anode to the cathode through the separator to receive the electrons. This redox reaction lasts until electrical equilibrium is reached. The capabilities of LIB such as specific energy density, volumetric density, and cycle durability have gradually improved since the 1990s, thanks to the development of new materials and manufacturing processes. Figure 1.8 shows an OPD of the concept of operations of LIB technology (type E(3)).

Fig. 1.7  Operating principle of Li-ion battery. (Source: Cadario et al. 2019)

1.3  Taxonomy of Technology

21

Fig. 1.8  OPD of LIB battery technology. (Source: Cadario et al. 2019)

While the classification of technologies in Table 1.2 has been generally accepted, it is also somewhat confined to a more limited view of technology.23 For one, the classical assumption that humans, natural systems, and technology are somehow distinct and completely separate from each other has recently been challenged (de Weck et al. 2011). Newer technologies that operate directly on biological systems such as DNA sequencing and gene editing, as well as technologies implanted directly in the body of humans (and other animals) show that living organisms, as opposed to “only” inorganic matter, as was implied in Table 1.2, are now an important class of operand in their own right.24 Another important aspect is that value  In physics, there are deep connections and equivalencies between mass and energy, for example, Einstein’s famous E = mc2, as well as Claude Shannon’s information theory which quantifies fundamental limits to information transport in terms of the maximum data rate Rmax, based on the bandwidth B and signal-to-noise ratio C/N that is available, Rmax = B log2(1 + C/N). It may be possible to collapse all technological operands into an energy equivalence, but we do not attempt this here, as this may force us to operate at a higher level of abstraction than is useful. 24  Some argue that living organisms can simply be classified as “matter,” but we disagree, as the requirements and value we place on life warrant a separate category. 23

22

1  What Is Technology?

(money) has become increasingly linked to technology. Technologies dealing with the flow of money are an important domain, which was perhaps not as much the case 100 years ago. Two additional functions, namely, that of control and that of exchange, are identified in an expanded technology classification matrix. The full 5x5 matrix for technological classification is shown in Table 1.3. It is interesting to note that many of the technologies we would consider more recent are to be found in the two bottom rows and two columns to the right. There are different explanations for this: • Value: The emergence of computer-assisted trading of financial assets has grown significantly since the 1970s. Automatic trading technology has generated (and occasionally destroyed) trillions of dollars in value. The use of technology in this domain is generally referred to as “financial technology” or FinTech for short. Even though information technology is increasingly used for the handling of financial value flows, the concepts of information and money are distinct. • Living Organisms: The progress in biology and biological engineering since the 1950s has been impressive, and the confluence of genetics, molecular imaging, and manipulation and computer modeling in systems biology has led the field of biological technology, or BioTech for short. • Exchange and Trade: While the trading and exchange of commodities has been practiced for Millenia, for example, along the famous Silk Road (de Weck 1989), and via maritime trading, the emergence of a more stable and peaceful world order after WWII and the end of the Cold War in the late twentieth century have Table 1.3  Expanded technology matrix (5 × 5) for technology classification Technology matrix (5 × 5) Transforming (1)

Matter (M) Basic open furnace (BOF) in steel making

Energy (E) Photovoltaic cells (PV) in a solar farm

Transporting distribute (2) Storing (3)

Transport aircraft (cargo)

High-voltage electric transmission Storage tank for Grid-level cryogenic lithium-ion hydrogen (LH2) storage battery

SWIFT financial network U.S. bullion depository (Fort Knox)

Exchanging (4)

Murray-Darling basin water trading system (Australia) Diesel engine emissions aftertreatment (NOx, PM)

Deep space network (DSN) Optical compact disk (CD) for data storage EEX European Electronic medical energy records (EMR) exchange

Blockchain distributed ledger

Online livestock trading

U.S. federal reserve automated clearing house ACH

Viral RNA testing, for example, SARSCoV-2

Controling (5)

Digital control of home air conditioning systems

Information (I) Graphical processing unit (GPU)

TCP/IP web server and switching technologies

Value (V) Crypto currencies (bitcoin ₿)

Organisms (L) Minimally invasive robotic surgery Self-driving automobiles Stem cell banking technology

1.4  Framework for Technology Management

23

led to the emergence of new technologies that facilitate trading and exchange across the globe. • Control and Regulation: While many systems have operated in “open loop” in the past, the increase in performance (and safety) due to feedback control and regulation to prevent instabilities in systems has led to dramatic advances in system performance and control technology. The upper left 3 × 3 technology matrix is the domain of “traditional” engineering where matter, energy, and information are transformed, transported, and stored. This 3 × 3 matrix is shown in Table 1.2. As can be seen in Table 1.3, the full 5 × 5 matrix provides a broader more comprehensive view of technology, including some technologies that were only conceived in the early twenty-first century. It is not impossible to think that this technology grid may expand further in the future as new technologies are invented and deployed. Also, since technologies are always part of a larger system and can themselves be decomposed into subsystems and parts, it is often the case that technological systems that fall into one particular cell of Table 1.3 contain within them a multitude of other technologies taken from the technology grid at different levels of decomposition. For example, self-driving electric passenger cars  – technology type L(2)  – contain with them energy storage technology, E(3), as well as information processing technologies, I(1), among others.

1.4  Framework for Technology Management ⇨ Exercise 1.5 Empty the cells in Table  1.2 (3  ×  3) or Table  1.3 (5  ×  5) and replace the examples given with different technologies of your own choosing. This may seem simple at first but is surprisingly challenging to do.

As one studies the evolution of technologies  – as we will  – it becomes quickly apparent that an overarching framework is needed to guide the overall development and deployment of technologies in an organization. This is a field generally known as Technology Management or Management of Technology (MOT). Several universities, including MIT, have created research and education programs around MOT over the years. While the names and instantiations of these programs are evolving – as are the underlying technologies themselves  – it is clear that a guiding set of principles and processes is needed to develop, deploy, and maintain technologies over time in those organizations where technology plays a pivotal role. This is typically the primary role of the Chief Technology Officer (CTO). A vast literature exists on technology management (Burgelman et  al. 2008; Roberts 2001) which sits squarely at the intersection of management science and engineering. Our intention in this book is not to review the scholarly work in this area in a complete and comprehensive manner, but to focus on the role of technology roadmapping and development in technology management.

24

1  What Is Technology?

One can think of roadmapping, in particular, as the control function for technology management in organizations. Without a clear understanding of what technologies exist in a firm (or agency), whether they are competitive, how fast they are evolving and what targets should be set for them, and most importantly, which future missions, products, or services require them, it is unlikely that the organization will be a leader in its own field or industry. Thus, we view technology roadmapping as central to technology management where all critical information about technology is integrated, consensus is achieved, and future actions and targets are decided and documented. Figure 1.9 depicts an object process model of technology management that will also serve as a basis for the Advanced Technology Roadmap Architecture (ATRA) in Fig. 1.10 that serves as the overall framework for this book. The technology management framework shows the different functions in the development and infusion of technology in the context of an organization25 that conceives, designs, implements, and operates missions, products, and services that are technology-based. In the upper left, we see (if they exist) current capabilities instantiated as products, services, or missions that are being purchased or used by a customer base. This creates results in the form of revenues or other benefits or social surplus. In markets

Fig. 1.9  Comprehensive technology management framework (shown in OPM)

 The primary organization we have in mind is a for-profit firm that develops, implements, and sells products and services that address societal and specific customer needs and that receives revenues in return. A portion of these is then reinvested to fund the development of new or improved technologies, products, and services. The framework can also be applied to nonprofit organizations such as government agencies, research institutes, or nongovernmental organizations (NGOs) that focus on missions.

25

1.4  Framework for Technology Management

25

Advanced Technology Roadmap Architecture (ATRA)

Inputs

Steps

Outputs

L1 Products and Missions L2 Technologies

1. Where are we today?

+10y

FOMj

Strategic Drivers for Technology

+5y Organization

Technology State of the Art and Competitive Benchmarking

Competitor 1 Competitor 2 FOMi Today

2. Where could we go? Technology Systems Modeling and Trends over Time

+10y FOMj

L1

+5y

?

Scenario Analysis and Technology Valuation

Technology Scouting Knowledge Management Intellectual Property Analytics

Scenario B

Scenario-based Technology Valuation

4. Where we are going! Technology Portfolio Valuation, Optimization and Selection

FOMi

Technology Investment

Technology Efficient Frontier Portfolio Technology Projects σ[NPV] - Risk

Cases

Foundations Definitions What is Technology?

Scenario A

3. Where should we go?

L2

E[NPV] - Return

Dependency Structure Matrix

Technology Systems Modeling

Technology Roadmaps

History Milestones of Technology

Ecosystems Nature Technology, Nature Technology Diffusion, Infusion and Industry and Humans

The Future Is there a Singularity ?

Case 1

Case 2

Automobiles

Aircraft

Case 3 Deep Space Network

Case 4 DNA Sequencing

Fig. 1.10  Advanced technology roadmap architecture (ATRA)

other than natural monopolies, a corporate strategy is needed to define which customers and market segments should be pursued and what products and services are needed to succeed in these market segments. The strategy is sanctioned by the senior leadership of the organization and takes into account the current and future requirements of the customer base. The Chief Technology Officer (CTO) is typically a member of the senior leadership team and drives the creation of a set of technology roadmaps which map both the existing products, services, and missions as well as the corporate strategy against specific targets for market share, performance, cost, profit, and other Figures of Merit (FOMs).26 The resulting roadmaps and targets need to take into account past and expected future technology trends. While it is often helpful to set ambitious targets for future product, service, or mission requirements along with a specific timeline, the setting of utopian targets should typically be avoided as it is generally counter-productive. This information is captured by a set of technology roadmaps, which facilitate the planning of a firm’s R&D (research and development) portfolio.27 This planning process can result in the launch, continuation, modification, or cancellation of R&D projects, including demonstrators and prototypes, and the shaping of a multiyear R&D budget. This budget is typically approved by the senior leadership of the

 The use of figures of merit (FOM) is central in our approach to technology management.  Some firms, particularly in Europe, make a distinction between R&T (research and technology development) and R&D (research and product development). However, this is not the case in most parts of the world where research, technology maturation, prototyping and the development and launch of new products, services, and missions are all considered to be part of R&D.

26 27

26

1  What Is Technology?

organization. Together these processes provide the necessary market “pull” for technology development. However, there may also be technology “push,” that is, the injection of new ideas from competitive analysis, the industrial ecosystem (suppliers, partners), and academia. Capturing and bundling these ideas, and quantifying them in credible existing technology trends, and future requirements is the job of the technology scouting function. Another important function is the actual execution of the R&D projects by the engineering organization which hopefully leads to tangible outcomes in the form of technological knowledge, new or improved technologies, and prototypes. Some of this technological knowledge may be explicitly recognized and managed as intellectual property (IP) through patent filings and, if necessary, protected through litigation. Other inventions may be managed more informally and internally as trade secrets. If technology development is successful, the senior leadership may decide to infuse new technologies into existing products, services, or missions to upgrade them, or to transition promising prototypes to become new products and services in the market. The degree to which current or new customers or users will value these new capabilities is crucial to understand which technologies and projects to prioritize. This prioritization is needed given the overall budget constraints and constantly shifting market conditions as well as threats and opportunities. The budget for R&D typically comes from a mix of internal and external sources. Deciding how much and where to spend on R&D is one of the most important decisions that firms and agencies have to make to ensure their long-term success and survival. Throughout this endeavor the availability of motivated and talented R&D staff, mainly scientists and engineers, is critical. Such staff may be “grown” internally or recruited externally from academia, suppliers, or even competitors.28 The organization of R&D into teams that can both sustain existing products, services, and missions while also developing new technologies and prototypes is one of the most challenging tasks of technology management. ⇨ Exercise 1.6 For your current (or past or future) organization, draw a diagram similar to Fig. 1.9. Who does technology scouting in your firm? Are there technology roadmaps? Who decides on and who implements the R&D project portfolio?

This book dedicates several chapters to the processes shown in Fig. 1.9, as summarized in Table 1.4. The sequence of chapters does not follow a linear chain but emphasizes foundational concepts first and gradually moves from considering only a single technology to a portfolio of technologies.

 Many competitors attempt to prevent this by inserting so-called noncompete clauses in their employment contracts. These are generally difficult, but not impossible, to enforce in a court of law.

28

1.4  Framework for Technology Management

27

Table 1.4  Mapping of processes in Fig. 1.9 against chapters in this book Technology management function Managing intellectual property Technology roadmapping Strategy development Executing research and development Infusing technology in products or systems Technology scouting Managing knowledge R&D portfolio planning Valuing technology

Chaptera 5 4, 8, 11 10 11, 12, 16 12 14 15 16, 17 17

Note that chapters not listed here contain complementary materials such as case studies (Chaps. 6, 9, 13, and 18) or special topics linked to technology such as defense and intelligence technologies (Chap. 20), technology and aging (Chap. 21) as well as the question of the existence of a singularity and the ultimate limits of technology (Chap. 22)

a

In smaller companies and startups, all of these functions may be carried out by a single person, such as the primary technologist or engineer among the co-founders. As organizations grow and mature, there will be teams and eventually departments responsible for each of these functions at which point the coordination and flow of information between strategy, marketing, technology (the CTO-led organization), engineering, manufacturing, and supply chain management, among others, becomes crucial and challenging to manage. At that point what is needed is a more prescriptive framework that implements Fig. 1.9 in a logical architecture that can be implemented and followed with confidence. Figure  1.10 shows what we will call the Advanced Technology Roadmap Architecture (ATRA) that also provides the guide map and signposts in this book. The foundational topics and case studies are shown at the bottom, while the four-­ step technology roadmapping process with inputs and outputs is shown at the top. As mentioned in the foreword, the author first implemented the ATRA technology roadmapping framework in a large aerospace firm with more than 100,000 employees and a €3 billion annual R&D budget. Many observations and recommendations in this book come from this experience, combined with the latest insights from the academic literature. However, since then the ATRA approach has also been selected by NASA’s Space Technology Mission Directorate (STMD), by other companies in aerospace, the energy sector, in medical devices, and even by startups, in a simplified form. It is now being taught as a coherent approach to technology management at several universities around the world, to both students and professionals. In the next chapter, we will review some of the technological milestones of humanity.

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1  What Is Technology?

Appendix Object process language (OPL) model of refrigerator, see Fig. 1.5. SD Refrigerator is physical and systemic. Thermostat Setting of Refrigerator is physical and systemic. Food is physical and systemic. Shelf Life of Food is physical and systemic. Human is physical and systemic. Temperature of Food is physical and systemic. Electrical Energy is physical and environmental. Waste Heat is physical and systemic. Exterior Air is physical and environmental. Refrigerator exhibits Thermostat Setting. Food exhibits Shelf Life and Temperature. Operating is physical and systemic. Operating requires Refrigerator. Operating affects Food. Operating consumes Electrical Energy. Operating yields Waste Heat. Setting is physical and systemic. Human handles Setting. Setting affects Thermostat Setting of Refrigerator. Convecting is physical and environmental. Convecting affects Exterior Air. Convecting consumes Waste Heat. SD1 (In-Zooming on “Operating”) Operating from SD zooms in SD1 into Condensing, Expanding, Evaporating, Compressing, and Regulating, as well as Coolant. Refrigerator is physical and systemic. Food is physical and systemic. Electrical Energy is physical and environmental. Waste Heat is physical and systemic. Compressor is physical and systemic. Pump is physical and systemic. Condenser is physical and systemic. Expansion Valve is physical and systemic. Evaporator is physical and systemic. Thermostat is physical and systemic. Coolant is physical and systemic. Refrigerator consists of Compressor, Condenser, Evaporator, Expansion Valve, Pump, and Thermostat. Operating is physical and systemic.

References

29

Operating requires Refrigerator. Compressing is physical and systemic. Compressing requires Compressor and Pump. Compressing affects Coolant. Compressing consumes Electrical Energy. Compressing invokes Condensing. Regulating is physical and systemic. Regulating requires Thermostat. Regulating invokes Compressing. Condensing is physical and systemic. Condensing requires Condenser. Condensing affects Coolant. Condensing yields Waste Heat. Condensing invokes Expanding. Evaporating is physical and systemic. Evaporating requires Evaporator. Evaporating affects Coolant and Food. Evaporating invokes Regulating. Expanding is physical and systemic. Expanding requires Expansion Valve. Expanding affects Coolant. Expanding invokes Evaporating.

References Burgelman RA, Christensen CM, Wheelwright SC. Strategic management of technology and innovation. McGraw-Hill/Irwin; 2008 Cadario A., et al. “Energy Storage Technology Roadmap”, MIT EM.427 Technology Roadmapping and Development, URL: http://34.233.193.13:32001/index.php/Energy_Storage_via_Battery December 2019, last accessed 27 Dec 2020 Chomsky, Noam. Language and mind. Cambridge University Press, 2006. de Weck, Christine. The Silk Road Today, Vantage Press, ISBN: 0-533-08031-2, 1989 de Weck, Olivier L., Daniel Roos, and Christopher L.  Magee. Engineering Systems: Meeting human needs in a complex technological world. MIT Press, 2011. Dori, Dov., “Object-Process Methodology: A Holistic Systems Paradigm”. Springer Science & Business Media, 2011 Duraiappah A.K., Munoz P.  Inclusive wealth: a tool for the United Nations. Environment and Development Economics. 2012 Jun 1;17(3):362–7. Friedenthal, S., Moore A., and Steiner R.. A practical guide to SysML: the systems modeling language. Morgan Kaufmann, 2014 Hughes T.P. Human-built world: How to think about technology and culture. University of Chicago Press; 2004 Hume, D. A Treatise of Human Nature, Book II, Part III, Sect. X, 1739–1740 Montbrun-Di Filippo, J., Delgado M., Brie C., and Paynter H.M.. "A survey of bond graphs: Theory, applications and programs." Journal of the Franklin Institute, 328, no. 5–6, 1991: 565–606.

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Roberts EB.  Benchmarking global strategic management of technology. Research-Technology Management. 2001 Mar 1;44(2):25–36. Schatzberg E. “Technik” Comes to America: Changing Meanings of “Technology” before 1930. Technology and Culture. 2006 Jul 1;47(3):486–512. Van Wyk, R.J., Management of technology: New frameworks. Technovation, 7(4), pp. 341–351, 1988. van Wyk, R., Technology: Its Fundamental Nature – To Explore further Ahead and farther Afield, Lambert Academic Publishing, ISBN: 978-620-2-00622-4, 2017 Wikipedia: https://en.wikipedia.org/wiki/Technology accessed 21 April 2019

Chapter 2

Technological Milestones of Humanity

Advanced Technology Roadmap Architecture (ATRA) Inputs

Steps

Outputs

L1 Products and Missions

1. Where are we today?

FOMjj

Strategic Drivers for Technology

Technology State of the Art and Competitive Benchmarking

L2 Technologies

+5y Organization

2. Where could we go? Technology Systems Modeling and Trends over Time

Technology Projects

FOMi

Today

+10y FOMj

Dependency Structure Matrix

L1

Figures of Merit (FOM) Current State of the Art (SOA) Technology Trends dFOM/dt

Competitor 1 Competitor 2

Technology Systems Modeling Tech Pul Pull

Technology Roadmaps

+10y

Scenario A

+5y

Scenario B ?

3. Where should we go?

L2

Scenario Analysis and Technology Valuation

1

2

Definitions What is Technology?

History Milestones of Technology

E[NPV] - Return

Intellectual Property Analytics

Foundations

4. Where we are going! Technology Portfolio Valuation, Optimization and Selection

Technology Investment Efficient Frontier Technology Portfolio Technology Projects σ[NPV] - Risk

Ecosystems Nature Technology, Nature Technology Diffusion, Infusion and Industry and Humans

The Future Is there a Singularity ?

© Springer Nature Switzerland AG 2022 O. L. de Weck, Technology Roadmapping and Development, https://doi.org/10.1007/978-3-030-88346-1_2

Design Reference Missions Future Scenarios T h l Technology V Valuation l ti Vector Charts

FOMi

Tech Push

Technology Scouting Knowledge Management

Scenario-based Technology Valuation

Case 1

Case 2

Automobiles

Aircraft

Pareto-optimal set of technology investment portfolios Recommended Technology Portfolio (Expected NPV and Risk)

C Cases Case 3 Deep Space Network

Case 4 DNA Sequencing

31

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2  Technological Milestones of Humanity

2.1  Prehistoric and Early Inventions The history of technology is a rich field of research and inquiry. It seeks to explain how our species, Homo sapiens sapiens, started to diverge from other so-called hominids (family: Hominidae) in terms of their development and use of artificially created tools that do not occur on their own in nature. This is a story of survival but also of displacement of other species on Earth. The evolution of our species, homo sapiens, in relationship to other species in the genus homo is shown in Fig. 2.1. In Fig. 2.1, blue shaded areas denote the presence of a certain species of Homo at a given time and place. Late survival of robust australopithecines (Paranthropus) in southern Africa alongside Homo is indicated in purple. Homo heidelbergensis is shown as diverging into Neanderthals, Denisovans, and Homo sapiens at about 400 [kya]. With the rapid expansion of Homo sapiens after [60 kya], Neanderthals, Denisovans, and unspecified archaic African hominins are shown as again subsumed into the H. sapiens lineage. Some of the earliest technological milestones of humanity are as follows, roughly in chronological order: –– Hand tools made of stone and bone, with the oldest at about 3.3 [mya] –– Deliberate use of fire at about 1.7–2.0 [mya]

Fig. 2.1  Schematic representation of the emergence of H. sapiens from earlier species of the genus Homo. The horizontal axis represents geographic location, and the vertical axis depicts time in millions of years ago [mya]. (Image adapted from: https://en.wikipedia.org/wiki/Homo_sapiens and Springer (2012))

1  The study of paleontology also concerns the same period of time before the Holocene which started about 11,700 years ago with the end of the last glacial period. However, paleontology excludes the study of human activity which is considered within the scope of archeology.

2.1  Prehistoric and Early Inventions

33

–– Earliest cooking of food at about 0.8 [mya] –– Clothing using animal skins and much later fabrics at 0.4 [mya] The study of these earliest traces of humanity’s ability to create their own tools and harness resources in their environment is both fascinating and complex. To a large extent, these studies rely on archeological finds,1 many in caves around the world. One of the most famous of these is the Drimolen Paleocave System near today’s Johannesburg in South Africa. This cave system has been designated by UNESCO as the “Cradle of Humankind” in 1999. The reliability of the estimates of which subspecies created what kind of tools and when depends to a large extent on the collocation of skeletal remains and other artifacts such as tools, and traces of other objects such as ash and animal bones. This type of analysis requires modern technologies such as carbon dating, X-ray crystallography, and spectroscopy, among others. Some of the most reliable findings with wide consensus among scholars are based on so-called lithic analysis which is the study of tools made of stone, flint, and related materials such as quartz. This is so because stones are often well preserved, compared to other biodegradable materials such as wood fibers that may or may not have been preserved through the process of fossilization. The deliberate use of fire by humans was a game changer as fire served multiple functions such as those shown in Fig. 2.2. The study of fire use by early humans relies to a significant extent on the microscopic and chemical analysis of ash particles as well as soot deposits on cave ceilings, among other traces. The deliberate use of fire by humans probably started in Africa and was initiated by early humans experiencing and harnessing wildfires. This preceded the initiation

Fig. 2.2  Ignition and use of fire by humans (OPM model)

34

2  Technological Milestones of Humanity Human Brain Development

Human Hand Dexterity

Human Language and Abstraction

Homo sapiens sapiens

us!

Homo sapiens Neandertalensis Homo erectus

1000 cc

Homo habilis Austrotopthecus

Singe ofriconus Anthropoide

Millions of years age:

500 cc -4 -3 -2 -1

0

Fig. 2.3  Human features allowing us to develop technology: brain, hand, and language

of fires using an ignition source based on flint stones or rubbing softwood and hardwood which required more advanced knowledge, and passing on information from one generation to the next. The first use of clothing and creation of artificial shelter by homo erectus is the subject of significant debate among scholars since the physical evidence is less clear compared to stone tools. Estimates of the first clothing used by humans range from 40 [kya] to 3 [mya], while the first artificial shelters which would have allowed humans to live outside of caves have been dated to 100 [kya] or younger. Early technologies for creating human housing included mud huts made from sun-dried and later oven-fired bricks or wooden structures in the mid-latitudes. This is a more difficult field of inquiry because preservation of such artifacts and structures is scarce. Another important transition that occurred after the last Ice Age ended around 11.7 [kya] is the transition from a society of hunters and gatherers to an agrarian society whereby food was grown in dedicated fields, which motivated the creation of human settlements close to those fields and reliable sources of water. Much remains to be discovered about this early period of human development. Researchers cite several factors in the development of our species that played an important role in the emergence of these primal technologies. These factors are anatomical, physiological, and cognitive, see Fig. 2.3, and include the following: • Increasing brain size, particularly of the frontal cortex. The average human brain size today is about 1130–1260 [cm3], whereas for Homo floresiensis (see Fig. 2.1) the brain size was estimated to be only about 380 [cm3]. The brain size for homo erectus was about 900 [cm3] on average. More recently, researchers have realized, however, that brain size alone is not a sufficient correlator with intelligence. Homo neanderthalensis, for example, is known to have had a bigger brain than Homo sapiens at about 1200–1900 [cm3] and also more rapid brain growth from

 The Encephalization Quotient (EQ) is the coefficient “C” as calculated in the following equation: E = CSr, where E is the weight of the brain, C is the cephalization factor, and S is the body weight, and r is an exponential constant. The EQ is normalized to 1.0 for the cat (Roth and Dicke 2005). 2

2.1  Prehistoric and Early Inventions

35

birth to adulthood (de León et al. 2008). Even today, elephants and whales have larger brains than humans, and while these animals are recognized as being some of the most intelligent on Earth, their intelligence is not believed to exceed that of humans as far as we know. The missing piece is the concept of encephalization which considers the ratio of brain size to body mass, specifically the Encephalization Quotient (EQ) which is about 7.4–7.8 for humans.2 More recently, neuroscience has been able to isolate, image, and count individual neurons, and it is now clear that the number of neurons and the synaptic network structure of the brain are what matters when it comes to enabling higher cognitive functions such as the ability to reason logically and to form abstractions of the real world. As an example (Azevedo et al. 2009) state that “the cerebral cortex of the elephant brain, which weighs 2848 [g] (gray and white matter combined), more than two times the mass of the human cerebral cortex, is composed of only 5.6 billion neurons, which amounts to only about one third of the average 16.3 billion neurons found in the human cerebral cortex.” This seems to indicate that it is the number of neurons and their interconnections in the cerebral cortex, and not raw brain mass alone, that may be at the root of humanity’s ability to reason in a way that allows advanced technology to emerge. • Evolutionary development of opposable thumbs greatly increasing manual dexterity. The presence of an opposable thumb and pad-to-pad grasping is an important feature of humans, in relation to most other primates. The opposable thumb can be found in other apes such as orangutans, but in many cases the thumb is shorter than that of humans and is optimized for grasping or hanging from tree branches. However, pad-to-pad grasping between the thumb and the index finger allows precision manipulation of objects by Homo sapiens. The evolution of primate and human hands and the role of gene enhancers such as HACNS1 during the evolution of our hands from Homo habilis and Homo erectus are the subjects of ongoing research in evolutionary biology (Rolian 2016). • The development of oral and written language and the ability to form abstractions (Chomsky 2006). While other animals such as primates, whales, dolphins, birds, etc. are able to communicate acoustically over large distances and using a sophisticated vocabulary, the number of words in all human languages exceeds that of any other species. The ability to formulate abstract concepts in human language and to transmit these concepts to other humans, including younger generations, has played a key role in the development of technology. It could be argued that language itself is a kind of technology. As far as we know humans are the only species on Earth capable of designing or inventing technology, and then abstracting this knowledge and passing it on to the next generation in a way that goes beyond a simple “copy and paste” process but includes the ability to understand why something works the way it does. This ability for abstraction and learning is of course enabled by our brains (the hardware) but very much relies on our linguistic and cognitive abilities (the software). One of the manifestations of this is the ability of humans to “run simulations in their heads,” meaning our ability to think through causal chains and come up with potential future out-

36

2  Technological Milestones of Humanity

comes of our actions, before we take such actions. Again, here we find a very active field of research associated with the brain and cognitive sciences.3 Beyond these three clearly identified and often cited human traits, we find humans to be curious and self-reflective in a way that is not yet fully understood. Therefore, another key distinction, as we will see in Chap. 3, is the ability to observe, reflect upon, and improve technology after and during use. ⇨ Exercise 2.1 Tie your shoes or a knot as you normally would and time yourself as a baseline value. Then tape both of your thumbs to their respective index fingers using masking tape. Have a friend or colleague help you. Now tie your shoes or the same knot again without the use of your opposable thumbs and record the time. What % increase in time did you record due to the lack of full use of your opposable thumbs?

One of the most important areas of early technology development was in what we call agriculture today. The deliberate and planned planting of seeds, raising of animals, and sedentary or partially nomadic lifestyle of tribes in the early to mid-­Holocene represents a turning point for our species. Technologies such as the irrigation of fields with the help of reservoirs and canals were already practiced in Egypt, in Mesopotamia, and in the Indus basin several thousands of years ago. Along irrigation and food production also came technologies for food preservation such as smoking, salting, and other ways to process food without the need for refrigeration which came much later. In the Americas, native tribes learned how to breed and raise nutritious and hardy crops like corn, beans, and squash, many varieties of which are still being cultivated to this day. We now transition to consider “early” technologies that are not prehistoric, meaning there is a written record of them, as well as preserved artifacts in good condition. These technologies precede the industrial revolution and feature prominently during the “Middle Ages” (e.g., twelfth to seventeenth century CE). While early technologies were primarily focused on the lower levels of what is generally known as Maslow’s (1989) pyramid of needs4 (food, shelter, etc.), it must be said that the interplay between groups of humans has also been an important impetus for the creation of technology. During times of peace, technology was developed to process, store, transport, and trade resources between humans or groups of humans (see Table 1.2). A good

 An interesting question is how to quantify and compare the ability of individual humans to form abstractions, see patterns, and correctly anticipate future outcomes of actions, thus understanding causal chains. This is often described as “intelligence” and while a variety of IQ tests exist, we are still actively researching this important area of cognitive science. The number of different words used in the spoken and written dictionary by humans can be used as a (imperfect) proxy for our ability to form abstractions. 4  Maslow’s hierarchy of needs has been intensively critiqued, and revisions have been proposed. For example, it has been pointed out that in some cultures the need for self-actualization and social interaction may actually be stronger than or precede physiological needs. 3

2.2  The First Industrial Revolution

37

Fig. 2.4  Left: Portuguese Caravela. Right: Ocean surface controlled by Portugal Sources: https://en.wikipedia.org/wiki/Caravel and according to Magee and Devezas (2011)

example is the use of sail ships and navigational aids (e.g., sextant) during the time of the great Chinese, Arabic, and European explorations. A well-known example is the Portuguese caravela shown in Fig. 2.4. It was optimized for coastal navigation, for example, along the West Coast of Africa, in the fourteenth and fifteenth centuries and could maneuver its sails rapidly to take advantage of changing winds, and tack upwind. It helped Portugal rapidly expand its influence.5 ⇨ Exercise 2.2 Select a technology that was invented and widely used before the year 1500 CE and describe it conceptually, for example, using OPM (see Chap. 1). Provide some calculations from first principles as to why this technology was useful. We will define Figures of Merit (FOM) for assessing technologies in Chap.4, so just keep it simple for now.

2.2  The First Industrial Revolution Prior to the eighteenth century, the main sources of power for performing the processes shown in Table 1.2, such as transporting matter from one location to another, were humans themselves, as well as domesticated animals such as horses and oxen, and – in a geographically more limited way – the sun, the wind, and water.

5  It has been pointed out that the seemingly exponential growth of Portuguese control suggested by Fig. 2.4 (right) did not continue forever. It peaked in the early 1600s after which competition with other European nations such as the Dutch and the British (e.g., East India Company) and the end of the Iberian Union in 1668 precipitated the Portuguese empire’s decline. 6  The invention of the broad horse collar or Dutch collar in the twelfth century was important, since it allowed horses to pull without experiencing the pain caused by narrower straps. 7  The first law of thermodynamics states that ΔU = Q − W. This means that the change in internal energy of a system U is equal to the amount of (heat) energy Q added to the system, minus the

38

2  Technological Milestones of Humanity

Table 2.1  Maximum and averages for speed, force, and power for different species Species Homo sapiens (human) Equus ferus (horse) Bovinae (ox)

Average body mass 70 [kg]

Maximum speed Maximum force Maximum running exerted power 12.5 [m/s] 4800 [N]a 1000 [W]

Average power 75 [W]

635 [kg]

24.6 [m/s]

35,500 [N]b

11,000 [W]c

745 [W]d

545 [kg]

13.3 [m/s]

17,300 [N]

7260 [W]

450 [W]

The current bench press world record is held by Ryan Kennelly at 487.6 [kg]. Assuming a gravitational acceleration of g = 9.81 [m/s2] on Earth’s surface, this corresponds to about 4800 N b A good rule of thumb is that a single adult horse can draw about 8000 [lbf] of force c In 1993 R. D. Stevenson and R. J. Wassersug published on this topic in the journal Nature d One mechanical horsepower is equivalent to lifting 550 [lbs] by 1 [ft] per second [s]. James Watt carried out experiments with actual horses to establish these numbers as a baseline to compare the performance of his steam engines. The equivalence is 1 [hp] = 745.7 [W]. Also note that the conversion factor between pounds of force and Newtons of force is 1 [N] = 4.4 [lbf] a

The question of how much force (or torque) and how much power an individual can develop without the aid of external tools is a key aspect for understanding the emergence of technology. Table  2.1 shows various estimates for the maximum speed, maximum force, and peak and average power that humans and selected domesticated animals such as the horse or the ox can generate. As a rule of thumb, a single adult horse6 can do the work of about ten adult humans during the same amount of time. An ox can do about two-thirds of the work of a horse. There are large variations between individuals and the numbers provided above represent only approximate averages. What should also be obvious is that the energy consumed by humans, horses, and oxen needs to come through their food intake and that the availability of both clean water and adequate calories through food is necessary for the numbers in Table 2.1 to materialize in practice. One of the implications of the above numbers is that – corresponding to the first law of thermodynamics7 – the amount of energy expended by an individual over a given amount of time needs to be equal to the amount of energy replenished, in steady state. If too little energy is provided, the individual will first tap into their own internal energy reserves (e.g., in the form of stored fat) and will eventually not be able to perform external work (W). In the worst case, they may not even have enough energy to provide to their bodies at rest, eventually leading to death from famine. This is applicable not only to humans, but to domesticated and wild animals as well. At the most basic level, technology was created by humans to be able to supply a sufficient amount of energy to their bodies in the form of food. In that sense, technology has had an important role in not only helping Homo sapiens survive but also in dramatically increasing population size. The fact that today a significant percentage of humans are in the obese or overweight category (Eknoyan 2006) can be attributed to the fact that the average amount of energy intake (number of calories) by individuals exceeds the amount of energy expended daily. This is one of the dark sides of technology, where “too much technology” has decreased our need to consume energy for survival on the one hand

2.2  The First Industrial Revolution

39

and has created an overabundance of food, and therefore energy, on the supply side on the other hand. To put it more simply, some of us now need gyms and scheduled workouts because we no longer work on farms with our own hands, where we used to burn large amounts of energy per day to generate our own food as a source of energy (see Exercise 2.3). ⇨ Exercise 2.3 Calculate the energy in [J] consumed by an average adult human per day in two situations: (a) working in an agricultural field with no machines for 10 h, and (b) working in a twenty-first-century office building for 8 h sitting at a desk. Refer to Table 2.1, but feel free to do your own research and make your own assumptions. What do you conclude in terms of energy needs (caloric intake) for humans? For situation (a) compare the caloric intake for a human versus a horse, for example, used for pulling a plow. Note: 1 [Cal] = 1000 [cal] =4184 [J]. Several early technologies helped humans increase the net amount of force or torque that they were able to generate, see Fig. 2.5: • The lever • The multi-wheel pulley • The geared wheel Suppose that for the rigid lever shown in Fig. 2.5 the multiplication of “human” force is obtained by a human located at location b, attempting to lift a rock at location a, against gravity. Given that at equilibrium the net moment M at the lever’s pivot point has to be zero, we obtain

Fig. 2.5  Schematic of simple early technologies: lever (upper left), geared wheels (lower left), and multi-wheel pulley system (right)

40



2  Technological Milestones of Humanity

∑ M = M a + M b = 0 = − Fa a + Fb b

(2.1)

From this, we can solve for the force that can be exerted at a as



Fa = Fb

b a

(2.2)

Thus, if b is 5 [m] and a is 0.5 [m], the force multiplier would be equal to 10. Practical limits to this lever “technology” are given by the flexibility of the lever itself and the yield stress of the material. In the pulley system of Fig. 2.5, we get the force multiplier n by looking at equilibrium using the free-body diagram:

W − nT = 0

(2.3)

where Fa = W is the weight being lifted, T is the tension in the rope, and n is the number of ropes obtained by virtually cutting through the system between the weight and downward force applied to the pulling rope, Fb. In the case of the double pulley system shown here n = 4. The price to pay for this mechanical advantage is that four times as much rope has to be pulled through, for every unit of vertical distance when the weight W is raised. Pulley systems were already used in Egypt around 1800 BCE and were employed in the construction of the pyramids. Finally, the geared wheel allows a change in the speed of rotation and torque M2 transmitted by a driven shaft M1 depending on the ratio of number of cogs (teeth) z1 and z2 between the smaller wheel (the pinion) and the larger geared wheel at the output. The gear ratio (mechanical advantage) is defined as m=

z2 z1

(2.4)

In this way, through empirical experimentation, humans developed new tools to leverage their own abilities further. The combination of humans and animals (see Table 2.1) as well as force- or torque-amplifying machines allowed humans, starting about 5000 years ago, to complete impressive projects. Some of the early “technologists” recognized that there are underlying laws and governing equations that – when properly understood – could be used to create such tools with repeatable outcomes. Some of the most famous were as follows: • Archimedes (ca. 287–212  BCE, Magna Graecia, today known as the Italian island of Sicily) approximated π and invented the famous screw that is named after him for lifting water to some height. • Hero of Alexandria (ca. 10–70 CE, Roman Egypt) was a prolific mathematician and inventor who created or described an early steam engine (aeolipile), the wind wheel, and the first vending machine. Technology was used in various temples of Alexandria to create optical illusions.

2.2  The First Industrial Revolution

41

• Cai Lun (50–121 CE, Luoyang, China) was a eunuch at the emperor’s court during the Han dynasty and is recorded as the inventor of paper. He documented the recipe for making paper from tree bark, hemp, and other ingredients such as rags. He was also the head of the imperial supply department. • Galileo Galilei (1564–1642  CE, Tuscany, Italy) developed the basic scientific method, worked on the strength of materials, and built his own telescopes. This list could easily include other names such as Isaac Newton, Leonardo da Vinci, and many others. What we know about these early inventors and the technologies they created or documented is probably only a fragment of reality, as much of the historical record has been lost, for example, due to the fire in the famous library of Alexandria, and due to destruction caused by wars and natural disasters. The diffusion of technologies through trade, for example, along the Silk Road and maritime exchanges, is also well documented. In some cases (such as Hero’s work), more has been learned through translations into Arabic and other languages. The study of early inventions and technologies remains a fascinating field worthy of further exploration. ➽ Discussion What technological invention that preceded the industrial revolution do you find to be particularly important or interesting and why? The Steam Engine A major advance in human technology was the invention and development of the steam engine. The steam engine can be classified as an energy transformation technology, (E1), according to our 5 × 5 technology grid (Table 1.3). One of the key individuals in the development and perfection of the steam engine was the Scotsman James Watt (1736–1819 CE). He is often assumed to be the original inventor of the steam engine, which is not the case. The first recorded mention of a steam engine, that is, using heat energy to boil water and produce steam to subsequently extract mechanical power from it is in Vitruvius, the Roman architect and inventor from the first century BCE. According to Vitruvius, the first confirmed use of a steam engine dates to the first century CE by Hero of Alexandria, who invented and used the so-called aeolipile, a device that created rotary motion by ejecting steam from two opposite openings of a spherical vessel. It is not known whether this device served a useful purpose and therefore qualifies as “technology” as we defined it in Chap. 1. However, it is suspected that it was used in the temples of Alexandria to create optical effects meant to impress worshipers. The first steam engine that was sold as a commercial product is credited to Thomas Newcomen in England (1712 CE), several decades before Watt. A schematic of a simple reciprocating beam-type steam engine is shown in Fig. 2.6. This engine uses the Rankine cycle described in thermodynamics (Rankine 1853) and seeks to reach as closely as possible the theoretical efficiency of the basic Carnot cycle which underlies all heat engines. A steam engine has six basic elements, the first four of which can also be found as the four segments of the Rankine cycle (see Fig. 2.7):

42

2  Technological Milestones of Humanity

Fig. 2.6  Simple schematic of a reciprocating beam-type steam engine (arrows indicate direction of motion or flow)

Fig. 2.7  T-s diagram of a typical Rankine cycle operating between pressures of 0.06  bar and 50 bar. Left of the critical point the water is liquid, right of it is gas, and under it is saturated liquid-­ vapor equilibrium. (Source: Ainsworth (2007), Wikipedia)

A. A water pump B. A boiler which acts as the steam generator C. An engine (or turbine) which converts the heat energy contained in the steam to useful mechanical energy D. A condenser which acts as a cold sink and recovers the water from the used steam in the engine E. A beam (or other mechanism) that transmits the mechanical forces and torques F. A flywheel (or other mechanism) that executes useful mechanical work by providing torque to an external mechanical load

2.2  The First Industrial Revolution

43

The energy conversion cycle of the steam engine is shown in Fig. 2.7. The cycle begins in the lower left corner with a water pump (A) providing freshwater to the boiler (B) (1 → 2). This process raises both the temperature of the water in [°C] or [K] and its entropy [kJ/kgK], but only by a small amount. Typically, the power consumed by the water pump is only about 1–2% of the power consumed by the steam engine as a whole and is often neglected in calculations. The major addition of heat energy, Qin, occurs in the boiler (B) and raises the temperature to above 100 [°C], which is the boiling point of water under standard atmospheric conditions. The ability to pressurize and superheat the steam above 100 [°C] was a major advancement in the development of the steam engine. This isentropic process is shown by the segment (2 → 3) in the T-s diagram. Isentropic means that it is an idealized thermodynamic process that is adiabatic (no external exchange of heat or mass) as well as reversible. As the superheated steam enters the engine (C), it pushes a cylinder or turbine and performs work at a rate w (3 → 4). This cools the steam to below the boiling point and creates a partial vacuum in the cylinder. As the used steam is pushed out of the cylinder, it is recovered as a liquid in the condenser (D) – Watt’s central contribution – which then acts as a cold sink and water recovery system, expelling heat as Q out . The recovered water is then reinjected into the boiler and the cycle repeats clockwise in the T-s diagram (4 → 1). The mechanical power thus generated is transmitted via a set of linkages, beams, and flywheels (E, F in Fig. 2.6) to perform work useful to humans such as pumping water from mines, driving a mill, or powering industrial machinery. It is estimated that by the year 1800  CE, there were about 500 of Watt’s engines deployed (mainly in Britain), each with a power of about 5–10 [hp], so about 4–8 [kW] each. Unlike a team of 5–10 horses which would require a large stable to work around the clock and water mills which were dependent on the seasons and were still dominant by 1800, the steam engine could work independently of the seasons and time of day. Watt compared the performance and cost of his engines against that of horses to justify the potential investment to his customers. Engines operating above the critical point on supercritical steam did not materialize until the 1920s. James Watt’s contribution was not the invention of the steam engine itself, but the realization of the importance of element “D,” that is, the cold sink and condenser, and the need to keep the engine itself (C) as close as possible to steam temperature. By quickly removing the spent steam from the engine, he was able to significantly increase the efficiency of his steam engines, initially by a factor of 3, and later by a factor of 10. He also invented the concept of “horsepower” as a way to benchmark and sell his machines more effectively. ⇨ Exercise 2.4 Calculate the theoretical steam engine efficiency for the Rankine cycle shown in Fig. 2.7. Estimate the step in efficiency gain achieved between Newcomen and Watt’s engines (see Fig. 2.8) based on your understanding of thermodynamics. How does this efficiency compare to the achievable theoretical efficiency of a Carnot heat engine?

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Fig. 2.8  Steam engine efficiency over time in units of [MJ/kg coal]. Note that a kilogram of bituminous coal has an energy content of about 24–35 [MJ/kg]. Many more smaller innovations occurred to improve steam engines, beyond the steps shown here

Figure 2.8 shows the historical improvement of steam engine efficiency over time. The evolution of steam engines is shown in Fig. 2.8 in terms of the amount of work, W, that a steam engine can provide per kilogram of bituminous coal as its energy source. Since such coal contains anywhere between 24 and 35 [MJ/kg] of energy per unit mass, this level of output can never be exceeded and represents a theoretical upper limit. The initial Newcomen engine was working well, but only had an efficiency of about 1%. Subsequent innovations such as operating at atmospheric pressure (Smeaton), the addition of a condenser (Watt), and operating at higher pressures above 1 [bar] increased this efficiency to about 10% by the mid- to late nineteenth century (Cornish). Steam engines were the main technology that powered the industrial revolution, and they were mainly used for stationary purposes such as driving machining tools or textile machines in factories and providing vertical lift in mines while starting to be used in mobile applications. Steam engines used in ships eventually displaced sail ships (see Chap. 7). They were also used successfully in railroad engines, especially after their efficiency increased further into the 10–20% range. Eventually, in the twentieth century, the reciprocating steam engine, and other innovations such as triple expansion systems, and the use of supercritical steam above 373 [°C] and 220 [bar] of pressure allowed steam engines to reach efficiencies in the range of 40–50% which is the state of the art today. Contemplating Fig. 2.8 in terms of the technological progress of steam engines raises several important points that we will return to many times:

work W performed by the system.

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• Technological progress is not immediate or sudden but occurs over decades and centuries. The period 1700–2000  CE represents a 300-year timeline of improvement. • We must choose a specific figure of merit (FOM) to understand technology progress. The specific definition and units of this FOM matter.8 • Technological progress is not a smooth continuous curve but looks like a “staircase” with discrete steps along the way. • Each step in the curve corresponds to a particular and discrete change in design configuration, material, or operating principle. • Major technologies should not be credited only to single individuals, even though some of these innovators are responsible for larger steps than others, but technology evolves, thanks to the contributions of many. • As technologies asymptotically approach fundamental limits, progress becomes more difficult to achieve. While steam engines are still in use around the world today,9 for example, for electricity generation in coal-fired power plants, many have been or are being gradually replaced by the following types of engines, mainly due to improved efficiency, better reliability, the ability to be mass produced, as well as lower mass and complexity: • Electric motors • Internal combustion engines (ICEs) • Steam turbines The replacement of steam engines highlights the importance of not just raw technical performance and efficiency, but of other figures of merit that drive the development and evolution of technologies. We often refer to these properties of systems as lifecycle properties, or “ilities.”10 One of these lifecycle properties is system safety (Leveson 2016). Some of the early steam engines exploded suddenly as pressure was increased (see Fig.  2.8) and caused injuries and even deaths. This occurred mainly due to the boiler over-pressurizing. Understanding and mitigating these failure modes to avoid accidents became an important part of technology development. In the twenty-first century, there is discussion of the internal combustion engine (ICE) eventually being replaced by high-power electric motors. The speed of this substitution is a matter of active debate (Helveston et al. 2015).

 More on how to define FOMs and quantify technological progress in Chap. 4.  One of the advantages of steam engines is that they are essentially fuel agnostic and can be powered by wood, coal, gas, oil, or even without fossil fuels such as concentrated solar power. This gives steam engines a degree of flexibility not available to other types of engines. The automobile (see Chap. 6) requires gasoline or diesel fuel which must be obtained from refined petroleum and relies on a complex supply chain that was scaled up by John D. Rockefeller’s Standard Oil in the early twentieth century. Creating this infrastructure created a captive audience. 10  A lifecycle property of a system is a characteristic that cannot easily be measured instantaneously but requires operating and observing the system over longer periods of time. 11  Note: the subsequent text on the competition between AC and DC is adapted from de Weck et al. (2011). 8 9

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2.3  Electrification Electrification, which began in the late nineteenth century, was the next wave of the industrial (r)evolution after steam power which dominated in the late eighteenth century and early nineteenth century.11 When Thomas Edison established his electricity generating station on Pearl Street in New York City, and it opened for business in 1882, it featured what have been called “the four key elements of a modern electric utility system: reliable central generation, efficient distribution , a successful end use – in 1882, the light bulb – and a competitive price.” As demand for electricity grew, though, the provision of electricity to end users was primarily through small generating stations, often many of them in one city, and each limited to supplying electricity for a few city blocks. These were owned by any number of competing power companies, and it was not unusual for people in the same apartment building to get their electricity from completely separate providers. This competition, however, did not drive down prices because an operating problem remained: the generating capacity was very much underused and thus the investment cost to serve outlying regions was much larger than desired by end users. There was not only competition for customers, though – there was also technological competition for which type of electricity would be used: alternating current (AC) or direct current (DC). In fact, historians of technology have dubbed what unfolded in the late 1880s the “War of the Currents.”12 Thomas Edison and George Westinghouse were the major adversaries. Edison promoted DC for electric power distribution, while Westinghouse and his ally Nikola Tesla were the AC proponents. Edison’s Pearl Street Station was a DC-generating plant, and there was no reliable AC generating system until Tesla devised one and partnered with Westinghouse to commercialize it. Meanwhile, Edison went on the warpath, mounting a massive public campaign against AC that included spreading disinformation about fatal accidents linked to AC, speaking out in public hearings, and even having his technicians preside over several deliberate killings of stray cats and dogs with AC electricity to “demonstrate” the alleged danger. When the first electric chair was constructed for the state of New York, to run on AC power, Edison tried to popularize the term “westinghoused” for being electrocuted. Technologically, direct current had and still has significant system limitations related to usability and operability. One was that DC power could not be transmitted very far (hence the many stations and their limited service areas in cities), so Edison’s solution was to generate power close to where it is consumed – a significant usability problem as rural residents also desired electrification. Another limitation of DC is that it could not easily be changed to lower or higher voltage, requiring that separate lines be installed to supply electricity to anything that used different voltages. Lots of extra wires were ugly, expensive, and hazardous. Even when Edison devised an innovation that used a three-wire distribution system at +110 Volts, 0 Volts, and  −  110 Volts relative potential, the voltage drop from the  A recent major Hollywood-produced motion picture, “The Current War” (2017), is recounting this era with Benedict Cumberbatch portraying Thomas Edison and Michael Shannon playing George Westinghouse.

12

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resistance of system conductors was so bad that generating plants had to be no more than a mile away from the end user (called the “load”). Alternating current, though, used transformers between the relatively high voltage distribution system and the customer loads. This allowed much larger transmission distances, which meant an AC-based system required fewer generating plants to serve the load in a given area; hence, these plants could be larger and more efficient due to the economies of scale that could be achieved by such large power plants. Westinghouse and Tesla set out to prove the superiority of their AC system. They were awarded a contract to harness Niagara Falls for generating electricity and began work in 1893 to produce power that could be transmitted as AC, all the way to Buffalo – about 25 miles away. In mid-November 1896, they succeeded, and it was not long before AC replaced DC for central station power generation and power distribution across the United States. The roots of the architecture of our current centralized electrical power system can thus be traced back to a fierce battle of technologies and personalities more than a century ago. Figure 2.9 shows the gradual deployment of electrical AC distribution systems in the Eastern United States (Hughes 1993).

Fig. 2.9  Evolution of the South East Pennsylvania electrical power system between 1900 and 1930 in 10-year increments. (Source: Hughes 1993)

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Most DC systems that remained, though, were for electric railways; that famous third rail typically employs DC power between 500 and 750 V, and the overhead catenary lines often use high-current DC. As more and more power came to be generated by AC stations, the needs of these large DC applications were met, thanks to the rotary converter. This device was invented in 1888 (Hughes 1993) and acts as a mechanical rectifier or inverter that could convert power from AC to DC (and vice versa when acting as an inverted rotary converter). The rotary converter, which has since been largely supplanted by solid-state power rectification, created increased usability and operability on the growing electric grid. ➽ Discussion What are other examples of “dueling” technologies that you know? Such technologies would fulfill the same function and be classified in the same cell of Table 1.3. What was the outcome of the competition?

The advancement of electrification was not limited to the United States. Germany, for example, emerged as a leading developer and adopter of electrical power. Both the underlying theory of electric systems and the development and refinement of electric machines became a major scientific and technological activity. Specifically, the emphasis was on characterizing and controlling the static and dynamic properties of electric machines, including electromagnetic energy conversion,

Fig. 2.10  Specific power [kg/kW] progression for AEG AC motors between 1891 and 1964. (Source: Buchheim and Sonnemann 1990)

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minimization of loss mechanisms as well as the conduction of waste heat. Figure 2.10 shows the evolution of the mass to power ratio (also known as the inverse of specific power) of AC motors developed and produced by AEG, the Allgemeine ElektricitätsGesellschaft A.G., the German General Electricity company. A logarithmic view of the progression of specific mass per power for such 4-pole AC motors shows that at the same power level the mass of electric motors decreased by about 20–25% every decade. Most applications of electric power were static such as in power stations or in ground vehicles such as trains, where the energy could be fed into the vehicles via a third rail or overhead catenary wires. More recently, however, further progress has been made whereby electric motors are increasingly used in mobile applications (cars, airplanes). For example, in 2017 Airbus, Siemens and Roll Royce announced the development of a 2 [MW] class electric motor to power the E-Fan X flight demonstrator.13 In May 2020, an electrified Cessna Grand Caravan became the largest all-electric aircraft to fly with a 750 [hp] electric motor. Another example of progress in electrification is the European project ASuMED whose aim is to develop a 1 [MW] superconducting electric motor for aviation applications. This motor will have cooled superconducting wires and achieve 1 [MW] at 6000 [RPM] with a target-specific power of 20 [kW/kg] at a motor efficiency of 99.9% and overall efficiency (including the energy required for the superconducting system) of 99%. A target value of 20 [kW/kg] corresponds to a value of 0.5 [kg/kW] in Fig. 2.10 which would represent an improvement by a factor of 15 compared to the last point shown in that figure for the year 1964. The electrification of automobiles will be discussed in greater detail in Chap. 6. Other trends include the development of high-temperature superconductors (to minimize resistive losses), the revival of DC for high-power propulsion systems – such as those used on high-speed trains, typically at 3 [kV] – as well as improvements of solar generation using wind and solar energy and energy storage using chemical batteries and supercapacitors.

2.4  The Information Revolution One of the major capabilities that enabled the technological evolution of humanity is our ability to process, transport, and store information. Information is also stored and processed in nature in two specific ways: • Information encoded in DNA14

 This project was stopped by Airbus in 2020. Nevertheless, there is an expectation in the aerospace community that electric propulsion will be used and improved for drones and light aircraft with few passengers and moderate range requirements. 14  A recent project at the Broad Institute, jointly operated by MIT and Harvard and funded by IARPA, aims at using synthetic DNA to store and retrieve nonbiological information similar to the hard drive on a computer (Jan 2020). 13

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Fig. 2.11  Gradual abstraction of cuneiform signs. (Source: Budge, E. A. Wallis (Ernest Alfred Wallis), Sir, 1857–1934; King, L. W. (Leonard William), 1869–1919 – A guide to the Babylonian and Assyrian Antiquities, published 1922)

• Information coded as memory in individual’s brains and transmitted to other individuals via auditory or visual messages It is generally agreed that the development of human language, initially spoken language only, and later written language, was a major enabler (or consequence?) of technological evolution. The causality and evolution of languages is a major topic in linguistics (Chomsky 2006) and philology. An early example of written language is cuneiform as shown in Fig. 2.11, where essential concepts for human living and survival such as sun, rain, etc. but also societal concepts such as man, house, king, etc. are encoded. This shows a gradual evolution from pictograms to the more abstract use of symbols whose semantics have to be transmitted and learned from one generation to the next. A major set of inventions marks the path of humanity’s ability to process, transmit, and store larger and larger amounts of information. Table  2.2 shows major milestones toward what has been called the information revolution. In contemplating Table 2.2, it is important to distinguish between the way the information is encoded, the medium used for its storage, and the carrier employed for its transmission. As we observe the transition of encoding information from pictograms to cuneiform to alphabetic and logographic writing – which became dominant during the Roman Empire as well as the Han Dynasty – to binary code, we also see that the physical forms in which information was stored began to change.

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Table 2.2  Milestones in humanity’s ability to process, store, and transmit information Invention Petroglyphs and cave paintings Cuneiforms, hieroglyphs, logograms Stone tablets, clay tablets Papyrus Paper Computer (Antikythera mechanism) Book press

Binary code Telegraph

Radio

Internet Communication satellites

Year and location 40,000–10,000 BCE Europe, Asia, Africa, Americas, Oceania 3200 BCE, Mesopotamia 3000 BCE, Egypt 2100 BCE, Ur, Mesopotamia 2000 BCE, Egypt

Description Depictions of animals, humans, and various symbols in caves and on rock surfaces Replacing or augmenting human messengers with a reliable written record First known law code recorded in history

Papyrus is made from plant material and used for writing and reading 200 BCE, China Paper is made from the cellulose pulp of wood or grasses, or rags (fibers) 100 BCE, Greece A computer enables the execution of arithmetic calculations at speeds higher than unaided humans can do 1432 CE, Johannes The mechanical printing press allowed the Gutenberg, Germany mass production and dissemination of books and ideas Invention of binary arithmetic and enabler of 1689 CE, Gottfried Leibniz digital computers with “on” and “off” gates Morse code and the telegraph systems allow 1844 CE, Samuel Morse sending messages over wired connections far apart 1901–1902 CE, The first wireless radio transmissions are sent Guglielmo Marconi across the Atlantic Ocean from Nova Scotia and Cape Cod 1960s, ARPANET Computers connected via a digital network enable global dissemination of information 1965, Intelsat I (“early First geosynchronous communications satellite bird”) in space to send live TV broadcasts back to Earth

One important transition was from stone tablets and clay tablets to papyrus (which was abundantly available along the shores of the Nile River). The major advantage of this transition was that papyrus could be rolled into scrolls and was much lighter to transport than stone or clay tablets. Thus, an intuitive figure of merit (FOM) to explain these technology transitions is the number of characters stored per unit mass, that is, [char/kg], or if considering a more universal conversion of information to binary code: [bits/kg]. The later success of paper as a carrier for information can be traced not so much to its lightness as compared to papyrus or animal skins, 15 but to the cost of producing the carrier of information itself, coupled with the machinery required for copying or duplication of the information. In the Middle Ages in Europe information was mainly

 The older parts of the state archives of Venice which cover over 1000 years of history in great detail are written in vellum, a kind of parchment, which uses animal skins as its basis.

15

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Fig. 2.12  Cost of Processing Information (computing = technology classification I(1)) over time in [MIPS/$], normalized to 2004. (Source: Koh and Magee 2006)

copied by hand, for example, by monks in monasteries who specialized in the reproduction of manuscripts by hand. Gutenberg’s contribution was the ability to rapidly reproduce information through the printing process. Here again we may think of a figure of merit (FOM) such as [chars/person-hour] or [bits/person-hour] in terms of how many labor hours of work are required to reproduce a certain number of bits of information. In modern parlance and using currency, we might express this as [bits/$].16 ➽ Discussion An interesting question is that of causality between paper and printing, starting in the Middle Ages. What came first, the availability of affordable paper, or the reliable printing press? Are there other historical examples of one technology enabling or requiring another?

In addition to storing and transporting information (see Table 1.3), it is also the ability to modify or process the information that has greatly contributed to the information revolution. At its most basic computational core, this is the ability to carry out the four elementary arithmetic operations of addition, subtraction, multiplication, and division. Each of these calculations is referred to as an “instruction” to a human, analog or digital computer. Here again the introduction of technologies to facilitate the processing of information, now generally referred to as “computing,” has led to rapid progression of humanity’s capabilities.

 There are other ways in which information can be and has been stored and transmitted as in the field of art and architecture, take, for example, Michelangelo’s work in the Sistine Chapel. 16

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Figure 2.12 shows the progression of our ability to process information per unit of effort (expressed as currency). Specifically, a [MIPS] is one million instructions per second and is used as a typical figure of merit to quantify the speed of computing. Dividing by US dollars (reference year 2004) makes this FOM one of economic efficiency for information processing. As can be seen in Fig. 2.12 (note the logarithmic y-axis), the floor is set by unaided human manual calculation by hand.17 Moving from mechanical to analog to digital computers and integrated circuits (ICs) in particular has improved our ability to process information by about 13 orders of magnitude over the last 150 years. We will return to this aspect in Chap. 4 on the quantification of technological progress. One of the most interesting questions in the research on computing today is whether or not quantum computing will provide the next paradigm shift in information processing. ⇨ Exercise 2.5 Check your ability to compute, by carrying out a number of random elementary calculations per minute, and then divide by a nominal wage of $1518 per hour. What is your personal [MIPS/$]? Compare it with what is shown in Fig. 2.12.

2.5  National Perspectives An interesting aspect of understanding the roots of technology is that the same or similar inventions were often made independently in different parts of the world. Asking for a list of technology milestones in different countries or cultures, for example, will invariably not lead to a unified global answer, but to a rather regional or national perspective. Each of the inventions discussed so far has a complex and interesting history in its own right. In the popular mind technologies are “invented” in one instant as a stroke of genius by a single inventor and at a distinct moment in time. Reality is more complex and interesting. Many, perhaps most, technologies we know and use today had some antecedents in antiquity and have evolved gradually19 over centuries

 Humans have used mechanical aids for computation – such as the abacus – for millennia greatly augmenting speed. An interesting phenomenon is abacus speed competitions (soroban), such as those held in Japan, where humans demonstrate impressive computing speeds. It is said that champions in this discipline no longer need the physical abacus but that they run these computations purely in their minds to achieve higher speeds (cf. flash anzan). 18  $15/h was recently introduced in several US cities such as San Francisco as a minimum “living” wage, exceeding the US federal minimum wage (2019) set at $ 7.25/h. 19  We saw in the case of the steam engine (Fig. 2.8), that while technological progress is continual, it looks like a discontinuous staircase and not like a smooth continuous curve. When averaging over long time periods of a century or more, however, it may be valid to work with a continuous and differentiable approximation of the “staircase,” see Chap. 4 for details. 17

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Table 2.3  Examples of technological firsts claimed by different countries Country Great Britain (UK) France Germany Chinaa Japan United States

Technological inventions Steam engine, jet engine, precision timekeeping Hot air balloons, photography, batteries, sterilization Printing press, clocks, gliders, digital computer Compass, gun powder, papermaking, printing Video recorder, optical disk, hybrid cars Light bulb, aircraft, telephone, telegraph, fission

These are often referred to as the four great inventions celebrated in Chinese culture: 四大发明: https://en.wikipedia.org/wiki/Four_Great_Inventions

a

and even millennia. Oftentimes they were invented and improved independently from each other and in different parts of the world. It has been observed that many of the foundations of technology can be traced back to early human civilizations such as the Sumerians in the third millennium BCE, followed by Egypt and Greece. A hotbed of technological innovation was China during the Han Dynasty in the second century BCE, then Europe during and after the Renaissance starting in the fourteenth century, then Great Britain in the eighteenth and early nineteenth centuries. France played a pivotal role in the middle of the nineteenth century as France was the most populous country in Europe and Paris was its largest city with over 200,000 inhabitants. The United States came to the party relatively late starting in the late nineteenth century and early twentieth century and was greatly bolstered technologically by its victories in both World Wars. Japan emerged as a major technological innovator starting in the 1970s, particularly in the area of automobiles and consumer electronics. Today, technological innovation is a global game involving competitors on all continents (see Chap. 10). The reasons for technological developments in different countries and at different times are varied. Some were compelled to invent and use technology due to a lack of natural resources (e.g., Japan), while others viewed technology as a path to building military strength (e.g., Germany). During the so-called Belle Époque in France – which lasted from 1870 to 1914 – there was a unique confluence of arts, culture, science, and technology that led to great advances and mutual inspiration of different professions. Later, economic drivers and consumerism  – such as in the United States after WWII – became major drivers of technological change. Table 2.3 summarizes some of the claims of technological firsts made by different countries, while Fig. 2.13 overlays the growth of the human population since 1700 CE with major technological milestones. Attempts at verifying such claims invariably uncover the complex, interesting, and interwoven history of our common technological past. An interesting question is whether technologies are created at a higher rate or advance faster during periods of war as compared to peacetime? This is not a settled question when we consider Fig. 2.13, and there are indicators in favor and against answering this question in the affirmative. Since humans have started competing with each other for resources and control of territory, the use of technology has played an important role. It is quite well

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Fig. 2.13  Evolution of human world population and major technological milestones. The growth of the human population in the last century has been exponential and can be approximated by the finite difference equation x(t) = (1 + r) × x(t−1), whereby r = 0.0105 = 1.05%

established that engineering as a field of study, research, and application started from military technology (de Weck et al. 2011). Before and during the Middle Ages fortifications, armor and the design of weapons such as trebuchets and cannons played an important role in the development of military technology (see also Chap. 20). Warfare played an important role in propelling the formalization of engineering. When armies needed more complex artillery and fortifications in the mid-­1600s, officers were educated in mechanics and mathematics. Their pioneering work branched into the field of civil engineering, over a long period of time. In 1747 CE, King Louis XV of France turned to Jean-Rodolphe Perronet, a noted architect and what today we would call a “structural engineer” (he was famous for stone arch bridges), and gave him the task of educating men20 to build bridges and highways. This effort eventually became the École des Ponts et Chaussées in 1775 and may be the first formal “School of Engineering” in the world. It is certainly the world’s oldest civil engineering school, prestigious to this day. The École Polytechnique in Paris, established in 1794, was converted by Napoléon to a military school in 1804. It has always educated engineers, and many of the greatest mathematicians (e.g., Benoit Mandelbrot the father of fractal geometry) and theoreticians of the nineteenth and twentieth centuries graced its faculty and student body. Until the recent

 The history of technology – at least as it is mainly recorded today – is dominated by men and we unfortunately only find few examples of women as recorded inventors of new technology. This is likely due to the societal norms of past centuries and millennia. However, in the late twentieth and twenty-first centuries, women have become more prominent as originators of new technology and innovations. An example we celebrated recently is Margaret Hamilton who led the development of flight software in the Apollo program that enabled the first human landing on the Moon (1969). 20

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Table 2.4  Technologies invented during periods of war Technology Trebuchet Solid rockets Penicillin Nerve gas Jet engine Fission bomb Fusion bomb

Year Fourth century BCE Tenth century CE 1928 1936 1940 (WWII) 1945 (WWII) 1948–55 (Cold War)

Inventor/country China China United Kingdom Germany UK/Germany USA USA, Soviet Union

past, students at Polytechnique received credit for military service as they pursued their studies in France. Table 2.4 shows examples of military technologies invented before or during periods of war, often under great time pressure. It may be surprising to find penicillin on this list, but the refinement of it as a medication to combat bacterial infections was considered a classified military technology during WWII and it probably greatly improved survival rates following battlefield trauma and injury during the war, despite the risk of serious allergic reactions (de Weck 1964). The development of specific military technologies, such as the development of military aviation during WWI and WWII, is well documented. This includes the development of the turbojet engine (see also Chap. 9) which owes its roots to the competition between the Allies and the Axis powers for air supremacy during WWII. ➽ Discussion How does conflict drive technological development and innovation? What is the evidence? Is technology developed during peacetime more useful for humans and more sustainable in the long run? Is the sum of total welfare for all sides involved in a conflict involving technological innovation greater or smaller due to the war?

On the other hand, there is evidence that prolonged periods of war can have a significant depressing effect on technological and societal development in general. For example, it is generally considered that the Thirty Years War in central Europe (1618–1648) between the Habsburg states and its enemies (including Sweden, France, and England) had a major chilling effect on societal development in general and technological progress, specifically. There is currently no quantified evidence that the general rate of technological progress21 is higher or lower during periods of war. However, anecdotal evidence is that nations expend great effort on technologies during periods of war. Most of these 21

 Chapter 4 introduces the formal notion of quantifying and tracking technological progress.

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technologies have specific offensive or defensive characteristics. Some of these technologies (but not all) are later translated to civilian applications for greater societal benefit. An example of this is the use of uranium enrichment technology for generating carbon-free electrical power. Another interesting example is the relationship between astrophysics and military technology (Tyson and Lang 2018). The relationship between technological invention and the relative rate of progress during periods of war and peace remains an open research question.22 The opposite of conflict is cooperation. In the last 50 years, we have seen many attempts at international collaboration when it comes to the development of new technology. Recent examples include the International Space Station (ISS) as well as the ITER project, whose declared goal is to demonstrate energy net positive plasma fusion at scale with peak power of 620 [MW] starting in 2025. The European Union (EU) in particular emphasizes technological collaboration among its member states, for example, through the Horizon 2020 research program. Again, it is an open question whether competition or cooperation, or some combination of the two modalities, is most effective when developing new technologies (see also Chap. 10).

2.6  What Is the Next Technological Revolution? As we consider the technological milestones of humanity, we encounter different ways to phase or group technological epochs. These are born out of a desire to simplify the history of humanity’s quest for technology. A typical grouping is as follows: First industrial revolution: Steam power replaces or augments the power of humans and horses. The beginnings of the industrial revolution are firmly placed in Great Britain in the mid-1700s. This first industrial revolution enabled the mechanization of mines and manufacturing of large quantities of goods in factories. Some positive effects of industrialization were the raising of the standard of living for millions of people as well as population growth, while on the negative side of the ledger we find increased air pollution (due to the burning of coal driving all those steam engines) and increasing economic disparity between factory owners and landowners and laborers. Second industrial revolution: Electrification powers lights and electric machines in the United States and Western Europe and in Asia starting in the 1880s to illuminate the night and provide power to machines and appliances. This enabled extended working hours and relative independence from animals and climatic conditions to carry out work. Some of the advantages of electrification were the ability to produce power from water (hydropower), and the emergence of electric appliances, greatly reducing the tedium of many daily tasks such as cooking, washing, etc. An important application of electrification in warmer climates is air conditioning. However, depending on the

22

 See also Chap. 20 for a more detailed discussion on military and intelligence technologies.

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nature of the energy conversion technology, electrification may also have contributed significantly to accelerating climate change, for example, via coal-fired power plants. Third industrial revolution: Computing and information processing is enabled, thanks to the advent of the analog and subsequently the digital computer. Alan Turing’s machine (the so-called Bombe) “beat” the German naval Enigma at Bletchley Park in 1940. The Z3 computer, built by German inventor Konrad Zuse in 1941, was the first working programmable, fully automatic computing machine. These inventions eventually paved the way for us to link together computers, thus creating the Internet and enabling the modern information society in which we live today. A more recent development is the link between computing and telecommunications (radio), allowing mobile data access to large amounts of data, almost independently of physical location. ➽ Discussion Is it possible to know that a technological revolution is underway, or does this only become obvious after the fact? Can there be multiple technological revolutions going on in parallel, at the same time?

Assuming that the prior technological developments in the history of humanity indeed gave rise to three industrial revolutions (steam power, electrification, computing), what is a useful way to distinguish technological epochs? There is currently an active debate as to what the next (fourth) industrial revolution may be. This debate is not settled and there are several candidates under discussion: • Industry 4.0 and Cyber-Physical systems: The interconnection of physical machines and the Internet enables to essentially create an “internet-of-things” (IoT) where physical machines can talk to each other and perform functions autonomously with no or only minimal human intervention. This unprecedented degree of autonomy would allow functions that previously required not only human labor, but also human control (such as in mining, subsea oil production, agriculture, and industrial manufacturing) to be carried out by machines and robots on their own, using artificial intelligence (AI), thus allowing humans to focus on less rote and potentially more creative activities. This can and has already had fundamental implications for the future of human work.23 • Genetics and Biological Engineering: Since Gregor Mendel’s (1822–1884) foundational research on inheritance and the discovery of the double-helix structure of DNA by Watson and Crick (1953) – in part based on data generated by Rosalind Franklin  – we have made rapid progress in sequencing the human

23

 MIT recently concluded (2020) a study on the Future of Work

References

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genome (see Chap. 18) giving rise to gene therapy and genetic editing technologies such as CRISPR. This has the potential to alter not only human lifespan and health, but the future of our species overall. A big leap forward in this area was the creation and massive global deployment of vaccines against the COVID-19 virus using mRNA technology in 2020 and 2021 by companies such as Moderna and Pfizer. • Quantum Technologies: The advent of quantum physics in the early twentieth century led to nuclear fission, both for peaceful purposes, harvesting the energy of the uranium atom by splitting it and using the heat generated for electricity generation, as well as for weapons of mass destruction, that is, the fission bomb. Fusion is being developed as a potential source of energy, essentially replicating the plasma fusion occurring in our star, the Sun, but at a smaller controlled scale. The most ambitious undertaking in this area is the international ITER project. Further mastering the spin states of individual electrons and quantum states of atoms could lead to significant advances in computing, encryption, and communications. ➽ Discussion Which of these developments will have the largest impact on humanity’s technologies and overall future as a species? This remains an open question.

Today, there is no end in sight to humanity’s journey in terms of invention, deployment, and maturation of technologies. We are looking back at about six millennia – since about 4000  BCE  – of recorded history of technological developments. The “dominance” of Homo sapiens on Earth dates back about 60,000 years (see Fig. 2.1). It can be attributed only in part to our mastery of technology, since technology spans a mere 10% of that timeframe. An important part of this debate is whether the negative and cumulative aspects of the use of technologies at a massive scale will eventually cause the destruction of our species or at least significantly dampen our future prospects. An example of this is the rapidly rising accumulation of greenhouse gases in Earth’s atmosphere and the subsequent risks associated with Climate Change (Smil 2017). While some technological answers may exist to these challenges (e.g., artificial carbon capture and storage), others are advocating a “return back to nature,” that is, a massive program of reforestation or simply forsaking modern technologies to return to a time where basic functions were carried out by humans and domesticated animals directly.24 The next chapter focuses on exactly this question: the relationship between humans, technology, and nature.

 A well-known example of such a society which voluntarily limits the use of modern technology are the Amish

24

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References Azevedo FA, Carvalho LR, Grinberg LT, Farfel JM, Ferretti RE, Leite RE, Filho WJ, Lent R, Herculano-Houzel S. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of Comparative Neurology. 2009 Apr 10;513(5):532–41. Buchheim, G., Sonnemann R., “Geschichte der Technikwissenschaften”, Birkhäuser Verlag, Basel, Boston, Berlin, ISBN 3-7643-2270-5, 1990. Chomsky, Noam. Language and mind. Cambridge University Press, 2006. de León MS, Golovanova L, Doronichev V, Romanova G, Akazawa T, Kondo O, Ishida H, Zollikofer CP. Neanderthal brain size at birth provides insights into the evolution of human life history. Proceedings of the National Academy of Sciences. 2008 Sep 16;105(37):13764–8. de Weck A.L.  Penicillin allergy: its detection by an improved haemagglutination technique. Nature. 1964 Jun 6;202:975–7. de Weck O., Roos D., Magee C., “Engineering Systems: Meeting Human Needs in a Complex Technological World”, MIT Press, ISBN: 978-0-262-01670-4, November 2011. Eknoyan G. A history of obesity, or how what was good became ugly and then bad. Advances in chronic kidney disease. 2006 Oct 1;13(4):421–7. Helveston JP, Liu Y, Feit EM, Fuchs E, Klampfl E, Michalek JJ. Will subsidies drive electric vehicle adoption? Measuring consumer preferences in the US and China. Transportation Research Part A: Policy and Practice. 2015 Mar 1;73:96–112. Hughes T.P. Networks of power: electrification in Western society, 1880–1930. JHU Press; 1993. Koh H. and Magee C.  L., “A Functional Approach for Studying Technological Progress: Application to Information Technology,” Technological Forecasting & Social Change. 2006; 73: 1061–1083. Leveson N.G. Engineering a safer world: Systems thinking applied to safety. The MIT Press; 2016. Magee, Christopher L., and Tessaleno C. Devezas. “How many singularities are near and how will they disrupt human history?.” Technological Forecasting and Social Change 78, no. 8 (2011): 1365–1378. Maslow AH. A theory of human motivation. Readings in managerial psychology. 1989;20:20–35. Rankine WJ. VII.—On the Mechanical Action of Heat, especially in Gases and Vapours. Earth and Environmental Science Transactions of the Royal Society of Edinburgh. 1853; 20(1):147–90. Rolian C. The role of genes and development in the evolution of the primate hand. In The evolution of the primate hand 2016 (pp. 101–130). Springer, New York, NY. Roth G, Dicke U. Evolution of the brain and intelligence. Trends in cognitive sciences. 2005 May 1;9(5):250–7. Smil V. Energy and civilization: a history. MIT Press; 2017 May 12. Stevenson RD, Wassersug RJ. Horsepower from a horse. Nature. 1993 Jul 15;364(6434):195–. Tyson N.D., Lang A. Accessory to war: The unspoken alliance between astrophysics and the military. WW Norton & Company; 2018 Sept 11.

Chapter 3

Nature and Technology

Advanced Technology Roadmap Architecture (ATRA) Inputs

Steps

Outputs FOMjj

Strategic Drivers for Technology 1. Where are we today?

L1 Products and Missions

Technology State of the Art and Competitive Benchmarking

L2 Technologies

+5y Organization

FOMi

Today

2. Where could we go?

+10y

Technology Systems Modeling and Trends over Time

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Dependency Structure Matrix

Tech Pul Pull

Figures of Merit (FOM) Current State of the Art (SOA) Technology Trends dFOM/dt

Competitor 1 Competitor 2

Technology Systems Modeling

L1

Technology Roadmaps

+10y

Technology Projects

Scenario A

+5y

Scenario B ?

3. Where should we go?

L2

Scenario Analysis and Technology Valuation

Technology Portfolio Valuation, Optimization and Selection

Intellectual Property Analytics

Technology Investment Efficient Frontier Technology Portfolio Technology Projects σ[NPV] - Risk

3

Foundations Definitions What is Technology?

E[NPV] - Return

4. Where we are going!

History Milestones of Technology

Ecosystems Nature Technology, Nature Technology Diffusion, Infusion and Industry and Humans

Design Reference Missions Future Scenarios T h l Technology V Valuation l ti Vector Charts

FOMi

Tech Push

Technology Scouting Knowledge Management

Scenario-based Technology Valuation

The Future Is there a Singularity ?

© Springer Nature Switzerland AG 2022 O. L. de Weck, Technology Roadmapping and Development, https://doi.org/10.1007/978-3-030-88346-1_3

Case 1

Case 2

Automobiles

Aircraft

Pareto-optimal set of technology investment portfolios Recommended Technology Portfolio (Expected NPV and Risk)

C Cases Case 3 Deep Space Network

Case 4 DNA Sequencing

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3.1  Examples of Technology in Nature For many centuries – in the human mind – there has been a strict separation between humans, nature, and technology. Many religions elevate humans above other animals and designate them as being special or different. Societal norms in many (but not all) cultures view Homo sapiens as being superior and endowed with the right or even obligation to master or control nature. This has had and continues to have profound consequences. As we saw in Chap. 2, technology emerged over the last few millennia and was believed to be a uniquely human creation. In this worldview, nature is often viewed as being distinct and separate, particularly by urban dwellers. How could coal mines, factories, large cities, and forests possibly have anything in common? It is fair to say that in the late twentieth century and especially the early twenty-first century, a realization is dawning that humans are still animals (Homo sapiens), and that technology may not be unique to humans. Also, a more humble attitude appears to be developing that we may still have much to learn from nature when it comes to the development of technology. ➽ Discussion Why has it taken humans until now to do “rediscover” the value of nature to society? What does it mean to be a naturalist in the twenty-first century? Do you agree that biology and technology are or can be closely linked?

Let us begin with an example of technology in nature that is near and dear to our heart: The beaver1 (genus: castor), see Fig. 3.1. Fig. 3.1  The beaver is MIT’s mascot and is considered “nature’s engineer”

1  The beaver was chosen as MIT’s mascot in 1914 and was later named “TIM” (MIT read backward). The main reason is that the beaver is often considered “nature’s engineer,” see “Tim the Beaver Mascot History.” MIT Division of Student Life. 1998.

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There are two distinct species of beaver, the North American one (castor canadensis) and the Eurasian one (castor fiber). They live in groups and are widespread throughout North America and in Northern Europe and Siberia. The beaver is equipped with a set of remarkable anatomical features, including self-sharpening teeth and a paddle-like tail. A beaver’s habitat is a complex construct that contains the main structure which can only be reached from under the water. This requires the beaver to artificially create a small lake or canal, which is done by felling trees, which are subsequently assembled to form a so-called beaver’s dam. If there is a leak in the dam, the beaver knows how to make it watertight by patching holes with branches and mud, thus carrying out a kind of “maintenance” operation. The main purpose of this elaborate approach to habitat design is the protection from predators.2 The main predators of the beaver (besides humans) are bears and coyotes. The body of water created around the habitat is also used to float building materials and food back and forth. This ability to build dams, canals, and lodges (homes) has earned the beaver the nickname “nature’s engineer.” The following description can leave no doubt that the beaver masters “technology” as we have defined it in Chap. 1: Beavers are known for their natural trait of building dams on rivers and streams, and building their homes (known as “lodges”) in the resulting pond. Beavers also build canals to float building materials that are difficult to haul over land. They use powerful front teeth to cut trees and other plants that they use both for building and for food. In the absence of existing ponds, beavers must construct dams before building their lodges. First they place vertical poles, then fill between the poles with a crisscross of horizontally placed branches. They fill in the gaps between the branches with a combination of weeds and mud until the dam impounds sufficient water to surround the lodge.3

The following processes are needed for a beaver to create their habitat from scratch and to maintain it over time: 1. Scouting for and selecting an appropriate site. 2. Felling trees and constructing a dam and/or canal to create a body of water (pond) that will support a habitat and surrounding ecosystem. 3. Collecting and assembling materials for the main lodge (habitat). 4. Building and living in the main habitat (see Fig. 3.3). 5. Improving the infrastructure as needed and providing food for the group, watching out for predators, and sounding the alarm if needed. 6. Relocating the habitat if necessary (starting at step 1 again). These processes and their relationship are shown in a simplified way in Fig. 3.2, including the following OPL: Beaver is physical and systemic. Trees are physical and systemic. Creek is physical and systemic. Water Source is physical and systemic. Dam is physical and systemic. Food is physical and systemic. Lodge is physical and systemic.

2  When beavers were introduced in Tierra del Fuego (Argentina), it was found that they had no natural predators, but that they still build dams and habitats as they do in Northern latitudes. 3  Source: https://en.wikipedia.org/wiki/Beaver

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Fig. 3.2  OPM model of the beaver’s habitat building process in nature

Ecosystem is physical and systemic. Materials are physical and systemic. Ecosystem consists of Trees and Water Source. Creek is a Water Source. Dam relates to Creek. Scouting is physical and systemic. Beaver handles Scouting. Scouting  requires Ecosystem. Felling is physical and systemic. Beaver handles Felling. Felling consumes Trees. Felling yields Materials. Building is physical and systemic. Beaver handles Building. Building consumes Materials. Building yields Dam and Lodge. Living is physical and systemic. Beaver handles Living. Living requires Lodge. Living consumes Food. Improving is physical and systemic. Beaver handles Improving. Improving affects Dam and Lodge. Improving consumes Food and Materials. We find here several process-operand combinations as shown in Table 1.3: • • • • •

Scouting ecosystems = information transporting (I2) Felling trees = matter processing (M1) Building dam and lodge = matter transforming (M1) Beaver living in lodge = organism housing (L3) Dam improving = matter regulating (M5)

It is difficult to argue that these feats do not represent “technology” – as we have defined it in Chap. 1 – which is there to solve an existential problem for the species castor, that is, the beaver. Figure 3.3 depicts the form of a Beaver’s habitat and its different elements. Key features are the presence of a dam, which artificially raises the water level and creates an artificial pond, a dedicated food cache which is partially submerged and designed in a way that it is also accessible in the winter if the surface of the pond is frozen. The main habitat itself, the lodge, is only accessible via one or more underwater passages. Inside the lodge the sleeping chamber is separated from the feeding chamber which features an elevated shelf – a kind of table – for food consumption. At the top of the lodge is an air intake to provide adequate ventilation. The skills and processes for building such structures are passed on from one generation of beaver to the next. Experiments with transplanting beavers from one location to

3.1 Examples of Technology in Nature

65 Air intake

Dam

Food cache

Ice

Lodge

Sleeping chamber Feeding chamber with water basin and elevated shelf

Fig. 3.3  Beaver (castor) habitat with its various elements and functions

another in Wyoming (McKinstry and Anderson 2002) showed that young beavers under the age of 2 had much higher mortality rates than older beavers (age 4+). This suggests that young beavers learn how to build dams and lodges and how to survive from older beavers. Looking at the sophistication of beaver dams and lodges, it is difficult to argue that “technology” is exclusively the domain of humans. While beavers maintain and improve their habitats in the short term (on average a beaver site is used for 2–3 years), it is currently unknown if beaver habitat “technology” has improved significantly in recent centuries, and over the estimated 24 million years that this species has existed. ⇨ Exercise 3.1 Find and describe other examples of what you would consider as “technology” in nature. These examples should not involve Homo sapiens but must rely on a deliberate intervention by an agent (usually an animal) to create an object(s) or process(es) that would not otherwise occur. Provide both text and an image or schematic and make sure you reference the source of your material.

We can find many other examples of what can be considered “technology” in nature according to our definition, see also Fig. 3.4. • Primates use tools such as rocks for different tasks such as cracking nuts (chimpanzee), walking in the water or on uneven ground with sticks (gorilla) or spearfishing (orangutan), see Schaik et  al. (1996). It is a matter of ongoing research to estimate what fraction of this tool use is self-taught within the species versus observed and copied from human behavior. a

b

c

Fig. 3.4  Examples of “technology” in nature: (a) a chimpanzee cracking nuts with a rock, (b) a rock pigeon’s nest with eggs, (c) a bee’s honeycomb structure

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• Many species such as birds, rodents, or ants build sophisticated nests or habitats by taking raw materials from nature (sticks, leaves, etc.) and combine them into three-dimensional structures that provide both physical protection and thermal insulation among other functions. • As we have already seen, the ability to gather energy in the form of food, and to then store this energy (technology type E3) for later consumption is a major necessity for many animals, including humans. This need for energy storage is a great driver for technology development. One of the most impressive examples is the honeycomb structures inside the nests or hives of the honeybee (subgenus Apis), see Fig. 3.4c. These instances of “technology in nature” share the feature that they are objects and processes deliberately created by animals to solve specific problems such as protection from predators. These things would not otherwise occur spontaneously, and by “spontaneously” we refer to the quasi-random action of the wind, water, and solar radiation, among others. Relatively recent research has shown that birds are not simply “programmed” genetically to build nests in a certain way, but that they learn this behavior and can learn from each design. Birds get better at building nests with experience. For example, they drop fewer leaves over time the more practice they accumulate (Walsh et al. 2011). Thus, technology in nature is not based on pure instinct and requires forms of knowledge transfer between individuals (see also Chap. 15). We see that technology is not unique to humans, as is often claimed. While “human technology” tends to initially appear to be more complex and capable than the examples we see in nature, organisms have produced and are producing very resilient and energy-efficient solutions that often surpass what humans can (today) do by “artificial” means. These observations lead us to the more general definition of technology we adopted in Chap. 1: Technology is the deliberate creation and use of objects and processes to solve specific problems. The emphasis in technology is not on humans as originators and users, but on the deliberate act of creation and the problem-driven nature of its specific purpose. This also applies to many, but not all, animals in nature on Earth and it may apply to life forms outside of our planet as well, we just do not know yet. One area where human technology stands out is its rate of improvement which is orders of magnitude faster than what we have observed in nature.4 In 2015, the BBC published a story titled “Chimpanzees and monkeys have entered the stone age,5” where it was suggested that chimpanzees too may have the ability to further improve stone tools and that they have entered their own equivalent of the “stone age.” There is no way to be sure, but primate archeologists suggest that it is the ability to control fire and cook food (see Chap. 2) and therefore satisfy the energy needs of our larger brains, which has allowed humans to enter a kind of reinforcing loop whereby our larger brains required even more energy in the form of food, which then led to the

 We discuss ways to measure technological progress in Chap. 4.  http://www.bbc.com/earth/story/20150818-chimps-living-in-the-stone-age

4 5

3.2 Bio-Inspired Design and Biomimetics

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invention of additional technologies to both generate more food energy and consume less energy (e.g., thanks to clothing). Natural technologies that have evolved slowly over millions of years may on the other hand be orders of magnitude more efficient or resilient than human-generated technologies. One of the most remarkable characteristics of biology is the ability for self-replication. While there have been concepts and even attempts at creating self-­ replicating robots  – robots that can create copies of themselves without external intervention – this has not yet been achieved.6 This gives rise to what we call bio-inspired design or biomimetics. ➽ Discussion Since humans are hominoids and are therefore part of the animal kingdom, the philosophical argument can be made that human-generated technologies are “natural” since we are ourselves still part of nature. Do you agree with this?

3.2  Bio-Inspired Design and Biomimetics Nature can inspire technology. In the engineering design community, this generally goes under the heading of so-called bio-inspired design. There are also other related terms such as biomimetics, bionics, and biomimicry7 (Fu et al. 2014; Wilson et al. 2010). This field, which is generally considered a part of engineering design, links engineering to biology, zoology, botany, chemistry, and material science. Its general approach is to observe systems as they occur in nature such as trees, ant colonies, seashells, etc. and to describe and study their underlying principles, forms, and behaviors and to then extract from these observations “rules” that can be applied in the design of artificial, that is, human-made systems. We generally differentiate between biomimetics which are designs patterned or copied directly from natural processes, versus more general bio-inspired designs whose engineering principles are inspired by nature, but more indirectly, by first abstracting nature into a set of guidelines (see Fig. 3.5). Examples of biomimetics include the following: • Echolocation. Whales and other ocean mammals, as well as bats, send out high-­ frequency sound waves, for example, in the range of 10–100 [kHz], with a

 Speculation on how human-generated technology may evolve is the subject of Chap. 22.  There are subtle differences between these terms which have been introduced in the literature starting in the 1950s with bionics (Steele, 1950s), biomimetics (Schmitt, 1950s), and then bioinspired design (French, 1988) often used as synonyms. Here, however, we draw some distinctions that will be important in practice. Biomimetics is the direct application of biological functions, and imitation of form and behavior in design. The resulting design may look very similar to its natural analog. Bio-inspired design on the other hand is the indirect application of natural principles that have been distilled at a higher level of abstraction. Since the 1974–1978 TV series “The Six Million Dollar Man” the term bionics has been associated with artificial technology used in cyborgs. Biomimicry is essentially synonymous with Biomimetics. 6 7

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Artificial

Biomimetic

Bio-inspired Bio inspired Design

Fig. 3.5  Top row: spider silk in nature and synthetic spider silk (e.g., Microsilk™) used in textiles, bottom row: natural seashells and corrugated roofs which increase their bending moment of inertia by applying the geometry of seashells

s­ pecialized organ in their heads and then interpret the reflected signals in terms of amplitude and time delay. This is used to accurately identify obstacles as well as predators and prey, even in complete darkness. This phenomenon is also known as “biosonar” and is applied in underwater systems such as the active sonar systems found on submarines. • Spider Silk. This material has extraordinary strength and can now be replicated as artificial spider silk using a combination of chemical engineering, genetic engineering, and nanotechnology. One attempt at producing the spider silk protein even involved genetically modifying goats to produce the protein in their milk (Vollrath and Knight 2001). Dragline spider silk has a tensile strength of about 1.3 [GPa] and is about five times stronger than steel, when normalized by its density. Recently, the mechanical properties of natural orb webs were measured noninvasively by using light scattering (Qin and Buehler 2013). • Biologically derived materials and chemicals include mushrooms grown for insulation and organic packaging materials such as EcoCradle™ which is grown from fungal mycelium and biological anti-scaling agents used for water softening that mimic chemicals excreted by several organisms. One example of the use of anti-scaling agents is to clean or maintain membranes in reverse osmosis (RO) systems used for desalination of seawater. Here the main purpose of the technology is to prevent unwanted buildup of calcium carbonate and biofouling. Examples of bio-inspired designs include the following: • Airplanes. For thousands of years (see Chap. 9), humans have wanted to emulate the flight of birds as retold in the Greek legend of Daedalus and his son Icarus. It

3.2 Bio-Inspired Design and Biomimetics

•

•

•

•

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is well documented that the first heavier-than-air sustained powered flight by the Wright brothers in 1903 was achieved, in part, due to their meticulous study of birds soaring over the sand dunes of North Carolina (McCullough 2015). One specific manifestation of their natural observations on the Wright Flyer was the wing warping mechanism used for roll control. Corrugated Structures: Seashells are exoskeletons of invertebrates living in the sea. They have a high strength-to-weight ratio and are very stiff. In nature, these stiff shells are difficult for predators to crack and they serve as both housing and protection for their inhabitants, such as mollusks. In man-made systems, these structural properties can be replicated by extruding or bending sheets of metal in a way that increases their bending stiffness. This works extremely well, provided that the ratios of height, to width, to thickness are close to optimal. The shape of seashells has been optimized by evolution and natural selection over millions of years, see also Fig. 3.5 (bottom row). Honeycomb Structures. The hexagon is the two-dimensional shape with the best area-to-circumference ratio of any polygon that maintains a close-packing property, see Fig.  3.4c. This can be and has been exploited in artificial composite materials and honeycomb structures in particular. The extraordinary stiffness and lightness of honeycomb structures are two of the reasons why they are used extensively in aeronautical, automotive, sports equipment, and other applications. Neuromorphic Sensors. The principles of biological systems (Mead 1990) can be embedded in neural networks that are often low power, analog, and highly specialized. An example of neuromorphic sensors is small “event-based” cameras whose only purpose is to detect whether or not an event or change is happening in a particular scene of interest. Neuromorphic sensing and computing is an active area of research in computer vision and artificial intelligence (AI) and holds great promise, for example, for the next generation of self-driving cars (Collin et al. 2020). Organic Agriculture: There is a growing movement to use a diversity of plants in agriculture as well as to rely on natural pest deterrents and forego artificial hormones and chemically produced pesticides. This approach to agriculture, in contrast to high-intensity monoculture, is inspired by the dynamics of natural ecosystems.

Figure 3.5 illustrates these two subtly different concepts. In biomimetics, the natural processes and objects are used directly, even if in an adapted form, while in bio-inspired design the working principles observed in nature are first observed and abstracted, and then indirectly applied to artificial systems. Bionic systems are discussed in the later section on Cyborgs. Several principles of bio-inspired design have been described over the years. Table 3.1 (adapted from Bhushan 2009) shows examples of biological functions and which organisms or objects exhibit them. Reading the quickly growing literature in biomimetics leaves one amazed at nature’s variety of solutions for problems at multiple length scales. A summary is provided by Bhushan (2009): Molecular scale devices, super-hydrophobicity, self-cleaning, drag reduction in fluid flow, energy conversion and conservation, high adhesion, reversible adhesion, aerodynamic lift, materials and fibres with high mechanical strength, biological self-assembly, antireflection,

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⇨ Exercise 3.2 Find examples of “artificial” designs made by humans that can reliably be traced back to natural principles. Describe the essence of the technology, its purpose, how it works, when it was first introduced, and its antecedent in nature. Table 3.1  Objects and organisms from nature and their selected functions Organism or object Bacteria Plants Insects, spiders, lizards, and frogs Aquatic animals Birds Seashells, bones, teeth Spider web Moth-eyes Polar bear skin and fur

Function(s) Biological motor powered by ATPa Chemical energy conversion, self-cleaning, drag reduction, hydrophilicity, adhesion, motion Super-hydrophobicity, reversible adhesion in dry and wet environments Low hydrodynamic drag, energy production Aerodynamic lift, light coloration, camouflage, insulation High mechanical strength for transmission of forces and torques Biological self-assembly (see Fig. 3.5) Antireflective surface coatings, structural coloration Thermal insulation

Adenosine triphosphate (ATP) is an organic compound that is used as the main energy source to power several processes in living cells

a

structural coloration, thermal insulation, self-healing and sensory-aid mechanisms are some of the examples found in nature that are of commercial interest.

The key to many of these biological processes is the nano-scale or micron-scale materials and properties and the arrangement of these into hierarchical structures. An example of this is the multifunctional surface properties of plants such as hydrophobicity (repelling water) and photosynthesis (converting sunlight). Figure 3.6 shows a schematic of functions supported by plant surfaces in nature. Plant surfaces are not just a protective skin,8 but also provide chemical, mechanical, and thermal properties that enable the exchange of useful resources across the plant surface boundary. Conversely, harmful exchanges such as those with pathogens are blocked. As for the human skin,9 there are many different functions that can be and must be enabled. One of the most famous examples of surface properties of animals is the feet of the gecko. The secret to the “sticking” property of this animal is the multiple hierarchical levels of scales, hairs, or hooks that are tuned in a way to provide optimal mobility and climbing capability to the animal, on almost any surface. In the case of the gecko “each toe contains hundreds of thousands of setae and each seta contains hundreds of spatula” (Bhushan 2009). Figure 3.7 shows a number of different animals and their corresponding body mass in grams [g], density of setae (hairs) per 100 [μm2], and whether or not the

 A successful commercial application of plants is aloe vera, which grows mainly in dry climates.  The importance of the human skin is often underappreciated. It enables at least three major functions in our bodies such as protecting, sensing, and regulating (temperature). It is the largest organ of the integumentary system. 8 9

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Fig. 3.6  Functions provided by hydrophobic plant surfaces in nature: (a) transport limitation to prevent water loss, (b) surface wettability, (c) anti-adhesive properties to prevent pathogen attacks and enable self-cleaning, (d) signaling provides cues for insect recognition and epidermal cell development, (e) optical properties protect against harmful radiation, (f) resistance against mechanical stresses and maintenance of physiological integrity, and (g) reduction of surface temperature by increasing turbulent airflow to promote convection. (Adapted from Bhushan 2009)

adhesive properties are targeted at dry adhesion on land or wet adhesion on the water. The length scales of these surface features vary between 1 and 100 [μm]. Thanks to progress in nanotechnology and robotics we are now able to partially replicate such fine structures using machines. Indeed, robotic geckos have been able to climb walls and take advantage of these biologically inspired features. At a deeper level, one may wonder why bio-inspired design works and why it has so much potential. The answer may be related to evolution, as first proposed by Charles Darwin (1809–1882). Many of the organisms discussed so far had billions or at least millions of years to evolve under changing environments. We saw in Fig. 2.1 that the evolution of humans goes back at least 2 [mya]. Some of the features of humans that helped us succeed (so far) are bipedal motion, a large and capable brain and highly dexterous hands. One of the principles underlying the “survival of the fittest” is the minimization of energy or resource consumption – such as mass – for a given function. Another and simpler way to say this is: “Energy is the currency of life.” A specific design application of this principle in engineering is in the field of structural topology optimization. The most important feature of structural topology optimization, for example, see Fig.  3.8, is that it generates structures that are optimized for minimal mass and therefore promotes the most efficient use of materials. A structurally optimized part uses just enough material (and not more) for a given mechanical load and allowable deflection. In other words, structural topology optimization can be used to minimize so-called compliance. Compliance is equal to the force Fi times the deflection distance zi under load, that is, the amount of elastic work (energy) done by a structure at a specific point “i”, when subjected to a particular mechanical load. Equation 3.1 shows a typical structural topology optimization formulation. Minimize ∫ F i z i d Ω, Ω



Subject to ∫ ρ d Ω ≤ M 0 , 0 ≤ ρ ≤1

(3.1)

Fig. 3.7 (a) Terminal elements of the hairy attachment pads of a (i) beetle, (ii) fly, (iii) spider, and (iv) gecko (Arzt et al. 2003) shown at different scales and (b) the dependence of terminal element density on body mass. Larger and heavier animals on land tend to have more terminal elements compared to smaller animals on water. (Adapted from Bhushan 2009)

Here, Fi is the force acting on the ith element, zi is the vertical displacement of the ith element, Ω is the domain under consideration, ρ is the normalized density of each cell, and Mo is an upper total mass limit. The optimized structures show webbed internal patterns or porosities as we often see them in nature, for example, in bone structures. Additionally, in this example, the optimization is carried out by a genetic algorithm using a progressively longer chromosome, emulating the way that natural selection worked over millions of years, but here replicated numerically on a digital computer within only seconds or minutes. The idea to replicate natural evolution on a computer for design purposes goes back to some of the seminal work of John Holland (1992) and others. In genetic algorithms (GA), designs are encoded into a string of binary chromosomes which are then subjected to a set of “genetic operators” such as selection, crossover, and mutation, in

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Fig. 3.8  Variable chromosome length genetic algorithm for progressive refinement in topology optimization. Results show bone-like structures for different stages of refinement (3,4), mass constraints (44%, 41%, 31%) and genetic algorithm (GA) population size (50, 100, 150). (Adapted from Kim and de Weck 2005)

order to comprehensively search the design space. This application of biological principles, not just in general, but in detail, using mathematical optimization to “discover” and apply biological design principles has now become mainstream and has been embedded in many professional computer programs used by engineers. This is an early example of “artificial intelligence” (AI) using biologically inspired principles. Figure 3.9 shows a recent example of a so-called bionic design at Airbus, one of the largest aircraft manufacturers in the world. In this instance, a “bionic” design10 was applied to an aircraft cabin partitioning wall. These partitioning walls separate different parts of the cabin, such as business class and economy class. While these components are important, they typically do not carry flight-critical loads such as those from the wings to the fuselage. Firms often experiment with new techniques, such as biologically inspired design, on non-flight-critical components first. The resulting design shown here is as stiff as a traditional solid partitioning wall, while reducing mass by at least 25%. The project description states that: Airbus’s bionic partition needed to meet strict parameters for weight, stress, and displacement in the event of a crash with the force of 16 [g]. To find the best way to meet these design requirements and optimize the structural skeleton, the team programmed the generative design software with algorithms based on two growth patterns found in nature: slime mold and mammal bones. The resulting design is a latticed structure that looks random, but is optimized to be strong and light, and to use the least amount of material to build.11

Examples of famous designers and architects who took their inspiration from nature are the architect Antoní Gaudi (1852–1926) or the industrial designer Luigi Colani (1928–2019), among others. While this section focused mainly on objects inspired by nature, we can also learn from behaviors observed in nature, without replicating the exact forms.  The company calls this “bionic” design, but it is in fact biologically inspired design using the definitions we provided above. A bionic design – in the more recent interpretation of the term – would be the insertion of artificial components into a natural system, see discussion on cyborgs in Sect. 3.4 and the earlier definitions in this chapter. 11  Source: https://www.autodesk.com/customer-stories/airbus Note that here [g] refers to acceleration in units of [9.81 m/s2] and not weight in grams. 10

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Fig. 3.9  Example of bio-inspired design: Airbus “bionic” cabin partition

Examples of this are the operations and roles and responsibilities observed in ant colonies or beehives. The particle swarm optimization (PSO) algorithm, for example, mimics the motion of flocks of birds to confuse and evade predators. It turns out that PSO is more efficient than GAs for some types of problems (Hassan et al. 2005). Another interesting observation is on the role of symmetry. While humans often prefer symmetric solutions from an aesthetic point of view, nature often produces asymmetric or irregular forms, because they can be more efficient, particularly when the stimulus provided to the system comes preferentially from one direction. A good example of that is the structure of tree trunks and branches that are in exposed areas subject to a dominant wind direction or the orientation of plants who follow the arc of the sun to maximize energy harvesting through photosynthesis.

3.3  Nature as Technology In our discussion of biomimetics and bio-inspired design, we saw several examples where humans have borrowed or adapted ideas that they first observed in nature. In this section, we consider cases where nature itself, in more or less unmodified form, is the technology.

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As our understanding of both technical possibilities, and nature, progress, the link between the two becomes increasingly blurred. Here, nature itself – whether molecules of DNA, living organisms, or entire ecosystems  – are designed and directed to carry out specific tasks. This trend and phenomenon is described in vivid detail in Susan Hockfield12’s book “The Age of Living Machines – How Biology Will Build the Next Technology Revolution” (2019) and is often referred to as Convergence 2.0 which is the merging of biology and engineering, while Convergence 1.0 refers to the integration of engineering and physics. On the one hand, this idea of biology as technology is very new and goes hand in hand with the emergence and rapid growth of the life sciences and biotechnology industry (“biotech”) in the twenty-first century. On the other hand, the use of biology as “technology” is very old. Agriculture itself, the cross-breeding of plants, the domestication of animals, and the fermentation process to brew beer and produce other foods are all good and common examples of directing nature to human ends. At some point in the nineteenth and twentieth centuries, the intervention in nature, however, may have been taken too far. For example, the excessive use of chemical pesticides has been harmful to entire ecosystems and even to humans, causing undesired side effects (Carson 1962). This has given rise to the environmental movement and institutions like the Environmental Protection Agency (EPA) in the United States. The struggle to better understand and manage the interactions between natural environments and human technology persist to this day. However, something is qualitatively different today as our rapidly increasing understanding of biological molecular systems at the micro-level, and ecosystems at the macro-level, is opening new frontiers in using natural systems as technology. Some specific examples of the use of biology and biological components in technology are as follows: • Antibiotics are derived from natural fungal organisms and have been used for nearly a century in medicine to suppress bacterial infections. The first antibiotic was penicillin which was discovered by Sir Alexander Fleming (1929), another Scotsman. It is derived from the penicillium molds and was first used clinically in 1942 during WWII. • Microbial Fuel Cells (MFCs). In this biological application, specific bacteria are inserted into an otherwise artificial fuel cell for achieving specific functions like cleaning water, producing methane, or even generating electricity. Figure 3.10 shows how MFCs work at a bacterial, device, and system level. • Biomass Production Systems in artificial human habitats. These are essentially greenhouses in off-world environments to produce oxygen and food for human crew consumption. An example of this is shown in the MarsOne mission analysis by Do et al. (2016) that quantified the needed size of a greenhouse as a function of crew size and mission duration. It has been proposed that future deep space habitats for humans should produce their own food and recycle gases (e.g., turning carbon dioxide into breathable oxygen) by bringing with them and nurturing

 Susan Hockfield served as MIT’s 16th President from 2004 to 2012 and launched two major new initiatives on the life sciences and energy during her tenure.

12

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Fig. 3.10  Microbial fuel cell. From left to right: (1) Electrically active biofilm made up of the bacteria Shewanella oneidensis, (2) Schematic of a Microbial Fuel Cell with an active biofilm coating the anode and digesting organic matter while producing clean water (H2O) as well as electricity and (3) MFC pilot plant installed for wastewater remediation at one of Foster’s breweries. Courtesy: Cambrian Innovation Inc. (formerly IntAct Labs)

a variety of organisms  and physico-chemical technologies. This was also the goal of the famous Biosphere 2 experiment in Arizona in the 1990s. Figure 3.11 shows a detailed layout of a potential future Mars settlement, whereby a pressurized habitable volume and a set of greenhouses are co-located. Technologies other than the BPS are necessary for recycling water and gases, as well as providing temperature and pressure control. An example of such a technology is the urine processor assembly (UPA) which converts the crew’s urine into drinking water using a set of physico-chemical processes. The detailed analysis performed by Do et al. (2016) showed that a biomass production system, that is, a greenhouse, is indeed helpful in producing food for the crew as well as recycling water and carbon dioxide-rich air. However, the detailed breakdown of plant species, potential gas species, and imbalances of oxygen, nitrogen, and carbon dioxide and the sizing of the greenhouse area and volume relative to the number of human crew members to be supported is very complex. It turns out that the amount of carbon dioxide, oxygen, and calories produced by the BPS when the crew must rely 100% on grown food is not perfectly in harmony. It was also found that, unlike what is depicted in Fig. 3.11, it is better to separate the crew quarters and the BPS from each other and to operate the greenhouse at a higher temperature, higher level of humidity, and greater CO2 concentration compared to what is optimal for human crew performance and well-being. On planet Earth, these “services” provided by biology are more or less taken for granted (they should not be) and we have the luxury of relatively large buffers, such as our oceans and our atmosphere, where such imbalances can be absorbed for a while and only manifest themselves over longer time periods such as decades or centuries. In a smaller confined volume, as in a human habitat on another planet such as Mars (Fig.  3.11), such incompatibilities between biological systems and physico-chemical technologies can be fatal. There are many other examples of “nature as technology” that are emerging and are currently at various active stages of research and development: • DNA sequencing and gene editing (CRISPR). These technologies are able to detect, read, and modify the genome of organisms, including humans. In gene

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Fig. 3.11  Design of a future Mars human habitat system based on the concept presented by MarsOne, combining physico-chemical technologies for life support with a greenhouse, also known as a biomass production system (BPS) (See Do et al. 2016)

therapy, the ambition is to cure diseases that are caused by genetic defects by “repairing” the faulty nucleotide sequence directly and then injecting the correct sequence in the patient. • Genetic engineering of pathways for fuels and chemicals production basically turns cells and bacteria into small “bio-factories” that are able to produce a certain valuable substance, such as a desired protein, at scale. An example of this is

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the work of Prof. Kristala Jones Prather at MIT13 who has been able to reprogram E Coli and other organisms to produce target substances. • Ecosystem services for wastewater treatment are under consideration as an alternative or complement to traditional anaerobic chemical wastewater treatment. One of the most interesting concepts in this space is the idea of “constructed wetlands” which are arranged in an artificial and optimized layout (and can therefore be considered as “technology” according to our definition in Chap. 1), but whose actual components are purely biological and therefore indistinguishable from nature. Ironically, the most advanced form of “nature as technology” is technology that exists but appears to be invisible and essentially indistinguishable from nature to the untrained eye. The above examples show that a clear separation between what is “natural” and what is “artificial” is often no longer possible. Perhaps, it was really never possible to make this clear distinction.14 This supposed separation may have been driven by philosophical and religious currents in recent centuries, both after the Renaissance and during the first Industrial Revolution. During this period, Homo sapiens was (and still is) viewed by many as a superior species, distinct from all other animals. The singular belief in technology and humanity’s superiority also drove a belief that “artificially” created technology or products must by definition be superior to any “natural” alternatives. This mindset, and its religious underpinnings, can also explain the initial rejection of Darwin’s (1859) theory of evolution based on natural selection. A theory, which is now generally accepted in scientific circles and most – but not all – of society.15 A significant reason for the initial rejection of Darwin’s theory was the notion that humans and apes, such as chimpanzees, have a common ancestor (see Fig. 2.1) which would make humans not so special, after all. More recently, and especially since the middle of the twentieth century, the downsides of technology have become apparent (pollution, depletion of natural resources, climate change, etc.) and a blended approach that combines natural and engineered technologies is emerging (Hockfield 2019). One interesting – but somewhat controversial – proposal by well-known naturalist E.O. Wilson (2016) is to set half of the Earth’s surface aside16 to be left completely untouched by humans in order to preserve biodiversity and the potentially large number of species that have not been discovered yet.

 See further details: https://news.mit.edu/2013/turning-bacteria-chemical-factories  Even a concept as complex as the aerodynamic airfoil can be found in nature. For example, the seed of the fruit Alsomitra macrocarpa produces an airfoil of about 13 [cm] in wingspan  that allows it to travel over great distances. 15  Darwin did not get everything right. For example, while he subscribed to the view that Earth is older than the 6000 years described in the Bible, he believed it would be around 100 million years old. Today, we know that Earth is about 4.5 billion years old, about a third of the lifetime of the known universe (13.8 billion years). The Cambrian Explosion which is at the root of most of the diversity of animal and plant life we observe on our planet today occurred about 540 [mya]. 16  This surface area would not necessarily be completely contiguous and would not require relocating major populations. However, it would expand and protect major existing wildlife sanctuaries 13 14

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3.4  Cyborgs The notion of so-called cyborgs, creatures that are half-human and half-machine,17 has been a part of science fiction and public consciousness for a long time going back over a century or more. We include this section here since this topic is an important emerging trend at the intersection of nature – that humans are a part of – and technology that we create. In a narrow sense, we already see today that this is not just a future possibility but is already reality. Specific examples of technology being implanted or integrated into the human body are as follows: • • • • • • • •

Artificial knee replacements (e.g., made of titanium) Artificial hip replacements (e.g., made of titanium), see also Chap. 22 Implanted pacemakers to regulate the heart’s rhythm Insulin pumps, known as subcutaneous insulin infusion (CSII) technology Electronic retina implants for patients who have lost vision Artificial hearts, in lieu of surgical repair or as a temporary measure18 Artificial limbs lost due to amputation or missing from birth Performance-enhancing drugs (PEDs) such as anabolics to stimulate muscle growth and nootropics to enhance cognitive performance • Gene therapy to modify the human DNA and reinject it in a person’s own cells as targeted therapy to treat a variety of diseases19 Figure 3.12 shows examples of technologies implanted in the human body. The process of designing, testing, and integrating technology inside or adjacent to the human body is driven by modern medicine. As human life expectancy and affluence have both increased in most countries of the world over the last century, there is a desire by some to extend human lifespan even further while at the same time increasing the quality of life. The current global average lifespan for humans on Earth was 67.3 years in 2010. There are significant differences in average lifespan by country, for example, Japan has one of the longest life expectancies at 83 years, as well as by gender with females living about 5–7 years longer than males.20

and would collectively make up about half of the Earth’s surface including the land and the oceans, thus about 50% of 510 million [km2]. This proposal may also mitigate climate change. 17  In order to qualify as a cyborg a creature may not necessarily be made up of exactly 50% natural and 50% artificial components. We may think of this as more of a continuous spectrum where on the one end we have 100% humans with no artificial components whatsoever and on the other hand “pure” robots with no biological or human features and 100% abiotic components. Increasingly, we observe and create instances along the spectrum such as humans with artificial implants (e.g., titanium hip joints or artificial retinas), or robots that learn from humans and are trained to behave like humans (e.g., see Nikolaidis and Shah 2013). 18  The development of the artificial heart goes back several decades with the first successful artificial heart implant in 1982 (the Jarvik-7). 19  Between 1989 and December 2018, over 2900 clinical trials were conducted in gene therapy worldwide. Source: https://en.wikipedia.org/wiki/Gene_therapy 20  We discuss the link between technology and aging in Chap. 21.

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Fig. 3.12  Examples of technologies implanted in the human body (Image Source: http://media. techeblog.com/images/bionic_technologies.jpg) from top left to lower right: contact lenses and artificial cornea or retina, small cameras and sensors that can be swallowed and pass through the gastrointestinal tract, artificial hearts, artificial and instrumented teeth, and robotic prosthetic hands. Another common example of such technologies are cochlear implants

Extending human longevity via technology is generally done by first identifying specific morbidities and addressing them using technology either during treatment and subsequent therapy or by inserting them in the human body directly. Examples of such technologies include new surgical robots, chemotherapy, pinpoint radiation, hormone therapy, precision drug delivery via nanotechnology, and many others. While it takes many years to mature and certify these technologies and thus prove that they are both effective and not harmful,21 there is an increasing possibility to not only “repair” or “rehabilitate” humans to their baseline performance but to provide augmentation beyond the baseline. Generally, these implanted technologies are intended to replace functionality that has been lost. However, in the future, it is conceivable that such technologies may be merged with human biology to augment or deliberately exceed the baseline  In the United States, such technologies have to be approved by the Food and Drug Administration (FDA). Medical Devices in the United States are classified as Class I, II, or III, with class III being those that carry the highest risk for patient safety should they malfunction.

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level of performance that humans can achieve without the use of such technologies.22 This possibility raises serious issues in the emerging field of bioethics. Bioethics does not focus on the question of what can be done to use or co-opt biology for human purposes, but whether it should be done. ⇨ Exercise 3.3 Find an example of technology that has its roots in nature, that is in biology, and that was subsequently modified, or adapted, and linked to or infused in the human body. Describe this technology and its uses and any ethical considerations that come with the use of the technology.

The overall trend toward the creation of cyborgs, a fusion of humans and technology, will potentially lead to big changes in our species and redefine what it means to be human. We discuss these trends in our final Chap. 22. ➽ Discussion Will humans ever forsake technology and “return” to nature? Should there be limits on the degree to which technology modifies nature? Should half the Earth’s surface be left alone and remain untouched? Will the integration of technology with humans prevent further evolution? In comparison to nature, human technologies often appear as being “crude” or “brute force.” However, in recent decades, thanks to a new mindset and set of instruments such as electron microscopes we are discovering how natural principles and designs can be adapted and harnessed for human-designed technologies. We also still have much to discover about the role of evolution.23 The success of nature in solving many problems is a testament to the power of competition in the ongoing struggle for survival. Only the best designs survive in the long term. We are just at the beginning of understanding the link between nature and technology. An interesting legal and ethical question is whether nature can be patented. We discuss patents and intellectual property in Chap. 5. The emergence of COVID-19 and the global pandemic caused by the SARS-CoV-2 virus has helped to further accelerate the development of biological technologies, such as mRNA-based vaccine synthesis.  A recent movement called “biohacking” involves individuals (usually those with technological knowledge and disposable incomes) using biological technology to “improve” their own bodies, including their brains, for improved performance and well-being. Some of these efforts are taking place outside of the medical and scientific establishment and may carry significant risks. 23  Both evolution and migration had and continue to have an important role to play. A surprising fact that was discovered by paleontologists is that the camel originated in North America about 45 [mya] during the Pleistocene and subsequently migrated across the Bering strait to Eurasia (Donlan 2005). 22

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References Arzt, E., Gorb, S. & Spolenak, R. 2003 From micro to nano contacts in biological attachment devices. Proc. Natl Acad. Sci. USA 100, 10 603–10 606. https://doi.org/10.1073/ pnas.1534701100. Bharat Bhushan, “Biomimetics: lessons from nature−an overview”, Phil. Trans. R. Soc. A 2009 367, 1445–1486, doi: https://doi.org/10.1098/rsta.2009.0011 Carson R. Silent spring. Houghton Mifflin Harcourt; 2002 Oct 22. originally published in 1962. Collin A, Siddiqi A, Imanishi Y, Rebentisch E, Tanimichi T, de Weck OL. Autonomous driving systems hardware and software architecture exploration: optimizing latency and cost under safety constraints. Systems Engineering. 2020 May;23(3):327–37. Darwin, Charles. “On the Origin of Species.”, 1859. Do S., Owens A., Ho K., Schreiner S., de Weck O., “An independent assessment of the technical feasibility of the Mars One mission plan  – Updated analysis”, Acta Astronautica, 120, 192–228, April-June 2016 Donlan J. Re-wilding North America. Nature. 2005 Aug;436(7053):913–4. Fleming A.  On the antibacterial action of cultures of a penicillium, with special reference to their use in the isolation of B. influenzae. British journal of experimental pathology. 1929 Jun;10(3):226. Fu K, Moreno D, Yang M, Wood KL. Bio-inspired design: an overview investigating open questions from the broader field of design-by-analogy. Journal of Mechanical Design. 2014 Nov 1;136(11). Hassan R, Cohanim B, de Weck O, Venter G. A comparison of particle swarm optimization and the genetic algorithm. In46th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, 2005 Apr 18 (p. 1897). Hockfield S. The Age of Living Machines: How Biology Will Build the Next Technology Revolution. WW Norton & Company; 2019 May 7. Holland J.H. Genetic algorithms. Scientific American. 1992 Jul 1;267(1):66–73. Kim I.  Y and de Weck O.L., “Variable chromosome length genetic algorithm for progressive refinement in topology optimization”, Structural and Multidisciplinary Optimization, 29 (6), 445–456, June 2005 Mead, Carver. “Neuromorphic electronic systems.” Proceedings of the IEEE, 78.10 (1990): 1629–1636 Nikolaidis S, Shah J. Human-robot cross-training: computational formulation, modeling and evaluation of a human team training strategy. In2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2013 Mar 3 (pp. 33–40). IEEE. McCullough, D., 2015. The Wright Brothers. Simon and Schuster. McKinstry MC, Anderson SH.  Survival, fates, and success of transplanted beavers, Castor canadensis, in Wyoming. Canadian Field-Naturalist. 2002 Jan 1;116(1):60–8. Qin Z, Buehler MJ. Spider silk: Webs measure up. Nature materials. 2013 Mar;12(3):185–7. Schaik, C.P., Fox, E.A., Sitompul, A.F. (1996). Manufacture and use of tools in wild Sumatran orangutans. Naturwissenschaften, 83(4), 186–188. DOI: https://doi.org/10.1007/BF01143062 Vollrath F, Knight DP. Liquid crystalline spinning of spider silk. Nature. 2001 Mar;410(6828):541–8. Walsh PT, Hansell M, Borello WD, Healy SD. Individuality in nest building: do southern masked weaver (Ploceus velatus) males vary in their nest-building behaviour?. Behavioural Processes. 2011 Sep 1;88(1):1–6. Wilson, Edward O. Half-earth: our planet's fight for life. WW Norton & Company, 2016 Wilson, Jamal O., David Rosen, Brent A. Nelson, and Jeannette Yen. “The effects of biological examples in idea generation.” Design Studies 31, no. 2 (2010): 169–186.

Chapter 4

Quantifying Technological Progress

“When you can measure what you are speaking about, and express it in numbers, you know something about it, when you cannot express it in numbers, your knowledge is of a meager and unsatisfactory kind; it may be the beginning of knowledge, but you have scarcely, in your thoughts advanced to the stage of science.” — Lord Kelvin

Advanced Technology Roadmap Architecture (ATRA) Inputs

4

Steps

L1 Products and Missions

FOMjj

Strategic Drivers for Technology 1. Where are we today? Technology State of the Art and Competitive Benchmarking

L2 Technologies

Outputs

+5y Organization

Technology Systems Modeling and Trends over Time Technology Projects

FOMi

Today

2. Where could we go?

+10y FOMj

Dependency Structure Matrix

Tech Pul Pull

Figures of Merit (FOM) Current State of the Art (SOA) Technology Trends dFOM/dt

Competitor 1 Competitor 2

Technology Systems Modeling

L1

Technology Roadmaps

+10y

Scenario A

+5y

Scenario B ?

3. Where should we go?

L2

Scenario Analysis and Technology Valuation

Technology Portfolio Valuation, Optimization and Selection

E[NPV] - Return

Intellectual Property Analytics

4. Where we are going!

Technology Investment Efficient Frontier Technology Portfolio Technology Projects σ[NPV] - Risk

Foundations Definitions What is Technology?

History Milestones of Technology

Ecosystems Nature Technology, Nature Technology Diffusion, Infusion and Industry and Humans

Design Reference Missions Future Scenarios T h l Technology V Valuation l ti Vector Charts

FOMi

Tech Push

Technology Scouting Knowledge Management

Scenario-based Technology Valuation

The Future Is there a Singularity ?

© Springer Nature Switzerland AG 2022 O. L. de Weck, Technology Roadmapping and Development, https://doi.org/10.1007/978-3-030-88346-1_4

Case 1

Case 2

Automobiles

Aircraft

Pareto-optimal set of technology investment portfolios Recommended Technology Portfolio (Expected NPV and Risk)

C Cases Case 3 Deep Space Network

Case 4 DNA Sequencing

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4.1  Figures of Merit In order to understand how to quantify technological progress over time, it is necessary to define so-called Figures of Merit (FOMs). A FOM is a scalar quantity, either nondimensional or with specific units of measurement, that allows quantification of how well a technology performs and, ideally, how valuable it is to its user or to society as a whole (de Weck 2017). FOMs can include things that are directly measurable with sensors, such as mass, energy, power, the quantity of data transmitted per unit time, cost, etc. or more complex quantities that are calculated from multiple sources of data such as operational reliability, cost, or safety. If a FOM1 is directly related to how a technology or system does its job, that is, how well it performs its function, we speak of Functional Performance Metrics (FPMs), see Magee et  al. (2006). FPMs can be considered a subset of FOMs, namely, those that specifically quantify the functional performance of a technology. An advantage of FPMs is that – if well chosen – we measure quantities that users or potential adopters of the technology actually care about. Consider a specific example as shown in Fig. 4.1. The chart in Fig. 4.1 is credited to Ray Kurzweil, an MIT graduate and a well-­ known technologist and “futurist.” Futurists, such as Kurzweil (2005), are

Fig. 4.1  Progress in computing since 1900. (Source: Kurzweil, 2005) (An earlier version of this chart was published by Hans Moravec of Carnegie Mellon University (CMU) in his 1988 Book “Mind Children” in which he provided predictions of technological development for artificial life)

1  Technology FOMs are distinct from the so-called Key Performance Indicators (KPIs) that are primarily used in project management and business to assess organizational performance.

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preoccupied with the role of technology in society, and they attempt to quantify the rate of technological progress in specific categories. An ambition of most futurists is to predict future technological and societal developments, even though most of them will admit that it is difficult to do so. Analyzing the chart in Fig. 4.1, we see that the x-axis represents calendar time, spanning about 125 years from 1900–2025 CE on a linear scale, while the y-axis shows a particular FOM selected to illustrate computational progress on a logarithmic (log10) scale. The chart was constructed by gathering a list of specific computers  – most of them available for purchase in the market at that time  – and by tabulating their specifications and cost. The FOM, “Calculations per second per $1000,” on the y-axis can be written in equation form as follows: y (t ) =

N calc Ccomp MIPS ÷ = 109 ⋅ ∆ t 1000 Ccomp   speed

cost

(4.1)

This FOM captures the speed of calculations done by a computer (number of calculations per second), divided by the cost of the computer in U.S. dollars. This is effectively a “capital efficiency of computing” kind of FOM where the creator of the chart made the assumption that this particular FOM is relevant to illustrate technological progress in computing. The numerator captures the speed of computation, while the denominator reflects the cost of the equipment. A subtlety in these calculations is that the dollars shown in the denominator have to be in a particular country’s currency (presumably the United States) and represent “current year” dollars for a particular reference year, presumably 2006, even though this is not explicitly stated.2 This means that any currency-related component of a FOM has to be inflation adjusted. Another, probably more familiar way to write this FOM is in terms of millions of instructions per second (MIPS) per unit cost of the computer, Ccomp. Whenever constructing a figure of merit, close attention needs to be paid to the units of measurement, referencing of a particular year or reference configuration, and any normalization factors.3 What is also interesting in this chart is the vertical shading indicating the transition of specific technological concepts or solutions for computing going from electromechanical devices (1900-ca. 1938) to solid-state relays (ca. 1939–1945), vacuum tubes (1946–1958), transistors (1959–1973), and ultimately to integrated circuits (1974–present). The dominant technology for computing in the future (photonic, quantum, or biological) is left open to speculation.

2  This highlights the fact that quantifying the cost or labor efficiency of historical or ancient technologies is not trivial, since the technology will usually predate the existence of any particular currency. Instead, one may attempt to normalize the cost by other quantities such as one hour of human labor, or the price of wheat in the Roman Empire (Kessler et al., 2008). 3  The scaling factor is 109 in this case to account for the millions of instructions per second, 106, multiplied by 103 for thousands of dollars.

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Another noteworthy feature of this chart is the horizontal lines shown as “mouse brain” at 1011 and “human brain” at 1015 calculations per second per $1000, respectively. This suggests that computing technology has now reached and is about to exceed, the computing capabilities of humans. This is one of the bases for predicting the existence of an upcoming singularity4 (see Chap. 22). According to Fig. 4.1, computers will surpass humans according to this FOM by about 2025–2030 CE. In order to make this chart and draw the horizontal lines, its creator had to make an assumption about the “cost” of a human, and that of a mouse, which is somewhat controversial.5 The most remarkable insight gained from Fig. 4.1, however, is that progress in computing has been exponential over the last 100+ years and that it continues unabated. It is important to note that the FOM chosen here is a functional performance metric (FPM), and that it is independent of the specific form that has been implemented to carry out the calculations (vacuum tubes, transistors, IC, etc.). As we will see later, individual technologies are often claimed to be subject to S-curvelike behavior due to the existence of presumed fundamental limits, while technologically enabled functions, such as computing, are not. Said in plain language, while progress in carrying out calculations using a machine has progressed exponentially over the last 125 years, the individual technological implementations of the computing machines themselves (e.g., using vacuum tubes) have not progressed exponentially over the same time. Individual technological forms, such as vacuum tubes, have experienced stagnation and have eventually been replaced by newer technologies, such as transistors and ICs. This stagnation is, however, not visible in Fig. 4.1 because when we look at the sequence of technologies for computing (= information transformation, I1, see Table 1.3) over a long time period of a century or more, we see continuous and exponential progress. We return to this important point below. ➽ Discussion Can you give examples of FOMs related to a technology or product you have worked on, and compare and contrast this to a key performance indicator (KPI) that was, or is, used in an organization that you have been affiliated with? Are you familiar with the term “Functional Performance Metric (FPM)”?

 A singularity is a sudden disruption or shift in a mathematical function or phenomenon. A technological singularity (Kurzweil 2005) is a point-of-no-return whereby technologies, and computers, in particular, become so intelligent that they can improve themselves at an ever-faster rate and eventually exceed human capabilities, potentially rendering us obsolete. 5  It is important to note that the horizontal lines in Fig. 4.1 do not represent asymptotic limits, that is, threshold values of technology, that can never be exceeded. The existence of such asymptotic values will be discussed later in this chapter. For purposes of policy-making, the value of a human life is often estimated, for example, to establish an upper threshold for the cost and benefit of medical interventions to save a human life. The World Health Organization (WHO) recommends using three times the GDP/capita/year as such a threshold. 4

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Fig. 4.2 (a) Bottom: discrete steam engine improvements, ΔFOM, over time in terms of [MJ] of work performed per [kg] of bituminous coal consumed, (b) top: integration of discrete technological improvements over time resulting in a discrete technology trajectory (“staircase”), FOM (t), and its continuous approximation y(t). This chart is a simplification of reality as there are thousands of additional patents and non-patented improvements on steam engines that collectively provided significant progress in addition to the major improvements shown here

As we saw in Chap. 2 with the evolution of the steam engine (Fig. 2.8), technological progress occurs in discrete steps that can be thought of as a sequence of discrete impulse functions, each with its own time interval and amplitude ΔFOM, see Fig. 4.2(a). Integrating these impulses over time yields a continuous “staircaselike” curve, which can then be approximated with a smooth continuous curve, as in Fig. 4.2(b). If we assume exponential progress6 for a technology using a specific FOM, we can approximate its staircase-like progress which is a continuous function y(t) and can write the following equation:

6  Exponential progress occurs when a technology improves at roughly a constant percentage year-­ over-­year, leading to a compounding effect, similar to financial investments that achieve a positive annual rate of return.

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y ( t ) = yo (1 + r )

t



(4.2)

where y(t) is the approximate value of the FOM at time t (e.g., expressed in years from a reference year to), yo is the value of the FOM at that reference year, to=0, and r is an average annual rate of improvement. The average annual technological improvement of steam engine efficiency that best approximates the staircase-like curve in Fig.  4.2(b) is r=0.017. This corresponds to a rate of improvement over the last 250 years of about 1.7% per year. Returning to the example of computing in Fig. 4.1, if we take 1900 as the reference year with yo(t=1900)=10-5 (Analytical Engine) and 2010 as the current year, y(t=2010)=1010 (Core i7 Quad), we can estimate the annual rate of progress for computing. Using the FOM defined in Eq. (4.1) we find that r~=0.37, that is, about a 37% annual rate of improvement over the last 100+ years using this FOM. This rate of improvement is about 20 times faster compared to steam engine efficiency.7 ⇨ Exercise 4.1 Find an example of a published technology progression curve similar to Fig. 4.1 or Fig. 4.2 in the scientific or trade literature. What does this curve show? What FOM was defined and what is the timespan of the analysis? Can you estimate the average annual rate of improvement, r, for this technology?

➽ Discussion Why is it important or useful to quantify the rate of technological progress?

How to Construct a Meaningful Figure of Merit (FOM)? As we saw in the prior example with computing, the specific choice of figure of merit (FOM) is critical when it comes to quantifying the rate of technological progress. A critical observer should always question and try to understand why a particular FOM was chosen when presented with charts such as those in Figs. 4.1 and 4.2(b). A general method for constructing meaningful FOMs starts by going back to the taxonomy of technologies in Chap. 1, and to create FOMs based on the fundamental inputs, outputs, processes, affectees, agents, and instruments involved in each function. In this sense, we can begin defining FOMs, by starting from a technology “template.” Consider “Transforming Matter,” the set of technologies shown in the first cell (M1) in Table 1.2 of our 3x3 grid used for technology classification, as an example. A general template for what is involved in this type of technology is shown in Fig. 4.3. Transforming matter requires certain inputs 1 through N (ingredients, reactants, etc.) and produces certain outputs 1 through M (desired products, but also waste products). Most matter transformation processes consume energy  We discuss below the reasons why some technologies progress faster than others.

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Fig. 4.3 Object Process Diagram (OPD) of objects and processes involved in a generic “Transforming” matter technology (M1)

(endothermal), but some “produce” energy in the form of exothermic reactions, where energy stored in chemical bonds is transformed into heat. Additionally, transforming matter may require one or more catalysts, a processor8 as well as a controller which can be a human or a machine. The controller adjusts the rate of the matter transforming process as needed. Each of these objects and processes involved in “matter transformation” has its own set of attributes that can be used to construct meaningful FOMs. The process attributes for “transforming” are explicitly called out in Fig. 4.3. The corresponding Object Process Language (OPL) for “Matter Transforming” is shown below. This formal description language was first introduced in Chap. 1, and it expresses the logical relationships shown in Fig. 4.3 in human natural language. Describing technologies in this way helps promote clarity. Input 1 through Input N are physical and systemic. Output 1 through Output M are physical and systemic. Energy is physical and environmental. Waste is physical and environmental. Controller, Processor, and Catalyst are physical and systemic. Transforming exhibits Process Attributes. Process Attributes of Transforming are informatical and systemic. Transforming is physical and systemic. Controller handles Transforming. Transforming requires Catalyst and Processor.

 A processor is a machine or device that facilitates the matter transformation.

8

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Transforming consumes Energy, and Input 1 through Input N. Transforming yields Output 1 through Output M, and Waste9. Example of Steelmaking To illustrate how the definition or formation of figures of merit works, let us look at the process of “Steelmaking” which was and still is one of the most important functional technology areas today.10 Fig.  4.4 shows the progress in steelmaking with Electric Arc Furnaces (EAF) using three different FOMs: • Tap-to-Tap Time • Electricity Consumption • Electrode Consumption As a general rule, units of measurement must always be used when defining figures of merit (FOMs) and they should be clearly indicated in tables and on charts showing technological progress. Tap-to-tap time is measured in minutes [min]. It is the time for the whole process of steelmaking to complete for one batch until the next batch of steel can be completed, that is, tapped.11 We see that over three decades (1970–2000) the tap-to-tap time has been reduced from 2.5 hours (150 min) to less than one hour (55 min). This corresponds to an annual improvement factor of r = -0.03312, that is, a reduction of tap-to-tap time of about 3.3% per year. This was achieved by infusing specific new technologies in the steelmaking process such as secondary metallurgy (the use of scrap metal), water-cooled panels, etc., see Fig. 4.4 (upper left).13 The electricity consumption for steelmaking is measured in units of kilowatt-­ hours per metric ton of steel [kWh/ton], which is equivalent to watt-hours per kg [Wh/kg]. Using electric arc furnaces (EAF) is increasingly popular in areas where electricity is abundant, particularly with overproduction of electricity during off-­ peak hours (making the cost of energy per kWh cheaper14). The improvement shown in Fig. 4.4 is from 550 to 375 [kWh/ton] between 1970 and 2000, corresponding to roughly a 1.3% reduction per year. Thus, it seems to have been “easier” to reduce production time (tap-to-tap) than specific energy consumption in steelmaking. The third FOM shown in Fig. 4.4 is electrode consumption which is measured in [lb/ton] of steel made. Here we see more than a threefold improvement from 6 to 1.8 [lb/ton] in the indicated timeframe. This suggests an annual rate of improvement of about 4%.

 In general in matter transforming processes there is conservation of mass, that is, the mass of the inputs needs to equal the mass of all outputs. There are exceptions, as in nuclear reactions, where mass equivalence of energy, E=mc2, needs to be taken into account and the mass of inputs and total mass of outputs may not be equal. 10  Much has been said and written recently about the “information revolution” in society (see Chap.2), giving the impression that “hardware”-centric technologies such as those used for mining, making chemicals, metals, food, and other materials are no longer important. Nothing could be further from the truth. 11  The technology progression discussed here is specifically for Electric Arc Furnaces (EAF), see here for details about electric arc furnaces: https://en.wikipedia.org/wiki/Electric_arc_furnace 9

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Fig. 4.4  (Left) Progress in steelmaking using three different figures of merit over the period 1970–2000, (right) Electric Arc Furnace (EAF) being tapped for steel. (Source: American Iron and Steel Institute, Steel Industry Technology Roadmap, 2001.) Ideally, FOMs are constructed to increase over time as technology improves, while the FOMs shown here show a decrease over time, since they focus on resource consumption (time, energy, electrodes) per ton of steel produced

⇨ Exercise 4.2 Estimate the theoretical lower limit of electricity consumption for melting scrap steel in terms of [kWh/ton]. It may be helpful to know that the melting temperature of steel is about 1,500 [°C] and that the heat capacity of steel is around 0.466 [J/g°C]. How far from this limit were EAF’s by the year 2000? It is possible to use nondimensional or unitless figures of merit. However, this should be clearly defined and often occurs when like units in the numerator and denominator of a FOM cancel each other out. A prime example for this is an efficiency metric where work done in units of [J] by a machine is in the numerator and energy provided as input to that machine in [J] is in the denominator.15 Thanks to these improvements, EAF technology has grown to about 25% of global capacity since the 1980s. EAF is competitive compared to Basic Oxygen Furnaces (BOF), since it relies mainly on recycled steel (e.g., from cars), and has therefore better economics and lower environmental impact compared to BOF

 Note that here r is negative, since the FOM decreases over time. In general, it is preferable to define technological FOMs that increase as the technology improves. The annual rate of improvement, r, was estimated using a least squares optimization to minimize the error between the actual technology improvement data (shown in Fig.  4.4) and the calculated improvement obtained by determining r in Eq. (4.2). 13  Chapter 12 is dedicated to the topic of technology infusion analysis. 14  Residential electricity rates in the United States vary from state to state in the range from 10 to 23 [¢/kWh]. The electricity cost for EAF is typically on the order of 100 [$/MWh] as of 2020. 15  It is not advised to use percentages or indices as a technological FOM, unless it is very clear what was used as a reference for normalization purposes. Comparing the progression of different tech12

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Fig. 4.5  Specialized OPD for “Steelmaking” (type M1). The inputs, outputs, operators, instrument (furnace), and attributes are shown. FOMs are the Tap-to-Tap time [min], Electricity Consumption [kWh/ton], and Electrode Consumption [lb/ton]

which requires primary iron ore and coke for steel production. The emergence of so-called mini-mills in the United States coincides with the rise of EAF technology. One of the economic limitations of EAF technology is the availability of scrap steel. Fig. 4.5 shows a specialized version of Fig. 4.3 for steelmaking. An OPL (Object Process Language) description of “Steelmaking” is as follows: Furnace exhibits Capital Cost. Steel Making exhibits16 Production Cost and Tap-to-Tap Time. Operators handle Steel Making. Steel Making requires Furnace. Steel Making consumes Coke/Coal, Crude Iron, Electricity, Electrodes, Oxygen, and Scrap Steel. Steel Making yields Carbon Dioxide, Slag, and Steel. The three particular FOMs for steelmaking we have considered so far can thus be “constructed” and explained from the specialized OPM model of the technology (Fig. 4.5) as follows: FOM1 = Tap-to-Tap Time [min] – this is an attribute of the process “Steelmaking” itself, and it represents the time elapsed between sequential batches of steel made nologies that use different reference baselines is not valid. 16  The word “exhibits” in OPL is reserved for attribute links.

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in the same furnace. This is an important metric to determine cycle time, production capacity, and ultimately capacity utilization of a steel mill. FOM2 = Electricity Consumption [kWh/ton] – this is a ratio of input (electricity in [kWh]) to output (steel in [tons]).17 This metric is a measure of energy intensity of the process, and this will drive both the production cost [$/ton] and environmental impact of the steel mill, depending on the source of electricity. Many FOMs used in technology roadmapping are ratios of inputs to outputs, or outputs to inputs and are therefore measures of efficiency or productivity of the system. In order to demonstrate progress, an input-over-output ratio should decrease over time (as in Fig. 4.4), while an output-over-input ratio should increase over time. Note that efficiency and productivity are not the same, even though they are often conflated. Efficiency is a technical metric that is used in engineering and is dimensionless. It takes the ratio of output over input for like units. For example, the amount of useful work produced by a machine, such as the steam engine discussed in Chap. 2, is divided by the amount of energy that is supplied to the machine, for example, in the form of coal. In this case, both the numerator and denominator are in units of Joules [J], and efficiency is then by definition nondimensional (unitless), because the two units on the input side and output side cancel each other out.18 Productivity is a concept from economics that measures the output of a system per unit time, for example, tons of steel per day, as a function of different factors of input into the system such as capital [$] and labor [person-hours]. As we will see in Chap. 17, improvements in productivity not directly attributable to capital and labor are generally associated with technical change, which includes technology, but also better working procedures, improved training, etc. Robert Solow (1957) is often credited as the first economist to isolate improvements in technology as a driver of enhanced productivity. The aggregate production function Q = F(K,L,t) is generally used to relate the quantity of output, Q, to inputs such as capital, K, and labor L, over time t19. The simplest form of the production function is linear whereby the total quantity produced per unit time Q is a linear function of labor L, or capital K. In such linear production functions, ratios such as Q/L [tons/hours] or Q/K [tons/$] are FOMs expressing productivity. However, unlike efficiency, the ratios are typically not dimensionless. Ultimately, however, in the field of economics, all calculations are converted to monetary value, that is, currency such as U.S. dollars, Euros or Renminbis.

 The company ArcelorMittal is the largest steelmaker in the world today with a total annual production volume approaching 100 million tons. The company began in the 1980s by converting older inefficient BOF furnaces to EOF (energy-optimized furnaces) by introducing a preheating system for scrap steel, using heat from off-gassing for the scrap preheater. 18  Normally, the efficiency of a machine cannot exceed 1.0 (or 100%) since there can usually not be more work generated than energy that enters the system boundary. An exception to this rule may be fusion reactors (energy conversion) where the goal is to achieve a fusion energy gain factor of at least Q=1, better Q=10, which is the ratio of energy released by the plasma over the external energy input needed to heat and maintain the plasma. The mega-project ITER which is being built in Southern France is aiming at Q=10. 19  For details, refer to Chap. 17. 17

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FOM3 = Electrode Consumption [lb/ton] – this is a ratio of input (electrodes in pounds) over output (steel produced in tons). The smaller this ratio, the better, since electrodes are non-recoverable consumables. In general, it is preferable to use ratios of outputs-over-inputs rather than inputs-over-outputs as FOMs such that (i) this more closely mirrors the definitions of efficiency or productivity and (ii) the FOM tends to increase rather than decrease with technological progress. We can see, based on Fig. 4.5, that there could have been several other FOMs that we might have used to quantify the evolution of steelmaking over time. Examples are as follows: • Normalized staffing level [ton/operator/hour] – this metric would quantify how many tons of steel can be produced per operator per unit time, for example, expressed in [ton/hour].20 This would be a surrogate measure for the degree of automation of the steel mill. • Capital intensity [ton/$] – this is a measure of how much money is required to build and install a functioning mill of a certain production capacity and is primarily a function of the design of the furnace itself. The way it is defined here represents the amount of steel production capacity per dollar invested. • Carbon emissions [kg of CO2/kg of steel] – this FOM quantifies how much CO2 is emitted as a waste product from steel production per unit mass of steel produced. This FOM can be used as one measure for the environmental impact of the steelmaking process. In this case, it is an output-over-output measure, and since CO2 is considered a waste product, it should decrease over time. In general, sustainability-type FOMs that capture the amount of waste in the output stream over the amount of valuable output should decrease over time. From this analysis of steelmaking, presented as an example to illustrate the quantification of technological progress, we can extract several important statements that apply to all technologies: 1. Quantifying the evolution or rate of progress of a particular technology requires the definition of one or more Figures of Merit (FOMs).21 2. FOMs must have clear units of measurement such as [kg], [m], [$], [W], [$/kg], [ton/person/hour], etc., and these units should be indicated and used in all data tables, graphs, papers, presentations, etc. where the FOM is used. The units of measurement should be applied consistently. The units do not always have to be in SI units and are often dependent on the industry context.22 3. A general statement such as “technology x has improved by y% per year” is incomplete. There is no such thing as “general improvement” when it comes to technology. Only with specific FOMs can a rate of improvement clearly be defined. Empirically, however, we find that some FOMs for the same technology are often highly correlated (but not identical).23  An interesting challenge is how to quantify the productivity or cost of ancient technologies, before the introduction of modern currencies, such as the Euro €, or time accounting systems. 21  Note that the selection of specific FOMs to compare different technologies may create a differential advantage of one technology over the other. 22  However, SI units are preferred to facilitate international comparisons of technologies. 20

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4. The rate of progress for the same technology can be different when considering multiple FOMs describing that same technology. A technology could have a high annual rate of progress in one FOM (say > 10%) and a low rate of progress in another (say 100) at 1.0

Figure 4.12 is a real-world example of such behavior in terms of photovoltaic (solar) cells’ efficiency [%] for different types of solar cells since 1976. The chart in Fig. 4.12 is quite famous and is updated on an annual basis by the National Renewable Energy Laboratory (NREL) in the United States of America. Each curve (and associated color) represents a different type of solar cell technology. The chart essentially captures the world record for solar cell efficiency of a particular kind in any given year, and it is established using a standardized test protocol. Let us focus on the most efficient cells available which are multijunction cells with solar concentrators. Specifically, we will first look at three-junction cells with solar concentrators. These cells are generally made from Gallium Arsenide (GaAs) and other semiconductor-type materials. Figure 4.13 illustrates the working principle of these types of cells. Solar cells absorb solar radiation along the solar spectrum in the form of photons at different energy levels [eV] and emit electrons in the form of electrical current [A] at a given voltage [V]. The efficiency of a cell is the fraction of incoming solar power (energy per unit time and unit area) that is converted to electrical power. Single-junction cells have a maximum theoretical efficiency of 33.16%. Multijunction cells with solar concentrators with theoretically infinitely many junctions have a theoretical maximum efficiency of 86.8%. The best achieved efficiency for triple-junction cells (▽) with high solar concentration (302x) is 44.4%, according to Fig.  4.12. This milestone was achieved in 2013 by Sharp. The world-record for multi-junction cells as of 2019 was held by NREL with a six-junction (6-J) solar cell at an efficiency of 47.1% and at a solar concentration of 143x.

 Or any of the other categories of FOM listed in section 4.1.  An interesting question is whether there is a relationship between the shape of the S-curve and the number of competitors involved in a particular technology. We discuss this point in Chap. 7 and especially in Chap. 10 (competition as a driver for technology). 32 33

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Fig. 4.12  Best solar research-cell efficiencies (1976–2020). (Source: NREL)

Fig. 4.13 (a) Left: Three-layered structure of a triple-junction solar cell with concentrated sunlight entering at the top, (b) right: incoming solar spectrum (gray) versus absorbed solar spectrum, see colored bands: blue, green, and red. The efficiency of the cell [%] is the ratio of the colored areas divided by the gray area. (Source: Fraunhofer Institute for Solar Energy Systems, 2010)

Given this historical information, we can extract technology performance data over time for solar cell technology. Our FOM is conversion efficiency (%). We select multijunction concentrators as the particular technology (top purple curve in Fig. 4.12) and obtain the following (rounded) data in Table 4.3. With these data, we can obtain a least squares fit to an S-curve. This is done by optimizing the parameters a, b, c, m, n, and 𝜏, such that the least squares error between the fitted logistic function (Eq.  4.4) and the actual data is minimized. Table 4.4 shows the resulting S-curve parameters. The resulting fitted “S-curve” (in black) versus the actual data (the magenta “staircase”) for triple-junction solar cells is shown in Fig. 4.14. A nonlinear extrapolation out to the year 2040 (in blue) predicts further improvement of multijunction

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Table 4.3  Efficiency [%] of multijunction solar cells over time Year 1983 1988 1990 1993 1995 1996 2000 2003 2007 2010 2013

Efficiency [%] 16 17 23 29 31 32 34 36 39 42 44

Notes NCSU Varian Spire NREL NREL Japan Energy Spectrolab Boeing Boeing Fraunhofer Sharp

Source: NREL Table 4.4  S-curve parameters for progress in solar cell efficiency [%] (best fit) a b c

3.15 2.45 13.36

m n 𝜏

−8.8 1.19 12.63

solar cells to slightly over 50% efficiency by 2040.34 We note a flattening of the curve as further improvements are harder and harder to obtain. For example, going from one to three junctions yields about a 9% improvement in efficiency (from ~35% to 44%), whereas doubling the number of junctions from three to six has so far only resulted in a 3% absolute improvement from ~ 44% to 47%. Knowledge of the absolute limit of efficiency of multijunction solar cells (86.6%) was not used in the regression of the S-curve (black). It was, however, used in the performance prediction (blue) curve which does a good job predicting the current world record for multijunction cells (47.1% in red). Thus, it is possible to use historical technology trajectories to predict future performance, but typically after 10–20 years from the last data point such predictions become quite uncertain. Actual “S-curves” rarely look smooth and continuous as the conceptual model would have us believe. Interestingly, the optimal S-curve fit in Fig. 4.14 does not show the slow ramp-up period in the beginning; however, it does capture the effect of slowing progress. This is due to the fact that each additional percent of efficiency improvement has to be “bought” with a significant increase in technological and system complexity.

 This may be both a conservative and realistic prediction as in 2020 the world record for multijunction solar cell efficiency stood at 47.1% for a six-junction solar cell (6-J) at NREL with 143x solar concentration. The parameters for the blue prediction curve in Fig. 4.14 are a=3.75, b=2.5, c=11.75, m=-10, n=2, and 𝜏=13.

34

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Fig. 4.14  Actual vs. S-curve model for multijunction solar cell efficiency [%]

Conceptually, the S-curve can be interpreted as follows (see Fig. 4.15). Along the S-curve we follow the lifecycle of a technology in terms of several discrete stages: initial proof of concept, incubation, takeoff, rapid progress, slowing, and stagnation. The maximum potential of the technology is capped by its theoretical limit, which may or may not be known. Besides relying on historical data, we can use “collective intelligence” (similar to the Delphi method) to poll experts or the general public for their perception in terms of where they think particular technologies fall along the S-curve. An important point is that when keeping track of technological progress, it is important to separate data about levels of technology performance achieved in the laboratory or prototype phase (e.g., TRL 3 versus TRL 6)35 and those based on specifications from commercially available products (TRL 9). It is expected that technology trajectories achieved during research and development, that is, in the laboratory or field testing, and technology demonstrated in commercially available ⇨ Exercise 4.4 Polling question: “Where would you place the following technologies along their lifecycle on the S-curve: Internal Combustion Engine, Robotic Surgery, Optical Laser Communications, DNA Sequencing?”, refer to Fig. 4.15. and fielded systems are offset in time, in some cases only by a few months or years, but in other cases it could be a decade or more.

 The Technology Readiness Level (TRL) scale goes from 1 to 9 and captures the degree of maturation of a technology all the way from a mere idea (such as a sketch on a cocktail napkin) to a certified product or service available in the marketplace. More discussion on the TRL scale follows in Chaps. 8 and 16. 35

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Fig. 4.15  Conceptual stages along the S-curve of a technology. (Commercial aircraft show saturation in terms of aircraft speed and size. Most large commercial airliners cruise at about Mach 0.83–0.85 and their size is mainly between 150 and 350 passengers. This saturation is, however, not driven by a theoretical limit  – we can fly at supersonic speeds as was done by the famous Concorde aircraft from 1969 to 2003 – but due to economic considerations. This trend is exemplified by the recent retirements of very large aircraft such as the B747 Jumbo Jet and the A380)

⇨ Exercise 4.5 For a technology of your choice, gather background information and data for at least one relevant Figure of Merit (FOM), see Exercise 4.1, over time. Find a theoretical limit if it exists. Attempt to model the rate of improvement quantitatively and plot the trajectory for this particular technology and FOM. Estimate where in its lifecycle the technology currently is (based on Fig. 4.15). Pareto Shift Model So far we have always drawn a technological FOM (y-axis) versus time (x-axis). Another important way to think about technological progress over time is the Pareto shift model (Smaling and de Weck 2007). A Pareto front is the best achievable tradeoff between two or more FOMs. This can be illustrated by plotting two or more FOMs against each other, making time an implicit variable. A Pareto front connects the points corresponding to the same timeframe (year). In order to improve on one of the FOMs on the Pareto frontier, we need to sacrifice at least along one of the other dimensions. Figure 4.16 shows an example of such a tradeoff between travel time (inverse of speed) versus breaking time for high-speed rail (HSR) systems around the world. Ideally, we want both travel time and braking time to be short, but there is a tradeoff between the two that is mediated by technology and human physiology.

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Fig. 4.16  Tradeoff between FOMs in high-speed rail (HSR) systems around the world in terms of Journey Time in [min] for a 100 [km] trip versus braking time [sec]. The current Pareto front is shown in gray connecting existing HSR systems (shown as brown dots). JRE= JR East (Japan). (Source: de Filippi et al. (2019))

Technological progress is visible by shifting the achievable Pareto front (shown in gray in Fig. 4.16), that is, the best feasible tradeoff at a point in time, closer to the Utopia point, that is, to a higher state of ideality. In Fig. 4.16, the ultimately achievable tradeoff is limited by the human body’s ability to withstand rapid deceleration without extreme discomfort, injury, or death as indicated by the solid black line in the lower left, representing the 8 [g] deceleration level. Figure 4.17 shows conceptually how the Pareto front shift model works. The solid dark lines show the best tradeoff between different FOMs of a product or system at a given time, t. As the technology progresses at times t+1, t+2, etc., the Pareto front shifts toward higher performance or value, closer to the so-called utopia point.36 On the left, we see a situation where minimizing FOM values is better, while on the right larger FOM values are better. Mathematically, this shift is happening because through improved design and technology a larger design space becomes accessible (for example, a new material becomes available with better properties), or earlier constraints are eliminated or shifted.37

 The “utopia point” is a mathematical concept from multiobjective optimization and multi-criteria decision-making, and it represents the best value along each separate FOM dimension that is achievable. The utopia point itself is not achievable since it ignores the existence of tradeoffs and constraints; however, it represents an aspirational goal or target for a technology to move toward over time.

36

4.3  S-Curves and Fundamental Asymptotic Limits

FOMj

109

FOMj

+

t+2

utopia point

t t+1 + utopia point

t+1

t+2

FOMi

FOMs: smaller is better (SIB)

t

FOMi

FOMs: larger is better (LIB)

Fig. 4.17  Technology progression modeled as a shift in the FOMi-FOMj Pareto front over time, left: for smaller is better FOMs, and right: for larger is better FOMs

Fig. 4.18  One of Frank Whittle’s first turbojet engines, the W2/700, in 1944, developed by Power Jets and eventually by Rolls Royce. (Source: UK Science Museum Group)

Example: Aircraft Jet Engines One of the most important technological inventions of the twentieth century was the turbojet engine. Frank Whittle, a gifted engineer and Royal Air Force (RAF) officer in the United Kingdom, is generally credited as the inventor of the turbojet engine (even though the German Hans von Ohain designed the first operational engine during WWII). Figure 4.18 shows one of Whittle’s turbojet engines, the W2/700, from 1944 now on display in a museum in Britain. The core of the engine containing the single compressor and turbine is shown in the center, and the radial combustion chambers are visible at the periphery of the engine. Figure 4.19 shows a Pareto front progression chart in terms of two key FOMs for jet engines: core thermal efficiency (the degree to which kerosene fuel is efficiently

37  We will discuss the role of constraints and so-called Lagrange multipliers (“shadow prices”) in technology development in Chap. 11 on technology sensitivity analysis.

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Fig. 4.19  Pareto progression chart for jet engines in terms of core thermal vs. propulsive transmission efficiency (Source: Pratt & Whitney). This chart is of the larger-is-better (LIB) type, see Fig. 4.17 (right)

combusted into thermal energy of the airflow) and propulsive transmission efficiency which measures the degree to which the heated airflow efficiently produces thrust. The overall efficiency is the product of these two efficiencies and is shown as iso-lines of overall efficiency in Fig. 4.19. This overall efficiency is also captured by an aggregate FOM called the Specific Fuel Consumption (SFC), as shown in the upper right. We see that Whittle’s original engine (shown by a black dot in the lower middle) only had an overall efficiency of about 10%. With each generation of engine technology (and changes in their underlying architecture), the efficiency was significantly improved from turbojets (about 0.15–0.18) to low bypass ratio (BPR) engines (0.21–0.25), current high bypass ratio engines (0.28–0.32), and new ultra-high bypass ratio engines (UHBR) (0.35–0.38). Future engines such as unducted fans (UDF) may achieve overall efficiencies in the 0.4–0.5 range but are not yet in operational service due to several unsolved issues including noise and safety concerns due to the possibility of an uncontained rotor failure. While high BPR engines are at TRL 9, UHBRs are today at about TRL 7, and UDFs at TRL 6, for commercial applications. While aircraft jet engines have improved in terms of Thrust Specific Fuel Consumption (TSFC),38 this improvement has come at the expense of increased system complexity, see Fig. 4.20.

 TSFC = thrust-specific fuel consumption in units of [kg/s/N] is a normalized measure of fuel efficiency for aircraft engines that allows to compare engines across different generations 38

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Fig. 4.20  Increase in engine complexity as a function of improved normalized performance: (a) single-stage turbojet (Whittle), (b) multistage turbojet, (c) high bypass ratio turbofan engine, and (d) geared turbofan engine. The equation relates performance, P, to complexity, C

4.4  Moore’s Law The third major model for quantifying technological progress (besides the S-curve and Pareto model) over time is Moore’s law. Gordon Moore observed in a well-known paper (Moore 1965) that the number of transistors on an integrated circuit (IC) doubled about every 2 years. This has

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Fig. 4.21  Plot of MOS transistor counts for microprocessors against dates of introduction. The curve shows counts doubling approximately every 2 years, per Moore’s law. (Source: Max Roser, https://en.wikipedia.org/wiki/Transistor_count)

become known as “Moore’s Law.” Note that this paper was written 3 years before Intel was founded in 1968. Moore then became chairman of Intel in 1979, 11 years later. The exponential progression in ICs was achieved by improved semiconductor fabrication techniques and going to smaller feature sizes. Greater production volumes over time impacted the cost of ICs but not directly their performance. Figure 4.21 shows an updated figure of transistor count over time and is a continuation of the analysis started by Moore. The implication of a “doubling per unit time” is that on a semilogarithmic graph with performance as the y-axis and linear time as the x-axis that progress appears nearly as a straight line, see Fig. 4.22. While the rate of progress may fluctuate over larger periods of time, the underlying assumption behind Moore’s law is that there is no saturation in this model of technological progress. This is in sharp contrast to what is assumed in the S-curve model, which is predicated on the fact that there is saturation.39 Mathematically, we can think of exponential growth both in discrete and continuous terms. In discrete terms, we say that a variable grows by a fixed percentage (or fraction r) over a fixed interval of time and we experience a compounding effect, similar to earning a fixed interest rate on capital, while making no withdrawals from the account. This can be written as

 Recently, there is a debate whether Moore’s law is running out of steam, that is, slowing progress. So far, however, there is no such evidence for a slowdown.

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Fig. 4.22  Moore’s law – exponential technological progress over time as exemplified by the number of transistors on a computer chip (1970–2020). A selected subset of CPUs from Fig. 4.21 is shown along with the red progress curve, assuming r=0.37

y ( t ) = yo (1 + r )



t



(4.5)

where y is our FOM of interest, t is the discrete time (as in year 0, 1, 2, …N), and r is the annual rate of progress. Figure  4.22 shows what Moore’s law looks like when applying Eq. 4.5. There appears to be no slowing down as some have claimed, and Moore’s law appears to hold, even after 50 years. It is interesting to note that exponential progress appears as a straight line in Fig.  4.22 and that the rate of progress for computer chips is indeed r = 37% as shown in Fig. 4.1, but using a different FOM. ✦ Definition Moore’s Law (adapted) The progress in technology is exponential and can be approximated by a fixed annual rate r for different technologies. In computing, the progress is such that capabilities double about every 2 years.40

 A true doubling every 2 years would require an annual rate of about 41%. The rate of 37% per year observed in computing over the last 50 years (see Figs. 4.1 and 4.22) comes very close to that. Our case studies in Chaps. 13 and 18 will exceed even these rates of improvement.

40

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Fig. 4.23  Effect of different rates of annual improvement on technology over 30 years

We can replace y(t) with any FOM of interest to reflect “performance” of the technology and r represents the (discretized) rate of performance improvement per year. In Fig. 4.23, the dramatic impact of seemingly small changes in the rate of progress, r, over time is shown. This impact can be summarized as the x-fold improvement in the technology over a period of 30 years, assuming a constant rate of progress, r. For example, a 2.5% improvement per year will result in approximately a twofold improvement over 30 years, a 5% per year improvement will yield a fourfold improvement over 30 years, and a 10% annual rate of improvement will accumulate to a sixteenfold improvement over the starting value. A 20% annual rate will yield better than 200x improvement over 30 years and r=37% will yield 107 (seven orders of magnitude) over 50 years. We have now learned how to empirically determine the average annual rate of progress of different technologies. Another example of this is timekeeping (Fig. 4.24) where we estimate that over the last 1000 years our annual improvement in technologies that allow us to keep track of time has been about 1.8%. Exponential growth, for example, in biology, is often shown as a continuous exponential equation in the form of Eq. 4.6.

y ( t ) = yo e kt



(4.6)

where e = 2.718281… and k is the exponential growth rate, also known as the constant of proportionality. Here, t is interpreted as a continuous variable, contrary to Eq. 4.5 where it was assumed to be a discrete variable, for example, in units of years. For k>0 we can convert from the continuous rate to the discretized rate as follows:



1 + r = ek r = ek − 1 k = ln (1 + r )

(4.7)

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Fig. 4.24  Progress in timekeeping accuracy over a period of about 1000 years is 1.8% per year. Here, technical progress in the function of timekeeping is expressed as a functional performance-­ type figure of merit FOM= A/B, where A = time/error in time, also known as drift, in [sec/sec] and B = volume in cubic centimeters. For example, a pendulum clock in 1670 had a drift of about 1 second every 2 hours (A =~7,000) and a volume of about 400,000 cubic centimeters (B=~ 4 x 105 [cm3]), leading to a FOM value of about 1.75 x 10-2 [cm-3]. The straight line shown in this figure corresponds to an annual improvement of about 1.8% in our ability to keep time over the last millennium (see also de Weck et al. 2011 for more details)

For example, the annual rate of progression predicted by Moore’s law (r=0.37) translates to a constant of proportionality of k=0.385. Magee et al. (2016) have done extensive work on finding the different rates of average annual progress in technologies over time and explaining these differences across functional technology domains. Figure 4.25 shows a comparison of two technologies: piston engines for automotive applications (see also Chap. 6) and magnetic resonance imaging (MRI). The rates are vastly different, and we will explore reasons for these differences in future chapters. In general, technologies that manipulate information (such as MRI and computing) have improved at significantly higher rates than those involving matter and energy. A ranking of 27 different technologies by Magee (2016) in terms of annual rate r of improvement shows optical telecommunications (see Chap. 13) as the fastest improving technology at nearly 60% per year, versus milling machines which only improve at about 2% per year. MRI as shown in Fig. 4.25 (right) is third out of 28 technologies, and internal combustion engines (Fig. 4.25 (left)) are in 24th position out of 28 technologies in terms of rate of improvement. Is there a paradox between technology progression models? At first, there appears to be a paradox between the S-curve model and Moore’s law. While the S-curve model predicts saturation of technological progress due to diminishing returns and asymptotic physical limits, Moore’s law does not feature any such saturation effects.

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Fig. 4.25  Comparison of annual rate of improvement of piston engines in terms of [W/kg] versus MRI in terms of [1/(resolution x scantime)]. MRI has improved at a much higher annual rate than piston engines, but over a shorter time period

➽ Discussion How can we resolve the apparent paradox between the S-curve model which predicts that a technology will eventually reach a plateau (or period of slow progress), and Moore’s law which predicts exponential progress?

The answer depends on your perspective. If we consider only a specific implementation, architecture, or technical instantiation of a technology, we do indeed observe asymptotic saturation. Examples of such saturation or slowdown shown in this chapter are the performance of computers with vacuum tubes (Fig. 4.1), silicon-­ based solar cells (Fig. 4.12), and mechanical clocks (Fig. 4.24). This maturation and then saturation of a single technology often occurs over the time horizon of several decades. If, however, we take a broader view and our FOM is a functional performance metric (FPM) that is functionally oriented (see Tables 1.2 and 1.3) and on top of that we take a longer perspective over centuries (Fig. 4.25) or even millennia (Fig. 4.24), we do not observe saturation and Moore’s law holds. This apparent conflict is resolved when we see Moore’s law as the concatenation of multiple interlocking S-curves as depicted in Fig. 4.26. As an “old technology” reaches maturity and its own saturation stage, a “new technology” which provides the same function, but in a better way, will eventually become dominant. If we focus only on the individual technological solutions, the S-curve model may be appropriate. If, however, we focus on the functional view over a longer period of time as expressed by a solution-neutral FOM, then the exponential growth model à la Moore prevails. Table 4.5 summarizes some examples of technology transition we have seen for far.

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Fig. 4.26 Interlocking S-curves and technology transitions. The solid lines show the S-curves of individual technologies, while the dashed line approximates Moore’s law

Table 4.5  Transitions between technology generations for different functions Gen 1 2 3 4 5 6

Computing (Fig. 4.1) Mechanical computer Solid-state relay Vacuum tube Transistor Integrated circuit Optical, DNA, Biological (?)

Propulsing (Fig. 4.19) Piston engine Turbojet Turbofan Hybrid-electric Hydrogen fuel cell (?)

Timekeeping (Fig. 4.24) Sundial Mechanical clock Quartz clock Atomic clock Quantum clock (?)

In this chapter, we have seen in Fig. 4.1 the transition of technologies for computing from electromechanical computers to vacuum tubes, transistors, and eventually ICs at an annual rate of progress of ~37% over a period of 100+ years. In Fig. 4.19 we saw the transitions in aircraft engine architectures, and in Fig. 4.24 we see the transitions in timekeeping technologies over a millennium from sundials, to mechanical, quartz, and atomic clocks. Further improvements in timekeeping, thanks to quantum clocks, can be expected. In this way, we can now see all three models of technological progress (S-curve, Pareto front shift, and Moore’s law) as complementary to each other. This brings up important questions for discussion.41 More on the topic of technology transitions will be discussed in Chap. 7. The key takeaway from this chapter is that in order to manage technology one has to quantify it using appropriate Figures of Merit (FOM). Once FOMs have been defined, we can then trace the progress of technology over time. In the next chapter, we will learn about patents as an important way to document and protect first-of-a-kind technological inventions.

 Several of these questions are the subject of active research in academia and in industry and may not have a definitive answer yet. Chapter 7 will discuss in some more detail the topic of technology transitions. 41

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➽ Discussion • Do we ever really retire technologies? • Can we predict the crossover time between the old and new technology? • Do functions that improve at higher annual rates see more frequent technology transitions than those that exhibit slower rates of progress? • To what extent can the ratio of the rate of improvement of the old and the new technology and their current gap in terms of performance or cost inform optimal R&D investments and timing? • Will Moore’s law eventually show saturation as humanity approaches the fundamental limits of physics in the large (cosmology) and in the small (quantum physics)? A good example of such a limit is the speed of light.42

References American Iron and Steel Institute, Steel Industry Technology Roadmap, December 2001, Committee led by Mark Atkinson and Robert Kolarik URL: https://steel.org/~/media/Files/ AISI/Making%20Steel/manf_roadmap_2001.pdf de Filippi, R. et al. “High Speed Rail Safety”, Technology Roadmap created at MIT in 16.887, 2019, URL: https://roadmaps.mit.edu/index.php/High-Speed_Rail_Safety de Weck, O. (2017). Lectures on Technology Progress, SDM Core, Massachusetts Institute of Technology, EM.412 de Weck, Olivier L., Daniel Roos, and Christopher L. Magee. Engineering systems: Meeting human needs in a complex technological world. Mit Press, 2011 Kessler, D., & Temin, P. (2008). Money and prices in the early Roman Empire. The monetary systems of the Greeks and Romans. 2008 Feb 14:137–59. Koh, Heebyung, and Christopher L.  Magee. "A functional approach for studying technological progress: Application to information technology." Technological Forecasting and Social Change 73, no. 9 (2006): 1061-1083. Kurzweil, Ray. “The singularity is near: When humans transcend biology”. Penguin, 2005. Magee, Christopher L., Subarna Basnet, Jeffrey L. Funk, and Christopher L. Benson. “Quantitative empirical trends in technical performance.” Technological Forecasting and Social Change 104 (2016): 237-246. Moore, G. E. (1965). “Cramming more components onto integrated circuits” (PDF). Electronics Magazine. p. 4. Retrieved 2006-11-11. Rogers, Everett M. “Diffusion of innovations”. Simon and Schuster, 1962 Smaling, R. and de Weck O.. "Assessing risks and opportunities of technology infusion in system design." Systems Engineering 10, no. 1 (2007): 1-25. Solow, R.M. (1957). Technical change and the aggregate production function. The Review of Economics and Statistics 1:312–20. 1957 Aug

 See Chap. 22 for a further discussion on this topic, including the potential existence of a technological singularity.

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Chapter 5

Patents and Intellectual Property

Advanced Technology Roadmap Architecture (ATRA) Inputs

Steps

Outputs

L1 Products and Missions

1. Where are we today?

FOMjj

Strategic Drivers for Technology

+5y Organization

Technology State of the Art and Competitive Benchmarking

L2 Technologies

Technology Systems Modeling and Trends over Time Technology Projects

FOMi

Today

2. Where could we go?

+10y FOMj

Dependency Structure Matrix

Tech Pul Pull

Figures of Merit (FOM) Current State of the Art (SOA) Technology Trends dFOM/dt

Competitor 1 Competitor 2

Technology Systems Modeling

L1

Technology Roadmaps

+10y

Scenario A

+5y

Scenario B ?

3. Where should we go?

L2

Scenario Analysis and Technology Valuation

Intellectual Property Analytics

Technology Portfolio Valuation, Optimization and Selection

E[NPV] - Return

5

4. Where we are going!

Technology Investment Efficient Frontier Technology Portfolio Technology Projects σ[NPV] - Risk

Foundations Definitions What is Technology?

History Milestones of Technology

Ecosystems Nature Technology, Nature Technology Diffusion, Infusion and Industry and Humans

The Future Is there a Singularity ?

© Springer Nature Switzerland AG 2022 O. L. de Weck, Technology Roadmapping and Development, https://doi.org/10.1007/978-3-030-88346-1_5

Design Reference Missions Future Scenarios T h l Technology V Valuation l ti Vector Charts

FOMi

Tech Push

Technology Scouting Knowledge Management

Scenario-based Technology Valuation

Case 1

Case 2

Automobiles

Aircraft

Pareto-optimal set of technology investment portfolios Recommended Technology Portfolio (Expected NPV and Risk)

C Cases Case 3 Deep Space Network

Case 4 DNA Sequencing

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5.1  Patenting So far we have discussed what technology is (Chap. 1), the history of technology (Chap. 2), the relationship between technology and nature (Chap. 3) as well as ways to quantify technological progress over time (Chap. 4). Most of this has been mainly “descriptive.” In other words, we have merely described how things are, or how they have been, not how they could be or should be. With this chapter we begin a more “prescriptive” discussion of technology, beginning with patents, the best known form of technology-related intellectual property (IP). The first patent for an invention was issued in the year 1474  CE in Venice (Meshbesher 1996). It is generally accepted that the Venetian Patent Statute of 1474 is the basis for most modern patent systems in the world today. There are indications that an earlier form of patent may have been issued in ancient Greece, but the historical record is generally not considered strong enough to establish this as the first instance of a patent. During medieval times, monarchs would issue “letters patent” to certain of their subjects granting them exclusivity over certain resources such as land grants. Venice became a major trading state in the twelfth and thirteenth centuries, and beyond trading commodities such as spices, textiles, and so forth, the exchange of knowledge about inventions  – essentially technology  – became an important consideration. Some of these inventions traveled along the major trade routes such as the famous Silk Road. A patent is a hybrid legal and technical document which describes an invention. The grant of a patent bestows on the patent owner a time-limited legal monopoly over the invention. More precisely, a patent is a government-issued document that provides its owner with the right to prevent anyone else from offering for sale, selling, using, or importing the invention as defined by the claims of the patent. ✦ Definition A patent is a government-issued and time-limited right or title to exclude others from making, using, importing, or selling an invention. An invention is a solution to a well-defined problem that is novel, nonobvious, and useful.

Patents are territorial. This means that a U.S. patent only has effect on infringing acts in the United States.1 There is no such thing as a patent with global reach. An inventor who wishes to obtain worldwide exclusivity has to file separate patents in all jurisdictions of interest. 1  Practically this means, for example, that if an individual or company in a country in Europe or Asia “infringes” on a U.S. patent whose underlying invention is not also patented in Europe or Asia, that this act does not represent infringement in the legal sense and that it cannot be enforced. This is true, as long as said “copied” products or processes are not sold in the United States. 2  One of the reasons for this is that a specific patent may rely on another preceding more general patent owned by a different owner and in order to exercise the later specific patent, a license from the original (underlying) patent owner may be required. This obligation to obtain license from the earlier patent disappears, however, once the earlier more general patent has expired.

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Fig. 5.1  Example of a patent for a helicopter with a main rotor and fixed wings. (Source: U.S. Patent and Trademark Office. One of the prior patents cited in this patent is US20100224721A1 “VTOL Aerial Vehicle” which is concurrently active and might have to be licensed in order to build and produce helicopters as described in the U.S. Patent 9,321,526. It is interesting to note that the VTOL patent US20100224721A1 was added to the list of cited patents not by the inventors, but by the patent examiner as part of their patent examination process.) The figure only shows one of the graphical representations of the invention, whereas the most important parts of a patent are the underlying claims which are contained in the written text

A patent effectively establishes a temporary monopoly but does not oblige the patent owner to enforce that monopoly right. A critical nuance is that a patent does not give its owner an affirmative right to make, use, or sell the invention defined by the patent claims.2 A patent only gives the right to exclude others. Patents are articles of (intangible) property and as such can be sold, assigned, and licensed. We should think of granted patents as an asset belonging to a specific owner who may or may not be identical to the listed inventor(s). Figure 5.1 shows an example of a relatively recent patent for a so-called “compound helicopter.” This flying machine essentially combines a traditional helicopter, whose main rotor provides vertical lift, with horizontal wings and “pusher” propellers as typically found on traditional fixed-wing aircraft. The public policy “deal” upon which the patent system is based is that the state grants to an inventor a time-limited exclusive monopoly to an invention in exchange for the inventor completely disclosing the idea. The objective is that after the patent expires, the technology is able to be used freely by anyone who wants it. This is intended to have a generally positive long-term economic effect. Scholars and practitioners actively debate to this day whether the patent system, as a whole, has had a net positive or negative impact on innovation and technological progress. One of the examples of a vocal critic of the patent system in the United Kingdom was the famous British civil engineer and industrialist Brunel (Whitehouse et al. 2016) who is quoted as saying:

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“Patentees were the equivalent of squatters on public land, or better, of uncouth market traders who planted their barrows in the middle of the highway and barred the way of the people.” Isambard Kingdom-Brunel

There are arguments both in favor and against the patent system. One sector where patents have been particularly influential is in the pharmaceutical industry where large investments in R&D are required to develop and get approval for new medicines. Patents have been essential for incentivizing life science companies to invest money into drug development, in hopes that their investment may be recovered during the 20-year life of the patent. The most successful patented drugs often continue to be produced as “generic” drugs – using the same underlying chemical formulation – once patent protection has expired. The pharmaceutical industry has been grappling with ways to preserve their profits from successful drugs coming off patent (Bulow 2004). There is a link between the notion of patents, and intellectual property more generally, and the concept of the “tragedy of the commons.” Similar to real estate which is privately owned, the right to exclusive ownership and control of the intellectual property as an asset, gives the owner an incentive to invest in it, since if the asset were freely available for free, it may not be cared for or invested in to the same degree. Some countervailing trends in patenting have recently emerged, such as the promise to not enforce exclusivity on technology patents, in hopes that this may stimulate innovation and the growth of a larger ecosystem (see also Chap. 19). A good example of this is the 2014 announcement by Elon Musk that all of Tesla’s patents would be open sourced.3 Patents are not simply granted. Patents generally follow a process of application, examination (sometimes called prosecution), one or more office actions, and this ultimately results in either a successful grant or a rejection. In most jurisdictions, there are three main requirements that a patent must fulfill: 1. Novelty. The invention must be new according to the prevailing legal definition in the patent’s jurisdiction. The invention must go beyond the state of the art at the time of the filing of the patent application. 2. Nonobviousness. The patent must represent an invention that is not obvious, that is, that requires some “inventive step” above and beyond the normal experimentation or development in the field. 3. Usefulness. The invention must address a problem of interest to society, and it must be capable of implementation. However, it is not required to build a prototype to demonstrate the invention before filing for a patent.

 Source: https://www.tesla.com/blog/all-our-patent-are-belong-you  This is an interesting point. Typically, it is not possible to simply take something observed in nature (e.g., plants or naturally occurring DNA sequences) and obtain a patent for it, since no “inventive” step was required. However, it is possible to obtain patents on plant varieties that have been generated through breeding as well as more recently, genetic modification, see Chap. 3. 5  An example of a patent related to specific spacecraft orbits around planet Earth is as follows: Castiel, David, John E. Draim, and Jay Brosius. “Elliptical orbit satellite, system, and deployment with controllable coverage characteristics.” U.S. Patent 5,669,585, issued September 23, 1997. 3 4

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A patent should not be granted for something that already exists in nature on its own.4 This relates to the second requirement and is particularly interesting as there may be instances of issued patents for things that can be argued to be occurring “naturally.” Some examples are patents issued for specific, geometrically configured orbits around the Earth5 or patents issued for DNA sequences. When such patents are, nevertheless, granted it is often for instances of natural components or phenomena that are embedded in or combined with engineered components. Again, as alluded to in Chap. 3, we increasingly see great challenges in drawing a sharp boundary between what is natural and what is artificial. Deciding on whether the three criteria (novelty, nonobviousness, usefulness) are met in a particular patent application is the main job of the patent examiner. They have the ultimate authority to decide on granting or denying a patent application. This is typically a multiyear process that requires both formal procedures and substantial domain knowledge. Patent examiners generally have advanced degrees in science and engineering.6 Some patents have been granted for inventions that the general public may find surprising because of their perceived simplicity. Figure 5.2 shows examples of two related patents that may fall into this category. The “beerbrella” (Fig. 5.2 left) is a small umbrella that snaps on to a beer bottle and is intended to shade it from solar radiation to slow the warming of the beer (and provide advertising opportunities). The cardboard sleeve for hot beverages (Fig. 5.2 right) prevents discomfort or burns to those holding hot beverages such as coffee or tea. Some readers might disagree that these are “worthy” patents, but they nevertheless successfully passed the patent prosecution process, and certainly the second example will be familiar to most readers from personal experience. While the examples of patents shown in Fig. 5.2 may have been chosen deliberately and be amusing in a sense, the underlying message is a serious one in that what may or may not be patentable is not always easy to predict. The economic value of a patent is really the essence of why the patent system exists (see below). Only by excluding others from exploiting the use of an invention, a form of legally enforced exclusivity, does a patent gain its economic value. It is perhaps the steam engine patents (see Sect. 5.4) by Watt that made the economic value of patents clearly apparent for the first time. Regarding novelty, products, and underlying processes presented as part of the patent application must not have been sold or publicly described before the date of filing of the patent. This would render the invention not new. In other words, artifact(s) associated with the invention should not previously have been sold or publicly described.

6  Some famous patent examiners (so-called patent clerks) were Thomas Jefferson, the third president of the United States, Alrich Altshuller, the inventor of the TRIZ method in the Soviet Union, as well as Albert Einstein who worked for the Swiss Patent Office from 1902 to 1909, including the “annus mirabilis” of 1905, see below. 7  As stated earlier there is no such thing as a “global patent,” but there are international agreements and processes that aim at harmonizing patent processes – such as the minimum duration of a patent’s lifetime – among different countries.

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Fig. 5.2  Nonobvious patent examples such as US 6,637,447 B2 “Beerbrella” issued on October 28, 2003, on the left, and US 8,056,757 B2 “Hot Beverage Cup Sleeve” issued on November 15, 2011, on the right Table 5.1  Novelty requirement in terms of the filing date In the United States of America − An inventor(s) can file their patent within 1 year after certain types of public disclosure of the invention. − Oral disclosures to other individuals do not start the clock. − Slides, posters, and maybe even markings on blackboards may be considered a public disclosure.

In most countries other than the United States − Generally, absolute novelty is required. However, in some countries disclosure at trade fairs or as a result of an evident abuse to the prejudice of the patent applicant is taken into account. − One must file a patent before first public disclosure. − Oral disclosure does count as a public disclosure.

While novelty requires that the invention in the form of a method or an apparatus must generally remain completely confidential up until the date of filing the patent, some countries, including the United States, have limited exceptions or “grace periods” to this rule as summarized in Table 5.1. In 2013, the United States switched to the “first-to-file” system from the “first-­ to-­invent” system as part of the America Invents Act (AIA). As a matter of prudence, one should always file a patent before any public disclosure of the invention occurs. This is particularly important where a US inventor may want to obtain international patents.7 Other countries do not recognize the US grace period of one year, and this legal provision therefore cannot preserve the legal novelty of the invention. One way to knowingly or unknowingly prevent an invention from being patented by oneself or another party is to publish it in a public forum such as at a conference or in a scientific journal, or by simply posting it openly on the Internet, before filing at least a provisional patent.

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Second, in order to avoid what might be considered a public disclosure, it is always recommended to use a so-called Nondisclosure Agreement (NDA) when discussing an invention with a third party prior to filing a patent. As noted above, it is critical to realize that patents are territorial. A patent will only have legal effect in its own jurisdiction. In terms of enforcement, a patent gives the patent owner the right, but not the obligation, to enforce their monopoly right on others, that is, prevent others from using the invention during the lifetime of the patent. This right is not enforced automatically by the granting authority (usually the patent office) but must be asserted by the inventor(s) or patent owner through the filing of an infringement lawsuit, generally under civil law before the courts. Below we review several examples of famous patent infringement lawsuits. Most durations of validity of patents are for 20 years, starting with the first application date. In the life sciences, for example, it takes 3–4 years for a patent application to be decided, and in effect this reduces the actual useful “economic” life of the patent to about 16 or 17 years. During the examination period products containing the invention are often sold with the label “patent pending” attached. Patents filed in the United States before 1995 may have patent durations of 17 years depending on the delay between filing and final action by the patent office. The steps required for applying for a patent and typical associated costs in the United States are listed in Table 5.2. Note that this is not simply a linear process but may be iterative where in step 6, multiple office actions and inventor responses may be going back and forth before a final decision is issued. The duration from initial filing to final action in the United States can vary greatly, depending on the backlog at the USPTO, and inherent complexity of the patent. A typical average total duration in recent years has been between 18 and 22 months. Maintenance fees (item 8) are government taxes required to keep a patent in force. In most countries these increase over time. This reflects a public policy posture which discourages unused patent rights being kept alive. The total cost for obtaining (and maintaining) a patent in the United States is roughly between $15,000 and $40,000. The cost of patent litigation is typically much higher and can run into the millions of dollars.8 When it comes to the management and litigation of intellectual property (IP), it is advisable to work with professional lawyers and staff trained and specialized in IP law. This is particularly important for firms that seek patent protection beyond a single national jurisdiction. The patent system has been described as a “race” where, after a specific patent is granted to a player, the clock is reset and the next round of competition starts. Chap. 10 will discuss the role of technology strategy and competition in this race. In summary, patents are based on a “contract” between society and inventors. They encourage the disclosure of powerful ideas, and the reduction of these ideas to 8  According to the American Intellectual Property Law Association, the cost of an average patent lawsuit, where one million dollars to $25 million is at risk, is $1.6 million through the end of discovery, and $2.8 million through final disposition (2013), Source: https://www.ipwatchdog. com/2013/02/05/managing-costs-of-patent-litigation

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Table 5.2  Steps, and typical time and cost for filing a utility patent in the United States (2018) Step 1 2 3 4 5 6 7 8

Description Conception Reduction to practicea Technology disclosureb Prior art searchc Patent applicationd Office action (clarification, rejection) Patent grant Maintenance fees

9

Patent expiration

Duration Months to years Months to years 2–4 weeks 2–3 months 1 day 3–6 months each 1 day 3.5 years 7.5 years 11.5 years 20 years after filing

Cost $100 – $10 M+ $100 – $1B+ Nominal $500 – $2000 $7500–$10,000 $3000 – $5000 per action $1240 $850 $1950 $2990 Nominal

Reduction to practice means that the invention has moved beyond the mind of the inventor(s), which is conception, to actual reduction to practice to show that the invention works, or constructive reduction to practice as in the form of a patent application that discloses the details of the invention. This was important in the FITF system to resolve disputes between competing applications to establish the actual date of invention, prior to the date of filing b A technology disclosure is an internal document used inside organizations that have a technology management group, such as a technology licensing office (TLO) or chief technology office (CTO) for individual inventors to announce or “disclose” their inventions so that the organization can decide whether or not to pursue a patent application or other form of intellectual property protection c This includes not only searching for other patents in the same jurisdiction, but public information as well, including conference and journal articles, trade information, and the internet d The USPTO filing fee is $300, whereas the majority of costs shown here are patent preparation fees usually paid to IP professionals a

practice for society’s benefit. They provide incentives for the inventors and protect the rights of those inventors to prevent others (who did not generate the ideas and inventions) from benefiting from the invention during a limited time, typically 20  years. In exchange, the full disclosure of the invention and expiration of said patents after 20 years gives society a rich base of technological knowledge that can subsequently be used and built upon by a wide range of stakeholders, beyond the original patent owners. During their active period, patents can be bought, sold, or licensed and are considered assets.

5.2  Structure of a Patent – Famous Patents Patents have a tightly prescribed language and structure, which facilitates understanding what a patent is about, the examination of patents, and the practice of intellectual property law. Patents are an unusual hybrid legal and technical document. 9  The company in that case owns the patent rights since the inventors were paid to make the invention as part of their job duties and all costs associated with it were carried by the firm. Many companies incentivize their employees to file patents by awarding them a one-time fee or better a recurring bonus based on the cash flows generated by the patent.

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They must be capable of interpretation by both the courts and the notional person familiar with the technical domain with which the patent is concerned. Generally, patents contain the following information: • Inventors. Information on one or more persons who are the inventor(s). In some cases, the inventors are private individuals but they are more commonly employees or scientific staff. Patents are items of intangible property and are always owned by someone. Along with the inventors, patents identify the assignees of the patent who are the owners of the property rights. Sole inventors are usually also the owners, whereas inventors who are employees usually designate their company as the assignee.9 • Problem addressed. The patent shall describe what problem is being addressed by the invention. This is often related to some function(s) or objective to be fulfilled (see Chap. 1) such as the production or refinement of raw materials,10 the processing of information, curing or diagnosing of diseases, and so forth. However, the problem can also relate to the design of a particular physical object. Inventions are classically divided into methods and apparatus. Oftentimes, the patent is associated with a particular industrial sector (e.g., see NAICS classification system) and is classified according to various taxonomies. For example, patent US 9321526B2 shown in Fig.  5.1 belongs to CPC (cooperative patent classification) category B64C which includes airplanes and helicopters. • Prior art. A description of the state of the art (SOA)11 at the date of filing the patent and how the problem has been solved, or attempted to be solved before, prior to the filing date. The SOA represents the latest and most advanced implementation of a certain product, process, or technology at the time of filing and is primarily used to assess the novelty of the patent. This also includes listing of prior patents that are related to the claimed invention. This reference to other (prior) patents allows network or topographical analysis on patent datasets to identify linked ensembles such as groups or subgraphs of patents (Yoon and Magee 2018). • Description of the invention. The invention is described using both a textual description in human natural language such as English, Chinese, French, Japanese, etc. and a set of diagrams which give a pictorial view of the invention. The  The first US patent was awarded on July 31, 1790, to Samuel Hopkins for a new way to make potash, a fertilizer ingredient containing potassium, for example, K2CO3, which is typically derived from mined salts. The purpose of fertilizers is to increase yields in agriculture. Feeding a growing nation was the main problem being addressed by this patent in the late eighteenth century. Source: https://www.uspto.gov/about-us/news-updates/first-us-patent-issued-today-1790 11  The state of the art (SOA) is different from the state of practice. The latter encapsulates the average or typical way how a particular problem is solved in society by a majority of people or entities at a certain moment in time, while the former captures the best possible solution which may not have been widely diffused into society yet, see Chap. 7. 12  Most patent diagrams used to be, and are still today, drawn by hand. This is somewhat of a tradition and has even given rise to the notion of “patent art,” which are beautifully framed specimens of diagrams contained in famous historical patents. Increasingly, patent diagrams are computer generated, a trend which started in the twentieth century and continues to this day. 13  This goal is of course aspirational, since actually replicating the invention independently may require specialized knowledge and equipment (e.g., a semiconductor fabrication facility) that may not be easily available once the patent expires and becomes available for broader use. Replicating the underlying technology is difficult for new and disruptive technologies. 10

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Fig. 5.3  Difference between a utility patent (left) and a design patent (right). Note that in the design patent only the solid and not the dashed lines are protected

diagram(s) are very important, as they label the complete set of objects and/or processes related to the invention, see Figs. 5.1 and 5.2. In many jurisdictions, these diagrams are mandatory to obtain a valid patent. In the case of a physical artifact, this may be an isometric or exploded view of the device (see also Fig. 5.3), whereas in the case of a procedure, algorithm, process, or recipe it might be a flowchart, pseudocode, or a structured list.12 The idea is that the description is detailed enough for an individual skilled in the art to replicate the invention independently, without help from the original inventor(s). This is important since the whole idea of the patent system is predicated on the notion that after the patent’s expiration (typically after 20 years) the invention can be used and freely copied without infringing on the patent owner’s original property rights.13 • Advantages and use. The patent filing should provide a list of advantages versus existing alternatives as well as examples of how the invention would be used in practice. The patent should clearly specify the “best mode,” that is, the nominal use case, that an adopter of the technology would implement to realize the claimed benefits. This is often done in the summary section of the patent. Some of the claimed benefits may be surprising such as in the case of the “beerbrella” shown in Fig. 5.2 (left): “However, the apparatus of the present invention may also be used to prevent rain or other precipitation from contaminating a beverage” (US Patent US 6,637,447 B2). • Claims. The claims are the most important part of the patent. The claims constitute a succinct set of statements and are written as a list of numbered clauses.14 Each claim should contain the smallest possible list of the “integers” or elements of the invention. The claims are structured in a numbered tree-like hierarchy with the lowest numbered claims known as the “base” claims. The base claims  In the vernacular of patent law, the individually listed and numbered claims in a patent are referred to as the “integers” of the patent. This is because the first level of indentation of the patent claims, that is, 1., 2., 3. (as opposed to say 1.2.3) refer to the primary or base claims. Some patents contain over 100 claims, even though the average is lower. The European Patent Office (EPO) reported in 2019 that the average number of claims per patent was 14.7. 14

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describe the most elemental form of the invention. Dependent claims are drafted to depend on or be based on earlier claims and recite particular embodiments or variants of the invention. The claims legally define the invention and are the point of reference during any infringement proceedings in court. Broadly speaking, the claims define the invention with the rest of the patent document being used to interpret the claims in terms of technical meaning and scope of the invention. One of the most important decisions when preparing and filing a patent is how broad or narrow to make the claims. Broad claims are potentially more valuable, but also more likely to be challenged with the patent office or in court. Narrow claims may be easier to defend but may have less economic value and may make it easier for competitors to “design around” the patent in question. There are different types of patents, depending on the country and specific type of invention being claimed. The following types of patents are recognized in the United States of America: • Provisional: This is a patent which is filed to establish a priority date for the inventors. A provisional patent contains no claims, but must “fully” describe the invention. This is a quick and relatively easy way to establish a priority date under the “first-to-file” system. In the United States, a provisional patent has to be followed by a regular non-provisional patent within one year. Provisional patents can be extended for up to 18 months for a total of 30 months for countries participating in the PCT system (patent cooperation treaty of 1970). • Utility: A utility patent is used for a technical invention containing all of the elements of a technological patent specification, including the claims, and can cover the following elements: –– –– –– ––

Machine Process Article of manufacture Composition of matter

The notion of “utility” is specific to the U.S. patent system and is based on the need to demonstrate usefulness, one of the three patentability criteria mentioned earlier. The European patent system does not apply this test but uses industrial applicability instead. • Design: This is a patent covering the purely aesthetic elements of a new design (shape, form, visual appearance). A design patent is designated by the leading letter “D” and does not protect functional or technical elements as is the case for a utility patent. An example of a famous design patent is D48,160, which patents the shape of the original Coca-Cola bottle and was issued to Alexander Samuelson in 1915. Design patents also have to satisfy the novelty and nonobviousness criteria, in order to be awarded and have to be linked or associated with an item

15

 This relates to the topic of Chap. 3, where we discussed “nature as technology.”

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Fig. 5.4  Animal trap (“mousetrap”) by W.C. Hooker of Abingdon, Illinois, patented on November 6, 1894. U.S. Patent No. 528,671

associated with utility. Figure 5.3 shows the difference between a utility patent and a design patent. • Plant Variety: This type of patent for a plant variety application protects a specific genotype or combination of genotypes of plants.15 Let us dig into an example of a patent to better understand how the description of the invention and the claims can be analyzed and why we should think of them as “technology” as we defined it in Chap. 1. We will consider the mousetrap (U.S. Patent 528,671) as an example of technology, see Fig. 5.4. This patent was filed in the United States in the late nineteenth century by William C. Hooker of Illinois (1894). It describes the classic “animal trap” used to trap undesired rodents such as mice or rats in indoor spaces. The process of “trapping,” that is, catching animals in artificial traps, was an important activity in the eighteenth and nineteenth centuries in North America and other parts of the world.

*Quote (U.S. Patent No. 528,67116), Page 217 “Figure 1 is a perspective view of a trap constructed in accordance with this invention and shown set. Fig. 2 is a longitudinal sectional view of the same. [...] Like numerals of reference indicate corresponding parts in all the figures of the drawings.

 Patent numbers are issued sequentially and it took about 100 years from 1790 to 1894 to arrive at half a million U.S. patents. This is roughly the number of patents issued today in a single year. 17  Note that important objects are highlighted in bold, while key processes and attributes or states are underlined. 16

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1 designates a base, upon which is mounted a spring-actuated jaw 2, formed integral with a spiral spring and adapted to be forced downward by the same against the front portion of the base and in contact with an animal for catching the same. The resilient wire, of which the spiral spring and the jaw are constructed, is bent to form an arm 3. It is then coiled into the transverse spring 4: and is extended from one end thereof to form the loop or jaw 2, which terminates at the other end of the transverse coil at 4. It is then passed through the longitudinal opening of the coil and terminates in an inward extension 5, which is arranged below the jaw, and which serves to support the same. The jaw and the spring are secured hingedly to the base by perforated ears 6, which are provided with shanks 7, passed through the base and bent upward against the lower face of the same, the perforations of the ears receiving the extension 5, and the rear terminal of one side of the jaw. The outer end of the arm 3 is bent downward, and inserted in the base, which is preferably constructed of wood. The spring is of sufficient strength to force the jaw violently against an animal, and the front end 8 of the jaw which is approximately V-shaped is bent downward at an angle to the body of the jaw beyond the base, to form a grip to prevent the animal caught from being forced outward, and to hold the same securely. The jaw is held backward, when the trap is set, against the action of the transverse spring by a locking-bar 9, which passes over the jaw and which has its rear end loosely connected to the base. The front end of the locking-bar is adapted to engage a catch 10, of a hinged trigger 11, which is centrally arranged at the front of the base. The catch consists of a piece of sheet metal, which is doubled above the trigger and which is bent rearward to form a shoulder, and which extends below the trigger and is bent to form a pintle-eye 13, to receive a pintle 14; and the ends of the sheet metal are extended forward forming securing-plates, which are fastened to the rear end of the trigger. The pintle may consist of a staple, or may have its ends bent to form shanks which are passed through the base. The rear end of the locking-bar is bent to form an eye which is linked into a staple or eye 14 at the rear end of the base. The particular construction of the catch forms a very sensitive trap, and the latter may be conveniently set by inverting it and arranging the front end of the locking-bar above the shoulder of the catch, which will automatically engage it. The slightest pressure on the trigger will cause the springing of the trap. The trigger is preferably constructed of wood, but may be made of any suitable material”.

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We chose this example because mousetraps based on this original design are still being made and sold today, over 100 years later. The device will be familiar to most readers from personal experience. An interesting exercise is to extract the main description of the apparatus and claims from the patent and to model it conceptually, for example, using OPM (see Chap. 1). This may seem trivial at first; however, after further inspection of Fig. 5.4 and the description of the patent it is both quite challenging and insightful. The description is quoted from the original patent and provides a textual description of the “technology” in the patent. We quote an excerpt of the patent, to subsequently model the technology in OPM as a demonstration of how a textual description can be translated to a formal conceptual model. Who may have thought that so much thought and subtlety went into designing, constructing, and using a relatively simple device such as an animal trap? It usually takes several readings of a patent to digest both the high-level purpose and operating principles of an invention, as well as its details. Given the textual and graphical information provided in a patent it is then possible to effect a detailed system architectural analysis of the technology described in the patent using a formal systems modeling language. Below we analyze U.S. patent 528,671 using Object Process Methodology (OPM).18 This analysis has to be done manually and is not automated, and it provides both Object Process Diagrams (OPDs) and Object Process Language (OPL) sentences describing the technology as shown in Figs. 5.5, 5.6 and 5.7. Comparing different technologies or patents using OPM (or another systems modeling language such as SysML) allows for a formal investigation of the similarities and differences between different technologies. This can support detailed patent analysis for various purposes such as technology roadmapping (Chap. 8), research and development (R&D) planning (Chap. 16), IP intelligence (Chap. 14), and discovery during patent infringement lawsuits (Chap. 5). Reading the animal trap patent carefully, we recognize that its description combines two important processes: (1) its construction and (2) its end use. Consequently, we first model the technology at what we will come to refer to as “level 0” (system diagram SD in OPM), that is, at a high level of abstraction where the details of the apparatus are hidden, followed by two lower level diagrams, SD1.1 for constructing the animal trap and SD1.2 for using it, respectively. The diagram shows that the animal trap is the result of constructing it by a human agent. This process consumes materials such as wood, wire, and sheet metal. The process of catching an animal, which changes its state from being “free” to “caught,” also changes the state of the trap from “set” to “sprung.” The catching process also

 We already introduced OPM in Chap. 1, and it can also be found as ISO Standard 19,450 (2015). There is currently no formal requirement for systems modeling of patents. 19  There is an ongoing debate about which type of animal traps are “humane” (an ironic term) to use and whether it is better to use traps that only catch animals while leaving them alive (technology type L5) versus technologies that kill the animal instantly (technology type L1). This patent does not address this particular question, even though in practice most of the time smaller rodents such as mice are killed by such traps. 18

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DIAGRAMS & OPL SD

Animal is a physical and environmental object. Animal can be caught or free. Mouse is a physical and environmental object. Rat is a physical and environmental object. Animal Trap is a physical and systemic object. Animal Trap can be set, sprung or unused. Materials is a physical and systemic object. Wood is a physical and systemic object. Wire is a physical and systemic object. Sheet Metal is a physical and systemic object. Bait is a physical and systemic object. Human is a physical and systemic object. Place is a physical and environmental object. Rat Hole is a physical and environmental object. Furniture is a physical and environmental object. Mouse and Rat are Animals. Sheet Metal, Wire and Wood are instances of Materials. Furniture and Rat Hole are Places. Catching is a physical and systemic process. Catching changes Animal from free to caught. Catching changes Animal Trap from set to sprung. Human handles Catching. Catching requires Place. Catching consumes Bait. Constructing is a physical and systemic process. Human handles Constructing. Constructing consumes Materials. Constructing yields Animal Trap.

Fig. 5.5  System level diagram (SD) for animal trap in OPM

requires a human operator, a place to put the trap and it consumes bait. It is the state change from “free” to “caught” that creates utility (or “usefulness”) for the animal trap owner and user. In terms of our 5 x 5 technology grid (Table 1.3), we would probably classify this technology as L5 (regulating organisms19).

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SD1: Constructing in-zoomed

Constructing from SD zooms in SD1 into parallel Mounting and Connecting, Cutting, Bending, Arranging, Coiling, Making, Perforating, Cutting, Passing Through, and Forming, as well as Sheet Metal, Wire and Wood. Human is a physical and systemic object. 1 Base is a physical and systemic object. 2 Jaw is a physical and systemic object. 2 Jaw is stateful. 3 Arm is a physical and systemic object. 4 Spring is a physical and systemic object. 4 Spring is stateful. 5 Extension is a physical and systemic object. 6 Ears is a physical and systemic object. 7 Shanks is a physical and systemic object. 8 Front End of 2 Jaw is an informatical and systemic object. 9 Locking-bar is a physical and systemic object. 9 Locking-bar is stateful. 10 Catch is a physical and systemic object. 10 Catch is stateful. 11 Trigger is a physical and systemic object. 13 Pintle-eye is a physical and systemic object. 14 Pintle is a physical and systemic object. 15 Bait Opening is a physical and systemic object. 15 Bait Opening is stateful. 16 Plate is a physical and systemic object. Sheet Metal is a physical and systemic object. Wood is a physical and systemic object.

Fig. 5.6  Subsystem level diagram (SD1.1) for animal trap constructing

We can then zoom into the first process labeled as “Constructing,” and this is shown in Fig. 5.6. Here, we find the ingredients for constructing the animal trap at the center (wood, wire, and sheet metal) and the detailed fabrication processes such as mounting, bending, coiling, perforating, etc. inside constructing. The resulting

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components 1-base, 2-jaw, 3-arm, etc. are depicted on the periphery of the main process, and they are linked to the animal trap (the main apparatus) through participation-­aggregation links. Each of the steps depicted in Fig. 5.6 can be found in the original patent’s textual description, and each labeled part is shown in the figures of the original patent. It is interesting to note that an object labeled as “12” seems to be missing from the patent’s text or any of its figures. This is probably either an oversight or deliberate omission in the final approved patent. The key to understanding the patent and how the animal trap technology actually works is shown in Fig. 5.7, which zooms into the “Catching” process of Fig. 5.5. Here we see that the process of catching an animal using the trap is initiated by the human, using the animal trap by setting the trap, which in turn invokes a number of other subprocesses in sequence such as adding bait, bending the jaw, and spring from an unloaded or backward position to a loaded or forward position. This process requires work and stores elastic energy in the coiled spring until the trigger is activated by the animal. To finish setting the trap, the human (agent) has to secure the locking bar and catch.20 Once the trap is set, it sits idle and waits for an animal to trigger the catch, which springs the trap. Thus, while the human is the agent of the setting process, the animal is the agent of the triggering process which in turn invokes the springing process. Springing releases the stored potential energy in the spring and rapidly moves the jaw from the backward to the forward position, thus catching the animal. To the untrained eye, the OPD visualization of the animal trap technology may seem unfamiliar at first. However, with some practice it becomes a powerful way of studying and more deeply understanding how patents are written and how technology works, in terms of the set of objects (parts, attributes) and processes (functions, actions, sequence of events) that constitute the technology described in a patent. In this way, we may extract from an existing patent (or set of patents) the essential objects, attributes, processes, and the detailed sequence of operations which constitute the technology. This point will be reiterated in Chap. 15 on knowledge management. The first claim for our animal trap example is a much shorter and succinct summary of the invention21: 1. A trap, comprising a base, a spring-actuated jaw constructed of a single piece of wire coiled to form a transverse spring and extended from one end of the latter and shaped into a loop terminating at the opposite side of the coil and continued to form a transverse portion arranged within the coil, bearings receiving the ends of the transverse portion, a locking-bar, and a trigger for setting the jaw, substantially as described As can be seen, the level of detail and care taken in describing an invention in a well-written patent is usually exquisite.

 This is a tricky operation as all those can attest to who have accidentally had their fingers pinched by an accidental release of a mousetrap (author included). 21  Claims 2 and 3 of U.S. patent 528,671 are for slightly different variants of the animal trap. 20

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SD2: Catching in-zoomed

Catching from SD zooms in SD2 into Setting, Securing, Triggering, Bending, Adding, and Springing, as well as 10 Catch, 15 Bait Opening, 2 Jaw and 9 Locking-bar. Animal is a physical and environmental object. Animal can be caught or free. Animal Trap is a physical and systemic object. Animal Trap can be set, sprung or unused. Bait is a physical and systemic object. Human is a physical and systemic object. Place is a physical and environmental object. 4 Spring is a physical and systemic object. 4 Spring can be loaded or unloaded. 2 Jaw is a physical and systemic object. 2 Jaw can be backward or forward. 9 Locking-bar is a physical and systemic object. 9 Locking-bar can be backward or forward. 15 Bait Opening is a physical and systemic object. 15 Bait Opening can be empty or full. 10 Catch is a physical and systemic object. 10 Catch can be engaged or sprung. Catching is a physical and systemic process. Catching requires Place. Catching affects Animal Trap. Setting is a physical and systemic process. Human handles Setting.

Fig. 5.7  Subsystem level diagram (SD1.2) for animal catching

The claims of a patent are intricately linked to these objects, processes, and attributes. Patents are particularly suitable for this type of detailed analysis because the same requirements that mandate compliance with patent jurisprudence inevitably

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also lead to patent claim language which is highly structured and (ideally) internally consistent. Understanding how patents are written and analyzing them in some detail is an important skill for any scientist, engineer, patent lawyer, and technologist. ⇨ Exercise 4.1 Select a patent of your choice and describe it in a 2–3 page summary. Make a conceptual model of the patent in OPM (Object Process Methodology). It does not matter if the patent is historical (= expired) or currently active.

Some patents become highly cited and lead to thousands or millions of products that are beneficially used by humans. Many patents are not very successful in the sense that they are not, or only rarely, cited, and they expire before they have a chance to generate any revenues for their owners. Some patents, on the other hand, have inspired scientists and engineers to make new discoveries. One of the most famous sets of patents examined by perhaps the most famous patent clerk of all time, Albert Einstein, are the patents on clock synchronization (Isaacson 2008). In Switzerland, being on time is highly valued in society today as it was in the past. Figure 5.8 shows a clock synchronization patent from the year 1906, the year after Einstein published his famous paper on special relativity. This is the kind of patent that Einstein examined during his tenure at the Swiss patent office in Bern between 1902 and 1909, before becoming a professor of physics at ETH in Zurich. These electromechanical mechanisms, many of which were patented between 1903 and 1906, generally established a master clock as representing “true time” and

Fig. 5.8  Swiss Patent Nr. 37,912 awarded to clockmaker and inventor Franz Morawetz of Vienna, Austria (1872–1924) in 1906 together with Max Reithoffer for wireless transmission of clock signals from a master clock to a set of dependent clocks

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transmitted a clock synchronization signal from this master clock to geographically distributed clocks using a set of wires and electrical signals traveling to them. Swiss Patent 37,912 (1906), depicted in Fig. 5.8, is one of the earliest approved applications devoted entirely to the radio transmission of time. Such schemes date almost to the first days of radio and were widely discussed in 1905. Examples of other Swiss patents on this topic are 33,700 (James Besançon and Jacob Steiger), 29,832 (Colonel David Perret), and 37,912 (Max Reithoffer and Franz Morawetz) discussed here (Galison 2004). One of the thoughts that occurred to Einstein while examining these patents is what it means to have simultaneous events at geographically distant places. Since the signal takes a finite amount of time to travel wirelessly (at the speed of light) from the master clock to a dependent clock, the received signal would indicate not the current time at the master clock, but some time in the past. Therefore, true time synchronization would require to move the dependent clock forward by a small amount of time to compensate for the travel time of the signal from the master clock to the dependent clock.22 This would work if the relative position of the two clocks was fixed, but what if the dependent clock, or the master clock for that matter, was located on a moving train?

5.3  U.S. Patent Office and WIPO The United States Patent Office was founded in 1790 when George Washington was president.23 It is thus one of the oldest offices of the U.S. Federal Government dating back to the beginning of the nation. The World Intellectual Property Office (WIPO) was created in 1967 by the World Trade Organization (WTO), headquartered in Geneva, Switzerland, as a way to harmonize not just the trade of physical goods, but also the intellectual property associated with them.24 Currently, there are 192 countries who belong to the WIPO. More recently, the five largest patent offices in the world have formed a group known as the “IP5”: they are the US Patent and Trademark Office (USPTO), the European Patent Office (EPO), the Japan Patent Office (JPO), the Korean Intellectual Property Office (KIPO), and the National Intellectual Property Administration (CNIPA formerly SIPO) in China. Together these five agencies grant more than one million patents per year. The first major international agreement relating to patents, and that which is most fundamental to international patent law, was the Paris Convention for the Protection of Industrial Property (1883). This agreement provided that all signatory  This was probably of little concern to the inventors since the difference would be a very small fraction of a second, since light travels in vacuum at about 300,000 [km/s]. 23  In 2000 the institution was renamed the United States Patent and Trademark Office (USPTO) with its headquarters in Alexandria, Virginia. 24  It is important to note that WIPO does not award patents, since these are only issued by national (territorial) patent offices. WIPO plays an international coordination role. 25  Provisional patent applications are recognized by the Paris Convention and are sufficient to establish a priority date with the WIPO. 22

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countries mutually recognize the priority (date) of inventors filing their patent applications. Under this agreement, a US inventor seeking patents in other countries can delay filing patents in those countries by up to a year. If this criterion is satisfied, all of the subsequently filed patents will be back-dated to the inventor’s original date of filing in the United States.25 The United States then grants reciprocal treatment to foreign inventors who file in the United States. A further major advance in international patent law was the Agreement on Trade-­ Related Aspects of Intellectual Property Rights (TRIPS) which came into effect in 1995. It further harmonized patent law around the world, and adherence to the stipulations of the TRIPS agreement is generally considered a prerequisite for full membership in the WTO. Important sources of patent information are online databases which are now generally freely available. Some of the most prominent of these are the USPTO database, the European Patent Office database as well as the WIPO database.26 Conducting a proper patent search is not trivial and often requires the assistance of trained librarians or specialized IP professionals.27 A search for prior art includes not only patent databases but also scientific and trade publications on sites such as Google Scholar, for example. Figure 5.9 shows recent trends in the number of patent applications filed per year by the IP5.

Fig. 5.9  Number of patents filed by country per year. (Source: WIPO (The WIPO maintains a useful global set of statistics: https://www3.wipo.int/ipstats))

26  USPTO: https://www.uspto.gov/patents-application-process/search-patents, EPO: https://www. epo.org/searching-for-patents/technical/espacenet.html, and WIPO: https://patentscope.wipo.int/ search/en/search.jsf 27  Anyone can search for keywords or patents over the Internet today and discover patents related to an invention. However, the specific use of keyword combinations, date ranges, and countryspecific databases requires both training and experience. It is important to note that if a patent has been granted in country A, but the inventors did not file in country B, then the novelty test would be failed in country B by another applicant, since the patent in country A would make the invention not “new,” therefore failing the novelty test anywhere in the world.

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Figure 5.9 shows the number of patents filed by office per year between 1980 and 2018. These data are from the World Intellectual Property Organization (WIPO) that collects data from patent offices worldwide. From 1980 to 2006, Japan was the world leader in patent applications, largely driven by its strong export industries such as consumer electronics and automobiles.28 From 2006 to 2012 the United States briefly regained the top spot, thanks mainly to its computer and information technology companies such as Microsoft, Apple, and IBM, among others. However, what is most noticeable is the sharp rise in Chinese patents since about the year 2010. China now receives between one and two million patent applications per year and it took over the top spot in 2012, as part of its national innovation policy.29 The patent applications shown in Fig. 5.9 include both domestic and foreign applications. While these data show global aggregate trends, it is also useful to show the number of patents normalized by GDP or population which shows a somewhat different picture. Figure 5.10 depicts the number of patents filed in 2018 per 1000 residents.

Fig. 5.10  Patent filings in 2018 per 1000 residents

➽ Discussion What is your personal experience with patents? Have you filed one or more patents as an inventor? Does your company license patents from someone else? Have you read or studied patents? Have you been involved in patent-related litigation?

 The number of patents by itself may not be a reliable indication of innovation as the number of unitary claims included in a patent may differ radically in countries like Japan, China, the United States, and Europe. For example, a US patent based on Japanese patents may combine five or more claims that are filed as separate patents in Japan. 29  The handling of IP in China has become significantly more professional and internationally aligned in the last two decades. However, there are also signs that companies are becoming more careful in filing patents, due to potential expropriation and infringement issues and the number of 28

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These data show that – normalized by their population size – Japan and especially Korea are extraordinarily productive on a per capita basis, and that the United States still holds the edge over China when considering the relative population size. Europe, on the other hand, appears to be less dynamic in terms of technological innovation, which has given rise to a number of initiatives by the European Union (EU) such as the Europe 2020 Flagship Innovation Initiative.30

5.4  Patent Litigation As stated earlier, an active patent gives the owner the right (but not the obligation) to prevent others from using an invention. A patent owner can give permission for others to use an invention by either granting them a license or promising not to sue for infringement.31 Enforcing this right does not happen automatically but requires the filing of a patent infringement lawsuit. For example, if someone were to copy and sell products based on the designs shown in Figs. 5.1, 5.2, 5.3 and 5.4 during the active period of these patents, without knowledge or permission of the patent owner, said patent owners may choose to file an infringement lawsuit to recover financial damages incurred due to the infringement. Most infringement lawsuits seek two kinds of remedies: first to stop the infringer from further selling the products containing the infringed-upon technology, and second to receive financial compensation from past sales. In some cases, infringement lawsuits are filed to send a signal to competitors or suppliers that a firm is prepared to vigorously defend its own IP. In practice, most infringement lawsuits in the United States are settled out of court (about 95% of them), since IP-related lawsuits tend to last for years and cost millions of dollars to prosecute, with uncertain outcomes. Some examples of famous patent lawsuits, both recent and past, are as follows: • James Watt v. Edward Bull (1793) for the use of a separate condenser in steam engines (see Fig. 2.6). Watt sued Bull because he had built Watt’s engines starting in 1781, but in 1792 started designing and making his own steam engines, with a separate condenser. And so the claim was that he infringed Watt’s patents. The lawsuit was won by Watt and the court issued an injunction against Bull, allowing Watt to recover payments. • Orville and Wilbur Wright filed and received a patent for the technology underlying the Wright Flyer, particularly with respect to flight controls (U.S. patent No. 821,393 – A Flying Machine, O & W Wright). This patent was awarded in 1906 after their first successful flight in 1903, see Fig.  5.11. They spent many years, particularly in the 1906–1916 timeframe, vigorously defending their patent by suing both domestic and foreign aircraft designers such as Glenn Curtiss with the

filed patents should not, by itself, be used as a measure of national innovativeness. There are indications that US companies are increasingly opting to protect their technologies through trade secrets, instead of patents which require full disclosure of technical details. 30  Various empirical studies have shown a positive correlation between innovation as measured by patenting activity and GDP growth (Ulku 2004).

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Fig. 5.11  Wright Flying Machine, U.S. patent 821,393, awarded in 1906

goal of collecting licensing fees.32 While the Wright brothers prevailed in their initial lawsuits against Curtiss – in part because of the broad claims allowed in their patent – some have argued that the Wright brothers were so busy with patent litigation that they neglected to spend enough time on further improving their flying machine, eventually allowing others in the United States and especially in Europe to overtake them. See further discussion in Whitehouse, Scott, and Scarrott (Royal Aeronautical Society, 2016). We further discuss the history of airplanes in Chap. 9. • Apple Inc. v. Samsung Electronics Co., Ltd. (starting in 2011). This is an ongoing set of international lawsuits between these two electronics companies which was initiated by Apple in 2011 for the alleged copying of the design of the iPhone (see Fig. 5.3 right) and iPad. At the core of the allegations are design patents, such as D504,889, showing handheld devices with rounded corners and flat touchscreens. Lawsuits are ongoing in several countries such as the United States, South Korea, Japan, Germany, France, United Kingdom, and Italy, among others. Samsung countersued Apple and attempted to block sales of iPhones in certain markets. In August 2012, a US jury awarded $1.049 billion in damages to Apple to be paid by Samsung. An injunction to block the sale of Samsung devices in the United States was less successful. This high-stakes patent litigation is ongoing. • Airbus v. Aviation Partners Inc. (2011–2018): This case illustrates the function of lower tribunals and the U.S. Patent Office in determining the validity of disputed patents. As part of a larger dispute between the parties, in 2011 Airbus filed what is known as an invalidity or re-examination action against Aviation Partners Inc. in relation to its design of a blended winglet. These winglets are used on many Boeing commercial aircraft to reduce drag and save on fuel burn. The allegedly proprietary IP had for decades formed the basis for the business of Aviation Partners Inc. Airbus asked the Patent Office to rule that the Aviation Partners blended winglet patent was invalid, and thus not enforceable against Airbus. The U.S.  Patent Office subsequently invalidated the main claim of the Aviation Partners patent confirming that

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the claimed winglet design was neither new nor inventive. In May 2018, this lawsuit was settled between Airbus and Aviation Partners Inc. under undisclosed terms. The above examples of intellectual property (IP)-related litigation demonstrate why IP protection is important. In summary, protection of intellectual property is important because it can: • Add market value, particularly for startups and small companies, sometimes at ratios greater than 50% of the value of the company. • Be a source of income through licensing. IBM is a good example of a company that owns many patents that collectively generate about 10% of the company’s revenues through licensing fees. • Block competitors from practicing a proprietary technology or design. • Attract funders, strategic partners, customers, and employees. • Allow a firm to maintain legal exclusivity to certain of its products for a limited period of time, thus increasing revenues and profits. • Reduce the risk of innovating, because successful outcomes from R&D projects that are filed as patents have clear outcomes that are well documented and that can then be infused into new products (see Chap. 12). • Enhance a firm’s branding and market effectiveness. However, history also shows that inventors and firms who overemphasize IP protection and litigation over continued innovation will eventually fall behind and be overtaken by their competition, even if they initially prevail in court. While the patent system has been criticized, it continues to be used and be an important consideration, both for technology roadmapping and technology development. Before launching a major technology development effort, a thorough search of prior art should be conducted to avoid unpleasant surprises, such as “reinventing the wheel” or infringing on someone else’s technology, down the road. ⇨ Exercise 4.2 Select a patent dispute of interest, describe it on one page, and include the resolution of the case (still ongoing, settlement, or court judgment). What is your personal opinion of this case?

5.5  Trade Secrets and Other Forms of Intellectual Property The rationale for patenting is strongly related to the need to establish a legally enforceable competitive edge in terms of new technologies. This is as true today as it was 200 years ago. A for-profit firm, operating in a competitive market, will be faced with pressures from its competitors who seek to increase their value

 Tesla recently “open sourced” all its patents in electric vehicle design with the hopes that it may stimulate the emergence of an innovative electric car ecosystem. 32  Source: https://en.wikipedia.org/wiki/Wright_brothers_patent_war 31

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proposition to customers by offering new functions (or so-called features), higher levels of performance, lower prices, or a combination of all these.33 In most industrial sectors, firms set aside some percentage of their annual revenues and reinvest the money in the business in the form of Research and Development (R&D) projects. The percentage of R&D expenditures generally varies greatly by firm and industry sector. Some typical R&D expenditures as a function of revenues are as follows: • Aerospace 5–8% • Biopharma 10–15% • Automotive 4–6%34 The aim of R&D is to establish a so-called virtuous cycle, whereby investments in R&D will yield new or improved technologies and operations, which subsequently enable more competitive products and services. Some of these R&D investments may yield new intellectual property. As will be discussed in greater detail in Chap. 17, R&D efforts generally show up in the Profit and Loss statement of the company (P/L) as an expenditure. These expenditures need to be fed from the revenue side of the P/L such as from the sales of goods and services, royalties, etc. In some jurisdictions, it is possible to claim a tax credit for R&D expenditures that meet certain criteria. For some R&D categories (e.g., maturation of immature technologies or concepts), the firm may be able to compete for and acquire financial government support for R&D, such as funding from DARPA, DOD, etc. in the United States or programs like Horizon 2020 in Europe. In that case some R&D may also appear on the revenue side of the P/L. Assuming an effective and successful outcome of R&D efforts, which are generally carried out through a portfolio of projects that each consume budget for labor, materials, and other expenditures (see Chap. 16), the firm will produce new or improved products and services, and potentially new IP in the form of patents. These in turn have the potential to fuel the revenue side of the P/L.  This in turn allows the firm to maintain or increase its expenditures for R&D and to become more competitive, thus creating a virtuous cycle. Under some circumstances, it may also be possible to capitalize IP as an asset on the balance sheet.35 The set of patents that are owned by a particular firm are part of the so-called intellectual property (IP) portfolio. There may, however, also be other forms of IP such as trade secrets, trademarks, designs, and other intangible assets as described below. The total IP portfolio can be one of the most important assets of a company because it allows: • Making, selling, and using exclusively a set of technologies, processes, or designs that give it a competitive advantage in the market. This advantage should ultimately be reflected in higher levels of revenues (sales) and profitability. This in turn allows maintaining or even increasing the percentage of revenues going to R&D.

 The role of competition in driving technological progress is discussed in Chap. 10.  Tesla is an exception in the automotive industry at 11.7% R&D intensity, while among the traditional automotive OEM’s, Mercedes Benz (see Chap. 6) is the leader at 8.5% R&D intensity. Source: Statista.com

33 34

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• Preventing competitors (or suppliers) from copying or infringing on the inventions contained in the IP portfolio. • Generating additional revenues through the sale of patent ownership rights or royalties from granting exclusive or nonexclusive usage rights to a set of licensees. The licensing income from these patents will appear on the income statement (P/L) and not on the balance sheet of the firm. ➽ Discussion What are examples of companies, organizations, or individual inventors – historical or current – you think have been particularly productive or influential in terms of generating intellectual property?

It is very difficult to objectively assess the financial value of patents or technologies in general (see Chap. 17), since they represent only potential future cash flows and not already realized ones. Unlike other assets on the balance sheet such as cash, securities, inventory, real estate, etc., there is no general and functioning market for the trading of patents. In the United States, for example, accounting rules preclude patents from being included and valued as an asset on the balance sheet of the company. The only exception to this rule is when the patent was explicitly purchased from another party or when it was acquired through a merger and acquisition (M&A) where the patent was explicitly valued during the company valuation and due diligence process. Many companies choose not to publish or patent their technologies, but to keep them hidden from the public (and their competitors) as trade secrets.36 Trade Secrets are a creature of legislation and may be different from so-called confidential information. Trade Secrets are defined by, and protected according to, national or state law. In 2016, the EU issued a “directive for the protection of trade secrets,” in an attempt to harmonize the definitions and practices across member states. Trade secrets generally meet three criteria: • They represent information about a company’s technologies, designs, or recipes that are not generally known to the public. • The owner of the trade secret derives economic benefit from it. • The holder of the trade secret makes reasonable efforts to maintain the secrecy of the trade secret and can also demonstrate that such efforts at maintaining secrecy are made.  In the United States, patents can only be listed as an asset on the balance sheet if they were acquired, as in purchased through a merger or acquisition. In that case a market price for the IP was established as part of the transaction. Firms are not allowed to estimate a capital value for the patents they self-generate, since this could be a potential way to artificially inflate the balance sheet. The accounting rules for valuation of IP differ by jurisdiction. 36  Companies should keep in mind that there is a cost to secrecy, including having all their employees sign NDAs, maintaining vaults and securing databases and networks, monitoring for IP leaks, and hiring lawyers to maintain legal pressure, as necessary. Technologies that are subject to classification due to defense or intelligence applications are the subject of Chap. 20. 35

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Confidential Information, on the other hand, and in contrast to trade secrets, is protected by way of an agreement between two or more parties to, put simply, keep each other’s secrets. Such agreements are generally known as nondisclosure agreements (NDAs). Violation of NDAs can also lead to lawsuits. One of the most famous trade secrets is the recipe for making “Coca-Cola.”37 Trade secrets are an ensemble of specialized knowledge about the design, manufacture, or use of a technology which are not shared with the outside world. Trade secrets are usually written down in a carefully safeguarded document, including their date of invention. This can be an effective strategy and has been practiced by companies for centuries, or even millennia. There is an ongoing debate among legal scholars whether or not trade secrets existed and were enforced in the Roman Empire. One of the downsides of trade secrets is that, since they are not published and not known to the public, a competitor may independently invent the same technology as that contained in a trade secret and choose to patent it. This assumes that the competitor obtained the information in a legal way such as through their own independent R&D efforts. The first firm, who owned the technology originally as a trade secret, may in this way potentially become an infringer, even though they may have known and used the particular technology for much longer than the second firm who discovered the invention later but chose to patent it. This is a difficult legal situation since first, the patent owner would have to be able to prove the infringement, which is not easy to do due to the secret nature of the information held by the first firm. Second, there is a so-called prior user defense (at least in the United States) which allows trade secret holders to claim that they knew about an invention and have been using it internally as a trade secret before the patent was filed and granted to another entity (Barney 2000). In such a situation, the trade secret owner may appeal to the patent office in order to have the patent of the second company invalidated on the grounds that it fails the novelty criterion. This, however, may require the trade secret holder to reveal part or all of the trade secret (at least to the patent office or the court). There is an interesting and complex interaction between patent law and trade secret law, a discussion of which goes beyond this text, but is summarized by McGurk and Lu (2015). Deciding which technologies or “recipes” to publish, to keep as trade secrets, or to patent is one of the most important functions of technology management in general, and intellectual property management in particular. Achieving a consistent approach to this decision-making in a large firm with many business units, product lines, and service offerings can be a major challenge. The first step is to maintain a clear inventory of intellectual property in the company. This goes beyond simply keeping a list of patents at various stages of their lifecycle. Next, for each of the IP assets identified, there should be a clear and deliberate decision made on how to best protect this asset and how to enforce such protection. As stated earlier, each means of IP protection has its own characteristics, advantages, disadvantages as well as costs and benefits. Table 5.3 shows a comparison of the advantages and disadvantages of open publication (including open sourcing of software), patents, and trade secrets. A point to remember is that patents are not the only form of IP. Figure 5.12 shows a summary of asset types (intellectual property) owned by a firm at the top and potential means to protect these intellectual property rights at the bottom.

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Table 5.3  Comparison of different instruments related to intellectual property Instrument Open publication

Patent

Advantage Establishes prior art and prevents others from patenting or claiming trade secrets (this is also known as a defensive publication) No patenting or secrecy costs are incurred Exclusivity for use and exploitation of invention for up to 20 years Demonstration of innovativeness to the market (reputation) Potential generation of royalties from issuance of licenses

Trade secret Not time limited (indefinite duration as long as secrecy can be maintained) No patent filing fees No need for public disclosure

Disadvantage Effort to undergo publication and peer review No differential advantage Vis-à-Vis competitors who also have access to the same information once published Patent filing and maintenance fees Competitors have access to the technical invention once the patent is granted and may choose to deliberately infringe Patent litigation fees Time limited (20 years) Danger of leakage of IP through unauthorized disclosure by employees or industrial espionage None or only limited reputational benefita Risk of having to license one’s own technology if others patent it first

There are some exceptions to this rule as the existence of some trade secrets (without revealing their detailed contents) have greatly enhanced the reputation of the respective trade secret holders. Examples include Coca-Cola in the United States, Chartreuse liqueur in France, or Meissen porcelain in Saxony, Germany

a

⇨ Exercise 4.3 Come up with an idea for an “unsolved” problem. Then do a patent search to see if any “prior art” exists. For example, a problem could be “I am frustrated with used pizza boxes. How do I dispose of them ?” Hint: Your search might bring up patents U.S. 5,305,949, and U.S. 5,110,038 (Brown 2002). How would you choose to protect your idea, using Table 5.3, and why? Many times patent data38 are (erroneously) used as the sole means of assessing a firm’s R&D productivity. A wider view is necessary to include all IP, much of which is not publicly visible. The distinction between intellectual property assets and intellectual property rights is shown in Fig. 5.12. A broad view of intellectual property management not only considers technical inventions which can be protected by utility patents. Other forms of IP include designs, trademarks, brands including so-called logos, and so forth. Matching the right form of intellectual property with the best mechanism for asserting these intellectual property rights is one of the major challenges of the IP function in the firm. Doing this well requires a constant and well-organized dialogue between the

37  Source: https://en.wikipedia.org/wiki/Coca-Cola To this day and for over 100 years the CocaCola company has been able to maintain the trade secret for the original recipe for Coca-Cola, and

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Fig. 5.12  Mapping from intellectual property assets to intellectual property rights via the appropriate legal, regulatory, or contractual framework (Patents, trade secrets, and NDAs) have already been discussed in this chapter. Trademarks are recognizable designs, signs, or expressions associated with a particular logo or brand. Trademarks are considered intellectual property, and they can be financially valued. Authored works (including books and software) can be protected by copyright. “Passing off” is a particular intellectual property recognized in common law (e.g., United Kingdom, Australia, New Zealand) which prevents others from pretending that a certain good or product is from a source which it is not. This is intended to prevent imitation or “look-alike” products from harming the original source or owner. The difference between a secret and confidential information is that a trade secret can be designated by a company unilaterally, whereas confidential information is exchanged as part of a bilateral or multilateral NDA.) (Source: Scott A. 2017)

intellectual property function – which is usually part of either the general counsel’s office or the chief technology office – as well as strategy, engineering, marketing, finance, and the senior leadership team.

5.6  Trends in Intellectual Property Management Given the importance of patents and their legal and financial implications for individual firms and entire industry sectors, a set of recent trends has emerged which generally makes the management of portfolios of patents more complex and challenging. Several trends that have recently been observed are as follows: • Patent Volume: The global number of patents filed per year has risen steadily, as shown in Fig.  5.9, and recently exceeded the number of three million patents worldwide per year. Nearly half of these are coming from China. While many of

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these patents are in newer areas such as artificial intelligence (AI) and the life sciences, this increase leads to a “densification” of the patent space with many patents filing similar claims, therefore resulting in the potential for more overlaps and claims of infringement. • Patent Trolling: Patent “trolls” are individuals or more likely legal entities who secure ownership rights to patents for the main purpose of filing infringement lawsuits against others. The purpose of this is to generate cash flows from infringement compensation awarded by courts or settlements agreed under the threat of lawsuits. A synonymous term to “patent trolling” is “patent hoarding.” Specific entities, such as Patent Holding Companies (PHCs), have been created since the mid-1990s for this purpose. Most of these entities do not design or manufacture any of the products linked to the infringement lawsuits they file. The outcomes of patent trolling are often counter to the original intent of the patent system, which is to stimulate innovation. In 2012 in the United States, over 2900 infringement lawsuits were filed by patent trolls, going up to 3600 by 2015. Legislation to counter the abusive aspects of patent trolls has been introduced in several countries and states starting in about 2012. It is not clear yet whether this has had the desired effect of reducing frivolous infringement lawsuits by nonpracticing entities (NPEs). For example, in the United States in 2015 about two-­thirds of all infringement lawsuits were filed by NPEs.39 • Patent Thickets: These are partially overlapping sets of patent claims in a particular area which make it difficult to “design around” a single patent to avoid a future patent infringement lawsuit. Patent thickets (Von Graevenitz et al. 2013) may be created deliberately as a defensive measure by a firm to minimize the risk of technology copying or they may emerge naturally over time based on approved patents with (partially) overlapping claims, filed by different inventors and ­entities. A famous lawsuit in the United States in the 1970s was SCM Corp. v. Xerox Corporation, whereby SCM claimed that Xerox had established a patent thicket to prevent competition, while Xerox refused to grant SCM licenses for its technologies on competitive grounds. When a patent thicket is owned by a single entity, it is possible that concerns about antitrust behavior, such as the Sherman Antitrust Act (15 U.S.C. §§ 1 and 2), may be raised. The complexity of interrelationships between patents can now be analyzed using network science as well as machine learning, see Fig. 5.13. There is a rapidly growing literature on patent analytics, also looking at the evolution of patent classification and patenting trends over time (see also Chap. 14). Firms operating in a technology-intensive industry should have a clearly articulated IP strategy. What exactly constitutes a coherent IP strategy is often less clear in practice. We begin by discussing how technology disclosures and patents are handled at universities. Over the last 50 years, a number of leading research universities worldwide have started and maintained so-called Technology Licensing Offices (TLOs). The functions of these offices are to:

this despite having twice been ordered by a court to reveal it. This trade secret is a major asset and also a source of reputation for the company, see Allen (2015).

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Fig. 5.13  Patent network graph for the drug Ritonavir. Nodes in patent citation graphs can include inventors, owners, patent categories, or patents, while links can refer to citations, co-­occurrence of names, or patent ownership relationships. (Source: Mailänder L., World Intellectual Property Office, 2013)

• Encourage and assist faculty and researchers (including students) to file technology disclosures and patents coming from original research. • File patent applications as appropriate. • Maintain an active IP portfolio, including filing patents in home countries and worldwide, and maintaining patents active through payment of renewal fees (some of these fees can be generated from royalties). • Generate royalties and other revenues for the university. In the United States, the most active university-based TLOs are the University of California system, MIT, and Stanford University. In the case of MIT, the TLO40 now receives about 800 technology disclosures per year, and about 300 U.S. patents are issued per year. This results in about 120 licenses issued per year, many to startup companies that are coming from within the university itself. In 2018, the MIT TLO generated $45.9 million in royalties for the university. The subset of inventions which generate the largest amount of royalties is often quite small. This generally

38

 Typical sources of patent data include USPTO, WIPO, The Lens, Google Patents, etc.

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follows the 20–80 or even the 10–90 rule (10% of filed patents generate 90% of the revenues). An interesting question that has arisen recently at universities is how to deal with inventions made by the students themselves, without direct involvement of faculty or principal investigators (PIs). Policies in this area are still evolving. In general, the elements of an IP strategy including in for-profit firms are as follows: • Situational Awareness: Establishing a database and good understanding of what intellectual property assets (technology patents, trade secrets, trademarks, designs, brands, etc.) a firm owns and how this ownership is spread across the different product lines and operating units.41 • Strategic Vision: Establishing a clear strategic vision about how the firm wants to position itself with respect to technological innovation and IP.42 Does the firm seek to be a first mover and preferentially establish first-of-a-kind patents in new areas? Does it seek to be a fast follower and patent “around” existing patents, or seek to license technologies from others and focus more on effective production and sales? Does it see itself primarily as an Original Equipment Manufacturer (OEM) and rely on its supplier base to establish technological IP and drive innovation? Without a clear vision and strategy in terms of IP, it is difficult to make consistent operational and tactical decisions, for example, see Fig. 5.12. • Staffing: A firm needs competent staff for patent filing, renewal, and offensive (filing infringement lawsuits) as well as defensive (defending against infringement lawsuits brought by others) actions. In most firms specialized outside counsel (law firms specialized in IP) is employed in addition to dedicated internal employees. An important decision is to find the right balance between internal staff and external counsel. • Risk and Opportunity analysis of the evolving IP portfolio. This activity is of a more strategic nature and includes IP intelligence (systematically studying patenting trends by others such as competitors and suppliers), identification of ­patenting thickets, new patent filings and patent grants that may infringe on a firm’s IP position, etc. This should ideally not be a one-time activity but a recurring effort. Small- to midsize firms may be advised to hire specialized IP monitoring services to scan for potential infringement by others. • Negotiations: In certain industries that are dominated by a duopoly or oligopoly (two or only few main competitors), there may be negotiations of an explicit or implicit nature to minimize the filing of lawsuits and counter-suits, to allow for cross-licensing, and to ensure smooth business operations and minimize unnecessary turbulence in the market. Such negotiations and agreements must comply with antitrust laws.

 Source: https://en.wikipedia.org/wiki/Patent_troll  Source: http://tlo.mit.edu/, URL accessed July 27, 2020 41  As discussed in Chap. 8, technology roadmaps should contain a summary of the IP landscape 42  Intel is a good example of an international firm with a clearly established IP position 39 40

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Fundamentally, the intellectual property strategy should be driven by the overall strategy of the company. The legal doctrines of intellectual property provide a toolset which can be used to further the objectives of any innovative entity. Typically, these will be commercial strategies, but, as is becoming more common, may include altruistic, social, and political ends. Overall, understanding IP in general, and the complementarity of patents and trade secrets specifically, has become an indispensable area of expertise in technology management. The study of both historical and currently active patents is an essential part of understanding technology evolution over time, as well as for developing actionable technology roadmaps for the future. In the next chapter, we consider our first in-depth case study: The Automobile.

References Allen F. Secret formula: The inside story of how Coca-Cola became the best-known brand in the world. Open Road Media; 2015 Oct 27. Barney, J. R. (2000). The prior user defense: A reprieve for trade secret owners or a disaster for the patent law. Journal of the Patent and Trademark Office Society, 82, 261. Brown S. Lecture on intellectual property, M.I.T. Technology Licensing Office, April 18, 2002. Bulow, J. (2004 Jan 1). The gaming of pharmaceutical patents. Innovation Policy and the Economy, 4, 145–187. Galison, P. (2004 Sep 17). Einstein’s clocks, Poincaré’s maps: Empires of time. WW Norton & Company. Isaacson, W. (2008 Sep 4). Einstein: His Life and Universe. Simon and Schuster. Mailänder L., et al. (2013). Promoting Access to Medical Technologies and Innovation Intersections between public health, intellectual property and trade. World Intellectual Property Office WIPO. McGurk, M. R., & Lu, J. W. (2015). Intersection of patents and trade secrets. Hastings Science and Technology Law Journal, 7, 189. Meshbesher, T. M. (1996). The role of history in comparative patent law. Journal of the Patent & Trademark Office Society, 78, 594. Ulku, H. (2004 Sep 1). R and D, innovation, and economic growth: An empirical analysis. International Monetary Fund. Von Graevenitz, G., Hall, B. H., Helmers, C., & Bondibene, C. R. (2013). A study of patent thickets. Intellectual Property Office UK. Whitehouse, I., Scott, A., & Scarrott, M. (2016). Aerodynamic design innovation, patents, and intellectual property law. Applied Aerodynamic Conference, Royal Aeronautical Society. Yoon, B., & Magee, C. L. (2018 Jul 1). Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction. Technological Forecasting and Social Change, 132, 105–117.

Chapter 6

Case 1: The Automobile

Advanced Technology Roadmap Architecture (ATRA) Inputs

Steps

Outputs

L1 Products and Missions

1. Where are we today?

FOMjj

Strategic Drivers for Technology

Technology State of the Art and Competitive Benchmarking

L2 Technologies

+5y Organization

Technology Systems Modeling and Trends over Time Technology Projects

FOMi

Today

2. Where could we go?

+10y FOMj

Dependency Structure Matrix

Tech Pul Pull

Figures of Merit (FOM) Current State of the Art (SOA) Technology Trends dFOM/dt

Competitor 1 Competitor 2

Technology Systems Modeling

L1

Technology Roadmaps

+10y

Scenario A

+5y

Scenario B ?

3. Where should we go?

L2

Scenario Analysis and Technology Valuation

Technology Portfolio Valuation, Optimization and Selection

Technology Investment Efficient Frontier Technology Portfolio Technology Projects

6

Foundations Definitions What is Technology?

E[NPV] - Return

Intellectual Property Analytics

4. Where we are going!

History Milestones of Technology

Ecosystems Nature Technology, Nature Technology Diffusion, Infusion and Industry and Humans

The Future Is there a Singularity ?

© Springer Nature Switzerland AG 2022 O. L. de Weck, Technology Roadmapping and Development, https://doi.org/10.1007/978-3-030-88346-1_6

Design Reference Missions Future Scenarios T h l Technology V Valuation l ti Vector Charts

FOMi

Tech Push

Technology Scouting Knowledge Management

Scenario-based Technology Valuation

σ[NPV] - Risk

Case 1

Case 2

Automobiles

Aircraft

Pareto-optimal set of technology investment portfolios Recommended Technology Portfolio (Expected NPV and Risk)

C Cases Case 3 Deep Space Network

Case 4 DNA Sequencing

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6.1  E  volution of the Automobile Starting in the Nineteenth Century The common modes of transportation up until the mid-to-late nineteenth century were walking, riding horses, taking a stagecoach, or traveling by train or ship. However, many of these modes of transportation required following a fixed schedule and they were limited in terms of speed and convenience. It was Carl Benz (1844–1929), a German engineer, who in 1886 patented what is generally considered to be the first practical automobile, the now-famous “Motorwagen,” see Fig. 6.1. His business partner and wife Bertha Ringer (1849–1944) also had a significant role in the early history of the automobile. She is the one who took out the Model 3 on the first long-distance automobile trip in history for over 100 [km] from Mannheim to visit her mother in Pforzheim on August 5, 1888. The trip became a sensation and generated much-needed publicity for this new mode of transportation. On this trip, she is said to also have invented the concept of brake pads, and the buzz generated by this first voyage led to the first sales of the Motorwagen. The Model 3 was publicly introduced at the 1889 World’s Fair in Paris.1 Leading up to this milestone, Carl Benz had worked diligently for more than a decade on his automobile. Starting with a single-cylinder two-stroke petrol engine (the design was finished on December 31, 1879, and patented on June 28, 1880), he then improved the engine to eventually a four-stroke cycle. After founding the Benz & Cie company and combining it with his interest in bicycling,2 the first automobile was conceived as a “horseless carriage” with wire Fig. 6.1  The Benz Motorwagen number 3 of 1888, used by Bertha Benz for the first long-distance journey by automobile (more than 106 km or approximately 60 miles). (Source: Carl Friedrich Benz, 1936)

 This is the same world’s fair in 1889 for which the Eiffel Tower was constructed.  It is interesting to see the parallels between Carl Benz’s embrace of bicycles and that of the Wright Brothers in Ohio about a decade later. The design and manufacturing of bicycles required lightweight materials and precision metal manufacturing, two capabilities that became essential for both early automobile and aircraft design, see also Chap. 9. 1 2

6.1  Evolution of the Automobile Starting in the Nineteenth Century

155

wheels instead of wooden wheels that are much heavier. Between 1879 and 1888, Benz showed his genius through a succession of increasingly sophisticated technologies, many of which are still in use today, 130 years later: • • • • • •

Speed regulator Ignition using spark plugs Batteries Carburetor Clutch and gear shift3 Water radiator

The initial Model 3 had an engine displacement of 1600 [ccm] and produced a mere three-quarters of a horsepower at a top speed of 13 [km/h]. Not only Germany but also France turned out to be an important initial market. The first automobiles were sold by bicycle shops, for example, that of Emile Roger in Paris. Orders started coming in and Benz & Cie grew rapidly over the last decade of the nineteenth century. For example, in 1899, the Benz & Cie company located in Mannheim, Germany had 430 employees and produced 572 units. This eventually grew to 3480 units by 1904. Over time more competition emerged and, in particular, the Daimler Motoren Gesellschaft (DMG) in Stuttgart became a formidable rival to Carl Benz and his company. Due to the poor economic situation in the mid-1920s (after WWI and during the Great Depression), the two companies decided to merge and they formed the Daimler Benz company in 1926. This company still exists today and has remained a leader in automotive innovation and technology over the last 100 years. Automotive design, technology, and production were not confined to Western Europe.4 In the United States,5 steady population growth and increased technical capability made the car desirable to more and more people, and in 1902, Ransom Olds, who had been tinkering with automobiles and their engines for years, debuted large-­ scale, production line manufacturing of affordable cars. The evolution of the train network had occurred earlier as a major motor of westward expansion in the United States. Henry Ford stood on his shoulders when, in 1908, he created the Ford assembly line. The Model T was an important development in its own right. For example, it featured much smaller suspensions than other cars due to the first intelligent use of heat-treated steels (Davies and Magee 1979) in automobiles, which led to a smaller and cheaper overall vehicle. The development of the moving assembly line came more than 5  years later as the Model T continued to evolve. Other 3  One of the recommendations of Bertha after her first long distance drive was the addition of a third gear in order to facilitate the climbing of hills. 4  One of the factors that favored the adoption of the automobile was hygiene. Cars avoided the issue of having to remove horse manure from city roads. This had been a problem in many cities during the age of horse-drawn carriages, including San Francisco with its hills and steep roads. 5  This section is adapted from de Weck, Roos and Magee (2011), Ch. 1 “From Invention to Systems.”

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innovations continued to push costs down such as cast iron engines and all-steel bodies as well as brakes on all four wheels were important developments during these early decades. The special role of the Ford Model T is discussed in more detail below. As affordable cars became accessible to the growing populations of the United States and Europe, governments began to think about the transportation infrastructure. The Germans conceived of building a national highway system during the Weimar Republic of the 1920s, and in 1921, the US Army was asked to provide a list of roads it considered necessary for national defense – the precursor to a nationwide highway system in the United States. New England had established its own network of “interstate” roads in 1922. The first US system of “National Roads” also emerged in the 1920s and was much wider than New England’s system. For example, Route 40 went from Washington through Maryland and Pennsylvania to St. Louis, whereas Route 20 went from Boston to Seattle and still exists today as a parallel road to I-90. This is true of most of the US Interstate System built from the 1950s to the 1980s. Meanwhile, the automobile manufacturers had begun to think beyond the technological aspects of the car as an invention and considered the business side of the equation to a far greater degree. Alfred P. Sloan had merged his roller and ball bearings company with the company that eventually became General Motors, and he rose through the firm’s executive ranks. As GM’s president beginning in the 1920s (Sloan 1963), Sloan introduced product differentiation and market segmentation, with a pricing structure for cars within the GM family that did not compete with each other and kept consumers buying from the company even as their income grew and preferences evolved. He established annual styling changes, an idea that led to the concept of planned obsolescence. He adopted from DuPont the measure of return on investment (ROI) as a staple of industrial finance. Under Sloan, GM eclipsed Ford to become the world’s leading car company, as well as the world’s largest and most profitable industrial enterprise for a long period. Years later, GM’s leadership  – indeed, that of the entire US automobile industry  – would be challenged by Toyota and its Toyota Production System (TPS), an idea hatched by an engineer named Taiichi Ohno and supported by Sakichi Toyoda and his son Kiichiro Toyoda. More on the importance of TPS to modern automotive manufacturing is written in the next section (Womack et al. 1990). One of the lessons learned from automotive history for technology is that there is a definite first-mover advantage. This advantage can be overcome by other competitors who also invent new and improved technologies and who adopt superior business practices in manufacturing and in sales and distribution. This explains the transitions among Daimler Benz, Ford, General Motors and, more recently, Toyota as the world’s leading automotive manufacturer (by volume).

6.2  The Ford Model T

157

Fig. 6.2  Ford Model T driving chassis. (Source: F. Clymer 1955)

6.2  The Ford Model T The Ford Model T has a special place in the history of the automobile (Fig. 6.2). It was produced by Ford between 1908 and 1927 and became the first truly mass-­ produced automobile in history. It was also the first globally produced car with manufacturing sites in the United States, Canada, England, Germany, Argentina, and several other countries. Through a series of continuous improvements of both the vehicle itself (and its different versions) as well as the underlying production processes, the vehicle became affordable for a significant portion of the US population. Its adoption6 also provided the impetus for the development of highways and a more robust automotive infrastructure, including a network of petrol stations. Some of the specifications of the Ford Model T were as follows:7 • • • • •

2.9-L inline four-cylinder engine that developed 20 [hp] (15 [kW]) Top speed 40–45 [mph] (64–72 [km/h]). Fuel consumption: 13–21 [mpg] (18–11 [l/100 km]). Rear-wheel drive, see diagonal drive shaft in Fig. 6.2. Three-speed transmission: with two forward gears and one reverse gear.

The Ford Model T also set a new standard in terms of its reliability and ease of maintenance. It was designed for the realities of life in the 1910s and 1920s which included mainly dirt roads and few paved roads. The vehicle has been praised for its ruggedness and ability to climb hills. While the Model T itself was improved over the course of its production life, it is mainly the improvements in the production process that benefited from successive architectural and technological innovations.

 Chap. 7 discusses the phenomena of technology adoption and disruption over time.  Some of these specifications of the Ford Model T changed over time from 1908 to 1927.

6 7

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6  Case 1: The Automobile

Fig. 6.3  Rationalization, continuous flow, and division of labor on the Ford Model T moving assembly line. (Source: Ford Motor Company)

For example, the whole manufacturing of the Ford Model T was decomposed into 84 different areas that could each be managed and monitored and where the skills needed for production workers were clearly prescribed and understood (see Fig. 6.3). Additionally, the conversion from the static to the moving assembly line reduced final assembly time from initially 12.5 hours to only 93 minutes. This rationalization of manufacturing was a major reason why the price of the product was able to be dropped over time, which led to an increase in sales and production. A virtuous circle of mass production was established (Alizon et  al. 2009; Hounshell 1978). Figure  6.4 shows the evolution of price and production volume during the years when the Ford Model T was in production. It can be argued that the Ford Model T did for automobiles what the DC-3 did for aviation (see Chap. 9). It created a mass market for this mode of transportation that was accessible to the larger population and the middle class in particular. The Ford Model T moving assembly line had its debut in 1913 once production volumes reached well over 100,000 units per year. Other changes in materials and design also contributed to cost reduction as did volume and scaling effects.8  The learning curve equation predicts the drop in cost as production volume is doubled as follows: Yx = Yo xn, whereby Yo is the first unit cost and the exponent n = log(b)/log(2) determines the cost Yx of the x-th production unit (serial number) on the line. The decrease in cost of the Ford Model T from 1908 to 1915 as production went from 10,000 units to 500,000 units per year was approxi8

6.2  The Ford Model T

159 Ford Model T Production and Price

2500

Price [$/unit] Annual Production [1000 units]

2000 1500 1000 500 0 1908

1910

1912

1914

1916

1918 1920 Year CE

1922

1924

1926

1928

Fig. 6.4  Evolution of the Ford Model T annual production and price from 1908 to 1927. (Source: https://en.wikipedia.org/wiki/Ford_Model_T, URL accessed on August 24, 2020)

An aspect that is often underappreciated is the tremendous thought and expansion that had to go into the supply chain for the Ford Model T given the large expansion in production volumes shown in Fig. 6.4. Ford created an expansive vertically integrated supply chain. The Ford Motor Company kept tight control over the supply of metal, rubber, and all other materials. Raw materials would enter the factory at one end and finished cars would exit the factory at the other end. The transformation from raw materials to finished goods required a vast array of machinery and armies of factory workers. Industrial Engineering started as a discipline in the early- to mid-twentieth century with a focus on efficiency in manufacturing. In order to keep the factories running one needed to split up the work in the most optimal way along the assembly line to avoid bottlenecks and assure synchronization. Frederick W. Taylor (1919) was one of the leaders in the emerging field of “scientific management” that created a theoretical basis for this large-scale industrialization. This was not too difficult as long as only one standard model was made “… any color as long as it is black,” Henry Ford famously said.9 During WWII, it was Taylorism10 that allowed the United States to ramp up production of wartime manufacturing in unprecedented ways. Between 1941 and 1945,

mately $500 per unit. This corresponds to a learning curve factor b = 0.95, meaning that with every doubling of the production volume, the cost of a single unit dropped to 95% of its prior value. This trend did not continue after the volume peaked at two million units in 1923. 9  This is an oversimplification of reality as the Ford Model T and its different variants were available in other colors as well depending on the specific production year, such as green, red, and blue, as well as gray for the town car variant. It is said that black was the best paint for mass manufacturing because it would dry the fastest. 10  Taylorism is known as a specific way to organize and rationalize manufacturing based on a series of techniques such as time-motion studies, to find the best way to allocate tasks to individual pro-

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the United States produced millions of aircraft, ships, ground vehicles, tanks, and other weapons at a rapid pace that allowed it to overcome its initial disadvantage at the outset of the war. In the years following the war, this division of labor and rationalization of production continued its success story into the mid-1960s, at which time new ways of thinking and lower-cost foreign imports started to challenge American industrial dominance. One of these challengers was Japan, and specifically the Toyota Production System (TPS). TPS organizes manufacturing and logistics, including interactions with suppliers and customers, and represents a fundamentally different logic and framework than mass production for the business of developing, making, and selling cars. Most importantly, TPS was conceived of as an evolving system, not as a “breakthrough” invention. The Toyota automotive company founders visited America as early as the 1950s to see how the Ford assembly line worked but left unimpressed by the large amounts of inventory kept on hand, the uneven quality of work, and the large amount of rework required before a Ford car was truly “done.” They found their inspiration, instead, at a “Piggly Wiggly” supermarket, where they saw how goods were reordered and restocked only once they had been bought by the store’s customers. The rest is history – and notable because Toyota not only shook the auto manufacturing world with its approach but directly challenged American and European carmakers as the global economy emerged and it became easier for Toyota first to sell its “better-made” cars globally and then, eventually, to build them globally as well. Every global auto company was forced to rethink not only the underlying technology of the car but also the management of the automobile research and development and car-building processes. Unintended Consequences With the growing success and deployment of the automobile between roughly 1910 and 1970 came some unintended consequences. Take, for instance, the traffic jam, something about which none of the early developers gave any apparent thought. On July 11, 1910, the headline in Jacksonville, Florida’s daily newspaper, the Florida Times-Union and Citizen, announced something the small city had never seen: “Autoists Spending Day At The Beach: All Made Rush For The City At The Same Time!” The subhead described how, at the ferry crossing that linked the city with the new paved highway (the first in the southeast United States) that went to the beach: “Upwards Of 50 Cars Were Waiting At One Period!” A year later, on June 25, 1911, the same newspaper wrote: “The constantly increasing number of automobiles in use in Jacksonville makes their safe navigation of the streets a more difficult problem in proportion. Hundreds of motorcars are using the streets every hour

duction workers. Taylorism initially had an enormous positive impact on large-scale manufacturing through the introduction of division of labor and specialization. However, it also generated some negative side effects such as an increased distance between management and workers. Other downsides coming from the monotony of doing the same work day in and day out were physical problems such as repetitive stress injuries as well as a sense of disempowerment by workers on the production floor.

6.2  The Ford Model T

161

Fig. 6.5  Ford’s River Rouge Plant in Michigan. (Source: Ford Motor Co.)

of the day and far into the night. In most cases, they are left to work out their own salvation …”.11 Traffic jams were assuredly not the only unintended consequence of a great invention. In fact, the general mindset in the decades immediately before and following World War II was that resources were, for all intents and purposes, essentially inexhaustible. Smoke could be seen spewing from the stacks of factories, such as Ford’s famous River Rouge plant in Michigan (Fig.  6.5), but these emissions were often regarded as negligible and even as a sign of real progress – as evidenced by the artwork and photographs in many corporate headquarters of the time depicting and celebrating factories billowing large amounts of smoke. Things changed when many systems, such as automobile traffic, reached a critical size or “tipping point.” While component technologies continued to evolve rapidly – also in automotive design – the underlying infrastructure networks that had formed, and especially the regulatory frameworks, stagnated, failed to anticipate changes, or simply did not keep up with growth. This mismatch between technological progress at the product level and the backwardness of infrastructures and regulations persists to some degree today. An   Source: John W.  Cowart, “Jacksonville’s Motorcar History,” at http://www.cowart.info/ Florida%20History/Auto%20History/Auto%20History.htm; URL accessed August 24, 2020. 11

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example of this is the recent emergence of the so-called self-driving cars (see Sect. 6.5), for which a coherent national or international certification protocol is still missing. Eventually, unintended consequences could no longer be ignored. Many of the most dramatic changes began in the 1960s – no doubt fueled in part by a younger generation coming of age after the “complacency” of the 1950s that viewed the world quite differently from their parents. Many of the technological innovations in automobiles were driven by the desire to minimize the negative, unintended consequences of this mode of transportation. However, many of these technological improvements were also directly traceable to increased needs and demands of automobile owners and drivers worldwide.

6.3  Technological Innovations in Automobiles These technological innovations in automobiles were introduced continually over time, both before and after WWII. In some cases, they were introduced based on customer demands, in other cases, they were driven by government regulations or a combination of the two. Some of the most important innovations and regulations associated with automobiles are in the following three areas, all related to minimizing the potential downsides of automobiles: • Driving safety: Safety for automobiles includes crashworthiness and associated crash testing. The need for passengers to survive car crashes with minimal or no injuries (or death) became an imperative starting in the 1930s and especially in the 1950s in the United States after the establishment of the Eisenhower Interstate Highway System. The increased speed and density of traffic led to an increase and severity in car accidents. Also, since many accidents occur at intersections of roads, the introduction of traffic lights, local speed limits, and other measures of traffic flow regulation became essential. This shows that automotive technological innovation was not confined to the vehicle itself, but included the supporting and enabling engineering infrastructure as well. • It has been estimated that there are 1.2 million fatal car accidents per year worldwide today and this is an ongoing area of concern and technological challenge. Some of the technological improvements for vehicle safety that have been introduced over the years include disk brakes, radial construction tires, seat belts, airbags, crumple zones, traffic lights, speed limits, as well as increased policing and traffic enforcement.12 Figure 6.6 shows the general trend toward decreased  The statistics on car safety worldwide show dramatic differences between developed and developing countries such as in the United States, Western Europe, India, Africa, and so forth. It is important to note that this is generally due to differences in the quality of the roads, driver behaviors, rigor of the traffic laws and enforcement, and not primarily vehicle design. This is potentially

12

6.3  Technological Innovations in Automobiles

163

Fig. 6.6  Annual US traffic fatalities per billion vehicle miles traveled (VMT) are shown in red. Total VMT in tens of billions in dark blue and US population in millions in light blue from 1921 to 2017. (Source: Wikipedia, URL accessed on August 24, 2020)

automobile fatalities in the United States over the last century. Total automobile deaths in the United States are currently between 30,000 and 40,000 per year. • Emissions: With the number of vehicles and VMT increasing worldwide, the amount of emissions and their mix (particulate matter, NOx, CO2, and other by-­ products of combustion) have kept increasing in recent decades. The current estimate is that automotive emissions globally are about one-fifth of all CO2 emissions. Countries like China have recently seen a deterioration of air quality in major cities (such as in Beijing or in Harbin) and have started to take active countermeasures. In the United States, the Environmental Protection Agency (EPA)13 and the state of California in particular have been leaders in reducing the emissions from automobiles. Another factor in mitigating emissions from automobiles is the development of improved public transit options in cities such as New  York, London, Tokyo, and many others. As discussed below this phenomenon of increased urbanization, coupled with enhanced public transportation, potentially leads to a reduction in per capita car ownership and emissions. one of the reasons why the widespread introduction of autonomous vehicles might lead to significantly fewer accidents over time, by taking control away from or by augmenting the often (but not always) “unreliable” human drivers. Examples of driver augmentation are rear view cameras, lane crossing warning devices, and nod-off alerting systems. 13  It must be acknowledged that the level of vigor with which the EPA enforces air quality standards varies from administration to administration.

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Fig. 6.7  Recent trends and projection for automotive CO2 emissions per km. (Source: URL accessed August 24, 2020, https://theicct.org/blogs/staff/improving-­conversions-­between-­ passenger-­vehicle-­efficiency-­standards) NEDC = New European Driving Cycle

Figure 6.7 illustrates some signs of improvement when considering normalized car emissions for different countries worldwide. This analysis is usually done over a standardized drive cycle and driving distance. This particular emissions analysis was carried out by the International Council on Clean Transportation and it suggests that since the year 2000 – and on a trajectory towards 2025 – the CO2 emissions per vehicle have been halved from about 200 grams of CO2 per km to below 100 grams of CO2 per km. The ways in which these reductions have been achieved are varied and include: • Aerodynamic improvements in cars by shaping the body and adding drag reduction features, thus reducing their net drag coefficient CD. • Engine improvements similar to the case of aircraft engines that were discussed in Chap. 4, but at a faster pace. • Lightweighting of cars using aluminum and the introduction of high strength steel (HSS) were important material innovations earlier in the twentieth century. To some extent, HSS and aluminum are still competing today in addition to plastics. The trade-offs among strength, safety, manufacturability, durability, aesthetics, and cost can lead to different decisions depending on the vehicle application. When considering NOx emissions (which are not shown in Fig. 6.7), the United States pushed for significant improvements already starting in the 1970s, driven in part by smog issues, for example, in California. The reduction in NOx emissions was also a central point in the Volkswagen emissions cheating scandal (Chossière et al. 2017), which has raised the profile of this issue worldwide.

6.3  Technological Innovations in Automobiles

165

Fig. 6.8  Evolution of CAFE fuel economy standard for cars (red) versus actual fuel economy of passenger cars (black) since 1975 in the United States. (Source: US Department of Transportation)

• Fuel economy: Fuel economy standards in the United States are defined by the so-called Corporate Average Fuel Economy (CAFE) standards, which were enacted by the US Congress starting in 1975 following the oil crisis of 1973–1974. The ability to reduce fuel consumption correlates closely with emissions as ­discussed above. Figure 6.8 shows the relative improvement of fuel economy in the United States for cars according to CAFE since 1975.14 While it can be seen that the average fuel economy for the new US car fleet has improved from about 20 to nearly 40  miles per gallon [mpg] between 1980 and 2020, this improvement is not monotonic. During periods of lower gasoline prices, as was seen during the 1990s, consumer behavior changes and shifts toward larger cars such as sports utility vehicles (SUVs). This trend toward larger and heavier cars drives higher fuel consumption and negates – to some extent – the technological progress made on emissions and fuel economy. In order to better understand the role that technology can play for improving fuel consumption, a better measure than CAFE (which is a fleet average) is the so-called brake-specific fuel consumption (BSFC) in units of [g/kWh]. Figure 6.9 shows a series of projections of BSFC versus torque [Nm] from different sources such as the Environmental Protection Agency (EPA), Sandia National Laboratory, and manufacturers such as Mazda and Delphi. Optimal results in terms of fuel economy can only be achieved when the internal combustion engine and the fuel are co-optimized.  Source: https://en.wikipedia.org/wiki/Corporate_average_fuel_economy, URL accessed on Aug 24, 2020.

14

166

6  Case 1: The Automobile

Fig. 6.9  BSFC versus torque optimization for ICE vehicles, scaled to a 120 kW engine. (Source: Paul Miles, Sandia National Laboratory, 2018)

A computer model of vehicle fuel economy developed by Sandia National Laboratory in 2018 predicts a possible further reduction in BSFC between 20 and 44% for cars and light trucks by 2045, see Fig. 6.9. This is based on a combination of the following technological improvements: • • • • •

Friction reduction (lubricants and mechanical design) Cylinder deactivation Accessory electrification Improved variable transmissions Low friction brakes

An additional 30% reduction in fuel consumption may be possible through hybridization with electrical components (see discussion of electrification below). Given the maturity of ICEs, most of these improvements are incremental. Recently, Popular Mechanics published a list (Table 6.1) of what they consider the 10 greatest technological innovations that enabled the modern automobile. Such lists can always be argued with, but it is interesting to consider and debate them.15 In order to assess the improvements of automobiles over time, it is important to observe not only the details but also several macrotrends while recognizing the maturity and availability of car models and data in this industry, which is now about 130 years old.

 Rong, Blake Z, “Popular Mechanics”, “10 Innovations that made the modern car”, Dec 4, 2018, https://www.popularmechanics.com/cars/car-technology/a25130393/innovations-modern-cars/

15

6.3  Technological Innovations in Automobiles

167

Table 6.1  Ten innovations that made the modern car Number Innovation 1 Enclosed fenders 2

Electrical systems

3 4 5

Front engine and front wheel drive Crumple zones High strength steel

6

Hybrid electric drivetrains

7

Global positioning system

8 9

Adaptive cruise control Better transmissions

10

Active aerodynamics

Benefit Improved aerodynamics and reduced vibrations transmitted to the cabin Electric lights, electronic injection, and better fuel efficiency Better weight distribution and traction Better safety and crashworthiness Improved metallurgy and manufacturing cost, and improved safety Improved emissions and efficiency (see discussion below) Better navigation, improved safety, and fuel savings by avoiding wrong turns Driver convenience and improved safety Automatic transmissions, improved fuel economy, and convenience Speed-regulated spoilers and shutters, and improved aerodynamics

Fig. 6.10  Automotive vehicle platforming trends in the early twenty-first century. Mega platforms are shown at the bottom in blue. (Source: J.-U. Wiese, AlixPartners)

One important trend since the Ford Model T is the trend toward diversification and the production of mass-customized vehicles for different market niches. Figure 6.10, for example, shows the trend to build several models from a common platform (Suh et al. 2007). The automotive industry has been a leader in developing the product family concept. Mega platforms are generally understood to be those from which more than one million vehicles are produced per year.

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Fig. 6.11  Comprehensive model of (automotive) products and their production system. (Source: de Weck, Olivier L. “Determining product platform extent.” In Product Platform and Product Family Design, pp. 241–301. Springer, New York, NY, 2006). There is no question that financial considerations are a major driver in the development and prioritization of automotive technologies

Technologically speaking, product platforming (producing different product variants from a set of common platforms or modules) is both a challenge and an opportunity. It is a challenge because a particular technology may not meet the requirements (or cost targets) of a particular product variant. On the other hand, it is an opportunity because – if infused in a clever way – a technology may be reused and leveraged across multiple product variants, thus providing greater opportunity for amortization of R&D costs and value (see Chap. 17). This shows that automotive technology can only be understood and evaluated by considering the “whole system” as depicted in Fig. 6.11. Referring to Fig.  6.11, product architecture (1) defines the value-generating functions of the product and maps these to physical components (parts) and modules which are assemblies of parts. Inputs to product architecture are regulations and standards with which the machine (in this case, the automobile) must comply.16 The choice of operating principles of the machine and its decomposition relate the physical components to the vector of independent design variables, x, for which engineers will find the most appropriate values. In order to accomplish this, engineering (2) creates models of functional product performance attributes, f, as a function of the design variables, x. The interface between engineering and marketing is primarily concerned with how the vector of performance attributes, f, translates to value, V, in the marketplace. The product value model (3) is also impacted by “soft attributes,” s, such as  We have already mentioned emissions, fuel economy, and crashworthiness standards, which are often tested and certified using standardized drive cycles such as FTP-75 16

6.3  Technological Innovations in Automobiles

169

styling, comfort, or dependability, which are only measurable via customer surveys but not directly via (physics-based) performance attributes and engineering models. We subscribe to Cook’s (1997) view that value is to be measured in the same monetary unit as price, for example, [$]. See Chap. 12 on Technology Infusion Analysis for a practical example of engineering value analysis. A general OPM model of an automobile architecture is shown in Fig. 6.12.

Fig. 6.12  Product architecture view (simplified) in OPM of a generic automobile

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Fig. 6.13  Detailed automotive development after the concept has been chosen (e.g., BMW Active Hybrid)

Simplified parametric models are helpful during early conceptual design and technology roadmapping; however, during actual automotive vehicle development (once a program has been officially launched), very detailed modeling and prototyping are usually required to ensure that the FOM-based targets can actually be met, for example, see Fig. 6.13 for such a detailed model.

6.4  New Age of Architectural Competition Several important trends in recent years, perhaps since about the year 2000, have begun to challenge the traditional well-established automotive architecture consisting of an internal combustion engine (ICE) (such as an in-line-4, V6, or V8 engine), a forward or all-wheel drivetrain, and human drivers with some electronically enabled systems that provide driver assistance. Some of the main trends observable in the auto industry are: • Electrification and hybridization (moving towards electric vehicles) • Autonomy (self-driving cars) • Ride-hailing services (e.g., UBER, Didi Chuxing, and Lyft) In their work, Gorbea and Fricke (2008) and Gorbea (2011) argue that the automotive industry has entered a New Age of Architectural Competition. What is meant by this is that the emergence of hybrid vehicles and purely electric cars has begun to challenge the dominance of the internal combustion engine (ICE) that has been so successful and dominant over the last 100 years.

6.3  Technological Innovations in Automobiles

171

Fig. 6.14  Spectrum of automotive vehicle architectures from all-ICE to all-electric

Figure 6.14 shows the full spectrum of vehicle powertrain architectures from pure ICE vehicles on the left to pure electric vehicles on the right. In-between are intermediate architectures such as parallel hybrids and serial hybrids with electric drive motors and/or a gasoline-powered range extenders that can kick in once the battery is depleted – or even earlier at a certain programmable threshold level – in order to recharge the battery while driving.17 This opens up a very large architectural design space for automobiles that is reminiscent of the early years in the industry (in the early twentieth century as described earlier). Figure 6.15 shows a systematic organization of the automotive architectural space starting with the primary energy sources at the top (fossil fuels, biomass, renewables such as solar, wind, and hydropower, and even nuclear), followed by the primary energy carriers (liquid or gaseous fuels or batteries), and the different possible powertrain architectures with different degrees of hybridization. The systematic hybridization of automotive powertrains can and already has enabled value-added functions, such as: • Electric start and stop, reducing noise levels and pollution in cities. • Overnight charging and batteries serving as auxiliary power at home. • Regenerative braking, especially in terrain with elevation changes. While many different companies now offer hybrid models, and even all-electric vehicles, it is generally the Toyota Prius that is viewed as the first commercially successful vehicle with a hybrid architecture (first entry into service in December 1997). It held about 48% of the US market share for hybrid vehicles in 2018. The advantages of hybrids, however, are not universally acknowledged. Hybrid cars are typically heavier (mainly due to the battery pack), more complex, and more expensive than their pure ICE or EV  equivalents. Despite their  This example shows that hybrid electric vehicles have significant complexity, and that software that determines when certain parts of the system turn on and off is becoming an increasingly important part of the design.

17

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6  Case 1: The Automobile

Fig. 6.15  Architectural design space for automotive powertrain architectures

introduction over 20 years ago, hybrid cars have never exceeded more than 3.5% market share in the United States and they started declining again after the 2008 financial crisis and have less than 2% market share in the United States today. One of the reasons for this is that the primary figures of merit (FOM) along which automobiles are competing are many, and fuel efficiency is just one of them. Some of the primary FOMs that customers use to choose a car are: • • • • • • •

Fuel economy [mpg] and range [km] Passenger volume, cargo volume [cft], and comfort Price per vehicle [$], operating cost [$/year, $/km, $/mile], and reliability Power [kW, hp] and acceleration [sec for 0–100 km/h or 0–60 mph] Emissions for CO2, NOx, and PM [g/km] Aesthetics and design Resale value [$ after x years, or $ after x miles]

Given a certain vehicle powertrain architecture, such as the hybrid one shown in Fig.  6.16, we can construct an architectural model (using  a Design Structure Matrix  DSM) as well as quantitative predictions of the technical, environmental, and financial performance of a particular vehicle given its competitive environment. These vehicle product models can initially be purely parametric and are first used by system architects and product managers to down-select from thousands to a handful

6.4  New Age of Architectural Competition

173

Fig. 6.16 Architectural modeling of hybrid vehicle architectures (Multi-Domain Mapping Matrix  MDM shown on right) and block diagram of an integrated starter generator (ISG) Hybrid architecture (left)

Fig. 6.17  Architecture Performance Index versus time for automobiles

of the most promising vehicle architectures. The most promising architectures are then refined using a combination of modeling and simulation18 as well as prototyping. The argument made by Gorbea and Fricke (2008) is that the automotive industry has entered a new age of architectural and technological innovation and competition, see Fig. 6.17. This renewed interest in different vehicle architectures is reminiscent of what occurred in the early twentieth century. An example of this trend was the announcement by Ford Motor Company (March 2022) that it would design and build its ICE and electric vehicle (EV) cars in different business units under the common Ford brand.  The automotive industry is investing heavily in MBSE (model-based systems engineering) and digital models, and mockups are increasingly replacing physical models that have been used for many decades during the design phase in the past (e.g., made from clay or wood).

18

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6  Case 1: The Automobile

Architectural Performance Index 

       



  P   P   W i W min    P  P   W max  W min     V  Vmin   MPGi  MPGmin     i  i   Vmax  Vmin   MPGmax  MPGmin   MSRP  MSRP  i max       MSRPmax  MSRPmin in 2008US $  

         4        (6.1)

The analysis by Gorbea and Fricke was done using a database of 91 cars for which five basic FOMs were collected from the scientific and trade literature, and from museums and archival documents: overall power P, curb weight W, maximum velocity V, fuel consumption in miles per gallon MPG, and the manufacturer’s suggested retail price (MSRP) in 2008 US$. These FOMs were then combined into an overall architecture performance index as follows: Here, P/W is the power-to-weight ratio of the vehicle, V is the maximum speed, MPG is the fuel economy, and the MSRP is the manufacturer recommended sales price in 2008 US$. The index is normalized between 0 and 1 and contains not “utopian” vehicles but those actually found in the database. The authors comment on the early years of the automotive market as follows: “From 1885–1905, a top speed of 20 mph in a city environment was considered plentiful as long distance driving was not possible due to a lack of a highway infrastructure. In this speed range, architectural competition flourished amongst steam, electric and internal combustion cars. Today most cars can comfortably achieve the 80 mph velocity and can reach upwards of 150 mph for sports cars.” It is interesting to note that in the early 2000s, in many cities around the world, the average actual driving speed may not exceed 20–30 mph either, mainly due to congestion. The different phases of architectural and technological competition depicted in Fig. 6.17 and delineated by the dashed vertical bars are described by Gorbea and Fricke (2008) as follows: 1. The first time period (1885–1915) shows that three different architectures – electric, steam, and internal combustion – were competing to dominate the market. At this early stage, automakers (large and small) innovated around the basic structure of a car but with significantly different concepts. Hence, the market was exhibiting an early age of architectural innovation where a variety of powertrain elements linked in different ways were able to achieve the function of propelling the car (see Fig. 6.15), each combination with its own advantages and disadvantages. 2. The second time period (1915–1998) shows a shakeout in the market that allowed one architecture to dominate over all others – the ICE car. Because the entire market adopted this dominant architecture, the basic risk of not knowing which architecture would prevail was completely eliminated. This allowed automotive

6.4  New Age of Architectural Competition

175

Fig. 6.18  Worldwide sales of plug-in electric vehicles (PEVs). (Source: Wikipedia https://en. wikipedia.org/wiki/Electric_car_use_by_country)

manufacturers to focus on (sustaining) innovations19 at the subsystem level as opposed to the overall system architecture. 3. The current time period (1998-present) shows a renewed focus on vehicle architecture. The key historical event that marks the beginning of this new age is the reintroduction of electric vehicles in the market and the first mass-produced hybrid electric cars. At the moment, some auto manufacturers are trying to shift their focus from incremental innovation to that of architectural innovation. The shift has not come easy as most organizations have been structured around the major subsystems within the automobile. Most auto manufacturers have invested in developing their core competencies in areas specific to the design of internal combustion engine cars. Now, automakers who compete on architecture are shifting to build competency in other areas pertinent to fuel-flexible architectures such as hybrid, fuel cell, and electric cars. As mentioned above some manufacturers, like Ford, are reorganizing the entire corporation around these architectures. The fact that this analysis is not only hypothetical but real is illustrated by the sales of electric cars that have risen sharply in recent years, especially in China, which has mandated the adoption of electric cars as part of its environmental policies. See Fig. 6.18 for recent statistics regarding the worldwide sale of electric cars.

 We will discuss the difference among incremental sustaining, incremental radical, and disruptive innovations in the following Chap. 7.

19

176

6  Case 1: The Automobile

The benefit of electric vehicle technology during operations in terms of emissions while driving is undisputed. However, some scholars have pointed out that the lifecycle environmental impact of plug-in electric vehicles (PEV) and battery electric vehicles (BEV) may not be better, in fact it may be worse than ICE cars once the production and replacement of the batteries and depletion of rare-earth metals for production of the high-power electronics are taken into account. There is ongoing academic research and debate in the industry about the total lifecycle impact of future automotive technologies, such as electric cars. The shift toward architectural competition is significant because it can place established firms in jeopardy of disappearing if they are not able to adapt to the new competitive landscape that is developing (see also Chap. 7). This was the case of most steam car manufacturers during the 1920s that failed to adapt to new market changes. Firms that develop systematic ways to achieve architectural innovations are considered to be better placed in generating a competitive advantage over firms that stay the course of incremental innovation in the future market for automobiles. It is interesting to note that the last version of Clay Christensen’s book “The Innovator’s Dilemma” dedicates its Chap. 10 to the emergence of electric vehicles. Today, however, electric vehicles still represent less than 1% of all vehicles on the road, despite the growth documented in Fig. 6.18. This slow uptake and changeover of the entire fleet is in part due to the moving average age of the automotive fleet which is about 8 years in the United States. Similar to the issue we will encounter in Chap. 9 for the fleet of commercial aircraft, there is a delay of the new technology’s impact due to the phenomenon of the moving fleet average.

Fig. 6.19  Potential future evolution of automotive powertrain architectures and technology in terms of architectural performance (scenarios)

6.4  New Age of Architectural Competition

177

Fig. 6.20  The Toyota Mirai, a hydrogen fuel-cell powered car currently in production features 115 [kW] in engine power and a proven range of 502 [km]. The rate of production has been ramped up recently from 15 [units/day] to 100 [units/day] in early 2021. The filling process of hydrogen only takes 2–3 minutes, and is more similar to the refilling of gasoline cars than the recharging of EVs

The ultimate outcome of this architectural competition is difficult to predict. Figure 6.19 shows different future scenarios that are possible (according to Gorbea and Fricke 2008). In terms of the internal combustion engine (ICE), it may indeed saturate and plateau according to the classical S-Curve model (see Chap. 4). However, the ICE may also experience new life and defeat the S-Curve (at least for a while) with new radical sustaining innovations such as higher compression ratios, reduced friction losses, and better emissions control (see Miles 2018). The energy density of gasoline and diesel fuel is about 45 [MJ/kg] and is difficult to compete with. There are signs, however, especially in Europe after the VW emissions scandal that diesel-driven automobiles are falling out of favor and are being banned from driving in city centers, for example, in Germany. The growth of hybrid electric vehicles (HEVs) shown in blue in Fig. 6.19 is somewhat in question due to their higher price and weight and lower adoption rates to date. Hybrid vehicles may end up being a transitional technology between ICEs and all-electric cars, the two extreme ends of the spectrum in Fig. 6.14. The battery electric vehicles (PEV/BEV) in Fig. 6.19 are shown in green and are most definitely experiencing large year-on-­ year growth rates, however, measured against a relatively small installed base. What is not shown in Fig. 6.19 (but is in Fig. 6.15) is the potential emergence of hydrogen powered (fuel-cell) vehicles, which could avoid some of the downsides of both ICE (emissions constrained) and purely battery-driven electric cars (battery lifetime constrained). Japan, for example, has invested heavily in hydrogen-­powered cars such as the Toyota Mirai (Fig. 6.20), which since January 2021 is being produced at a rate of about 30,000 [units/year].

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6  Case 1: The Automobile

6.5  The Future of Automobiles In this section, we want to speculate a bit about the long-range future of cars. A relatively recent trend in the automotive market is the development of autonomous and, therefore, potentially self-driving cars. The increase in automation and higher levels of autonomy in cars is not per se a new phenomenon. The following driver-assist functions have been introduced gradually over the years, roughly in chronological order: • • • • • • • •

Electric engine start (no more hand cranking) Automatic electric lights, turn on at dusk and in tunnels and garages Automatic windshield wipers, turn on when it rains Cruise control, maintains constant speed but requires human steering Adaptive cruise control, follows the car in front and can stop when needed Self-parking function and summoning function20 Valet mode (car drops off and parks itself) Lane following (warns if a car moves off the centerline of a lane)

There are currently fully self-driving vehicles in operation but only on an experimental basis (usually with safety drivers present at the wheel who can take over in difficult situations) as well as autonomous buses on closed circuits. Recently, Tesla has introduced a nearly complete self-driving mode in its cars, however supervisory control by the driver is still required. There is active research in terms of the optimal set of technologies – such as sensors and processors  – needed to implement self-driving cars with high levels of safety and performance. See Fig. 6.21 for results from a tradespace exploration in terms of navigation performance for SLAM (simultaneous localization and mapping) and cost ($) for different sensor combinations for autonomous cars (Collin et al. 2020). One of the key technologies for enabling self-driving cars is LIDAR, which is an active sensor that floods its surroundings with laser light and builds a 3D map of its environment based on the light reflected from objects in the environment. For driving in inclement weather conditions (e.g., fog, rain, etc.) and at high speeds, it has been shown that the use of radar technology is also important to maintain safety. There is not a one-size-fits-all answer at this time, and similar to the architectural competition in terms of powertrains (Fig. 6.19), there is an active competition in terms of autonomy architectures for vehicles at this time. There are many open questions regarding the future of autonomous cars: • How should self-driving cars be certified and licensed? • Should self-driving cars be restricted to closed environments and dedicated lanes or can they mix into regular traffic with human drivers?   Tesla is beta-testing this function with early customers: https://www.theverge. com/2019/9/30/20891343/tesla-smart-summon-feature-videos-parking-accidents

20

6.5  The Future of Automobiles

179

Fig. 6.21  top: (a) Tradespace for normalized SLAM performance versus cost ($) and (b) normalized SLAM performance versus energy/power [W], bottom: sensor suites for self-driving cars from left to right: no LIDAR, mid-range LIDAR, and long-range LIDAR.

• What are the legal ramifications in an accident between a self-driving car and a human driver or between two self-driving cars? Who is liable? The driver(s)? The occupants? The car manufacturers? The software providers? • Will self-driving cars lead to a net loss or gain of jobs? As in many other areas where global standards never emerged (e.g., driving on the left side in the UK/Commonwealth vs. driving on the right side in most of the rest of the world), it is probable that there will not be a common and globally enforceable standard for self-driving cars, even though organizations such as the ISO and SAE are doing their best – in collaboration with manufacturers and government authorities – to develop such standards for autonomous cars. Ultimately, however, it may be economic and cultural factors that may determine the mid- to long-range future of the automobile. In the early- to mid-twentieth

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century, the automobile became not only a way to enhance personal mobility and drive economic and social development,21 but it also became a status symbol of individual prosperity. The excitement of car racing (e.g., Formula 1, NASCAR etc.…) helped promote the positive image of the automobile and also served as a test bed for new technological developments. More recent generations such as the millennials and generation Z, however, may be developing different preferences. Particularly urbanization, the use of digital technologies and online presence and the high cost of automobile ownership including fuel, insurance, gasoline, taxes, loans, parking, fines, etc. are dissuading an increasing number of young people from owning and operating their own motor vehicles. For example, in Switzerland, which has an excellent public transit system, the number of young people between the ages of 18 and 25 who obtain drivers’ licenses now drops between 2% and 3% per year and has fallen over 10% in the last 15 years.22 In the not too distant future, less than half of young people in certain countries with good public transportation such as in Western Europe, Scandinavia, and Japan will own drivers licenses. This, coupled with the emergence of hire-for-ride online platforms such as UBER, Didi, and Lyft, may over time begin eroding the number of vehicles built and sold worldwide. Major car companies such as Toyota, GM, Ford, Nissan, BMW, Mercedes-Benz, and others are carefully monitoring these trends and a possible global disruption of the automotive market as it has existed over the last 100+ years. Shifting toward more electric cars, fleets of self-driving vehicles and other models has also brought new entrants into the industry such as Google, Baidu, and others. On top of this, the long-term effect of the COVID-19 global pandemic on car ownership remains uncertain. No Cars? There is little doubt that the need for personal freedom and mobility will persist in the future. However, the mode split between automobiles, buses, trains, or even Urban Air Mobility (UAM) is a wide open question today. In a twist of irony, the use of bicycle sharing services in many cities around the world is on the rise and e-­bicycle-type vehicles (bicycles augmented by an electric drive motor and a built­in battery) have been proposed for the creation of future sustainable urban transportation, see Fig. 6.22 for an example. Personal mobility started with bicycles in the late nineteenth century (such as the ones beloved and promoted by Karl Benz) and we may indeed return yet again to this earlier mode of transportation, however, enabled by new technologies such as CFRP materials, electric drives, and driver-assist navigation systems. As in the case of environmental technologies for water treatment (see Chap. 3), we may witness over time a gradual return to earlier, more “natural” and less

 With all its positive and negative side effects to society, such as urban sprawl.  https://www.swissinfo.ch/eng/lack-of-drive_why-young-people-are-falling-out-of-love-withcars/43024836

21

22

6.5  The Future of Automobiles

181

Fig. 6.22  E-Bicycle like urban mobility vehicle. (Source: MIT Media Lab)

energy-­intensive solutions as we had them in the late nineteenth century, but this time reinvented and enhanced with twenty-first century technologies and materials.

References Alizon, Fabrice, Steven B.  Shooter, and Timothy W.  Simpson. "Henry Ford and the Model T: lessons for product platforming and mass customization." Design Studies 30, no. 5 (2009): 588-605. Benz, Carl Friedrich: Lebensfahrt eines deutschen Erfinders. Die Erfindung des Automobils, Erinnerungen eines Achtzigjährigen. Leipzig 1936 Chossière GP, Malina R, Ashok A, Dedoussi IC, Eastham SD, Speth RL, Barrett SR. Public health impacts of excess NOx emissions from Volkswagen diesel passenger vehicles in Germany. Environmental Research Letters 2017 Mar 3;12(3):034014. Clymer F. , Henry’s Wonderful Model T, Bonanza Books, New York, 1955 Collin A, Siddiqi A, Imanishi Y, Rebentisch E, Tanimichi T, de Weck OL. Autonomous driving systems hardware and software architecture exploration: optimizing latency and cost under safety constraints. Systems Engineering. 2020 May; 23(3):327–37. Davies RG, Magee CL. Physical metallurgy of automotive high-strength steels. JOM. 1979 Nov 1;31(11):17–23. de Weck, Olivier L., Daniel Roos, and Christopher L.  Magee. Engineering Systems: Meeting human needs in a complex technological world. MIT Press, 2011. Gorbea C., “Vehicle Architecture and Lifecycle Cost Analysis in a New Age of Architectural Competition”, 2011, PhD Thesis, TU Munich Gorbea C., Fricke E., “The Design of Future Cars in a new age of architectural competition,” paper DETC2008/DTM-49722, Proceedings of the ASME 2008 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, IDETC/CIE 2008, August 3–6, 2008, Brooklyn, New York, USA

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Hounshell, David Allen. “From the American system to mass production: the development of manufacturing technology in the United States, 1850–1920.” PhD diss., University of Delaware, 1978. Miles, Paul C. Potential of advanced combustion for fuel economy reduction in the light-duty fleet. No. SAND2018-4022C. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States), 2018. Sloan AP. My years with General Motors. Currency; 1963. Suh, Eun Suk, Olivier L. De Weck, and David Chang. "Flexible product platforms: framework and case study." Research in Engineering Design, 18, no. 2 (2007): 67-89. Taylor, Frederick Winslow. The principles of scientific management. Harper & Brothers, 1919. Womack JP, Jones DT, Roos D. The machine that changed the world: The story of lean production – Toyota’s secret weapon in the global car wars that is now revolutionizing world industry. Simon and Schuster; 1990

Chapter 7

Technological Diffusion and Disruption

Advanced Technology Roadmap Architecture (ATRA) Inputs

Steps

Outputs

L1 Products and Missions

1. Where are we today?

FOMjj

Strategic Drivers for Technology

Technology State of the Art and Competitive Benchmarking

L2 Technologies

+5y Organization

Technology Projects

FOMi

Today

2. Where could we go? Technology Systems Modeling and Trends over Time

+10y FOMj

Dependency Structure Matrix

Tech Pul Pull

Figures of Merit (FOM) Current State of the Art (SOA) Technology Trends dFOM/dt

Competitor 1 Competitor 2

Technology Systems Modeling

L1

Technology Roadmaps

+10y

Scenario A

+5y

Scenario B ?

3. Where should we go?

L2

Scenario Analysis and Technology Valuation

Definitions What is Technology?

E[NPV] - Return

Intellectual Property Analytics

Foundations

4. Where we are going! Technology Portfolio Valuation, Optimization and Selection

Technology Investment Efficient Frontier Technology Portfolio Technology Projects σ[NPV] - Risk

7 History Milestones of Technology

Ecosystems Nature Technology, Nature Technology Diffusion, Infusion and Industry and Humans

Design Reference Missions Future Scenarios T h l Technology V Valuation l ti Vector Charts

FOMi

Tech Push

Technology Scouting Knowledge Management

Scenario-based Technology Valuation

The Future Is there a Singularity ?

© Springer Nature Switzerland AG 2022 O. L. de Weck, Technology Roadmapping and Development, https://doi.org/10.1007/978-3-030-88346-1_7

Case 1

Case 2

Automobiles

Aircraft

Pareto-optimal set of technology investment portfolios Recommended Technology Portfolio (Expected NPV and Risk)

C Cases Case 3 Deep Space Network

Case 4 DNA Sequencing

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7.1  Technology Adoption and Diffusion Once invented and “launched” a technology will initially have few followers or adopters. This is normal as the technology is often initially unknown, except to the inventors themselves, and some opinion leaders who may become “early adopters.” Some of the earliest work on the topic of “Diffusion of Innovations” was done by Everett M. Rogers1 in his landmark book “Diffusion of Innovations” first published in 1962 (Rogers, 2003).2 The book was based on his 1957 doctoral dissertation, which was on the topic of adoption of agricultural innovations in the rural community of Collins, Iowa. As a social scientist, he interviewed many of the 148 farmers in that community to better understand what prompted them to adopt early or delay adopting agricultural innovations. In fact, the term “early adopters” was coined by him. Rogers grew up on a rural farm in Iowa and witnessed his father, who was a farmer, readily adopt electro-mechanical innovations (such as the tractor) but be much slower when it came to bio-chemical innovations such as hybrid corn seeds, or 2,4-D weed spray. This sparked his interest in how individuals decide if and when to adopt innovations, such as new technologies. His study of diffusion of innovations was not confined to the adoption of new technologies per se. He also studied other “innovations” or policy interventions such as practices to slow the spread of HIV/AIDS, family planning, and nutrition. Rogers defines innovation as follows: ✦ Definition Innovation is defined as an idea, practice, or object that is perceived as new by an individual or other unit of adoption. An innovation presents an individual or an organization with a new alternative or alternatives, as well as new means of solving problems. The main contribution of Rogers work is the elaboration of a general model of diffusion of innovations which is independent of the nature of the innovation itself (technological or other) but does assume differences in individual’s willingness to adopt new solutions. It is based on the Gaussian distribution (the well-known “Bell curve”) shown in blue in Fig. 7.1. Integrating under the blue curve which shows the number of adopters that adopt early or late yields the yellow cumulative “S-curve” which is based on the logistics function, which was already discussed in Chap. 4. The different categories of technology adopters and assumed fractions of the population of potential adopters are: innovators 2.5%, early adopters 13.5%, early majority 34%, late majority 34%, and laggards 16%. These are defined by the -3σ, -2σ, -1σ, +1σ, and +2σ (extended to infinity, i.e., those that never adopt) boundaries of the underlying Gaussian distribution. Rogers found in his interviews that innovators – the first individuals in a system to adopt an innovation – tended to be individuals who travel a lot, read widely, and have a “cosmopolitan” mindset.  The first edition appeared in 1962, while the fifth and latest edition was published in 2003.  Some point out that even though Rogers is better known, that it is really Zvi Griliches, a Harvard economist who should deserve the credit for being the first to rigorously study technology adoption (1957).

1 2

7.1  Technology Adoption and Diffusion

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Fig. 7.1  The diffusion of innovations according to Rogers. With successive groups of consumers adopting a new technology (shown in blue), its market share (yellow) will eventually saturate. (Source: https://en.wikipedia.org/wiki/Everett_Rogers#/media/File:Diffusionofideas.PNG). This assumes total substitution and fixed market size

This general diffusion model has had an enormous impact3 and is generally still viewed as a valid way to think about technological adoption as a universal process of social change. Is there empirical evidence that this diffusion of innovations model is correct when it comes to the adoption of technologies? Fig. 7.2 highlights some of the original research done by Rogers on the adoption of hybrid seed corn in Iowa in the 1930s and 1940s. The facts of whether and when an individual farmer became an adopter of hybrid seed corn were painstakingly established mainly through personal interviews in the community. The cumulative curve clearly features an S-shape, albeit an asymmetric one. Figure 7.2 shows, it took a full decade from when the first farmer adopted hybrid seed corn in 1927, until the peak of adoption in a single year which was 1937. As mentioned, Rogers’ own father was not an early adopter. During the Iowa drought of 1936, however, while the hybrid seed corn stood tall on the neighbor’s farm, the crop on the Rogers’ farm wilted. Rogers’ father was finally convinced. The 1936 drought and peak in 1937 help explain the location of the year with the largest number of new adopters (1937). This shows that the process of deciding whether or not and when to adopt the new technology is an individual choice. The results of innovation take time to manifest themselves since the skeptics want to first see “proof” of the value of the new technology. In agriculture, for example, one has to wait for one or more annual growing seasons to see the net results of an innovation. Also, interpersonal contacts and opinions shared across one’s personal or professional  In the early 2000s, Diffusion of Innovations was the second most cited text in the social sciences.

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Fig. 7.2  The number of new adopters each year, and the cumulative number of adopters, of hybrid seed corn in two Iowa farming communities (Source: Rogers 1962)

network of peers appear to play a large role. Consider, for example, Fig. 7.3, which shows a social network of early adopters reconstructed by Rogers through his interviews. Adopter No.1 heard about the innovation from an agricultural scientist (middle right) and first tried the new weed spray in 1948. Then in 1950 (2 years later!), farmer No.2, who knew No.1, also adopted the weed spray. This farmer then became an opinion leader for eight other farmers who also became adopters between 1951 and 1956. Clearly, the social network and credibility of farmer No.2 played a large role in the diffusion of this technology in this particular community. ➽ Discussion Can you think of an example where you personally were trying to decide whether or not to adopt a new technology or wait until later? Who or what influenced your decision? How would you classify yourself in terms of the groups shown in Fig. 7.1?

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Fig. 7.3  The diffusion of new weed spray in an Iowa farm neighborhood. Note the direction of the dashed arrows is from the later adopter to the earlier adopter. For example, farmer No.10 (1954) followed No.3 (1951), who followed No.2 (1950), who in turn followed No.1 (1948), who was the original adopter

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Fig. 7.4  Layout of the QWERTY (top) and Dvorak (bottom) keyboards

An important part of understanding the diffusion model is the role of (epistemic) uncertainty.4 Individuals or organizations, when confronted with the decision to hold on to an existing solution versus adopting a new, lesser known one, are facing a decision under uncertainty. In order to reduce this uncertainty (the downside of this uncertainty is what we call “risk” and the upside is “opportunity”), potential adopters seek information from any possible source such as subjective opinions of peers, publications in the trade press, and increasingly the Internet. As Rogers stated: “This information exchange about a new idea occurs through a convergence process involving interpersonal networks. The diffusion of innovations is essentially a social process in which subjectively perceived information about a new idea is communicated from person to person.” In this sense, manifested technological diffusion is essentially the cumulative effect of thousands or millions of individual decisions. The main decision alternatives are: adopt now – wait – don’t adopt. An important point is that even when a specific technology is shown to be beneficial and superior when it comes to certain figures of merit (FOMs), there is no guarantee that a particular innovation or technology will be broadly adopted. A classic example that has been claimed by Paul David (1985) is the layout of the computer keyboard, see Fig. 7.4.5 While the Dvorak keyboard is optimized for typing efficiency in the English language and takes into account the frequency of words in the alphabet (e.g., notice 4  Epistemic uncertainty is that uncertainty where the information is unknown to the decision maker, but the facts are already established and knowable. This is in contrast to aleatoric uncertainty where the facts are not yet established and are subject to a random stochastic process that unfolds in the future. 5  However, in a more recent article by Liebowitz and Margolis (1990), the claim of the superiority of the Dvorak keyboard over QWERTY has been severely challenged, and some would say debunked.

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the prominent locations of the letters “E” and “T” in the home row), it was never widely adopted. The QWERTY keyboard, on the other hand, which was designed more than a century ago in 1873 to slow down typists so as to prevent the jamming of neighboring keys on a mechanical typewriter was never displaced. More on the factors that can promote or hinder technological diffusion and disruption will be discussed in a later section. In summary: successful technological diffusion is not inevitable. And, invention and diffusion are distinct processes that must be considered in their own right. ⇨ Exercise 7.1 Think of a technology that you have heard about that may have been superior based on technical merits, but that was ultimately not adopted at a wide scale. What may have contributed to it not being adopted? According to Rogers, the four key ingredients needed in the successful diffusion of an innovation are: (1) the innovation itself – for example, a new technology; (2) communications about the new innovation through one or more channels; (3) time; and (4) a social system through which information about the innovation travels and which will (or not) adopt the new technology over time. The absence or lack of any of these four ingredients can doom the diffusion process. In his fifth (and last) edition of Diffusion of Innovations, which was published in 2003,6 Rogers also looked at the diffusion of new communications technologies such as mobile phones and the Internet. Fig. 7.5 shows the estimated adoption curve for cellular (mobile) telephones in Finland between 1981 and 2002. It is interesting to note that while Finland was a pioneer in mobile radio communications (e.g., for some time Nokia was a dominant player in that industry) even after 20 years there were still over 20% of the population who had not adopted mobile phones. The reasons for late or no adoption can be varied, including lack of financial resources, mistrust, lack of perceived need, or simply unawareness, which can be linked to a lack of communication and isolation of individuals. There is generally an assumption that older adults (those over age 65) adopt technologies at a lower rate than younger adults or adolescents. We will examine this aspect in Chap. 21. Making individual technology adoption decisions is based on several factors, including subjective information from peers about the effectiveness (and, therefore, value) of new technologies. The Internet has had a large effect on technology adoption due to its peer-rating systems such as the now well-established five-star (*****) ratings on many sites (such as amazon.com). In a sense, the Internet has not only itself been adopted at a very fast rate (see Fig. 7.6), but access to the Internet has indirectly acted as an accelerator of technological adoption. After the first computer network, ARPANET, was established by the US Department of Defense in 1969, it took almost 20 years for the Internet, as we know

 Everett M. Rogers passed away on October 21, 2004, in Albuquerque, New Mexico.

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Fig. 7.5  Rate of adoption of mobile telephones in Finland (1981–2002)

it today, to emerge. Keys to broad diffusion of the Internet were complementary innovations such as the html protocol (the World Wide Web), web browsers, and the appearance of commercial applications online. An important takeaway from the study of diffusion of innovations is that the innovation, whether it is a new technology or not, will not be adopted “on its own” or automatically as the example of QWERTY shows. A complex social system is required along with careful and targeted communication in order to lead to successful adoption. An interesting distinction is that between a centralized and a decentralized innovation diffusion system, see Fig. 7.7. In the centralized approach, the innovation is deliberately incubated in the Research and Development (R&D) part of an organization. While (hopefully) based on a real or latent demand, the innovation is then “pushed” by a change agent toward the innovators and early adopters, some of whom may act as opinion leaders. An example of a commercial version of a centralized diffusion system is the highly successful launch and adoption of the iPhone by Apple. This product has not only found many millions of buyers worldwide, it has also cannibalized – we may say disrupted – the sales of classic mobile phones (such as the still existing but almost extinct flip phones) and created a whole new software industry for Apps. Another example of centralized diffusion of technological innovation, where the Change Agent was a national government, is the adoption of nuclear power for electricity generation in France (Doufene et  al. 2019). Fig.  7.8 shows the generation capacity of nuclear power in France between 1945 and 2012, relative to its

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Fig. 7.6  Cumulative rate of adoption of the Internet worldwide (Rogers 2003)

competitors: renewables and fossil fuels. The technological change was triggered by the oil crisis of 1973 and a strategic decision by the French government (due in large part to the lack of domestic sources of oil and gas) to build a large number of nuclear power plants and to develop the associated supporting industry. This high fraction of nuclear power is one of the reasons for the potential of successful adoption of electric vehicles (see Chap. 6) in France.

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Fig. 7.7  Centralized (top) and decentralized (bottom) diffusion systems (Rogers 2003)

Fig. 7.8  Market share of electricity generation technologies in France over time. (Source: IDCH (2001), Varon (1947), INSEE database (2014))

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It is surprisingly straightforward to create a mathematical model of diffusion of innovations and implement it in a software simulation. Here, we develop such a model using the so-called agent-based modeling (ABM) approach. The simplest kind of these models assumes a normally distributed (Gaussian) population of potential adopters – each individual being an “agent” – who at the start, t=0, are all using the existing technology, that is, their new technology adoption “flag” is set to a=0. At t=0, we assume that one agent out of a population of N individuals will be the first adopter. They will then interact with C other randomly chosen individuals in the existing population of nonadopters at each time period. It is implicitly assumed that each interaction will either lead the other individuals (who are not yet adopters) to either adopt (or not) the technology. This is mathematically done by comparing the individuals’ initial predisposition to adopt, that is, the normally distributed variable n against a uniform random number nr on the interval [0,1], see distribution in Fig. 7.1. If nr 10  years), as well as a long-range vision. The result of applying this framework should be a strategic and aligned plan for purposeful innovation in the organization.

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Fig. 8.4  A potential technology roadmapping framework. (Phaal and Muller 2009)

• As a result of the technology roadmapping framework, there are different questions answered: When something is needed? What is needed? Why is it needed? • The type of information and knowledge contained in a roadmap includes strategic drivers, market needs, form, function, and performance levels of future ­products and services, as well as the solutions and resources needed to implement the plan. In a later section, we will see a different – and yet related – implementation of technology planning and roadmapping that embodies the “MIT approach” to technology roadmapping. There are a few important points to keep in mind for organizations that decide to adopt technology roadmapping. Some of these are often overlooked, which can substantially reduce the benefits obtained from roadmapping: • Technology roadmapping is not a purely technical function, it requires bringing together people from marketing, strategy, engineering, research, manufacturing, procurement, finance, and even HR under a common umbrella. It is one of the most multidisciplinary activities that can be done in a technology-enabled firm. • Creating a set of high-quality technology roadmaps is not a side activity but central to the long-term success and survival of the company. As we saw in Chap. 7, the lack of foresight and proper balance between sustaining and disruptive innovations can lead to the downfall of an enterprise. This can happen surprisingly quickly. • The effort for creating and maintaining technology roadmaps is substantial if it is to be done well with real impact on the direction of the R&D portfolio. While it is useful to begin with a set of qualitative workshops, eventually the roadmaps should be maintained and refreshed regularly by dedicated roadmap owners (RMOs).2  We estimate that it takes about $250 K per year (2019 figures) to create and properly maintain a quality technology roadmap. This means that an organization that has about 20 technology road2

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Fig. 8.5  Example roadmap structure (“architecture”) proposed by Phaal and Muller (2009)

• It is important that technology roadmaps are well organized and somewhat standardized such that different technologies can be compared on an equal footing. Figure 8.5 presents a roadmap structure as proposed by (Phaal and Muller 2009). At the top of the roadmap is the market and business view. In what markets and segments is the company active today? Where does it want to compete in 3 years? In 10 years? What are the different business units (BUs) and what is their competitive position? What are the different products and services offered by the company? The example provided here is from a European Tier 1 supplier of off-highway vehicles, therefore the list of their products and services contains things such as: wheels, axles, transmissions, driveline systems, tractor attachments and hitches, cabs, as well as product distribution and servicing. In terms of technologies, the firm has identified the following main technologies it perceives as enabling or enhancing (see Table 8.1): computer-aided engineering (CAE), manufacturing (e.g., milling and casting), electronics, driveline technologies, materials, and other. Finally, the roadmap lists at the bottom the resources needed to actually implement the roadmap in practice. This includes finance (what are the necessary R&D project investments?), skills and competencies (impacts on HR planning, recruiting, and training programs), alliances, and supply chain impact (are we doing everything

maps should plan to spend about $5 million per year on technology roadmapping.

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alone? Are we partnering? Make or buy?). And finally any impacts or actions to be taken with respect to the firm’s organization and culture. From our experience in creating and implementing technology roadmaps at several global Fortune 500 firms, there are a few lessons learned: • Technology planning and roadmapping should have the full support of the CEO, CTO, Head of Engineering, and the board. Without that active support, it becomes a less than impactful activity. • Technology roadmaps must be validated with quantitative technical and financial models. Many technology roadmaps in practice are purely qualitative in nature. It is, therefore, “easy” to make plans and set quantitative FOM targets and so forth. However, without a quantitative analysis, whether these targets are (i) too easy, (ii) about right, or (iii) too difficult to achieve within the resources and timeframe available, technology roadmaps will not have much credibility. In other words, technology roadmaps need to be validated by data, analysis, and an organized review process involving experts and senior management. • Individuals who are selected for technology roadmapping should be a mix of personnel from more experienced technical staff (e.g., chief engineers, senior technical experts, and chief scientists) and more junior staff such as new research scientists and junior engineers. The more senior staff will typically focus more on sustaining incremental innovations and throw up warning flags why something new cannot or should not be done. The junior staff will typically push for more radical and disruptive innovation. This dialogue and tension is healthy and can lead to a well-balanced strategy. Next, we will consider an example of a “complete” roadmap for a new product in an aerospace company based on solar-electric flight. This roadmap is based on publicly available information.

8.2  E  xample of Technology Roadmap: Solar-Electric Aircraft August 10, 2018, was an exciting day for aviation. An aircraft named “Zephyr” made history and established a new world record for sustained flight of a heavier-­ than-­air aircraft without burning a drop of fuel. The aircraft is a solar-electric unmanned aerial vehicle (UAV) flying at the edge of the stratosphere and at an altitude of about 70′000 feet, twice as high as most commercial airliners. See Fig. 8.6 for an infographic on Zephyr which is designed and manufactured by Airbus Defense and Space, and was originated by the firm QinetiQ (2003) based on an earlier project at Newcastle University in the UK. While solar-electric aircraft have been developed for the last three decades or so, it is only now that the enabling technologies, such as thin-film photovoltaics (see Fig. 4.12), lithium-based rechargeable batteries, lightweight composite structures, and miniaturized electronics (payload cameras and communications electronics),

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Fig. 8.6  Zephyr solar-electric aircraft infographic (World Record 2018)

have progressed to the point where sustained flight based only on solar energy through the day-night cycle has become possible. The endurance world record that Zephyr established in Arizona in 2018 stands at 25  days, 23  hours, and 57  minutes. This record is sure to be broken in the coming years, but what will it take? In this section, we provide a notional technology roadmap for solar-electric aircraft as a new business category. The potential market and business applications for this type of aircraft, also known as High-Altitude Pseudo-Satellites (HAPS), include military surveillance, civilian research, observation, and acting as a radio communications relay, among others. The first point to make when starting a new technology roadmap is that each technology roadmap should have a clear and unique identifier and name:

8.2.1  2SEA – Solar-Electric Aircraft This indicates that we are dealing with a “level 2” roadmap at the product level (see Fig. 8.4), whereas “level 1” would indicate a market-level roadmap and “level 3” or “level 4” would indicate an individual technology roadmap at the subsystem or component level. Next, the technology roadmap needs an outline or “table of contents.” Many technology roadmaps only consist of a single slide or page (similar to Figs. 8.1, 8.2, 8.3 and 8.4). However, this is usually not sufficient to rationalize, quantify, and explain the recommendations made by the roadmap. Here, we propose the following outline for 2SEA3: 3  These 12 elements are a general recommendation for the outline and content of a technology roadmap. In our technology roadmapping and development class at MIT, we follow this outline

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1. Roadmap overview. 2. DSM allocation (interdependencies with others roadmaps). 3. Roadmap model (e.g., using OPM ISO 19450). 4. Figures of merit (FOM): Definition, name, unit, and trends dFOM/dt. 5. Alignment with company strategic drivers: FOM targets. 6. Positioning of company vs. competition: FOM charts. 7. Technical model: Morphological matrix and tradespace. 8. Financial model: Technology value (∆NPV). 9. Portfolio of R&D projects and prototypes. 10. Keys publications, presentations, and patents. 11. Technology strategy statement (incl. “arrow” or “swoosh” chart). 12. Roadmap maturity assessment (optional). We now demonstrate what these elements might look like for the 2SEA roadmap. 1. Roadmap Overview Solar-electric aircraft are built from lightweight materials such as carbon-fiber reinforced polymers (CFRP) and harvest solar energy through the photoelectric effect by bonding thin-film solar cells to the surface of the main wings, and potentially the fuselage and empennage as well. The electrical energy harvested during the day is then stored in onboard chemical batteries (e.g., lithium-ion, lithium-­ sulfur, etc.) or regenerative fuel cells and used for propelling the aircraft at all times, including at night. For the system to work, there needs to be an overproduction of energy during the day, so that the aircraft can use the stored energy to stay aloft at night. The flight altitude of about 60,000–70,000 feet is critical to staying above the clouds and not to interfere with commercial air traffic. Depending on the length of day, that is, the diurnal cycle that determines the number of sunshine hours per day, which itself depends on the latitude and time of year (seasonality), the problem is easier or harder. The reference case in this technology roadmap is an equatorial mission (latitude = zero) with 12 hours of day and 12 hours of night. The working principle and architecture of a typical solar-electric aircraft are depicted in Fig. 8.7. Such diagrams are helpful in depicting the key elements of a technology. 2. Design Structure Matrix (DSM) Allocation In a dependency structure matrix (DSM),  also known as a design structure matrix, we identify other roadmaps at the same or at other levels that are coupled to this roadmap. The coupling can be due to coinvestment relationships where an R&D project or demonstrator (prototype) requires progress in another technology as well. Coupling also exists when competing (mutually exclusive) technologies are being pursued at the same time, leading to an eventual down-select of the winning technology. The 2-SEA roadmap tree that we can extract from the DSM (Fig.  8.8 right) shows us that the solar-electric aircraft (2SEA) roadmap is part of a larger company-­ wide initiative on electrification of flight (1ELE) and that it requires the following and add between 15 and 20 technology roadmaps per year, see http://roadmaps.mit.edu

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Fig. 8.7  Working principle and architecture of a typical solar-electric aircraft

Fig. 8.8  DSM links of the 2SEA roadmap to other roadmaps at other levels

key enabling technologies at the subsystem level: 3CFP carbon fiber polymers, 3HEP hybrid electric propulsion, and 3EPS nonpropulsive energy management (e.g., this includes the management of the charge-discharge cycles of the batteries during the day-night cycle). In turn, these level 3 technologies require enabling technologies at level 4, the technology component level: 4CMP components made from CFRP4 (spars, wing box, and fairings), 4EMT electric machines (motors and generators), 4ENS energy sources (such as thin-film photovoltaics bonded to flight surfaces), and 4STO (energy storage in the form of lithium-type batteries or regenerative fuel cells). This hierarchy of roadmaps and the DSM allows to view a technology roadmap not in isolation but in the context of the higher-level (i.e., the market viewpoint), which sets performance, cost, safety, and reliability targets, and the lower-level more  CFRP = carbon fiber reinforced polymers.

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Fig. 8.9  Object-process diagram (OPD) of the 2SEA solar-electric roadmap

detailed technology roadmaps that contain the enabling and supporting technologies needed to achieve the higher-level targets. 3. Roadmap Model Using Object-Process Methodology (OPM) An important aspect of technology roadmapping is to clearly define the scope of the technology covered by the roadmap. This sounds simple, but in practice may not always be so clear. For example, does a roadmap on “high power electronics” include only switches (e.g., MOSFETs) or does it also contain the filters, cables, and control software? In this spirit, we provide an object-process diagram (OPD)5 of the 2SEA roadmap in Fig. 8.9. This diagram captures the main object of the roadmap (solar-electric aircraft), its various instances including the main competitors, its decomposition into subsystems (wing, battery, e-motor, etc.), its characterization by figures of merit (FOMs), as well as the main processes (flying and recharging). An object-process language (OPL) description of the roadmap scope is auto-­ generated and given in the Appendix. It reflects the same content as Fig. 8.9, but in a formal natural language. While initially awkward for the uninitiated, this kind of semantically rigorous and formal description helps avoid unnecessary ambiguities and confusion in terms of technology roadmap scope. 4. Figures of Merit (FOM) Definition The roadmap should also be unambiguous when it comes to the figures of merit (FOMs) that will be used to establish the status quo of the technology, its historical trends, and where it should be heading in the future. Table 8.2 shows a list of FOMs by which solar electric aircraft can be assessed. The first four (shown in bold) are used to assess the aircraft itself. They are very similar to the FOMs that are used to compare traditional aircraft that are propelled by fossil fuels. The big difference is that 2SEA is emission free during flight operations.  OPD and OPL are based on ISO Standard 19,450 (2015) for object-process methodology (OPM).

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Table 8.2  FOMs for 2SEA solar-electric aircraft roadmap FOM name Unit Cost

Units [€]

Operating Cost

[€/FH]

Maximum Payload [kg] Endurance Energy Storage Density Recharging Rate Electrical Max Power Photovoltaic Cell Efficiency Availability

[hrs] [kWh/ kg] [kWh/ hr] [kW] [%] [hrs/y]

Description Unit cost to manufacture the aircraft (incl. amortization of R&D) Cost per flight-hour, including all variable cost (e.g. energy recharging, battery replacement), and maintenance Useful payload that can be carried (includes cargo, payload, sensors and comm equipment) and passengers Time-aloft without recharging on the ground Energy stored onboard per unit mass of energy storage devices (e.g. batteries) Rate at which batteries can be recharged on the ground Total maximum electrical power generated on board by e-machines, for both propulsive and non-propulsive use Conversion efficiency from incoming photon flux to useable electric current (electron flux) Expected number of flight hours the aircraft is available for service per year (excludes maintenance downtime)

The other rows in Table 8.2 represent subordinated FOMs which impact the performance and cost of solar electric aircraft, but are provided as outputs (primary FOMs) from lower-level roadmaps at level 3 or level 4, see Fig. 8.8. Besides defining what the FOMs are, this section of the roadmap should also contain the FOM trends over time dFOM/dt as well as some of the key governing equations that underpin the technology. These governing equations can be derived from physics (or chemistry, biology, etc..) or they can be empirically derived from a multivariate regression model.6 Fig.  8.10 shows an example of a key governing equation governing (solar-) electric aircraft. The equation shown here is the electric version of the famous Bréguet range equation (which will be introduced in Chap. 9) and estimates the all-electric range as a function of key aerodynamic, structural, and electrical parameters. Some of the improvement trends for photovoltaic cells were shown in Chap. 4. For example, single crystalline silicon cells have been improving at a rate of about +0.4% per year, but are subject to a maximum theoretical efficiency bound of 33.16%. 5. Alignment with Company Strategic Drivers This section of the roadmap creates a link between the market-facing strategies of the company (the top two layers shown in Fig. 8.5): Market and business and the product-level FOMs and targets that should be achieved. Note that the analysis of current and evolving markets and the setting of the business strategy is not part of technology roadmapping, but feeds into it.

6  In general, physics-based models are preferred since empirically derived models are only valid over the interval of training data that were used on the input side. As technology progresses, the correlations derived for the empirical models may no longer be valid.

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Fig. 8.10  Governing equation with inputs and outputs for (solar-) electric aircraft Table 8.3  Strategic drivers for the 2SEA roadmap and statements of alignment Number Strategic driver 1 To develop a multipurpose solar-powered HAPS (UAV) that has enough endurance and payload to provide a new commercially viable service that will generate $X million in revenue by 2030 2

Alignment and targets The 2SEA technology roadmap will target a solar-powered UAV with a useful payload of at least 10 kg and an endurance of 500 days. This driver is currently aligned with the 2SEA technology roadmap To develop autonomous flight capabilities The 2SEA technology roadmap will help develop and test a certifiable stack of for HAPS and low Earth orbit (LEO) autonomy software that will reduce the satellites that will avoid the need for operating cost compared to current UAVs dedicated ground stations by 50%. This driver is currently not aligned with 2SEA

Table 8.3 shows an example of potential strategic drivers and alignment of the 2SEA technology roadmap with it.7 The list of drivers shows that the company views HAPS as a potential new business and wants to develop it as a commercially viable (for profit) business (1). In order to do so, the technology roadmap performs some analysis – using the governing equations in the previous section – and formulates a set of FOM targets that state that such a UAV needs to achieve an endurance of 500 days (as opposed to the world record of 26  days that was demonstrated in 2018) and should be able to carry a payload of 10  kg. The roadmap confirms that it is aligned with this driver. This means that the analysis, technology targets, and R&D projects contained in the roadmap (and hopefully funded by the R&D budget) support the strategic ambition stated by driver 1. The second driver, however, which is to use the HAPS program as a platform for developing an autonomy stack for both UAVs and satellites, is not currently aligned with the roadmap.8 7  Disclaimer: While we have used the Zephyr as a motivating example at the beginning of this section, the strategic drivers in this section should not be taken as a direct reflection of the Airbus Defense and Space business strategy in the area of solar electric aircraft. 8  Not all targets or ambitions stated in a technology roadmap may initially be funded or fundable by the R&D budget. That is fundamentally okay, since the technology roadmap is a statement of ambitions, translated to quantified targets. However, once converged, the technology roadmap targets should be achievable both fiscally and in terms of their feasibility within physical limits.

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As can be seen in Fig. 8.8, there are currently no autonomy or software-related elements in this roadmap. Therefore, the roadmap has an internal conflict between the strategic ambition expressed by strategic driver 2 and what is actually planned in the roadmap. This conflict needs to be first acknowledged and ultimately resolved, either by expanding the scope of the 2SEA roadmap or by explicitly removing strategic driver 2 as a requirement. 6. Positioning: Company Versus Competition FOM Charts The next portion of the roadmap is a careful qualitative and quantitative benchmarking of the company’s position against the present and potential future (if known) competition in this particular segment. This benchmarking is best done via a set of FOM charts so that the “gap” between the company and its competitors can be quantified and visualized. In some FOMs, the company may be the leader, while in others a follower. This task requires the gathering of data through the technology scouting function (see Chap. 14 for details). Figure 8.11 shows a summary of current and past electric and solar-electric aircraft from public data. This is an important exercise to bring some realism to the technology roadmap in terms of already fielded products, services, and systems and those under development. The aerobatic aircraft Extra 330LE by Siemens had the world record for the most powerful flight-certified electric motor (260 kW) at the time of writing. The Pipistrel Alpha Electro is a small electric training aircraft which is not solar powered, but it is in serial production. The Zephyr 7 is the previous version of Zephyr which established the prior endurance world record for solar-electric aircraft (14 days) in 2010. The Solar Impulse 2 was a single-piloted solar-powered aircraft that circumnavigated the globe in 2015–2016 in 17 stages, the longest being the one from Japan to Hawaii (118 hours). SolarEagle9 and Solara 50 were both very ambitious projects that aimed to launch solar-electric aircraft with very aggressive targets (endurance up to 5  years) and

Fig. 8.11  Benchmarking of (solar-) electric aircraft (approximations are made where necessary)

9  This project was partially funded by the DARPA Vulture program whose aim it was to develop a solar-powered UAV that could fly for 5 years without landing. The project was canceled in 2012.

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Fig. 8.12  Endurance [hrs] versus payload [kg] for all-electric and solar-electric aircraft

payloads up to 450  kg. Both of these projects were canceled prematurely. Why is that? The answer is shown in Fig. 8.12. The Pareto front (see Chap. 4, Fig. 4.17 for a definition) shown in black in the lower-left corner of the graph shows the best trade-off between endurance and payload for actually achieved electric flights by 2017. The Airbus Zephyr, Solar Impulse 2, and Pipistrel Alpha Electro all have certified flight records that anchor their position on this FOM chart. It is interesting to note that Solar Impulse 2 overheated its battery pack during its longest leg in 2015–2016 and, therefore, pushed the limits of battery technology available at that time. We can now see that both Solar Eagle in the upper right corner and Solara 50 in the upper left corner were chasing FOM targets that were unachievable with the technology available at that time. The progression of the Pareto front shown in red corresponds to what might be a realistic Pareto front progression between 2017 and 2020. Airbus Zephyr Next-­ Generation (NG) has already shown with its world record (624 hours endurance) that the upper left target (low payload mass of about 5 kg and high endurance of 600+ hours) is feasible. There are currently no plans for a Solar Impulse 3, which would be a non-stop solar-electric circumnavigation of Earth with one pilot, and which would require a nonstop flight of about 450 hours. A next-generation E-Fan aircraft with an endurance of about 2.5 hours (all electric) also seems within reach for 2020. Then, in green we set a potentially more ambitious target Pareto front for 2030. This is the ambition of the 2SEA technology roadmap as expressed by strategic driver 1. We see that in the upper left the Solara 50 project, which was started by Titan Aerospace, and then acquired by Google, then cancelled, and which ran from about 2013 to 2017, had the right target for about a 2030 entry into service (EIS), but not

8.2 Example of Technology Roadmap: Solar-Electric Aircraft

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for 2020 or sooner. The target set by Solar Eagle was even more utopian and may not be achievable before 2050 according to this 2SEA roadmap. The positioning (where are we today?), benchmarking (where is our competition?), and target setting (where do we want to be in 2 years? 5 years? 10 years?) and Pareto front progression are an essential part of a technology roadmap. It is this kind of information that allows technical leaders to push back against unrealistic business targets and to set the right expectations. The existence of this kind of quantitative and validated information is what distinguishes useful and high-­ quality roadmaps from “pseudo-roadmaps” that are mainly qualitative in nature and primarily useful as a visual aid (usually in the form of a PowerPoint chart) or conceptual guideline but not for detailed and serious technical planning. More on this topic in Sect. 8.5 on Technology Roadmapping Maturity Levels below. 7. Technical Model In order to assess the feasibility of technical (and financial) targets at the level of the 2SEA roadmap, it is necessary to develop a technical model. The purpose of such a model is to explore the design tradespace and establish what are the active constraints in the system. The first step can be to establish a morphological matrix that shows the main technology selection alternatives that exist at the first level of decomposition, see Fig. 8.13. It is interesting to note that the architecture and technology selections for the three aircraft on the 2017 Pareto front (Zephyr, Solar Impulse 2, and E-Fan 2.0) are quite different. While Zephyr uses lithium-sulfur batteries, the other two use the more conventional lithium-ion batteries. Solar Impulse uses the less efficient (but more affordable) single-cell silicon-based photovoltaics, while Zephyr uses specially manufactured thin-film multijunction cells. The technical model centers on the E-range and E-endurance equations and compares different aircraft sizing (e.g., wingspan, engine power, and battery capacity) taking into account aerodynamics, weights and balance, the performance of the aircraft, and also its manufacturing cost. It is recommended to use multidisciplinary design optimization (MDO) when selecting and sizing technologies in order to get the most out of them and to compare them fairly (Fig. 8.14). 8. Financial Model While technology roadmapping can also be important for not-for-profit enterprises, such as the NASA technology roadmaps discussed in Sect. 8.3, it is essential in a technology-based for-profit business. How much should the company expect to spend on R&D and on what projects? What % improvement in key FOMs can be expected and by when? How much are customers willing to pay for such improvements? How much internal cost reduction can be achieved due to new technologies (see also Chap. 12)? A financial model is akin to a “business plan,” but not necessarily for the product as a whole, but for the “delta” or relative impact that a specific technology can have on a baseline business plan. Imagine that a business plan for a product includes only wellestablished technologies. How would the business plan change with the “new”

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Fig. 8.13  Morphological matrix for (solar-) electric aircraft

Fig. 8.14  Multidisciplinary design optimization model of solar-electric aircraft

technology included? Would it be better or worse? How would the uncertainty of the business plan (standard deviation of net present value (NPV)) be affected by the technology? Figure 8.15 contains a sample NPV analysis underlying the 2SEA roadmap. It shows the nonrecurring cost (PDP NRC) of the product development project, which

8.2 Example of Technology Roadmap: Solar-Electric Aircraft

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Fig. 8.15  Hypothetical financial model for the 2SEA roadmap, PDP NRC Product Develeopment Project Non Recurring Cost, MFG RC Manufacturing Recurring Cost

includes the R&D expenditures as negative numbers. A ramp-up period of 4 years is planned with a flat revenue plateau (of 400 M€ per year) and a total program duration of 24 years. Such a model can then be used as a mechanism for quantifying the relative impact of different technologies such as extended endurance and larger payload (more revenue) or using a more expensive battery technology (more cost per unit). Ultimately, the decisions on product and service launch (or not) and which technologies to incorporate are linked to specific financial and technical FOM-based targets and milestones on a timeline and are the decisions of the senior management (and in some cases the board) of the company. 9. Portfolio of R&D Projects and Prototypes In order to know whether the technology roadmap will be able to meet its targets, it is necessary to define and prioritize which R&D projects should be carried out and

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funded in the overall portfolio. This is an important section of the technology roadmap since it creates a link between the higher-level financial and technical FOM-­ based targets and the specific R&D activities and projects that the technical organizations (research centers, R&D departments, engineering, etc.) will carry out, either internally or in collaboration with partners. In order to select and prioritize R&D projects, we recommend using the technical and financial models developed as part of the roadmap to rank-order projects based on an objective set of criteria and analyses.10 Figure 8.16 illustrates how technical models can be used to make technology project selections, for example, based on the previously stated 2030 performance targets (see Fig. 8.12). Figure 8.17 shows the outcome if none of the three potential R&D projects is selected. This model makes an important assumption: even if the company decides not to invest in any of the three proposed projects (battery, solar cell, and structural improvements), those technologies will still progress “on their own.” This is due to the fact – as shown in Chap. 4 – that long-term technology improvement trends are quite predictable and that, for most or at least for many technologies, there are several competing players and suppliers around the world. Major technological improvements are almost never achieved by just one company or organization (despite some claims made by these firms or the media) and often rely on a complex web of contributions from many organizations and individuals.

Fig. 8.16  R&D project evaluation using a product-specific technical model

 In many organizations, R&D projects are selected based mainly on “intuition” alone and the voices of a few – usually senior and very experienced – individuals. This is potentially a dangerous way to go as Christensen shows (Chap. 7) due to the innovator’s dilemma. Usually this intuitionbased process by entrenched senior engineers and executives will favor sustaining incremental technology investments, instead of sustaining radical or even disruptive ones. The dynamics and pitfalls of R&D project selection and R&D portfolio management are discussed further in Chap. 16.

10

8.2 Example of Technology Roadmap: Solar-Electric Aircraft

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Fig. 8.17  Expected outcome if none of the three proposed R&D projects are selected

So, for the owner of the 2SEA roadmap, the fundamental question is: “Can I sit back and wait until my subsystem and component technologies have matured ‘naturally’ based on their expected ‘natural’ rate of progression (the solid blue lines in Fig. 8.16 left), or do I need to proactively invest in them to remain or become a leader and accelerate their development (the dashed red lines in Fig. 8.16 left)?” For the 2030 target set in Fig. 8.17 (right), the answer is clear: We are unable to meet the target with no R&D investments in individual technologies. If we scale back the target to a payload of less than 10 kg and an endurance of less than 500 days, the target could potentially be met. We now consider investing in each project, one at a time, as shown in Fig. 8.18. The results of the analysis show that the largest impact on the performance of the aircraft is the battery technology (in this case, using lithium-sulfur chemistry). This makes sense since at its current size the aircraft is able to generate enough electrical power during the day (at least at an equatorial latitude and 12-hour day); however, it is its ability to store and release this energy efficiently at night in terms of energy density [J/kg] where a large improvement is needed. The problem is compounded by the deterioration of the battery with each cycle. Interestingly, further improving solar cell efficiency has no impact since it is not an active constraint in the system. Also, structural improvements alone (lightweighting of the structure) are insufficient. A further analysis would look at the net effect of combinations of different projects and technologies (this will be further discussed in Chap. 16 on R&D portfolio management). For now, the company decides on two projects in the 2SEA roadmap: 1. A Li-S battery improvement project with the FOM target of raising the number of charge-discharge cycles from 100 to 500 by 2025. This project will be allocated to the linked 4STO Energy Storage Roadmap and executed with a partner who specializes in lithium-sulfur chemistry-based battery development and certification (with shared IP, see Chap. 5).

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Fig. 8.18  Impact of individual R&D project investments on the Pareto frontier: top: Li-S battery improvements alone, middle: solar cell efficiency improvements alone, and bottom: structural improvements alone

2. A flight demonstrator project will be launched as part of the 2SEA roadmap to demonstrate a 10 kg payload and 365-day (1 full year) capability by 2027 as a prototype, with an intended entry into service (EIS) of a commercial 500-day-­10kg-capable product and associated profitable service by 2030. 10. Key Publications, Presentations, and Patents. A technology roadmap should contain a comprehensive list of publications, presentations, and key patents as shown in Fig. 8.19. This includes literature trends, papers published at key conferences and in the trade literature, and the trade

8.2 Example of Technology Roadmap: Solar-Electric Aircraft

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Fig. 8.19  Key scientific publications, trade press summaries, patent analysis, and publication trends should be included in a high-quality technology roadmap

press. Depending on legal considerations the technology roadmap may or may not contain patent information (since this could affect potential discovery in a future infringement lawsuit). Given the continual nature of technology progress, a technology roadmap cannot be created once and then left unattended for long periods of time. The best-in class companies that use technology roadmapping effectively have dedicated roadmap owners. This can be a full time or part-time job depending on the complexity and strategic importance of the roadmap. Technology roadmaps need to be refreshed regularly. Refresh rates of technology roadmaps depend on the industry and the dynamics of innovation. A yearly refresh that is synchronized with the annual planning and budget cycle is the minimum refresh rate that should be expected for technology roadmaps. 11. Technology Strategy Statement A technology roadmap should conclude and be summarized by both a written statement that summarizes the technology strategy coming out of the roadmap as well as a graphic that shows the key R&D investments, targets, and a vision for this technology (and associated product or service) over time. The technology roadmap could also insert a “swoosh” chart at this point. A maturity assessment of the roadmap (section 12) is optional, but recommended. For the 2SEA roadmap, the statement could read as follows: Our target is to develop a new solar-powered and electrically driven UAV as a HAPS service platform with an entry-into-service date of 2030. To achieve the target of an endurance of 500 days and useful payload of 10 kg, we will invest in two R&D projects. The first is a flight demonstrator with a first flight by 2027 to demonstrate a full-year aloft (365 days) at an equatorial latitude with a payload of 10 kg. The second project is an accelerated development of Li-S batteries with our partner XYZ with a target lifetime performance of 500 charge-discharge cycles by 2027. This is an enabling technology to reach our 2030 technical and business targets.

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8.3  NASA’s Technology Roadmaps (TA1–15) Technology roadmaps are not only in use in the industrial (for profit) sector. One of the organizations that has developed and made extensive use of technology roadmaps is the National Aeronautics and Space Administration (NASA) in the United States. There was a major effort in the agency to create an initial set of roadmaps in 2012. These were then updated in 2015 and decomposed into the 15 technical areas (TAs) shown in Fig. 8.20. One interesting fact about the NASA technology roadmaps is that when they were first published in 2012 that only TA1-TA14 existed. In other words, the technology roadmaps focused only on technologies related to human and robotic space missions. Later, in 2015, the TA15 roadmap was added which includes all of aeronautics. Given the breadth of NASA’s missions and activities, each of these roadmaps contains many levels of decomposition in order to capture comprehensively the technological base. Let’s consider as an example area TA9 which covers entry, descent and landing (EDL) systems. Inside the roadmap we find three levels of technology decomposition as shown in Fig. 8.21. Within the roadmap we can then deep dive into a set of missions that create the “technology pull” or “need” for new or enhanced technologies. For example, Fig. 8.22 shows a “Venus In-Situ Explorer” as a potential mission with an originally

Fig. 8.20  NASA’s technology roadmaps grouped into 15 technical areas (TAs)

8.3 NASA’s Technology Roadmaps (TA1–15)

Fig. 8.21  Decomposition inside the NASA TA9 EDL Roadmap

Fig. 8.22  Timeline for TA9 technology roadmap from missions to technologies

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Fig. 8.23 Illustration of Rigid Venus Entry Probe (left) and a Mechanically-Deployable Aeroshell (right)

planned launch date in 2024 (green triangle on the upper right). Entering the atmosphere of Venus which is hotter and denser than the atmosphere of Earth or Mars will require a heat shield that can withstand the thermal loading during ballistic re-­ entry as shown in Fig. 8.23. The timeline then backtracks from the planned mission launch date (2024) to the point of “need,” where key technologies should be available at a given TRL level. For example, the technologies under 9.1.1. and 9.1.2. (thermal protection systems for rigid and deployable decelerators) are shown to be “needed” by 2016 and should start development in 2014. The rule of thumb is that technologies should have been matured to at least TRL 6 before they are taken onboard by a flight program. A similar rule of thumb exists in the commercial sector as well. Figure 8.24 provides a description of the technology and its key challenges (top), quantification of the current technological state of the art (left), technology performance goal (right), and technology interdependencies on research or on other technologies (bottom). We see here that the FOM-based targets for this technology are ambitious but not utopian: a peak heating rate of 50–100 [W/cm2] (a factor of 2 improvement), an integrated heat load during reentry of [12 kJ/cm2] (a factor > 2 improvement), a peak temperature of 400 degrees [C] (a 30% improvement), and a deployed diameter of 10–25 meters (a factor of 2–4 improvement). Clearly, this technology must be considered as an enabling technology if we desire to enter the atmosphere of Venus at high speeds. How are these Technology Roadmaps Used at NASA? Figure 8.25 depicts the nominal NASA technology roadmapping process. The TA1–15 roadmaps are shown on the upper left. They serve as input to the NASA Technology Executive Council (NTEC). This is the decision body that sets technology policy, and prioritizes strategic technology investments. The roadmaps contain a larger “wishlist” than what can be funded (this is usually true in all organizations) and so a down-selection and prioritization is necessary. This then influences NASA’s annual budget process, leading to a certain number of technology projects that are

8.3 NASA’s Technology Roadmaps (TA1–15)

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Fig. 8.24  Technology description and performance goal for EDL heat shield technology Fig. 8.25 NASA technology roadmapping and budget process

funded. These projects are then documented and the portfolio is analyzed and reflected in “TechPort” which is an agency-wide technology database. A portion of this database known as “Tech Finder” is then made available to the public, containing information on patents, licenses, and software agreements. The loop is then closed periodically by injecting new information into the roadmaps from TechPort.

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⇨ Exercise 8.1 Pick a published roadmap, for example, from NASA or any other you can obtain (however, do not use or share company confidential materials). Perform a careful review of the roadmap and critique it on 1–2 pages. What technology is it about? What elements does it contain? Is anything missing according to the proposed outline? Is it fit for purpose? Since 2016, NASA has gone to a more decentralized approach to technology planning with every directorate and major program setting their own priorities for technology investment, usually based on the strategic input coming from decadal surveys compiled by the National Research Council (NRC) of the National Academies of Science, Engineering and Medicine.11

8.4  Advanced Technology Roadmap Architecture (ATRA) This section briefly describes the technology planning and roadmapping approach as it was implemented and refined at a major aerospace company by the author and his team. It is based on the principles of technology roadmapping described in this book. Figure 8.26 shows the overall ATRA methodology and its four major steps. The inputs to the ATRA methodology are as follows and are shown on the left side of Fig. 8.26: • A hierarchical decomposition of the product, service, and technology portfolio into different mapped levels. The simplest decomposition is one with two levels with products (and services or missions) at level 1 and technologies at level 2 (see also Fig. 8.1). A more fine-grained decomposition was shown in Fig. 8.8 with four levels of decomposition: markets or missions (L1), products and services (L2), subsystems (L3), and components (L4). • Based on the DSM of each individual roadmap (see Fig. 8.8), which shows the interdependencies with other products and technologies (a technology can and should ideally serve more than one product), a global Dependency Structure Matrix (DSM) can be constructed which shows an overview of the total system of roadmaps, potentially including the selected R&D projects. • Strategic drivers coming from marketing, strategy, and senior management. See Table 8.3 for an example of strategic drivers. • Other inputs such as those coming from technology scouting, IP analytics, and subject matter experts (SMEs) both inside and outside the company.

 NASA has recently selected the ATRA framework for researching improved ways of managing its technology portfolio, see: https://www.nasa.gov/directorates/spacetech/strg/early-stage-innovations-esi/esi2020/astra/

11

8.4 Advanced Technology Roadmap Architecture (ATRA)

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Fig. 8.26  Advanced technology roadmap architecture (ATRA)

The ATRA methodology then proceeds in four steps (see Fig. 8.26 middle column), each asking a very specific question that must be answered by the roadmap. 1. Where Are We Today? This question asks for the current status quo in terms of market position, products, services, technology performance (FOM-based), and running R&D projects. The corresponding sections in the technology roadmaps that capture the status quo are several as demonstrated in the 2SEA example. When starting technology roadmapping from scratch or building on a rather thin initial set of roadmaps, this can be a rather laborious process, involving several workshops (Fig. 8.27), and potentially dozens or even hundreds of stakeholders in the organization. Depending on the size of the organization and the complexity of its product and service portfolio, the set of R&D projects, and the number of subject matter experts involved, this can yield thousands of pieces of information that need to be collated, grouped, linked, and validated. In some cases, there may be ambiguities in terms of which roadmap a project belongs to or what is the primary product in need of a particular technology. Given this initial set of information, roadmap owners (RMOs) or technology committees are then appointed to develop the individual roadmaps. The content of the roadmaps should be in a more or less standardized format. One of the key outputs of step 1 is a set of FOM charts, as shown in Fig. 8.26 (top right). It shows the current position(s) of the company compared to its competitors and compared to the current state of the art (SOA), expressed as a Pareto frontier. See Fig. 8.12 for a quantitative example in the 2SEA roadmap. This will give a clear sense in which technology areas the company is leading, where it is about equal to its peers, and where it is behind its competitors.

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Fig. 8.27  Interactive and hands-on workshops are recommended to map all the running R&D projects against the technology roadmaps, subject matter experts, including roadmap owners, and against target products and services (or missions)

2. Where Could We Go? The second step asks the question of what are the possible new products, services, or technologies that the company could pursue. Some of these could be based on a market or customer “pull,” while some could be based on technology “push” based on the ideas of the science and engineering community or the leadership within the company. The existence of a so-called Concurrent Design Facility (CDF) is very helpful as a central focal point and supporting infrastructure for this exploratory activity (Fig. 8.28). This is an important phase of roadmapping which is often neglected or cut short. It is important to explore new concepts, missions, and ideas, and to do so using both qualitative concepts and quantitative models. An effective methodology during this stage is “Concurrent Engineering” (Knoll et al. 2018), where different experts from inside and outside the organization are brought together to explore potential technological directions. As shown in Fig. 8.26 (middle), we would expect a discrete set of potential scenarios for the evolution of existing products and technologies to emerge from step 2 and the associated exploratory CDF sessions. Figure 8.26 shows a scenario A and scenario B which are initially common, but then split after about 3 years (decision point). 3. Where Should We Go? This step applies a prioritization scheme to the proposed product and technology scenarios. It is here that the specific FOM targets are set and agreed for individual products and technologies. If these are very different from the original input received from marketing or strategy, there may have to be an iteration loop to close any gaps or inconsistencies.

8.4 Advanced Technology Roadmap Architecture (ATRA)

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Fig. 8.28  Concurrent Design Facility (CDF) in support of technology roadmapping

Fig. 8.29  Network chart (left) coming from step 1 “as is” analysis, reorganized as a “DSM” in steps 2 and 3 with clearly formed clusters of technologies (referred to as “thrusts” shown right) which represent strategic investment areas for technologies

Figure 8.29 shows an example of the clarification that should come from steps 1 and 2 moving into step 3. We see on the left a network diagram that shows the interdependencies between different roadmaps across the ATRA, with some technologies having a central role as enabling technologies for several products. On the right, we see the same information, but now organized as a DSM with L1 products in the upper left and L2 technologies on the lower right. The technologies are grouped into technology clusters: digital design and manufacturing (DDM), materials, autonomy, connectivity, and electrification as an example. These clusters of roadmaps then become the focal points for targeted technological investments including R&D projects and new prototypes and demonstrators.

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4. Where We Are Going! This is the final step in technology roadmapping and technology planning. This is where the set of proposed scenarios and R&D projects and new demonstrators has to be fitted into an overall budget envelope for R&D. Most companies spend anywhere between 1% and 20% of their revenues on R&D to “prepare for the future.” The goal of technology roadmapping is to turn this difficult process from a more intuitive and personality-driven exercise into a disciplined and rational one. At this stage, each potential project in the R&D portfolio should have clear targets, a statement of work (SOW), and a well fleshed out budget to completion. To build a competitive R&D portfolio, following the recommendations of the roadmaps, and hopefully achieving a consensus among the senior technical leadership, the following decisions have to be made on a project-by-project basis: • START: Which new technology projects should be started and with what targets? • STOP: Which projects are coming to completion naturally (handoff to product development) and which projects should be terminated prematurely either because they have stalled or because their deliverables are no longer needed due to a change in strategic drivers? • KEEP: Continue funding projects that are on track and that are still needed. • CHANGE: Modify ongoing projects for a variety of reasons. For example, projects may be accelerated or slowed down depending on changes in product entry-into-service (EIS) dates. Or similar projects in the portfolio may be merged to achieve better synergies. The ability to stop or change projects may be limited by external commitments that have been made such as collaborations with development partners on a given project or external funding agencies. Figure 8.30 shows an example of the type of technology projects that have been funded and rationalized in the R&D portfolio of a major aerospace company based on the ATRA methodology.

Fig. 8.30  Sample of projects recommended by technology roadmaps in the R&D portfolio of a major aerospace company. These projects line up with the technology clusters in Fig. 8.29 (right)

8.5 Maturity Scale for Technology Roadmapping

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8.5  Maturity Scale for Technology Roadmapping As stated by Phaal and Muller (2009), technology roadmapping is not really new. It has been practiced since about the 1970s. However, the speed of technology development, the number of companies that have been disrupted (see Chap. 7) or that have disappeared due to a lack of technological investment or foresight, has increased sharply in recent decades. As a result, technology planning and roadmapping are now viewed as a key strategic function in many technology-intensive industries such as aerospace, automotive, consumer electronics, software, life sciences, medical devices, and many more. Recently, Schimpf and Abele (2019) have conducted an empirical survey of technology roadmapping in N = 81 German industrial firms, including smaller and mid-­ sized ones. Figure 8.31 shows a quantitative result from their survey in terms of the mentioned application areas of roadmapping. They found that: *Quote

“Companies apply roadmapping within an average of 3.37 application areas with a standard deviation of 1.17 and roughly one-third (32.1%) of participants apply roadmapping for two application areas or less. This leads to a rejection of hypothesis H01, recognizing that a majority of participating companies apply roadmapping to more than two application areas. Within the content of roadmaps in companies, products (79.7%), technologies (68.4%) and projects (57.0%) are the most common options mentioned by participants.”

This confirms also the soundness of the ATRA approach which emphasizes a clear mapping from products to technologies to projects. While roadmapping is becoming more common among technologically intensive companies, the quality and impact of technology roadmaps can vary greatly.

Fig. 8.31  Frequency of application areas for roadmapping in German companies (N = 81; multiple responses possible). Source: Schimpf and Abele (2019)

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Table 8.4  Technology Roadmapping Maturity Scale Maturity level I

II

III

IV

V

Name Exploration

Characteristics at that level Partial list of only the most important technologies Focus mainly on technology scouting and finding “blind spots” Uneven format, quality, and depth of roadmaps Not used at all for decision-making, just for information Canvassing Complete list of roadmaps across the firm Centralized project inventory mapped to roadmaps Standardization of format and dedicated roadmap owners “Flat” list of technologies, no explicit link to products Evaluation Explicit hierarchy of roadmaps with link to products or missions Clear definition of FOMs and setting of targets Anticipated entry-in-service dates are used to set pace Find and exploit synergies across business units Prescription Roadmaps are the main way to decide on R&D investments Value for money is calculated for sustaining technologies Quantified route-to-target options (vector charts) evaluated with risk levels in products where multiple technologies are used Clearly prioritized and ranked list of R&D projects in each roadmap Optimization Calculation of FOM targets and value with calibrated technical models for each product, including the mapped technologies Validated multiyear cost models for NRC and RC Prioritization of R&D investments across product divisions Portfolio optimization for value versus risk to maximize the NPV for the firm with explicit expectations on ROI of the R&D portfolio

In Table 8.4, we propose a five-level maturity scale to assess technology roadmapping in a given organization. The higher the level, the more advanced technology roadmapping is practiced. A company that is new to technology roadmapping should expect to start at level I and – with the proper support (incl. financial resources) of the senior management should be able to – progress about one level per year. Thus, the full roadmapping journey from level I to level V may realistically take 5 years or more. It is also possible for a company to achieve a high level of maturity in technology roadmapping at one point, but to regress again for a variety of reasons, such as changes in management, lack of support from senior management, or due to mergers and acquisitions of companies at vastly different levels of technology roadmapping maturity one from the other. ⇨ Exercise 8.2 Develop a technology roadmap for a technology of your choice. Make sure you are passionate about the technology you choose. This can be a quick exercise to arrive at a sketch of a roadmap, or a big effort over multiple weeks

 Appendix

249

or months. Use 2SEA as an example for the format of the roadmap, but feel free to add, modify, or remove elements as you see fit. Summarize your roadmap in a document or digital wiki (including the use of hyperlinks between the elements) and present it to your peers or management for feedback.

Appendix

Object-process language (OPL) for the 2SEA roadmap

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References Bernal, Luis, et al. "Technology roadmapping handbook." International SEPT Program, University of Leipzig (2009) Kerr C, Phaal R. Technology roadmapping: Industrial roots, forgotten history and unknown origins. Technological Forecasting and Social Change. 2020 Jun 1;155:119967. Knoll, Dominik, Alessandro Golkar, and Olivier de Weck. "A concurrent design approach for model-based technology roadmapping." In 2018 Annual IEEE International Systems Conference (SysCon), pp. 1-6. IEEE, 2018. NASA Technology Roadmaps, Office of the Chief Technologist (OCT): https://www.nasa.gov/ offices/oct/home/roadmaps/index.html Phaal, Robert, and Muller, Gerrit, “An architectural framework for roadmapping: Towards visual strategy,” Technological Forecasting and Social Change, Volume 76, Issue 1, 2009,Pages 39-49, ISSN 0040-1625 Schimpf, Sven, and Thomas Abele. "How German Companies apply Roadmapping: Evidence from an Empirical Study." Journal of Engineering and Technology Management, 52 (2019): 74-88.

Chapter 9

Case 2: The Aircraft

Advanced Technology Roadmap Architecture (ATRA) Inputs

Steps

Outputs

L1 Products and Missions

1. Where are we today?

FOMjj

Strategic Drivers for Technology

Technology State of the Art and Competitive Benchmarking

L2 Technologies

+5y Organization

Technology Projects

FOMi

Today

2. Where could we go? Technology Systems Modeling and Trends over Time

+10y FOMj

Dependency Structure Matrix

Tech Pul Pull

Figures of Merit (FOM) Current State of the Art (SOA) Technology Trends dFOM/dt

Competitor 1 Competitor 2

Technology Systems Modeling

L1

Technology Roadmaps

+10y

Scenario A

+5y

Scenario B ?

3. Where should we go?

L2

Scenario Analysis and Technology Valuation E[NPV] - Return

Intellectual Property Analytics

4. Where we are going! Technology Portfolio Valuation, Optimization and Selection

Technology Investment Efficient Frontier Technology Portfolio Technology Projects σ[NPV] - Risk

Definitions What is Technology?

Ecosystems Nature Technology, Nature Technology Diffusion, Infusion and Industry and Humans

The Future Is there a Singularity ?

© Springer Nature Switzerland AG 2022 O. L. de Weck, Technology Roadmapping and Development, https://doi.org/10.1007/978-3-030-88346-1_9

Pareto-optimal set of technology investment portfolios Recommended Technology Portfolio (Expected NPV and Risk)

9

C Cases

Case 1

Case 2

Automobiles

Aircraft

Case 3 Deep Space Network

Foundations History Milestones of Technology

Design Reference Missions Future Scenarios T h l Technology V Valuation l ti Vector Charts

FOMi

Tech Push

Technology Scouting Knowledge Management

Scenario-based Technology Valuation

Case 4 DNA Sequencing

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9  Case 2: The Aircraft

Fig. 9.1  Three fundamental mechanisms of flight: (left) buoyancy in balloons, (middle) aerodynamic lift in aircraft, and (right) conservation of momentum in rockets

9.1  Principles of Flight The dream of humans to be able to fly “like birds” is as old as human civilization itself. There are Egyptian tombs that have depictions of humans flying (into the afterlife) and the famous Greek legend of Icarus, who flew too close to the sun and came plummeting down after the wax in his wings melted only to drown in what is known today as the Icarian sea (not far from the Island Samos). It is important to mention that it was not a heavier-than-air vehicle that first allowed humans to fly, but hot air balloons. The Montgolfier brothers and Pilâtre de Rozier (1783) in France were the first to achieve and demonstrate such flights, first tethered, then untethered. On January 7, 1785, Frenchman Jean-Pierre Blanchard and his American copilot John Jeffries completed the first successful crossing of the English Channel in a balloon.1 This is much earlier than most people realize. However, hot air balloons have several disadvantages. They are cumbersome to setup and launch (it may take a few hours to set up the balloon and get the air hot enough so that it produces lift), they can only be launched in fair weather to avoid strong crosswinds or lightning strikes and they have to be recovered by land at the destination (wherever that may be). In fact, over the centuries, we have discovered that there are fundamentally only three known mechanisms that allow for flight in Earth’s (or any other) atmosphere, as far as we know,2 see Fig.  9.1 (de Weck et al. 2003). The key to successful powered flight is to control the forces acting on the vehicle in a careful manner and at all times. 1  Source: https://www.historyhit.com/1785-english-channel-balloon-crossing/ Their competitor de Rozier died in the attempt to cross the channel two years earlier, becoming (with his copilot Pierre Romain) the first documented aviation fatality, Icarus notwithstanding. 2  I hesitate to say that there are absolutely no other ways than these three concepts to fly. I stated these are the three known mechanisms. Predictions about what is and is not possible with technology should only be made with extreme caution.

9.1 Principles of Flight

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Fig. 9.2  Trajectory and forces in ballistic flight (top) and powered flight (bottom)

In the case of balloon flight lift, FL is produced by displacing a certain volume of air, V, with a gas (such as heated air) that has a density less than the surrounding air ρgas