Navigating the Digitalization of R&D

By Jim Euchner, VP, Global Innovation, Goodyear; Editor-in-Chief, Research-Technology Management (RTM)

“Well something’s lost, but something’s gained In living every day.”

—Joni Mitchell, “Both Sides Now”

This is an optimistic issue. In it, you will read about a wide variety of ways in which digitalization will improve the practice of R&D. Ted Farrington, in his summary article, “On the Impact of Digitalization on R&D,” gives a broad view of the changes we will see. He looks at the three major trends discussed in this issue—virtual experimentation and simulation, digital collaboration, and big data—through the lens of the four scenarios for the future explored in the IRI2038 program. His introduction to the issue makes clear not only that much is possible, but also that there are many harbingers of the future in what we see happening today.

Each of these major trends is discussed in its own article in the issue. Anita Friis Sommer, Ajith Rao, and Chris Koh discuss the first major trend in their paper, “Leveraging Virtual Experimentation and Simulation to Improve R&D Performance.” As simulation technologies have become more sophisticated, they have become increasingly important to competitive advantage, allowing leading practitioners to improve design processes while reducing the time to market. In their discussion, Sommer, Rao, and Koh offer a maturity framework that organizations can use to assess their current effectiveness in deploying virtual technologies and explore the many opportunities they may offer.

In her Point of View article, “The Digitalization of R&D Collaboration,” Stephanie Orellana discusses how digitalizing collaboration can add value for R&D and for the organization as a whole. She considers the forms that future R&D collaboration might take, from tools that enable people to work better together to create, capture, and share knowledge to immersive technologies like virtual reality that allow collaborative processes to be captured and, perhaps, recreated. She also discusses the primary benefits of broader, faster, more interactive collaboration—including collaboration across traditional corporate boundaries.

Finally, Michael Blackburn and colleagues address “Big Data and the Future of R&D Management.” Their study, based on interviews and workshops with thought leaders and practitioners across industries, explores the potential of big data to “inform, enable, and transform or disrupt R&D management.” They find a wide variance in the state of adoption across industries but expect profound change as industrial firms seek the kind of transformation that big data has enabled in pharmaceutical R&D.

These papers present a very positive vision of the future. But, as Joni Mitchell suggests, we need to look at these technologies from “both sides now.” When something is gained, something is often lost as well. What are the trade-offs we can expect on the way to what is likely an inevitable future?

Marshall McLuhan provides a framework for understanding the impact of new technologies in The Laws of Media (McLuhan and McLuhan 1992McLuhan, M., and McLuhan, E1992The Laws of Media: The New ScienceToronto, CanadaUniversity of Toronto Press. [Google Scholar]). For McLuhan, every new medium (or technology or “extension of man”) triggers at once four effects:


When taken together, these effects represent what McLuhan calls the message of the medium. Virtual experimentation and simulation technologies, for example, extend or intensify speed of development, design iterations, systems integration, and precision. They make obsolete (over time) physical prototyping, lab experimentation, and the intuition that comes from working hands-on with physical material. In the extreme, they may reverse (or flip into) a world where researchers act at such abstract levels that they lose touch with their own designs and where speed as an imperative devolves into frenetic activity.

Digital collaboration will extend our ability to work with a wide range of experts anywhere on the globe. Although truly usable advances in this area have been slow to materialize, as collaboration tools become increasingly immersive, rich, and natural to use, we may be able, collectively, to solve problems that have until now been beyond the reach of even powerful R&D organizations. By enabling a new kind of connection across distance and allowing people to work together without an extensive bureaucracy, collaboration tools retrieve a sense of community, making people feel again the sense of being part of a small, cooperative village. They may also make obsolete the very idea of a research lab and reverse into a world of fragmented tasks, one in which the lab of the future becomes the factory of the past, to paraphrase Barbara Garson (1998Garson, B. 1998The Electronic Sweatshop: How Computers Are Transforming the Office of the Future into the Factory of the PastNew YorkSimon & Schuster. [Google Scholar]).

Finally, big data holds the potential to help us learn more about everything and build on that knowledge to create better products, better customer experiences, and better work. In the ideal, we will discover what people really want and need and do so in newly efficient ways. Intuition, market research, and human judgment will first be extended, then replaced by data analytics. At the same time, mass customization informed by big data can retrieve the kinds of made-to-order products once available from individual craftsmen. But this world can reverse into one with little privacy and a creeping depersonalization, even amidst pervasive customization.

My point is that we should approach the benefits digitalization will bring with excitement and enthusiasm but also with care. We must manage their side effects even as we embrace the opportunities they offer. As researchers, we are not just brains but bodies as well—bodies that interact with things and people and the built world. In the excitement that digitalization promises, we cannot forget this. The new capabilities are coming, but the blind pursuit of speed, efficiency, and rationalization cannot be permitted to lead to an R&D that is more productive but somehow less human.


  • Garson, B. 1998The Electronic Sweatshop: How Computers Are Transforming the Office of the Future into the Factory of the PastNew YorkSimon & Schuster.
  • McLuhan, M., and McLuhan, E1992The Laws of Media: The New ScienceToronto, CanadaUniversity of Toronto Press.

One thought on “Navigating the Digitalization of R&D

  1. William Miller September 28, 2017 / 10:56 AM

    The articles in RTM on Digitalization of R&D seem to fit into the model of 3rd Generation of R&D that assumes the market need and business model have already been correctly identified and therefore the task for R&D is just to do technology, product or service development to serve the need and fit within the business model. Digitalization helps the performance of that task in a linear stage gate process. But 4th Generation R&D is required when the market need and business model have not been correctly identified. In that situation, real physical prototypes are needed inside a non-linear iterative process to permit potential customers to use and test the prototypes to determine their value and to test a business model. In addition, 4G permits testing of a collection of products from multiple suppliers which together provide a solution. A subset of 4G is the Lean Start-up Methodology described by Steve Blank which is taught in the NSF iCorps. 4G is required for radical innovation when customers have never experienced the “new to the world products or services” which exist in a new dominant design for products, services and the structure of a market or industry. An example of the application of 4G was the Apple iPhone which required products and services from multiple suppliers to be tested. The market need and the required “ecosystem” to serve the need could never have been identified with traditional marketing such as focus groups, surveys or methods such as “the voice of the customer”. The scope of 4G is broader that 3G in that it includes determination and validation of the market need and multiple business models with capabilities linked together ( knowledge, tools, applications, platforms, components, technologies, processes, products and services) in a value chain from multiple suppliers.

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