Experimentation, Innovation and Technological Change Advancing Knowledge and the Knowledge Economy Washington, D.C. January 10, 2005 Stefan Thomke Harvard Business School

The Process, Economics and Management of Experimentation ©Experimentation is critical to product development and needs to be managed explicitly (e.g., organization, process design choices, technology adoption). ©New technologies (e.g., computer modeling and simulation) amplify the impact of experimentation and create the potential for higher R&D performance, product innovation and value creation for customers. ©The integration of these new technologies have organizational, managerial and process implications for innovators. Today

Why Experimentation Matters to Learning by Doing and Innovation Source of uncertainty How can uncertainty be resolved? ©Technical ©Can it work? ©Production ©Can it be effectively produced? ©Need ©Does it address customer needs? ©Market ©Does the market size justify the resource investment? 1. Experimentation 2. “First Principles” 3. Experience

Technology Potential: The Changing Economics of Experimentation ©New technologies (e.g., computer modeling/simulation, rapid prototyping) are making it easier than ever to conduct complex experiments quickly and cheaply. ©Potential increases in efficiency and speed (e.g., substitution of costly prototypes with long build times). ©New possibilities through “what-if” experiments that were previously too costly and impractical (e.g., simulation models in integrated circuits, flight simulators). ©These technologies are being broadly adopted in many industries (e.g., automotive, semiconductors, pharmaceuticals) and professional fields (e.g., engineering, science, medicine).

Research Example: Crash Simulation and Modeling in Automotive R&D  1982: Simulation model using CRASHMAS (3,000 finite elements). zRun time nearly three months. zNo real significance in design decisions.  2002: Simulation model of X5 using PAMCRASH (about 700,000 finite elements). zRun time less than 30 hours (at less than $10/hour). zDrives important design decisions. Source: BMW AG.

Technology-Performance Paradox: Empirical Findings from Car Industry ©Large-scale empirical study of project performance drivers in global car industry (started in 1980s with Clark/Fujimoto) ©Late 1990s: U.S. companies were leading in the deployment of sophisticated technologies (e.g., CAE, 3-D CAD). ©But Japanese companies integrated these technologies more effectively into their development organizations by: ©Earlier use of simulation that forces earlier problem-solving. ©Having fewer organizational interfaces in order to accelerate experimentation and problem-solving cycles. ©Using fewer functional prototypes; they are built rapidly for immediate feedback on design problems and solutions.

How Can Companies Unlock the Potential of New Technologies? ©My research focused on what does and does not work when the value of experimentation is captured. ©Large empirical studies, grounded field research, and analytical methods revealed a set of principles that are robust across companies and industries. ©While some of the management principles seem intuitive, the reasons why companies do not follow them are complex and subtle. ©These reasons can be understood through the lens of experimentation.

Unlocking the Potential: Principles for Managing Experimentation zOrganizing for Rapid Iteration. yOrganizing for rapid experimentation. yFail early and often but avoid “mistakes”. yManage projects as experiments. zExperimenting Early and Often. yAnticipate and exploit early information through front-loaded process. yExperiment frequently but do not overload your organization. yIntegrate new and traditional technologies to unlock performance. zShifting the Locus of Experimentation & Innovation (with Eric von Hippel, MIT).

Shifting Experimentation to Customers via Design Toolkits Traditional Model Solution Information (“What is possible?”) Need Information (“What do I want”) Supplier Customers New Model: Reverse Information Flow Solution Information (“What is possible?”) Need Information (“What do I want”) Supplier Customers “User Research” “Innovation Toolkits”

LSI Logic Founded in 1981: A Radical Approach Transforms Chip Design Focus Solution Information (“What is possible?”) Need Information (“What do I want”)

zCustomers design chips that are produced by LSI. zUser-friendly and integrated toolkit (using simulation and CAD technology). zTraditional suppliers were reluctant to make tools available to markets (intellectual property). zFujitsu even refused to share its tools with US division. Source: LSI Logic. Shifting Design Locus: LSI’s Development Toolkit

From Design Toolkits to New Markets: The LSI Model

An Industry Transformation: Creating Value by Shifting the Locus of Design

BACK-UP

Industry Study of Product Development: Global Automotive Project at HBS ©Background of research project: ©1980s: First study by Kim Clark and Takahiro Fujimoto; 29 car development projects; book published in ©Early 1990s: Second study by Kim Clark, David Ellison and Takahiro Fujimoto; 27 projects; David’s thesis. ©Late 1990s: Third study by Takahiro Fujimoto and myself (with support from Kim); 22 projects; new book project. ©In the third study, companies answered about 400 questions per project, including on the use of new technologies (e.g., CAD, simulation, rapid prototyping).

Learning by Experimentation: Testing of Innovative “What-If” Ideas Department ADepartment B Design (new concepts) Analyze Data (modify understanding) Build Model (prototypes) Run Test (collect data) Learn Iterations Interface(s)

Global Automotive Study at HBS: Actual vs. Expected Engineering Hours Source: Global Automotive Development Study at HBS.

Global Automotive Study at HBS: Actual vs. Expected Development Time Source: Global Automotive Development Study at HBS.

The Pattern is Repeated: The Rise of Field Programmable Technologies

Creating Value with Toolkits: Experiences at GE Plastics z30 years of in-house expertise on website (tools): $5 mill. cost. zPotential customers can solve their own design problems. yHelpline calls dropped >50%. y400 e-seminar for 8,000 potential customers per year. zAbout one million visitors p.a. yAutomatic screening and tracking of potential customers. yOne third of new customers. zSales threshold dropped by more than 60%. Source: GE Plastics.

Summary ©Managing Experimentation: Experimentation is critical to product development and needs to be managed explicitly (e.g., organization, process choices, technology adoption). ©The Changing Economics of Experimentation: New technologies (e.g., computer modeling and simulation) amplify the impact of experimentation and create the potential for higher R&D performance, product innovation and value creation for customers. ©How to Unlock The Potential of New Technologies: Companies that master and integrate these technologies must change their processes, organization and management of innovation.

How “Best Practice” Moves Big Markets Out of Reach for Companies