An Open Innovation Machine Through Rapid Technology Intelligence Processes

Transcription

1 An Open Innovation Machine Through Rapid Technology Intelligence Processes Paul Frey President Nils Newman Director, New Business Development

2 Most innovations fail. And companies that don t innovate die. Open Innovation Chesbrough, H. W. (2003). Open Innovation The New Imperative for Creating and Profiting from Technology, Harvard Business School Press, Boston, MA.

3 Goal: Improve R&D Efficiency Get profitable innovations into the market at lowest cost Internal Research and Development Mergers, Acquisitions, Divestitures License Intellectual Property (in/out) Leverage Knowledge Economy $$$$ $$$ $$ $

4 Example: Connect and Develop Procter & Gamble s New Model for Innovation produces more than 35% of company s innovation and billions of dollars in revenue R&D productivity has increased by nearly 60 percent R&D investment as a percentage of sales is down from 4.8 percent in 2000 to 3.4 percent today "Connect and Develop: Inside Procter & Gamble's New Model for Innovation," Harvard Business Review, Vol. 84, No. 3, March 2006, External collaboration plays a key role in nearly 50 percent of P&G's products. We've collaborated with outside partners for generations but the importance of these alliances has never been greater. Our vision is simple. We want P&G to be known as the company that collaborates inside and out better than any other company in the world. A.G. Lafley Chairman of the Board and Chief Executive Officer April 2008

5 Networks/Brokers A quick search yields: and there are many others

6 A Network/Broker Example Yet2.com Apr 16, 2008

7 The Process of using the brokers Bi-directional marketplace Sellers post ideas and capabilities Buyers post needs and requirements Yields people that want to be found

8 The Challenge Qualify the people (and ideas) that want to be found Find the people (and ideas) that have not yet entered the knowledge economy marketplace

9 The Decision Process Reality Expert Opinion Drives Technology Decisions Information Expert Opinion

10 The Balanced Decision Process Information Good Decisions demand both Information and Expert Interpretation Expert Opinion

11 Six information types Technical Information ST&I (Science, Technology & Innovation) Databases (e.g., Web of Science, INSPEC, Micropatents) Internet Sources (e.g., Googling) Technical Expertise Contextual Information Business, competition, customer, popular, policy content Databases (e.g., Lexus-Nexus, Factiva) Internet Sources (e.g., blogs, website profiling) Business Expertise

12 How to use these information sources effectively? Model of Innovation Framework of Influences Process to analyze and integrate information

13 A Linear view of Innovation Processes Functionality Incremental Innovation Maturation Adoption Commercial Introduction New Product Development Licensing, Collaborative Innovation Development; Patenting Basic to Applied Research Time

14 Research Arena Internal R&D A 1 Contextual Arena Business, Competitors, Markets, Customers, Regulatory Knowledge Flow Existing PSPS* External R&D A 3 A 4 A 2 Research Knowledge Flow Incremental innovation design New PSPS B 5 A 5 design Radical innovation Really New PSPS C 5 *PSPS = Products, Services, Processes &/or Systems

15 Technology Delivery System (TDS) Push vs. Pull Pressure of Scientific Discovery Suction of Societal Need VCR Example Scientific Push Societal Pull

16 TDS: Mapping External Influences Public Interests Stakeholders Competitors Government Bodies Customers Impact Assessment Management/Organization Resources needed: Capital Skills Materials Software Products Impacts

17 Sample TDS Federal R&D funds for solar energy R & D Performers for solar energy R & D Results Lending Institutions R&D Results & Manpower Tech. needs R&D Results & Manpower Local Gov t Codes and Regulations Approvals Lending Institutions Equipment Home Builders Equip. Manufacturers & Developers Tech. Info Architect/ Engineering Companies Lending Institutions Approvals Tech. Info Tech. needs Homes $ $ Private Housing Market

18 How do you answer critical technology management questions? Tech Mining Alan L. Porter and Scott W. Cunningham John Wiley & Sons Inc., 2005

19 Tech Mining Questions and Indicators Issues Questions Indicators Tech Mining

20 How to get Management to hear information-based knowledge products Define the Management of Technology (MOT) Issues Break out particular MOT Questions Identify candidate empirical Indicators Identify appropriate Data Source(s) Identify appropriate Analytical Tool(s) Design Effective composite Representations that can be rapidly built one-pagers in one day.

21 13 MOT Issues 39 MOT Questions ~200 Innovation Indicators R&D Portfolio Mgt R&D Project Initiation Engr Project Initiation New Product Development Strategic Planning Track/forecast emerging or breakthrough technologies etc. What? What s hot? Fit into tech landscape? New frontiers at fringe? Drivers? Competing technologies? Likely development paths? Who? Who are available experts? Which universities or labs lead? MOT Issues, Questions, and Indicators Mapping of topic clusters within the technology 3-D trend charts for topic clusters Ratio of conference to journal papers (benchmarked) Scorecard rate-of-change metrics for topic clusters Time slices to show evolution of topical emphases Topic growth modeling (S-curve) fit & extrapolation Profile table of main players Pie chart: Company vs. Academic vs. Government publishing Spreading (or constricting) # of players by topic

22 First Cut Indicators Top Researchers Top Organizations Time Trends

23 Knowledge Networks Authors by index term Inventors by IPC Co-authors and co-inventors

24 Profiles

25 Integrated indicators Clusters Categorization Concept Migration Year Records New Terms New Terms (normalized) Acoustic devices [3 of 3] Wavelet transforms [2 of 2] Unmanned vehicles [3 of 11] Remotely operated vehicles [12 of 13] Pattern matching [2 of 8] World Wide Web [2 of 2] Motion estimation [3 of 4] Bombs (ordnance [3 of 3] Martian surface analysis [2 of 2] Web browsers [2 of 2] Uncertain systems [3 of 5] Radar antennas [1 of 1] Personal computers [2 of 5] Arid regions [2 of 2] Internet [3 of 3] Radio systems [1 of 1] Backpropagation [2 of 3] Visualization [2 of 2] Ordinary differential equations [2 of 2] Safety devices [1 of 1] Electrostatics [2 of 3] Tracking radar [2 of 2] Multiplexing [2 of 2] Monolithic microwave integrated circuits [1 of 1] Jamming [1 of 2] Graphical user interfaces [2 of 6] Handicapped persons [2 of 2] Semantics [1 of 1] Mine trucks [1 of 3] Radar target recognition [2 of 2] Theorem proving [1 of 1] Receiving antennas [1 of 1] Ordnance [1 of 1] Information retrieval [2 of 3] Traffic surveys [1 of 2] Redundant manipulators [1 of 1] Optical beam splitters [1 of 1] Nonlinear filtering [1 of 1] Autonomous agents [1 of 4] Railroad tracks [1 of 1] Ballast tanks [1 of 1] Antenna arrays [1 of 2] Maximum likelihood estimation [1 of 1] Vector quantization [1 of 1]

26 Polymer Biomaterials : fibrous structural proteins : skin (68 patents)

27 Polymer Biomaterials : fibrous structural proteins : skin (470 patents)

28 Agriculture Geoscience Infectious diseases Clinical medicine Ecology Environ. Sci. General medicine Chemistry Biological Sciences Neurosciences Materials Sci Computer Sciences Engineering GLOBAL MAP OF SCIENCE Leydesdorff&Rafols (2007, submitted) Physics

29 Map of Science Quantum Dot 1995

30 Map of Science Quantum Dot 2005

31 Ceramic Engine Example Overcoming Management Resistance Mechanical vs. Electrical Discovering new technology Building the prototype

32 The Timeliness of Information Information Professional Technology Analysts Researchers & Technology Managers Decision Maker

33 Automation Aim first for automation of cleanup, normalization, integration, and creation of individual indicators Manageable and maintainable Fosters incremental and evolutionary development Usable in a variety of ways Seek opportunities for sequencing these components into analytical machines

34 The Tech Mining Process (recap) 1. Understand & scope the questions, set in an Innovation Process context 2. Identify suitable databases (especially R&D publication or patent abstracts) 3. Search & download topical records (iteration likely) 4. Import into software tools 5. Clean the data 6. Analyze & interpret (integrate) 7. Represent the information effectively to communicate well 8. Standardize and semi-automate where possible

35 The Balanced Decision Process Patent, R&D Publication & other Databases Analysis & representation Data Software KP s Knowledge Products Good Decisions demand both timely Knowledge Products and Expert Interpretation Expert Opinion

36 Summary Open Innovation depends on effectively exploiting external research knowledge Treat text like Data Mine it for patterns! Patterns speak to innovation prospects: maturation, contextual forces, market prospects Answer who, what, where & when Innovation Management questions for business decision processes

37 Better informed technology management Faster decisions and better results Competitive Advantage The Payoff