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TASC FUTURES GROUP

NORTHROP GRUMMAN. D E F I N I N G T H E F U T U R E. TASC FUTURES GROUP. TASC FUTURES GROUP Approach. Seven Fundamentals Lay the Foundation Change the Frame Challenge the Experts Tell Stories Live and Work in the Future Walk Back to Today Shape the Future An application:

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TASC FUTURES GROUP

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  1. NORTHROP GRUMMAN D E F I N I N GT H E F U T U R E TASC FUTURES GROUP

  2. TASC FUTURES GROUP Approach • Seven Fundamentals • Lay the Foundation • Change the Frame • Challenge the Experts • Tell Stories • Live and Work in the Future • Walk Back to Today • Shape the Future • An application: • Future Economic Impacts of Investments in Intelligent Machine Technology, 2006-2025 • Methodology • Results & Implications Anticipation is not widely practiced by decision makers because when things are going well they can manage without it and when things are going badly, it is too late to see beyond the ends of their noses! –Michel Godet

  3. Technology Scouting:Lessons from Future Impact Studies David Leech Senior Analyst for Technology & Industry Evaluation Futures, Forecasting, & Change Management Northrop Grumman Information Technology Intelligence Group (TASC)

  4. Outline • Overarching “Lessons Learned” • Future Economic Impacts of IMT, 2006-2025 • Analytical Approaches to Technology Scouting • Building bridges from lab concerns to industry concerns

  5. Overarching “Lessons Learned” • There is a cultural and operational disconnect among scientific institutions, technology development institutions, and business institutions • Yet, ultimately they act as a whole to create value added • Increases in national productivity rely on a national innovation system • If you don’t understand the system you can’t make the pieces fit • At the most basic level, S&T institutions aim to advance technical performance parameters and business institutions aim to advance return on investment (ROI) • Thinking about the future of S&T in a business context is hard! No easy answers! • Bridging the two (or so) worlds is essential for actionable futures and for technology scouting • ROI is largely about incremental cost and quality improvements • These are tractable (w/ difficulty) • Radical change is much harder to assess • To some extent, its more critical to get a bead on these implications (e.g., IMT) • What we try to do in our futures work is walk out to the future and bring it back to today

  6. Sophisticated Understanding of the Private/Public National Innovation System A important part of our proposed approach to IMT A important part of our proposed approach to “scouting”

  7. Future Economic Impacts of Investments in Intelligent Machine Technology, 2006-2025 • Challenge and Methodology • Results • Implications

  8. The Challenge

  9. The Ultimate Challenge of this approach to Futures • What does the future hold for IMT investors? • Private and public • Without some reasoned sense of what the future holds, allocating the right amount of scarce investment dollars to IMT research and development (R&D) is extremely difficult • Most technologists are not futurists • Our purpose: to shine some light onto a path that likely represents the future of machine intelligence and to do so in “business case” language

  10. The Methodological Challenge • Forecast the future • What do we expect from the application of intelligent machine technology? • Three narrowly defined industries • Automotive • Aerospace • Capital Construction • Quantify future economic impact using conventional impact analysis tools • Disaggregated technology production function • Productivity growth rates • Social rate of returnon investment • Forecasting technology trends is a challenge • Quantifying economic impacts of technology is a challenge • Quantifying economic impact of future technologies is a realchallenge!

  11. Future IMT Scenarios — Conservative & Optimistic

  12. Future IMT Scenarios — Application-Specific

  13. State-of-the-Art Survey Population Targeting • R&D capital stock model is standard empirical approach • We adapted to focus on a few variables that survey respondents could estimate • Who possesses IMT R&D capital stock? • “Hidden” knowledge stocks identified through patent filter construction • “Leading inventors” analyzed and identified • A ready source of interest, motivation, and applied domain-specific knowledge

  14. Identifying Survey Population

  15. Industry Survey Population

  16. The Results

  17. IMT R&D Productivity Growth Rates and ROI

  18. The Implications

  19. “It Takes a Village” — Disaggregated Production Function • Surveyed firms report that they would invest much more in IMT R&D if they could appropriate all of those social returns • We asked how much they would invest if they could appropriate all of the returns generated by their IMT R&D investments • IMT R&D intensities roughly double • This underlines the importance of government supported IMT-R&D investments to counteract spillovers • Surveyed firms believe that government funding of IMT R&D will be importantif the anticipated developments in IMT are to be achieved by 2025 • Respondents believe compliance with industry technical standards is increasingly important for the success of the next generations of IMT-based products • Some concern about whether the government and industry will fulfill their roles with regard to the level and composition of funding to achieve the 2025 level of advancement • These findings are consistent with the New Innovation Economics thrust being promoted by the Information Technology & Innovation Foundation (ITIF)

  20. Disaggregated Production Function

  21. Short version: • David P. Leech and John T. Scott,“Intelligent Machine Technology and Productivity Growth,” Economics of Innovation and New Technology, September, 2008 • Focuses on economic framework the impact estimates Long version: • Leech, et al, Future Economic Impacts of Investments inIntelligent Machine Technology, 2006-2025(Final report to the National Institute of Standards and Technology, 2006.) • Includes economic framework and impact estimates • Introductory discussion of the R&D capital stock model and its evolution • Extensive discussion of national innovation system model • Extensive discussion of the scenarios • Extensive discussion of overall methodology

  22. Analytical Approaches to Technology Scouting— “Bridge Building” —

  23. Technology-to-Product KPP Mapping • A framework for determining where your technology gets the greatest bang for the buck • Automated Technology Frontiers • Automated (low-cost), quantitative technology frontiers generator • Innovation Communication Protocols (ICPs) • An infrastructure tool for improving the effectiveness of technology transfer in general

  24. Underlying Concepts Products & Services Product & Service Attributes Products (Embodied) Technologies (Disembodied) • Wine • Car • Computer • Dryness • Fruitiness • Alcohol content Science • Acceleration • Speed • Prestige Innovation Market Structure • Software availability • Convenience • MIPS

  25. 1 3 5 Y Y 2 Y Y Technology-to-Product KPP Mapping — Tying Laboratory Advances to Commercially Relevant Market-Drivers Describe Innovation Market Structure Identify Cost- & Quality- Driving Performance Attributes 4 Technology Users (Manufacturers) Assess 2 w/Respect to 3 C o s t & Q u a l P e r f o r m a n c e Develop & Test Assessment Methodology Technology Suppliers (Corporate, Government, & University Labs) Selected Interviews Either =Cost -Driving Attribute • Pull Cost ? • Future ROI ? $4X X = Identify Key Invention Advances $10Y E-mail Survey Or $2Z Z Similar to TFP Model used in IMT Laboratory Scientist TASC Analyst

  26. Automated (Low Cost) Technology Frontiers • • A technology frontier is a small set of key performance parameters (KPPs) that define the state of technology and its progress over time • • Unique to a community of practice (CoP) and reflected in a set of technical terms by which technical progress is defined intrinsically • • Optics, for example, define progress in terms of “surface accuracy” measured in “microns RMS”; or IR detectors, define progress in terms of “detectivity,” measured in Watts-1, and “element size”, measured in “mils.” • • Patents categorize technologies in ~110,000 categories (infinitely mixed and matched) to identify horizontally- and vertically-related technology development efforts • • With sophisticated search techniques KPPs can be identified in temporal cohorts of patents and rates of progress modeled • • A low-cost approach to identifying KPPS over time! Stand-alone or in combination with KPP mapping

  27. Innovation Communication Protocols • Science and Technology Estates • Organizations and individuals have different drivers • Sources place a high value of novelty • Brokers place a high value on profitability • Users place a high value on utility • Members of one estate, type of organization, or subfield do not have an effective and efficient means to communicate the readiness of a scientific or technological innovation to transfer to other estates, organizations, or fields* • Organizations talk and think differently about S&T innovation because they have different goals and incentive structures • Ineffective communication increases technical and commercial risk, while slowing the flow innovation from one estate to another Solution: “Innovation Communication Protocols” orICPs

  28. Innovation Communication Protocols (Continued) • Standard tool for effectively and efficiently communicating information among innovation estates • Think in terms of inter-estate TRLs • ICPs are multi-tiered, qualitative heuristic algorithms that grade innovation characteristics along a continuum from theory to product in a format that is readily comprehensible across estates • Initial ICPs • will focus on innovation maturity and innovation scope • will be field or subfield specific • will be developed in close collaboration with all innovation estates • Post-project work will extend ICPs to organizational maturity and financial maturity and look for commonality among field ICPs

  29. Summary • The recognition that the National Innovation System is comprised of multiple estates is essential to effective business and government policy • Bridging the real differences between estates is essential for actionable futures and for technology scouting • Actually building the analytical bridges — KPP Mapping or ICP — is interesting, useful, and hard • Progress depends on it!

  30. Contact Information David P. Leech Senior Analyst for Industry & Technology Evaluation Intelligence Group (TASC) Northrop Grumman Corporation 703-907-4075 (Rosslyn office) 410-346-6338 (home office) david.leech@ngc.com Thank You!

  31. Backup

  32. Intelligent Machine Technology • IMT refers to any computational technology or system that senses its environment and adjusts its behavior based on sophisticated world modeling and value judgment to achieve its goals • Encapsulated in a computer program, intelligent sensor, or a robot • IMT embraces • Computer–aided design technologies • Computer numerically controlled (CNC) machine tools • Computer-controlled inspection systems • Enterprise integration information systems • Just-in-time production scheduling and inventory control technologies • Internet technologies that enable out-sourcing to the most efficient suppliers • Multi-spectral measurement systems for construction site metrology

  33. What is a “Social Rate of Return (SRR)”? • The social rate of return (SRR) is a form of a standard financial metric known as the internal rate of return (IRR) • The IRR is derived from the calculation of Net Present Value (NPV) • IRR is the discount rate that makes the NPV of an investment equal to zero • NPV=0 is the breakeven condition for an investment • Accept the project if IRR > the discount rate • Reject the project if the IRR < the discount rate • When the IRR is calculated to evaluate the impact of an R&D investment by a single organization it is called the private rate of return (PRR) • When the IRR is calculated to evaluate the impact of R&D across a number of firms, it is called the social rate of return (SRR)

  34. Compared to what? • Past econometric studies of R&D impacts employing a total factor productivity (TFP) approach report industry-level annual social rates of return between 61percent and 162 percent • Annual social rates of return of 72%-77% look modest by comparison • However, past studies plagued with measurement difficulties • Overestimation due to difficulties with holding constant forces other than R&D spending • Inability to account for launch costs (would tend to reduce RoR) • Our approach • Focuses on R&D productivity impact alone • Directly accounts for launch costs (which are quite substantial) • Reports relatively high productivity increases and social rates of return that represent a very impressive indicator of future economic impact

  35. IMT R&D Productivity Growth Rates and ROI

  36. IMT R&D Productivity Growth Rates and ROI

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