1. Viterbi School of Engineering Technology Transfer Center Portfolio Defense February 2006 Ken Dozier

2. Viterbi School of Engineering Technology Transfer Center A System of Forces in Organization Efficiency Direction Proficiency Competition Concentration Innovation Cooperation Source: “The Effective Organization: Forces and Form”, Sloan Management Review, Henry Mintzberg, McGill University 1991

3. Viterbi School of Engineering Technology Transfer Center Make & Sell vs Sense & Respond Chart Source:“Corporate Information Systems and Management”, Applegate, 2000

4. Viterbi School of Engineering Technology Transfer Center Supply Chain (Firm) Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002

5. Viterbi School of Engineering Technology Transfer Center Supply Chain (Government) Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002

6. Viterbi School of Engineering Technology Transfer Center Supply Chain (Framework) Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002

7. Viterbi School of Engineering Technology Transfer Center Supply Chain (Interactions) Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002

8. Viterbi School of Engineering Technology Transfer Center Theoretical Environment Seven Organizational Change Propositions Framework, “Framing the Domains of IT Management” Zmud 2002 Business Process Improvement Business Process Redesign Business Model Refinement Business Model Redefinition Supply-chain Discovery Supply-chain Expansion Market Redefinition

9. Viterbi School of Engineering Technology Transfer Center Data Provider 52 acre complex located on the Alameda Corridor in Lynwood, CA. The Business Park is a master planned development with 12 separate facilities consisting 15,000 to 200,000 square foot buildings. Houses 45 tenants who occupy anywhere from 2000 square feet to 100,000 square feet and employing approximately 1300 individuals.

10. Viterbi School of Engineering Technology Transfer Center Statistical Physics Approach

11. Viterbi School of Engineering Technology Transfer Center Outline Introduction 1 –Why a framework? 3 –Why statistical physics? 4 –What technology transfer measures? 5 Statistical physics program tasks for technology transfer to an industrial sector –Quasi-static phenomena Task 1. Reduce unit cost of production [T2S 2004] 6-12 Task 2. Improve productivity (output/employee) [CITSA 04 & JITTA] 13-16 Task 3. Increase total output 17 Task 4. Reduce R&D costs 18 –Dynamic phenomena Task 5. Understand implications of supply chain oscillations for tech. transfer [CITSA 05] 19-20 Task 6. Increase rate of production [T2S 2005] 21 Task 7. Understand T2 implications of instabilities in supply chain oscillations 22 Task 8. Dampen disruptive cyclic phenomena by technology transfer 23 Task 9. Increase rate of technology spread and adoption 24 –Reality check Task 10. Compare the theory with actual data 25 –Report Task 11. Prepare final report

12. Viterbi School of Engineering Technology Transfer Center Why a framework? Current understanding of technology transfer impact –Anecdotally-based –Not comprehensive or convincing Advantages of an non-anecdotal framework –Impact on relevant performance parameters and interrelationships –Comprehensive and systematic approach

13. Viterbi School of Engineering Technology Transfer Center Why statistical physics? Proven formalism for “seeing the forest past the trees” –Well established in physical and chemical sciences –Our recent verification with data in economic realm Simple procedure for focusing on macro- parameters –Most likely distributions obtained by maximizing the number of micro-states corresponding to a measurable macro-state –Straightforward extension from original focus on energy to economic quantities Unit cost of production Productivity R&D costs –Self-consistency check provided by distribution functions

14. Viterbi School of Engineering Technology Transfer Center What technology transfer measures? Value-added goals for an industrial sector –Reduce unit cost of production & reduce entropy –Improve productivity (output/employee) –Increase total output –Reduce R&D costs –Increase rate of production –Dampen disruptive cyclic phenomena –Increase rate of technology spread

15. Viterbi School of Engineering Technology Transfer Center Task 1. Reduce unit cost of production [Presented at 2004 T2S meeting in Albany, N.Y.] Background question –What is required for technology transfer to reduce production costs throughout an industrial sector? Approach –Apply statistical physics approach to develop a “first law of thermodynamics” for technology transfer, where “energy” is replaced by “unit cost of production” Result & significance –Find that technology transfer impact can be increased if “entropy” term and “work” term act synergistically rather than antagonistically Technology Transfer: Quasi-static

16. Viterbi School of Engineering Technology Transfer Center Task 1 approach: Why does unit production cost play the role of energy in a statistical physics of production? Problem [simplest case] Given: Total output N of sector Total costs of production for sector C Unit costs c(i) of production at sites i within sector Find: Most likely distribution of outputs n(i) within sector Approach Let W{n(i)} be the number of possible ways that a set of outputs {n(i)} can be realized. Maximize W{n(i)} subject to given constraints N, C, and c(i)  /  n(i) [ lnW +  {N-Σn(i)} +β{C-Σc(i)}] =0 [1] Solution for simplest case n(i) = P exp{-βc(i)} [Maxwell-Boltzmann distribution] [2] where the parameters characterizing the sector are: P is a “productivity factor” for the sector β is an “inverse temperature” or “bureaucratic factor” Technology Transfer : Quasi-static

17. Viterbi School of Engineering Technology Transfer Center Task 1. Comparison of Statistical Formalism in Physics and in Economics VariablePhysicsEconomics State (i)Hamiltonian eigenfunctionProduction site EnergyHamiltonian eigenvalue Ei Unit prod. cost Ci Occupation number Number in state Ni Output Ni = exp[-βCi+βF] Partition function Z ∑exp[-(1/k B T)Ei]∑exp[-βCi] Free energy FkBT lnZ(1/β) lnZ Generalized force fξ ∂F/∂ξ∂F/∂ξ ExamplePressureTechnology ExampleElectric field x chargeKnowledge Entropy (randomness)- ∂F / ∂T k B β 2 ∂F/∂  Technology Transfer : Quasi-static

18. Viterbi School of Engineering Technology Transfer Center Total cost of production C = ∑ C(ξ;i) exp [-β(C(ξ;i) – F(ξ ))] [1] Task 1 approach. Conservation law for Technology Transfer Effect of a change dξ in a parameter ξ in the system and a change dβ In bureaucratic factor dC = - dξ + β [  2 F/  β  ξ] dξ + [  2 [βF]/  β 2 ] dβ [2] which can be rewritten dC = - dξ + TdS [3] Significance First term on the RHS describes lowering of unit cost of production. Second term on RHS describes increase in entropy (temperature) Technology Transfer : Quasi-static

19. Viterbi School of Engineering Technology Transfer Center Technology Transfer : Quasi-static Ln Output Unit costs High output N, High “temperature”  High output N, Low “temperature” 1/  Low output N, High “temperature” 1/  Low output N, Low “temperature” 1/  Costs down Entropy up Task 1. Approach

20. Viterbi School of Engineering Technology Transfer Center Task 1. Semiconductor example: Movement between 1992 and 1997 on Maxwell Boltzmann plot Ln Output Unit costs 1997: High output N, Low “temperature” 1/  1992: Low output N, High “temperature” 1/  Technology Transfer : Quasi-static

21. Viterbi School of Engineering Technology Transfer Center Task 1. Heavy spring example: Movement between 1992 and 1997 on Maxwell Boltzmann plot Ln Output Unit costs 1997: Low output N, High “temperature” 1/  1992: Low output N, Low “temperature” 1/  Technology Transfer : Quasi-static

22. Viterbi School of Engineering Technology Transfer Center Technology Transfer: Quasi-static Task 2. Improve productivity (output/employee) [Paper submitted to JITTA for publication (March, 2005) following well-received presentation at CITSA ’04 conference (July, 2004)] Background –Information paradox: Value of technology transfer – and more generally, of information – on productivity has been called into question Approach –Apply statistical physics approach to show how productivity is distributed across an industry sector –Compare evolution of distributions for information-rich and information-poor sectors [US economic census data for LA] Results & significance –Find that productivity decreases but output increases in small company sectors that invest in information, while productivity increases in information-rich large company sectors

23. Viterbi School of Engineering Technology Transfer Center Task 2. Normalized cumulative distribution of companies N(S)/N vs shipments per company S for β = 0.5 (bottom curve), 1, and 5 Technology Transfer: Quasi-static

24. Viterbi School of Engineering Technology Transfer Center Task 2. Comparison of U.S. economic census cumulative number of companies vs shipments/company (diamond points) in LACMSA in 1992 and the statistical physics cumulative distribution curve (square points) with β = 0.167 per $106 Technology Transfer: Quasi-static

25. Viterbi School of Engineering Technology Transfer Center Company size: Large Intermediate Small IT rank 59 70 81 # 0.86 1.0 0.90 E(1000s) 0.78 0.98 1.08 #/company 0.91 1.0 1.21 Sh ($million) 1.53 1.24 1.42 Sh/E ($1000) 1.66 1.34 1.35 β 1.11 0.90 0.99 Findings: Sectors with large companies spend a larger percentage on IT. Largest % increases in shipments are in large & small company sectors. Small companies increased in size while large companies decreased. Number of large and small companies decreased by 10%. Employment decreased 20% in large companies, but increased 8% in small companies. Largest productivity occurred in large companies. Task 2. Ratio (‘97/’92) of the statistical parameters Technology Transfer: Quasi-static

26. Viterbi School of Engineering Technology Transfer Center Background question –What are the parameters involved in determining an increase in output as well as a decrease in unit costs of production? Approach –Maximize number of microstates corresponding to macrostate defined by total cost of production ratio of total output/total cost of production –Obtain equivalent of a “chemical potential” Result –Conservation equation containing a uniquely defined technology transfer “force” that affects chemical potential for increasing output Technology Transfer: Quasi-static Task 3. Increase total output

27. Viterbi School of Engineering Technology Transfer Center Background question –Is there a systematic way of reducing barriers to industry use of government R&D and vice versa (diffusion and infusion)? Approach –Maximize number of microstates corresponding to macrostate defined by total cost of R&D ratio of total innovation output/total R&D cost –Obtain equivalent of an “innovation potential” Result & significance –Conservation equation containing a uniquely defined technology transfer “force” that affects innovation potential for increasing innovation output Technology Transfer: Quasi-static Task 4. Reduce R&D costs

28. Viterbi School of Engineering Technology Transfer Center Background –National resources are wasted by disruptive and ubiquitous economic cycles –Collective oscillations are evident in industry sector supply chains Approach –Develop a simple model of important interactions between supply chain companies that give rise to oscillations –Determine structure of normal mode oscillations –Find governing dispersion relation for supply chain normal modes Results & significance –Identify opportunities for resonant, adiabatic, and short-time technology transfer efforts Task 5. Understand implications of supply chain oscillations for technology transfer [Paper accepted for CITSA 05 conference in July, 2005] Technology Transfer: Dynamic

29. Viterbi School of Engineering Technology Transfer Center Supply chain normal mode equation y(n-1) – 2y(n) + y(n+1) +(  T)2 y(n) = 0[1] Normal mode form for N companies in chain y(p:(n) = exp[i2  pn/N] [2] Normal mode dispersion relation  =  (2/T) sin(  p/N) where p is any integer [3] Task 5. Normal modes in a supply chain with uniform processing times Technology Transfer: Dynamic

30. Viterbi School of Engineering Technology Transfer Center Background question –How should government technology transfer policy be focused to realize the value associated with increased production rates? Approach –Understand flow (overall production rate) in a supply chain –Develop normal modes for flow oscillations –Apply quasilinear theory to describe effect of resonant interactions with normal modes on overall flow velocity Results & significance –Find criteria for timing and position focus of technology transfer efforts that will maximize impact on rate of production throughout a supply chain Task 6. Increase rate of production [Paper accepted for presentation at T2S meeting in September, 2005] Technology Transfer: Dynamic

31. Viterbi School of Engineering Technology Transfer Center Background –MIT’s “beer game” simulation has demonstrated that costly and disruptive supply chain inventory oscillations with phase change and growing amplitudes occur consistently. Approach –Extend normal mode analysis of supply chains to accommodate instabilities due to overcompensation –Apply eikonal (Hamilton-Jacobi) analysis to identify critical damping potential Result & significance –Determine the degree to which slowly-responding government technology transfer efforts can impact instabilities Task 7. Understand technology transfer implications of instabilities in supply chain oscillations Technology Transfer: Dynamic

32. Viterbi School of Engineering Technology Transfer Center Background questions –Inventory oscillations in supply chains can be reduced somewhat by adiabatic technology transfer efforts, but is there a more effective technology transfer focus? –Asynchronous SBIR program more appropriate? Approach –Introduce a Wigner-type distribution function –Develop associated Fokker-Planck equation for describing the evolution of oscillatory phenomena in supply chains –Solve evolution equation by multi-time-scale formalism Result & significance –The effects of adiabatic, resonant, and short time-scale technology transfer efforts will be systematically described. –Criteria will be established for the timing and focus of technology transfer efforts for most effectively controlling instabilities Technology Transfer: Dynamic Task 8. Optimize damping of disruptive cyclic phenomena by focusing technology transfer

33. Viterbi School of Engineering Technology Transfer Center Background –W. Mansfield and others have pointed out the economic benefits of rapidly spreading new technology within and between industry sectors Approach –Adapt the Pastor-Satorras equation for virus spreading in scale-free networks to technology transfer –Generalize further by adding a Fokker-Planck term to the PS equations Result & significance –Identify thresholds for successful technology spread, and determine parameter-dependencies of spreading rates Task 9. Increase rate of technology spread and adoption Technology Transfer: Dynamic

34. Viterbi School of Engineering Technology Transfer Center Background –Applications of statistical physics to understand the impact of information on productivity growth has been demonstrated with U.S. economic census data for the Los Angeles area. A more general test of the predictions for technology transfer is needed. Approach –Mine the technology transfer data of government agencies (NASA, DOE, DOD) to determine the impact on specific statistical physics parameters (e.g. productivity, output, bureaucratic factor) and on their distribution functions Result & significance This should providing convincing support for the statistical physics framework for the guidance and analysis of technology transfer efforts. Actual data in statistical physics framework will provide calibration for assessing DOLLAR VALUE of technology transfer Task 10. Compare the theory with actual data Technology Transfer: Reality Check

35. Viterbi School of Engineering Technology Transfer Center SUMMARY This statistical physics-based program should help put NASA in a leadership position to: design and implement optimal technology transfer programs systematically measure value-added impact

36. Viterbi School of Engineering Technology Transfer Center Future Work Examine NAICS consistent 2002 and 1997 U.S. manufacturing economic census data Use seven organizational change proposition strata to further explore the linkage between organizational size and productivity. Compare results across the strata and within each stratum Check for compliance to thermodynamic model Expand to technology transfer