Viterbi School of Engineering Technology Transfer Center Portfolio Defense February 2006 Ken Dozier.
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Transcript of Viterbi School of Engineering Technology Transfer Center Portfolio Defense February 2006 Ken Dozier.
Viterbi School of Engineering Technology Transfer Center
Portfolio Defense February 2006
Ken Dozier
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
Viterbi School of Engineering Technology Transfer Center
Make & Sell vs Sense & Respond
Chart Source:“Corporate Information Systems and Management”, Applegate, 2000
Viterbi School of Engineering Technology Transfer Center
Supply Chain (Firm)
Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002
Viterbi School of Engineering Technology Transfer Center
Supply Chain (Government)
Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002
Viterbi School of Engineering Technology Transfer Center
Supply Chain (Framework)
Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002
Viterbi School of Engineering Technology Transfer Center
Supply Chain (Interactions)
Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002
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
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.
Viterbi School of Engineering Technology Transfer Center
Statistical Physics Approach
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
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
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
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
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
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 sectorTotal costs of production for sector CUnit 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 sectorβ is an “inverse temperature” or “bureaucratic factor”
Technology Transfer : Quasi-static
Viterbi School of Engineering Technology Transfer Center
Task 1. Comparison of Statistical Formalism in Physics and in Economics
Variable Physics Economics
State (i) Hamiltonian eigenfunction Production site
Energy Hamiltonian eigenvalue Ei Unit prod. cost Ci
Occupation number Number in state Ni Output Ni = exp[-βCi+βF]
Partition function Z ∑exp[-(1/kBT)Ei] ∑exp[-βCi]
Free energy F kBT lnZ (1/β) lnZ
Generalized force fξ ∂F/∂ξ ∂F/∂ξ
Example Pressure TechnologyExample Electric field x charge Knowledge
Entropy (randomness) - ∂F / ∂T kBβ2∂F/∂
Technology Transfer : Quasi-static
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 dβIn bureaucratic factor
dC = - <fξ > dξ + β [2F/ βξ] dξ + [2[βF]/ β2] dβ [2]
which can be rewritten
dC = - <fξ > 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
Viterbi School of Engineering Technology Transfer Center
Technology Transfer : Quasi-static
Ln O
utpu
t
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
Viterbi School of Engineering Technology Transfer Center
Task 1. Semiconductor example: Movement between 1992 and 1997 on Maxwell Boltzmann plot
Ln O
utpu
t
Unit costs
1997:High output N,Low “temperature” 1/
1992:Low output N,High “temperature” 1/
Technology Transfer : Quasi-static
Viterbi School of Engineering Technology Transfer Center
Task 1. Heavy spring example: Movement between 1992 and 1997 on Maxwell Boltzmann plot
Ln
Ou
tput
Unit costs
1997:Low output N,High “temperature” 1/
1992: Low output N,Low “temperature” 1/
Technology Transfer : Quasi-static
Viterbi School of Engineering Technology Transfer Center
Technology Transfer: Quasi-staticTask 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
Viterbi School of Engineering Technology Transfer Center
0.5 1 1.5 2
0.2
0.4
0.6
0.8
1
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
Viterbi School of Engineering Technology Transfer Center
0
500
1000
1500
2000
2500
3000
3500
4000
0 10 20 30 40 50 60
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 $106
Technology Transfer: Quasi-static
Viterbi School of Engineering Technology Transfer Center
Company size: Large Intermediate Small
IT rank 59 70 81# 0.86 1.0 0.90E(1000s) 0.78 0.98 1.08#/company 0.91 1.0 1.21Sh ($million) 1.53 1.24 1.42Sh/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
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-staticTask 3. Increase total output
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
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
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[i2pn/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
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
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
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
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
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
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
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