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


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


Make & Sell vs Sense & Respond

Chart Source:“Corporate Information Systems and Management”, Applegate, 2000


Supply Chain (Firm)

Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002


Supply Chain (Government)



Supply Chain (Framework)



Supply Chain (Interactions)



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


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.


Statistical Physics Approach


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


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


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


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


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


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


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/∂



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)




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


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/



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-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


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



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



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



• 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


• 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


• 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


• 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



• 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]



• 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



• 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


• 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



• 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


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


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

viterbi school of engineering technology transfer center portfolio defense february 2006 ken dozier

Documents

technology transfer

transfer citsa

rate of technology

report task

static phenomena task

dynamic phenomena task

ken dozier slide

reality check task