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Statistical Physics Framework for Analysis of the Economic Impact of Technology

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Statistical Physics Framework for Analysis of the Economic Impact of Technology Ken Dozier David Chang USC Viterbi School of Engineering Technology Transfer Center – PowerPoint PPT presentation

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Title: Statistical Physics Framework for Analysis of the Economic Impact of Technology


1
Statistical Physics Framework for Analysis of the
Economic Impact of Technology
  • Ken Dozier
  • David Chang
  • USC Viterbi School of Engineering Technology
    Transfer Center
  • 8/11/05

2
A System of Forces in Organization
Direction
Cooperation
Efficiency
Proficiency
Competition
Concentration
Innovation
Source The Effective Organization Forces and
Form, Sloan Management Review, Henry Mintzberg,
McGill University 1991
3
Make Sell vs Sense Respond
Chart SourceCorporate Information Systems and
Management, Applegate, 2000
4
Supply Chain (Firm)
Source Gus Koehler, University of Southern
California Department of Policy and Planning,
2002
5
Supply Chain (Government)
Source Gus Koehler, University of Southern
California Department of Policy and Planning,
2002
6
Supply Chain (Framework)
Source Gus Koehler, University of Southern
California Department of Policy and Planning,
2002
7
Supply Chain (Interactions)
Source Gus Koehler, University of Southern
California Department of Policy and Planning,
2002
8
Theoretical Environment
Seven Organizational Change Propositions
Framework, Framing the Domains of IT
Management Zmud 2002
9
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
Statistical Physics Approach
11
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 RD 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

12
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
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
  • RD costs
  • Self-consistency check provided by distribution
    functions

14
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 RD costs
  • Increase rate of production
  • Dampen disruptive cyclic phenomena
  • Increase rate of technology spread

15

Technology Transfer Quasi-static
  • Task 1. Reduce unit cost of productionPresented
    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

16
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
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
Wn(i) be the number of possible ways that a set
of outputs n(i) can be realized. Maximize
Wn(i) subject to given constraints N, C, and
c(i) d /d n(i) lnW ?N-Sn(i)
ßC-Sc(i) 0

1 Solution for simplest case n(i) P
exp-ßc(i) Maxwell-Boltzmann
distribution 2 where the
parameters characterizing the sector are P is
a productivity factor for the sector ß is an
inverse temperature or bureaucratic factor
17
Technology Transfer Quasi-static
Task 1. Comparison of Statistical Formalism in
Physics and in Economics Variable Physics Eco
nomics State (i) Hamiltonian
eigenfunction Production site Energy Hamiltoni
an eigenvalue Ei Unit prod. cost
Ci Occupation number Number in state Ni
Output Ni exp-ßCißF Partition function Z
?exp-(1/kBT)Ei ?exp-ßCi Free energy F kBT
lnZ (1/ß) lnZ Generalized force f?
?F/?? ?F/?? Example Pressure Technology Ex
ample Electric field x charge Knowledge Entropy
(randomness) - ?F / ?T kBß2?F/?b
18
Technology Transfer Quasi-static
Task 1 approach. Conservation law for Technology
Transfer
Total cost of production C ? C(?i) exp
-ß(C(?i) F(? )) 1
Effect of a change d? in a parameter ? in the
system and a change dß In bureaucratic factor
dC - ltf? gt d? ß d2F/ dßd? d? d2ßF/
dß2 dß 2
which can be rewritten
dC - ltf? gt d? TdS 3
Significance First term on the RHS
describes lowering of unit cost of production.
Second term on RHS describes increase in
entropy (temperature)
19
Technology Transfer Quasi-static
Task 1. Approach
High output N, High temperature 1/b
Ln Output
Costs down
High output N, Low temperature 1/b
Low output N, High temperature 1/b
Entropy up
Low output N, Low temperature 1/b
Unit costs
20
Technology Transfer Quasi-static
Task 1. Semiconductor example Movement
between 1992 and 1997 on Maxwell Boltzmann plot
1997 High output N, Low temperature 1/b
Ln Output
1992 Low output N, High temperature 1/b
Unit costs
21
Technology Transfer Quasi-static
Task 1. Heavy spring example Movement between
1992 and 1997 on Maxwell Boltzmann plot
Ln Output
1997 Low output N, High temperature 1/b
1992 Low output N, Low temperature 1/b
Unit costs
22
Technology Transfer Quasi-static
  • Task 2. Improve productivity (output/employee)P
    aper 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
Technology Transfer Quasi-static
Task 2. Normalized cumulative distribution of
companies N(S)/N vs shipments per company S for
ß 0.5 (bottom curve), 1, and 5
24
Technology Transfer Quasi-static
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 ß 0.167
per 106
25
Technology Transfer Quasi-static
Task 2. Ratio (97/92) of the statistical
parameters
  • 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
  • ß 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.

26
Technology Transfer Quasi-static
Task 3. Increase total output
  • 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

27
Technology Transfer Quasi-static
Task 4. Reduce RD costs
  • Background question
  • Is there a systematic way of reducing barriers to
    industry use of government RD and vice versa
    (diffusion and infusion)?
  • Approach
  • Maximize number of microstates corresponding to
    macrostate defined by
  • total cost of RD
  • ratio of total innovation output/total RD 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

28
Technology Transfer Dynamic
Task 5. Understand implications of supply chain
oscillations for technology transfer Paper
accepted for CITSA 05 conference in July, 2005
  • 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

29
Technology Transfer Dynamic
Task 5. Normal modes in a supply chain with
uniform processing times
  • Supply chain normal mode equation
  • y(n-1) 2y(n) y(n1) (?T)2 y(n)
    0 1
  • Normal mode form for N companies in chain
  • y(p(n) expi2?pn/N 2
  • Normal mode dispersion relation
  • ? ? (2/T) sin(?p/N) where p is any
    integer 3

30
Technology Transfer Dynamic
Task 6. Increase rate of productionPaper
accepted for presentation at T2S meeting in
September, 2005
  • 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

31
Technology Transfer Dynamic
Task 7. Understand technology transfer
implications of instabilities in supply chain
oscillations
  • Background
  • MITs 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

32
Technology Transfer Dynamic
Task 8. Optimize damping of disruptive cyclic
phenomena by focusing technology transfer
  • 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

33
Technology Transfer Dynamic
Task 9. Increase rate of technology spread and
adoption
  • 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

34
Technology Transfer Reality Check
Task 10. Compare the theory with actual data
  • 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

35
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
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
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