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Improvement of Supply Chain Efficiency with a Learning Game

Improvement of Supply Chain Efficiency with a Learning Game. Ken Dozier & David Chang Western Research Application Center World Society and Engineering Academy and Society Conference February 25, 2012 Harvard, Cambridge USA. Bio – David Semiotics Syntactics: Plasma Physics.

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Improvement of Supply Chain Efficiency with a Learning Game

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  1. Improvement of Supply Chain Efficiency with a Learning Game Ken Dozier & David Chang Western Research Application Center World Society and Engineering Academy and Society Conference February 25, 2012 Harvard, Cambridge USA

  2. Bio – DavidSemiotics Syntactics: Plasma Physics * Stamper, Information, Norms and Systems (1996)

  3. Introduction • Used a Design Research Framework to create a Learning Object Game Immersed in Complex Systems (LOGICS) process and product artifact. • Used the NetLogo “Hubnet” capability to immerse a human agents into the simulate the dynamics of the classic Sterman “Beer Game”. • Create a persuasive experience that nudged players abandon heuristics and invest in IT rationality to improve economic performance in a counter intuitive dynamic business system.

  4. 2. Background • Observations • Cyclic phenomena in complex dynamic business systems • Ubiquitous & Disruptive • Example: Wild oscillations in supply chain inventories • Results of Observations and Simulations • Negative Feedback Systems with Delays Oscillate • Phase dependence of oscillations on position in chain • Understanding of Managements Personality Impact • Commitment to Rationality Dramatically Increases Financial Performance • MIT Sterman “Beer Game” Simulation • Single Customer, Single Product, 4 Tier Supply Chain • Published Suboptimal Performance 1 Order of Magnitude • Experimental Suboptimal Performance 2 Orders of Magnitude

  5. 2.1 Beer Game 23rd Order Differential Equation Model Developed by Dr. Nathan B. Forrester of A.T. Kearney, Atlanta, 2000

  6. 2.1.1 Sterman Single Repetition Teaching Takes Off: Flight Simulators for Management Education, Sterman (1992)

  7. 2.1.2NetLogo Demonstration Steady state at 4 cases per order. Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo. Center for Connected Learning and Computer-Based Modeling. Northwestern University, Evanston, IL Beer Game Demo Densmore, O. June 2004

  8. 2.1.3A Single Change A 4 Unit Constant Demand is Changed to an 8 Unit Constant Demand Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo. Center for Connected Learning and Computer-Based Modeling. Northwestern University, Evanston, IL Beer Game Demo Densmore, O. June 2004

  9. 2.2 Analytical Models Direction Cooperation Efficiency Proficiency Competition Concentration Innovation Source: “The Effective Organization: Forces and Form”, Sloan Management Review, Henry Mintzberg, McGill University 1991

  10. 2.2.1 Conflicting Temporal Layers Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002

  11. 2.2.2 Layers of Communication Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002

  12. 2.2.3 Government Dynamics Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002

  13. 2.2.4 High Level of Complexity Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002

  14. Market Redefinition Supply-chain Expansion Supply-chain Discovery Business Model Redefinition Business Model Refinement Business Process Redesign Business Process Improvement 2.2.5 Bureaucratic Factor Low β High β Seven Organizational Change Propositions Framework, “Framing the Domains of IT Management” Zmud 2002

  15. 2.2.6 Plasma Theories • Advanced plasma theories are extremely important when one tries to explain the various waves and instabilities found in the plasma environment. Since plasma consist of a very large number of interacting particles, in order to provide a macroscopic description of plasma phenomena it is appropriate to adopt a statistical approach. This leads to a great reduction in the amount of information to be handled. In the kinetic theory it is necessary to know only the distribution function for the system of particles. Source: University of Oulu, FInland

  16. 2.2.7 JITTA • Investigated the β bureaucratic factor and it’s inverse organizational temperature T (dispersion) in the 5 metropolitan service areas surrounding Los Angeles. • Investigated the ability of Stratification to Differentiate impact of IT Investment on output and job creation • Large firms invest in IT to increase output and eliminate jobs • Small firms invest in IT to increase output and expand workforce • Investigate Partition Function Z, Cumulative Distribution Function opened the linkage to Statistical Physics • Dozier-Chang (06) Journal of Information Technology Theory and Application • Math Intensive Solution Ideal for Cloud Architectures • Marginal Cost on a per transaction basis • “Give them a Browser Razor Sale them Transaction Blades” • Accommodates Both Large and Small Firms

  17. 4000 3500 3000 2500 2000 1500 1000 500 0 0 10 20 30 40 50 60 2.2.8Maxwell-Boltzmann Distribution Comparison of U.S. economic census cumulative number of companies vs shipments/company (blue diamond points) in LACMSA in 1992 and the statistical physics cumulative distribution curve (square pink points) with β = 0.167 per $106

  18. 2.2.9 Advantages • This model does allow examination of the optimal timing for interventions of these propagations and parametric forces. Something not possible in simulation models to date • The most effective paramedic interventions will be those that use information technologies to harmonize with naturally occurring normal modes of the system. • Disturbances from non optimal interventions move up and down the supply chain.

  19. 3. Learning Object Game Design Science Framework Takeda, Veerkamp, Tomiyama, Yoshikawa (AI) Purao (IS&T) Dasgupta (IS&T) Vaishnavi & Kuechler, Design Science Research Methods and Patterns (2008)

  20. 3.1 Kernel Theories

  21. 3.2 Integrated Development Environment

  22. 3.3 Player Interface

  23. 3.4 Instructors Console

  24. 3.5 Results

  25. 3.6 Design1 versus Design5 Design5 Highly Skewed Design1 Normally Distributed

  26. 4. Discussion Design 5 TrialPlayer 1 Player 2 Player 3 1 4356 15588 6243 2 4362 4289 16650 3 3393 8156 5840 4 179.3 1563 1491 5 179.3 2885 1876 Modest Sample Size IT Orders 87% 21% 55%

  27. 4.1 Binomial Distribution • Results can be interpreted in a self consistent manner since each trial can be considered independent. • A “success” occurs when the total cost is less that some target • Binomial Distribution is Appropriate for a Modest Sample Size • There is a 7 fold improvement in target teaching costs in only five trials • This is a dramatic improvement in past experience with reinforced learning for the Beer Game problem Teaching Target Costs from Box Plots N=3 p=1/7

  28. 4.2 Someone is Getting Rich on the Waste

  29. 4.3 Findings and Video Demos • Game 1: Manual ordering with repetition. My control group average 10 times larger than Sterman. • Game 2: Manual ordering using Senge’s “Do nothing strategy” Retail absorbs the bulk of the costs • Game 3: Local computing enables Wal-Mart's to kill supplier factories • Game 4: Any technical solution is better than intuition • Game 5: If we cooperate we make 100 times more money. • The Optimal to Average performance ration of 100 to 1 • Much room for improvement • Asynchronous interaction between tiers. • Realistic demand time series • Realistic normal modes and associated optimal values • Larger number of participants • World side distribution on the Cloud

  30. Contact Information For more information, please Visit the Learning Center http://wesrac.usc.edu kdozier@usc.edu Google WESRAC Google Ken Dozier

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