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This study explores the use of a learning game, based on the Sterman "Beer Game," to improve supply chain efficiency by nudging players to abandon heuristics and invest in rationality. The game creates a persuasive experience immersed in complex systems and aims to improve economic performance in counter-intuitive business systems.
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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 – DavidSemiotics Syntactics: Plasma Physics * Stamper, Information, Norms and Systems (1996)
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.
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
2.1 Beer Game 23rd Order Differential Equation Model Developed by Dr. Nathan B. Forrester of A.T. Kearney, Atlanta, 2000
2.1.1 Sterman Single Repetition Teaching Takes Off: Flight Simulators for Management Education, Sterman (1992)
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
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
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
2.2.1 Conflicting Temporal Layers Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002
2.2.2 Layers of Communication Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002
2.2.3 Government Dynamics Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002
2.2.4 High Level of Complexity Source: Gus Koehler, University of Southern California Department of Policy and Planning, 2002
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
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
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
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
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.
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)
3.6 Design1 versus Design5 Design5 Highly Skewed Design1 Normally Distributed
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%
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
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
Contact Information For more information, please Visit the Learning Center http://wesrac.usc.edu kdozier@usc.edu Google WESRAC Google Ken Dozier