beer game slides
TRANSCRIPT
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Artificial Agents Play the Beer Game Eliminate the Bullwhip Effect
and Whip the MBAs
Steven O. Kimbrough
D.-J. Wu
Fang ZhongFMEC, Philadelphia, June 2000; file: beergameslides.ppt
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The MIT Beer Game • Players
– Retailer, Wholesaler, Distributor and Manufacturer.
• Goal– Minimize system-wide (chain) long-run average cost.
• Information sharing: Mail. • Demand: Deterministic.• Costs
– Holding cost: $1.00/case/week.
– Penalty cost: $2.00/case/week.
• Leadtime: 2 weeks physical delay
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Timing
1. New shipments delivered.
2. Orders arrive.
3. Fill orders plus backlog.
4. Decide how much to order.
5. Calculate inventory costs.
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Game Board
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The Bullwhip Effect
• Order variability is amplified upstream in the supply chain.
• Industry examples (P&G, HP).
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Observed Bullwhip effect from undergraduates game playing
Retailer's Order
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Wholesaler's Order
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Distributor's Order
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Factory's Order
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Bullwhip Effect Example (P & G)Lee et al., 1997, Sloan Management Review
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Analytic Results: Deterministic Demand
• Assumptions:– Fixed lead time.– Players work as a team.– Manufacturer has unlimited capacity.
• “1-1” policy is optimal -- order whatever amount is ordered from your customer.
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Analytic Results: Stochastic Demand (Chen, 1999, Management Science)
• Additional assumptions:– Only the Retailer incurs penalty cost.– Demand distribution is common knowledge.– Fixed information lead time.– Decreasing holding costs upstream in the chain.
• Order-up-to (base stock installation) policy is optimal.
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Agent-Based Approach
• Agents work as a team.
• No agent has knowledge on demand distribution.
• No information sharing among agents.
• Agents learn via genetic algorithms.
• Fixed or stochastic leadtime.
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Research Questions
• Can the agents track the demand?
• Can the agents eliminate the Bullwhip effect?
• Can the agents discover the optimal policies if they exist?
• Can the agents discover reasonably good policies under complex scenarios where analytical solutions are not available?
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Flowchart
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Summary
• Agents are capable of playing the Beer Game– Track demand.
– Eliminate the Bullwhip effect.
– Discover the optimal policies if exist.
– Discover good policies under complex scenarios where analytical solutions not available.
• Intelligent and agile supply chain.• Multi-agent enterprise modeling.