beer game slides

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

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

Timing

1. New shipments delivered.

2. Orders arrive.

3. Fill orders plus backlog.

4. Decide how much to order.

5. Calculate inventory costs.

Game Board

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The Bullwhip Effect

• Order variability is amplified upstream in the supply chain.

• Industry examples (P&G, HP).

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

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.

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.

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.

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?

Flowchart

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.