Strategies%20for%20a%20Intelligent%20Agent%20in%20TAC-SCM

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Strategies for a Intelligent Agent in TAC-SCM

• 28th September, 2006
• Based on studies of MinneTAC (TAC-SCM 2003)

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Quick Overview

• The TAC-SCM game actually consists of 2 separate,
  but inter-related sub-games.
• One game is played in the the market where the
  agents have to buy supplies
• Second game is played in the market where agents
  must sell their finished goods

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MinneTAC Agent Outline

• Component-based architecture (similar to
  DeepMaize)
• Decision Responsibilities delegated to
  components
• Raw Materials Manager Manages Purchases
• Assembly Manager Decides what to assemble
• Sales Manager What RFQs to respond to, and with
  what price quotes
• Since the Sales Manager is the where the actual
  action starts, we'll look at the strategies for
  it...

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What Strategies Are There?

• Customer-Demand Driven (Build-to-Order)
• Supply Driven

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Customer-Demand Driven

• Environment
• Assumes that customer demand decides what how
  much to make
• Goal of Sales Manager
• Maximize profit on a bagged order (via Raw
  Materials Manager)
• Immediate Benefit
• Flexibility to stop doing business in
  unprofitable environment

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Strategy Maximize Sales Profit

• The strategy relies only on details in RFQ to
  decide the offer price
• This gives a 6-dimensional Order Probability
• OrderProbability offer_price x
• quantity x
• lead_time x
• reserved_price x
• penalty x
• product_type
• And Profit...
• Expected Profit Profit x Probability of
  acceptance

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Supply Driven

• Environment
• Assumes what customer demand could be, coupled
  with decides as per past history of its offers'
  acceptance what how much to make
• Goal of Sales Manager
• Predict a target acceptance rate as close to the
  actual acceptance rate
• Immediate Benefit
• More dynamic in an even more uninformed market

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Strategy Optimize Sales With Demand

• The strategy relies on details in RFQ to
  decide the offer price, and also calculates
  Acceptance rates and demand estimates
• This gives a 5-dimensional Order Probability
• OrderProbability offer_price x
• customer_demand x
• lead_time x
• reserved_price x
• product_type
• And Target Acceptance Rate...
• TARproduct (available_inventory) x
  (products_produced) x (num_of_days_left)
• Optimistic Demand Estimate

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What are the differences?

• Customer-Driven
• Work on restricted data set
• Tries to sell out its inventory of Finished Goods
  towards the end
• Doesn't rework price calculations as regularly

• Supply-Driven
• Work on a more expansive, probabilistic set of
  data
• Tries to sell out its inventory of Finished Goods
  from the start
• On basis of target acceptance and actual
  acceptance rates

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What was observed
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What was observed...
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What Fits Best?

• Customer-Driven
• Profitable in an overall increasing price
  scenario
• Works best if customer demand is not 100
  satisfied
• Tends to hold on to the finished goods in the
  inventory till better prices come along
• Towards the end, a lot of the inventory may be
  sold of cheaply

• Supply-Driven
• Adapts rapidly to demand and price fluctuations
  in the market
• Tends to sell finished goods in the inventory
  rapidly from the start with a pessimistic view,
  making it more competitive with agents having
  similar traits
• Due to relative low inventory of finished goods,
  it will also sell of fairly cheaply, bu the
  cumulative loss incurred for this stage is low
• On an overall game play, this fails to make most
  of the market

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Conclusion

• Agent clearly cannot adopt any one strategy
  alone. Balance is required.
• Knowledge of the nature of competing agents
  helps
• Estimation of customer-demand can solve the
  bottle-neck
• Split the strategies between the Raw Materials
  Mgr and Sales Mgr to share cooperate on
  information

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Reference Source

• Strategies for a Sales Component of an
  Intelligent Agent for TAC-SCM 2003
• Elena V. Kryzhnyaya
• University of Minnesota

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Thank You!

• Kunal Khatua
• kunal_at_cs.utexas.edu
• Dept. of Computer Science
• Univ. of Texas at Austin