strategies for a intelligent agent in tac-scm 28 th september, 2006 based on studies of minnetac...
TRANSCRIPT
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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