Strategies for a Intelligent Agent
in TAC-SCM
28th September, 2006
Based on studies of MinneTAC (TAC-SCM 2003)
Quick Overview
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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
MinneTAC : Agent Outline
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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
What Strategies Are There?
➢
➢
Customer-Demand Driven
(Build-to-Order)
Supply Driven
Customer-Demand Driven
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Environment:

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Goal of Sales Manager:

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Assumes that customer demand decides what &
how much to make
Maximize profit on a bagged order (via Raw
Materials Manager)
Immediate Benefit:

Flexibility to stop doing business in unprofitable
environment
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
Supply Driven
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Environment:

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Goal of Sales Manager:

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Assumes what customer demand could be,
coupled with decides as per past history of its
offers' acceptance what & how much to make
Predict a target acceptance rate as close to the
actual acceptance rate
Immediate Benefit:

More dynamic in an even more uninformed
market
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
What are the differences?
Customer-Driven
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Supply-Driven
Work on restricted data set
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Tries to sell out its inventory of
Finished Goods towards the end
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Doesn't rework price calculations
as regularly
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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
What was observed
What was observed...
What Fits Best?
Customer-Driven
✔
Profitable in an overall increasing
price scenario
Supply-Driven
✔
✔
✔
✔
✗
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
✔
✗
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
Conclusion
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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
Reference Source
Strategies for a Sales Component of an Intelligent
Agent for TAC-SCM 2003
Elena V.
Kryzhnyaya
University of Minnesota
Thank You!
Kunal Khatua
kunal@cs.utexas.edu
Dept. of Computer Science
Univ. of Texas at Austin