Strategies for a Intelligent Agent in TAC-SCM

28th September, 2006

Based on studies of MinneTAC (TAC-SCM 2003)

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

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...

What Strategies Are There?

➢ Customer-Demand Driven (Build-to-Order)

➢ Supply Driven

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

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

● 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

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

● 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

What was observed

What was observed...

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

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

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