Decision coordination in a supply-chain agent

Why is decision coordination an important problem? Decision support is especially critical when human decision makers are faced with combinatorial problems, uncertain and partially-visible data, and our built-in cognitive biases. When the results of decisions in different domains interact with each other, we have an additional level of complexity that is easy to ignore.

We use the TAC SCM simulation to model complex, interacting decisions, and the fully-autonomous MinneTAC trading agent to test our ideas for decision support. The agent must trade in two competitive markets simultaneously, while managing its own inventory and production facility. A successful agent must buy parts it needs to manufacture and sell finished products, and it must sell its finished products at a profit. Inventory is relatively costly, competition is stiff, and agents that cannot effectively coordinate their decisions are easily defeated.

We reviewed the coordination strategies employed by a number of successful agents. These include a "sales pull" method, inventory-centric approaches, a production-centric approach that fills its future production schedule with the products that are expected to give the highest marginal profit, an approach that projects a "target demand" into the future that is expected to satisfy profit targets, and a multi-layer system of internal "markets" in which projected customer demand bids on products, which in turn bid on parts and production capacity, etc.

We wrapped up by looking in some detail at the behaviors of the two top agents in the 2009 competition, DeepMaize from the University of Michigan and TacTex from the University of Texas. They were very nearly tied, although TacTex bought and sold considerably more volume and carried much larger inventories. We looked at an example where TacTex built up a large finished-goods inventory during a period of low customer demand, when parts are inexpensive, and used that inventory to keep prices depressed during a later period of high demand, when other agents were competing for parts and were consequently squeezed for profits. This is clearly a very risky strategy, but we assume that the TacTex team has used its machine-learning expertise to recognize the market signal patterns that indicate a reasonable probability of such a situation occuring. The regime model used by MinneTAC could presumably predict such a situation also, but so far it's not being used to drive strategic decisions.