Identifying and Predicting Economic Regimes in TAC SCM
2009
In this thesis, the effects of adding procurement information to a sales-based
regime model, which is used for predicting price trends in a simulated supply
chain, are researched. This supply chain is simulated in the TAC SCM game,
which is an annual international competition held for several years, where
researchers from around the world submit their artificial trading agents. The
regime model extended in this thesis is used by the MinneTAC agent of the
University of Minnesota.
We find that component offer prices can be used to extend the regime
model, which is currently based on a one-dimensional Gaussian Mixture
Model where probabilities are clustered. The resulting clusters hold as
regimes. Extending the model with a new dimension results in newly defined
regime clusters. Implementing the new regime model, MinneTAC increases
its customer orders significantly. However, because the agent configuration
shows a structural error in predicting future price trends – possibly due to
an insufficient pricing mechanism – we have strong indications that our new
approach leads to lower profits, although the decrease of the amount of cash
at the end of a game is not significant. We believe that this decrease of
profits can be tackled in the future by research into price trend prediction
in the newly defined regime model.
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