Strategic Sales Management Guided By Economic Regimes

Wolf Ketter, John Collins, Maria Gini, A Gupta, Paul Schrater

Research output: Chapter/Conference proceedingChapterAcademic

Abstract

We present methods to predict future market conditions and price trends from historical data, and we describe how these predictions can be used by an autonomous agent to make strategic and tactical sales decisions. The methods are based on learning dominant market conditions, such as over-supply or scarcity, from historical data and using this knowledge, together with real-time observable information, to identify the current market conditions. We use a Gaussian Mixture Model to represent the price density and a Markov process to forecast market changes and to predict price density over a planning horizon. We validate our methods by presenting experimental results in predicting price trends in the customer market for the Trading Agent Competition for Supply Chain Management.
Original languageEnglish
Title of host publicationEdited Volume of the 2nd Smart Business Network Initiative Discovery Event
EditorsPeter Vervest, Eric van Heck, Kenneth Preiss, Louis-Francois Pau
PublisherSpringer-Verlag
Pages245-264
Number of pages20
Publication statusPublished - 2008
Externally publishedYes

Bibliographical note

KetterW_SBNi06

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