Tactical and Strategic Sales Management for Intelligent Agents Guided by Economic Regimes

27 Pages Posted: 21 Nov 2008

See all articles by Wolfgang Ketter

Wolfgang Ketter

University of Cologne - Institute of Energy Economics; Erasmus University Rotterdam (EUR) - Department of Technology and Operations Management; Erasmus Research Institute of Management (ERIM)

John Collins

University of Minnesota - Twin Cities

Maria Gini

University of Minnesota - Twin Cities - Computer Science and Engineering

Alok Gupta

University of Minnesota - Twin Cities - Carlson School of Management

Paul Schrater

University of Minnesota - Twin Cities

Date Written: October 20, 2008

Abstract

We present a computational approach that autonomous software agents can adopt to make tactical decisions, such as product pricing, and strategic decisions, such as product mix and production planning, to maximize profit in markets with supply and demand uncertainties. Using a combination of machine learning and optimization techniques, the agent is able to characterize economic regimes, which are historical microeconomic conditions reflecting situations such as over-supply and scarcity. We assume an agent is capable of using real-time observable information to identify the current dominant market condition and we show how it can forecast regime changes over a planning horizon. We demonstrate how the agent can then use regime characterization to predict prices, price trends, and the probability of receiving a customer order in a dynamic supply chain environment. We validate our methods by presenting experimental results from a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM). The results show that our agent outperforms traditional short- and long-term predictive methodologies (such as exponential smoothing) significantly, resulting in accurate prediction of customer order probabilities, and competitive market prices. This, in turn, has the potential to produce higher profits. We also demonstrate the versatility of our computational approach by applying the methodology to prediction of stock price trends.

Keywords: dynamic pricing, machine learning, agent-mediated electronic commerce, market forecasting, rational decision making

JEL Classification: M, O32, L15, C63

Suggested Citation

Ketter, Wolfgang and Collins, John and Gini, Maria L and Gupta, Alok and Schrater, Paul, Tactical and Strategic Sales Management for Intelligent Agents Guided by Economic Regimes (October 20, 2008). ERIM Report Series Reference No. ERS-2008-061-LIS, Available at SSRN: https://ssrn.com/abstract=1303877

Wolfgang Ketter (Contact Author)

University of Cologne - Institute of Energy Economics ( email )

Alte Wagenfabrik
Vogelsanger Strasse 321a
Cologne, 50827
Germany

HOME PAGE: http://ewi.uni-koeln.de/en/

Erasmus University Rotterdam (EUR) - Department of Technology and Operations Management ( email )

RSM Erasmus University
PO Box 1738
3000 DR Rotterdam
Netherlands

Erasmus Research Institute of Management (ERIM) ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

HOME PAGE: http://www.rsm.nl/energy

John Collins

University of Minnesota - Twin Cities ( email )

420 Delaware St. SE
Minneapolis, MN 55455
United States

Maria L Gini

University of Minnesota - Twin Cities - Computer Science and Engineering ( email )

200 Union St SE, #4-192
Minneapolis, MN 55455
United States

HOME PAGE: http://www,cs,umn.edu/~gini

Alok Gupta

University of Minnesota - Twin Cities - Carlson School of Management ( email )

19th Avenue South
Minneapolis, MN 55455
United States

Paul Schrater

University of Minnesota - Twin Cities ( email )

420 Delaware St. SE
Minneapolis, MN 55455
United States

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