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Integer Programming Models for Sales Resource Allocation

Author

Listed:
  • Andris A. Zoltners

    (Northwestern University)

  • Prabhakant Sinha

    (University of Georgia)

Abstract

A practical conceptual framework for sales resource allocation modeling is presented in this paper. A literature review of sales resource allocation models is described in terms of this framework. The conceptual framework also lends itself to several integer programming models which may be used to address the variety of sales resource allocation decisions faced by every sales organization. A general model for sales resource allocation is developed which incorporates multiple sales resources, multiple time periods and carryover effects, non-separability, and risk. Several actual model implementations are discussed which illustrate the practical application of the integer programming models. The model implementations utilize recent advances in integer programming theory which enables sales managers and sales representatives to quickly develop and evaluate alternative sales resource allocation strategies.

Suggested Citation

  • Andris A. Zoltners & Prabhakant Sinha, 1980. "Integer Programming Models for Sales Resource Allocation," Management Science, INFORMS, vol. 26(3), pages 242-260, March.
    • Handle: RePEc:inm:ormnsc:v:26:y:1980:i:3:p:242-260
    DOI: 10.1287/mnsc.26.3.242
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    Citations

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    Cited by:

    1. Darmon, Rene Y., 2002. "Salespeople's management of customer information: Impact on optimal territory and sales force sizes," European Journal of Operational Research, Elsevier, vol. 137(1), pages 162-176, February.
    2. Brian Lunday & Hanif Sherali & Kevin Lunday, 2012. "The coastal seaspace patrol sector design and allocation problem," Computational Management Science, Springer, vol. 9(4), pages 483-514, November.
    3. Berk, Lauren & Bertsimas, Dimitris & Weinstein, Alexander M. & Yan, Julia, 2019. "Prescriptive analytics for human resource planning in the professional services industry," European Journal of Operational Research, Elsevier, vol. 272(2), pages 636-641.
    4. Drexl, Andreas & Haase, Knut, 1996. "Fast approximation methods for sales force deployment," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 411, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    5. Fabio Caldieraro & Anne T. Coughlan, 2009. "Optimal Sales Force Diversification and Group Incentive Payments," Marketing Science, INFORMS, vol. 28(6), pages 1009-1026, 11-12.
    6. David Godes, 2003. "In the Eye of the Beholder: An Analysis of the Relative Value of a Top Sales Rep Across Firms and Products," Marketing Science, INFORMS, vol. 22(2), pages 161-187, May.
    7. Lin, Edward Y. H. & Bricker, Dennis L., 1996. "Computational comparison on the partitioning strategies in multiple choice integer programming," European Journal of Operational Research, Elsevier, vol. 88(1), pages 182-202, January.
    8. AgralI, Semra & Geunes, Joseph, 2009. "Solving knapsack problems with S-curve return functions," European Journal of Operational Research, Elsevier, vol. 193(2), pages 605-615, March.
    9. Shouvanik Chakrabarti & Pierre Minssen & Romina Yalovetzky & Marco Pistoia, 2022. "Universal Quantum Speedup for Branch-and-Bound, Branch-and-Cut, and Tree-Search Algorithms," Papers 2210.03210, arXiv.org.
    10. Amin Sayedi & Jeffrey D. Shulman, 2017. "Strategic compliments in sales," Quantitative Marketing and Economics (QME), Springer, vol. 15(1), pages 57-84, March.
    11. Andreas Drexl & Knut Haase, 1999. "Fast Approximation Methods for Sales Force Deployment," Management Science, INFORMS, vol. 45(10), pages 1307-1323, October.

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