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Decentralization for Multidivision Enterprises

Author

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  • Cornelis van de Panne

    (University of Calgary, Calgary, Alberta, Canada)

Abstract

The paper deals with the organization of decision making for multidivision enterprises. If decisions can be represented by linear programming models with divisions sharing resources, an organization is proposed with one division or a combination of these setting resource prices, while the remaining ones determine quantities. The role of each division is determined by the numerical coefficient values as well as the models' structure. This approach is related to, but quite different from, the Dantzig–Wolfe decomposition principle. Interactions between divisions are like ordinary commercial transactions with one party setting the price and the other the quantity traded. The usefulness of this organizational structure depends on its stability for expected variations of model data, which can be determined by parametric variations and simulations. These concepts are applied to a model by R. M. Burton and B. Obel to decide whether the M-form or the U-form organization is preferable. For central values of the model data the M-form turns out to be appropriate, and remains valid for large variations of the coefficients. A comparison with Dantzig–Wolfe decomposition indicates that the proposed approach has significant advantages.

Suggested Citation

  • Cornelis van de Panne, 1991. "Decentralization for Multidivision Enterprises," Operations Research, INFORMS, vol. 39(5), pages 786-797, October.
    • Handle: RePEc:inm:oropre:v:39:y:1991:i:5:p:786-797
    DOI: 10.1287/opre.39.5.786
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    Cited by:

    1. S. Leitner & D.A. Behrens, 2015. "On the efficiency of hurdle rate-based coordination mechanisms," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 21(5), pages 413-431, September.
    2. Jornsten, Kurt & Leisten, Rainer, 1995. "Decomposition and iterative aggregation in hierarchical and decentralised planning structures," European Journal of Operational Research, Elsevier, vol. 86(1), pages 120-141, October.

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