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On Uncertainty, Ambiguity, and Complexity in Project Management

  • Michael T. Pich

    ()

    (INSEAD, 1 Ayer Rajah Avenue, Singapore 138676)

  • Christoph H. Loch

    ()

    (INSEAD, Boulevard de Constance, 77305 Fontainebleau Cedex, France)

  • Arnoud De Meyer

    ()

    (INSEAD, 1 Ayer Rajah Avenue, Singapore 138676)

Registered author(s):

    This article develops a model of a project as a payoff function that depends on the state of the world and the choice of a sequence of actions. A causal mapping, which may be incompletely known by the project team, represents the impact of possible actions on the states of the world. An underlying probability space represents available information about the state of the world. Interactions among actions and states of the world determine the complexity of the payoff function. Activities are endogenous, in that they are the result of a policy that maximizes the expected project payoff. A key concept is the adequacy of the available information about states of the world and action effects. We express uncertainty, ambiguity, and complexity in terms of information adequacy. We identify three fundamental project management strategies: instructionism, learning, and selectionism. We show that classic project management methods emphasize adequate information and instructionism, and demonstrate how modern methods fit into the three fundamental strategies. The appropriate strategy is contingent on the type of uncertainty present and the complexity of the project payoff function. Our model establishes a rigorous language that allows the project manager to judge the adequacy of the available project information at the outset, choose an appropriate combination of strategies, and set a supporting project infrastructure---that is, systems for planning, coordination and incentives, and monitoring.

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    File URL: http://dx.doi.org/10.1287/mnsc.48.8.1008.163
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    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 48 (2002)
    Issue (Month): 8 (August)
    Pages: 1008-1023

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    Handle: RePEc:inm:ormnsc:v:48:y:2002:i:8:p:1008-1023
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    1. Christoph H. Loch & Christian Terwiesch & Stefan Thomke, 2001. "Parallel and Sequential Testing of Design Alternatives," Management Science, INFORMS, vol. 47(5), pages 663-678, May.
    2. Brucker, Peter & Drexl, Andreas & Mohring, Rolf & Neumann, Klaus & Pesch, Erwin, 1999. "Resource-constrained project scheduling: Notation, classification, models, and methods," European Journal of Operational Research, Elsevier, vol. 112(1), pages 3-41, January.
    3. Williams, Terry, 1995. "A classified bibliography of recent research relating to project risk management," European Journal of Operational Research, Elsevier, vol. 85(1), pages 18-38, August.
    4. Dvir, D. & Lipovetsky, S. & Shenhar, A. & Tishler, A., 1998. "In search of project classification: a non-universal approach to project success factors," Research Policy, Elsevier, vol. 27(9), pages 915-935, December.
    5. Robert P. Smith & Steven D. Eppinger, 1997. "A Predictive Model of Sequential Iteration in Engineering Design," Management Science, INFORMS, vol. 43(8), pages 1104-1120, August.
    6. Stefan H. Thomke, 1998. "Managing Experimentation in the Design of New Products," Management Science, INFORMS, vol. 44(6), pages 743-762, June.
    7. Salah E. Elmaghraby, 1964. "An Algebra for the Analysis of Generalized Activity Networks," Management Science, INFORMS, vol. 10(3), pages 494-514, April.
    8. Shantanu Bhattacharya & V. Krishnan & Vijay Mahajan, 1998. "Managing New Product Definition in Highly Dynamic Environments," Management Science, INFORMS, vol. 44(11-Part-2), pages S50-S64, November.
    9. Genaro J. Gutierrez & Panagiotis Kouvelis, 1991. "Parkinson's Law and Its Implications for Project Management," Management Science, INFORMS, vol. 37(8), pages 990-1001, August.
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