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When and how to use scenario planning: A heuristic approach with illustration

Schoemaker, Paul J. H.·1991·Journal of Forecasting

Abstract

This paper offers a step‐by‐step analysis of a heuristic approach to scenario planning, taking a managerial perspective. The scenario method is contrasted in general with more traditional planning techniques, which tend to perform less well when faced with high uncertainty and complexity. An actual case involving a manufacturing company is used to illustrate the main steps of the proposed heuristic. Its essence is to identify relevant trends and uncertainties, and blend them into scenarios that are internally consistent. In addition, the scenarios should bound the range of plausible uncertainties and challenge managerial thinking. Links to decision making are examined next, including administrative policies as well as integrative techniques. At the strategic level, a key‐success‐factor matrix is proposed for integrating scenarios, competitor analysis and strategic vision. At the operational level, Monte Carlo simulation is suggested and illustrated as one useful technique for combining scenario thinking with formal project evaluation (after appropriate translations). The paper concludes with a general discussion of scenario planning, to place it in a broader perspective.

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Metadata

Authors
Schoemaker, P. J. H.
Venue
Journal of Forecasting
Publisher
Wiley
Published
1991-11
Volume
10
Issue
6
Pages
549-564
ISSN
  • 0277-6693 (ISSN-L)
  • 1099-131X
OpenAlex
W1971143298
Cited by
371
References
0
FWCI
16.98
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: When and how to use scenario planning: A heuristic approach with illustration. https://app.cochat.ai/papers/10.1002/for.3980100602
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Schoemaker, P. J. H. (1991). When and how to use scenario planning: A heuristic approach with illustration. Journal of Forecasting. https://doi.org/10.1002/for.3980100602

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.