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Decision making and planning under low levels of predictability: Enhancing the scenario method

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Listed author(s):
  • Wright, George
  • Goodwin, Paul
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    Abstract

    In this paper we review and analyse scenario planning as an aid to anticipation of the future under conditions of low predictability. We examine how successful the method is in mitigating issues to do with inappropriate framing, cognitive and motivational bias, and inappropriate attributions of causality. Although we demonstrate that the scenario method contains weaknesses, we identify a potential for improvement. Four general principles that should help to enhance the role of scenario planning when predictability is low are discussed: (i) challenging mental frames, (ii) understanding human motivations, (iii) augmenting scenario planning through adopting the approach of crisis management, and (iv) assessing the flexibility, diversity, and insurability of strategic options in a structured option-against-scenario evaluation.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0169-2070(09)00091-0
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    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 25 (2009)
    Issue (Month): 4 (October)
    Pages: 813-825

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    Handle: RePEc:eee:intfor:v:25:y:2009:i:4:p:813-825
    Contact details of provider: Web page: http://www.elsevier.com/locate/ijforecast

    Keywords: Scenario planning Crisis management Framing Judgment Heuristics and biases Low predictability Rare events;

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    1. Paul Goodwin, 2001. "Enhancing Strategy Evaluation in Scenario Planning: a Role for Decision Analysis," Journal of Management Studies, Wiley Blackwell, vol. 38(1), pages 1-16, 01.
    2. Wright, George & Cairns, George & Goodwin, Paul, 2009. "Teaching scenario planning: Lessons from practice in academe and business," European Journal of Operational Research, Elsevier, vol. 194(1), pages 323-335, April.
    3. Wright, George, 2002. "Game theory, game theorists, university students, role-playing and forecasting ability," International Journal of Forecasting, Elsevier, vol. 18(3), pages 383-387.
    4. Orrell, David & McSharry, Patrick, 2009. "System economics: Overcoming the pitfalls of forecasting models via a multidisciplinary approach," International Journal of Forecasting, Elsevier, vol. 25(4), pages 734-743, October.
    5. Robert Fildes & Paul Goodwin, 2007. "Good and Bad Judgment in Forecasting: Lessons from Four Companies," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 8, pages 5-10, Fall.
    6. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
    7. John M Bryson, 2004. "What to do when Stakeholders matter," Public Management Review, Taylor & Francis Journals, vol. 6(1), pages 21-53, March.
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    Cited by:

    1. Önkal, Dilek & Lawrence, Michael & Zeynep SayIm, K., 2011. "Influence of differentiated roles on group forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 27(1), pages 50-68, January.
    2. Meissner, Philip & Brands, Christian & Wulf, Torsten, 2017. "Quantifiying blind spots and weak signals in executive judgment: A structured integration of expert judgment into the scenario development process," International Journal of Forecasting, Elsevier, vol. 33(1), pages 244-253.
    3. Wright, George & Rowe, Gene, 2011. "Group-based judgmental forecasting: An integration of extant knowledge and the development of priorities for a new research agenda," International Journal of Forecasting, Elsevier, vol. 27(1), pages 1-13, January.
    4. Derbyshire, James & Wright, George, 2017. "Augmenting the intuitive logics scenario planning method for a more comprehensive analysis of causation," International Journal of Forecasting, Elsevier, vol. 33(1), pages 254-266.
    5. Malekpour, Shirin & de Haan, Fjalar J. & Brown, Rebekah R., 2016. "A methodology to enable exploratory thinking in strategic planning," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 192-202.
    6. Jan Anne Annema & Hugo Priemus, 2013. "Mega-projects: new challenges to cope with climate change and energy transition," Chapters,in: International Handbook on Mega-Projects, chapter 18, pages 398-417 Edward Elgar Publishing.
    7. repec:eee:intfor:v:27:y:2011:i:1:p:1-13 is not listed on IDEAS
    8. Makridakis, Spyros & Taleb, Nassim, 2009. "Decision making and planning under low levels of predictability," International Journal of Forecasting, Elsevier, vol. 25(4), pages 716-733, October.
    9. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    10. Önkal, Dilek & Lawrence, Michael & Zeynep Sayım, K., 2011. "Influence of differentiated roles on group forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 27(1), pages 50-68.
    11. Cairns, George & Wright, George & Fairbrother, Peter, 2016. "Promoting articulated action from diverse stakeholders in response to public policy scenarios: A case analysis of the use of ‘scenario improvisation’ method," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 97-108.
    12. Machani, Mahdi & Nourelfath, Mustapha & D’Amours, Sophie, 2015. "A scenario-based modelling approach to identify robust transformation strategies for pulp and paper companies," International Journal of Production Economics, Elsevier, vol. 168(C), pages 41-63.
    13. Klenk, Nicole L. & Hickey, Gordon M., 2011. "A virtual and anonymous, deliberative and analytic participation process for planning and evaluation: The Concept Mapping Policy Delphi," International Journal of Forecasting, Elsevier, vol. 27(1), pages 152-165.
    14. Dohnal, Mirko, 2016. "Complex biofuels related scenarios generated by qualitative reasoning under severe information shortages: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 676-684.
    15. repec:gam:jeners:v:9:y:2016:i:2:p:77:d:62990 is not listed on IDEAS
    16. Alexandros Flamos, 2016. "A Sectoral Micro-Economic Approach to Scenario Selection and Development: The Case of the Greek Power Sector," Energies, MDPI, Open Access Journal, vol. 9(2), pages 1-12, January.
    17. Klenk, Nicole L. & Hickey, Gordon M., 2011. "A virtual and anonymous, deliberative and analytic participation process for planning and evaluation: The Concept Mapping Policy Delphi," International Journal of Forecasting, Elsevier, vol. 27(1), pages 152-165, January.
    18. Önkal, Dilek & Sinan Gönül, M. & Goodwin, Paul & Thomson, Mary & Öz, Esra, 2017. "Evaluating expert advice in forecasting: Users’ reactions to presumed vs. experienced credibility," International Journal of Forecasting, Elsevier, vol. 33(1), pages 280-297.
    19. Zanoli, Raffaele & Gambelli, Danilo & Vairo, Daniela, 2012. "Scenarios of the organic food market in Europe," Food Policy, Elsevier, vol. 37(1), pages 41-57.
    20. Spickermann, Alexander & Grienitz, Volker & von der Gracht, Heiko A., 2014. "Heading towards a multimodal city of the future?," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 201-221.

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