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Problem structuring using computer-aided morphological analysis

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  • T Ritchey

    (Swedish Defence Research Agency)

Abstract

General morphological analysis (GMA) is a method for structuring and investigating the total set of relationships contained in multidimensional, usually non-quantifiable, problem complexes. Pioneered by Fritz Zwicky at the California Institute of Technology in the 1930s and 1940s, it relies on a constructed parameter space, linked by way of logical relationships, rather than on causal relationships and a hierarchal structure. During the past 10 years, GMA has been computerized and extended for structuring and analysing complex policy spaces, developing futures scenarios and modelling strategy alternatives. This article gives a historical and theoretical background to GMA as a problem structuring method, compares it with a number of other ‘soft-OR’ methods, and presents a recent application in structuring a complex policy issue. The issue involves the development of an extended producer responsibility (EPR) system in Sweden.

Suggested Citation

  • T Ritchey, 2006. "Problem structuring using computer-aided morphological analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(7), pages 792-801, July.
    • Handle: RePEc:pal:jorsoc:v:57:y:2006:i:7:d:10.1057_palgrave.jors.2602177
    DOI: 10.1057/palgrave.jors.2602177
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    References listed on IDEAS

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    1. Jonathan Rosenhead, 1996. "What's the Problem? An Introduction to Problem Structuring Methods," Interfaces, INFORMS, vol. 26(6), pages 117-131, December.
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    Cited by:

    1. J S Edwards & B Ababneh & M Hall & D Shaw, 2009. "Knowledge management: a review of the field and of OR's contribution," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 114-125, May.
    2. Zec, Marin & Matthes, Florian, 2018. "Web-based software-support for collaborative morphological analysis in real-time," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 168-181.
    3. Lijie Feng & Yuxiang Niu & Zhenfeng Liu & Jinfeng Wang & Ke Zhang, 2019. "Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT," Sustainability, MDPI, vol. 12(1), pages 1-35, December.
    4. Kemp-Benedict, Eric & Carlsen, Henrik & Kartha, Sivan, 2019. "Large-scale scenarios as ‘boundary conditions’: A cross-impact balance simulated annealing (CIBSA) approach," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 55-63.
    5. Pereverza, Kateryna & Pasichnyi, Oleksii & Lazarevic, David & Kordas, Olga, 2017. "Strategic planning for sustainable heating in cities: A morphological method for scenario development and selection," Applied Energy, Elsevier, vol. 186(P2), pages 115-125.
    6. Minkyu Kwon & Jihwan Lee & Yoo S. Hong, 2019. "Product-Service System Business Modelling Methodology Using Morphological Analysis," Sustainability, MDPI, vol. 11(5), pages 1-16, March.
    7. Zivkovic, Marija & Pereverza, Kateryna & Pasichnyi, Oleksii & Madzarevic, Aleksandar & Ivezic, Dejan & Kordas, Olga, 2016. "Exploring scenarios for more sustainable heating: The case of Niš, Serbia," Energy, Elsevier, vol. 115(P3), pages 1758-1770.
    8. Klerkx, Rik & Pelsser, Antoon, 2022. "Narrative-based robust stochastic optimization," Journal of Economic Behavior & Organization, Elsevier, vol. 196(C), pages 266-277.
    9. Duczynski, Guy, 2018. "Investigating traffic congestion: Targeting technological and social interdependencies through general morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 161-167.
    10. Durugbo, Christopher M., 2020. "Affordance-based problem structuring for workplace innovation," European Journal of Operational Research, Elsevier, vol. 284(2), pages 617-631.
    11. Ram, Camelia, 2020. "Scenario presentation and scenario generation in multi-criteria assessments: An exploratory study," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    12. Jannie Coenen & Rob van der Heijden & Allard C. R. van Riel, 2019. "Making a Transition toward more Mature Closed-Loop Supply Chain Management under Deep Uncertainty and Dynamic Complexity: A Methodology," Sustainability, MDPI, vol. 11(8), pages 1-27, April.
    13. Vilkkumaa, Eeva & Liesiö, Juuso & Salo, Ahti & Ilmola-Sheppard, Leena, 2018. "Scenario-based portfolio model for building robust and proactive strategies," European Journal of Operational Research, Elsevier, vol. 266(1), pages 205-220.
    14. C Ram & G Montibeller & A Morton, 2011. "Extending the use of scenario planning and MCDA for the evaluation of strategic options," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(5), pages 817-829, May.
    15. Markus Spiekermann, 2019. "Data Marketplaces: Trends and Monetisation of Data Goods," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 54(4), pages 208-216, July.
    16. Panula-Ontto, Juha & Luukkanen, Jyrki & Kaivo-oja, Jari & O'Mahony, Tadhg & Vehmas, Jarmo & Valkealahti, Seppo & Björkqvist, Tomas & Korpela, Timo & Järventausta, Pertti & Majanne, Yrjö & Kojo, Matti , 2018. "Cross-impact analysis of Finnish electricity system with increased renewables: Long-run energy policy challenges in balancing supply and consumption," Energy Policy, Elsevier, vol. 118(C), pages 504-513.
    17. Johansen, Iver, 2018. "Scenario modelling with morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 116-125.
    18. Lee, Changyong & Kim, Juram & Lee, Sungjoo, 2016. "Towards robust technology roadmapping: How to diagnose the vulnerability of organisational plans," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 164-175.
    19. Ram, Camelia & Montibeller, Gilberto & Morton, Alec, 2011. "Extending the use of scenario planning and MCDA for the evaluation of strategic options," LSE Research Online Documents on Economics 32215, London School of Economics and Political Science, LSE Library.
    20. Soon Goo Hong & DonHee Lee, 2023. "Development of a citizen participation public service innovation model based on smart governance," Service Business, Springer;Pan-Pacific Business Association, vol. 17(3), pages 669-694, September.
    21. Panula-Ontto, Juha, 2019. "The AXIOM approach for probabilistic and causal modeling with expert elicited inputs," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 292-308.
    22. Seeve, Teemu & Vilkkumaa, Eeva, 2022. "Identifying and visualizing a diverse set of plausible scenarios for strategic planning," European Journal of Operational Research, Elsevier, vol. 298(2), pages 596-610.
    23. D Champion & J M Wilson, 2010. "The impact of contingency factors on validation of problem structuring methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(9), pages 1420-1431, September.
    24. Voros, Joseph, 2018. "On a morphology of contact scenario space," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 126-137.

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