IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v11y2024i1d10.1057_s41599-024-03180-5.html
   My bibliography  Save this article

Beyond probability-impact matrices in project risk management: A quantitative methodology for risk prioritisation

Author

Listed:
  • F. Acebes

    (Universidad de Valladolid)

  • J. M. González-Varona

    (Universidad de Málaga)

  • A. López-Paredes

    (Universidad de Málaga)

  • J. Pajares

    (Universidad de Valladolid)

Abstract

The project managers who deal with risk management are often faced with the difficult task of determining the relative importance of the various sources of risk that affect the project. This prioritisation is crucial to direct management efforts to ensure higher project profitability. Risk matrices are widely recognised tools by academics and practitioners in various sectors to assess and rank risks according to their likelihood of occurrence and impact on project objectives. However, the existing literature highlights several limitations to use the risk matrix. In response to the weaknesses of its use, this paper proposes a novel approach for prioritising project risks. Monte Carlo Simulation (MCS) is used to perform a quantitative prioritisation of risks with the simulation software MCSimulRisk. Together with the definition of project activities, the simulation includes the identified risks by modelling their probability and impact on cost and duration. With this novel methodology, a quantitative assessment of the impact of each risk is provided, as measured by the effect that it would have on project duration and its total cost. This allows the differentiation of critical risks according to their impact on project duration, which may differ if cost is taken as a priority objective. This proposal is interesting for project managers because they will, on the one hand, know the absolute impact of each risk on their project duration and cost objectives and, on the other hand, be able to discriminate the impacts of each risk independently on the duration objective and the cost objective.

Suggested Citation

  • F. Acebes & J. M. González-Varona & A. López-Paredes & J. Pajares, 2024. "Beyond probability-impact matrices in project risk management: A quantitative methodology for risk prioritisation," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
  • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03180-5
    DOI: 10.1057/s41599-024-03180-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-024-03180-5
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1057/s41599-024-03180-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Stefano Gatti & Alvaro Rigamonti & Francesco Saita & Mauro Senati, 2007. "Measuring Value‐at‐Risk in Project Finance Transactions," European Financial Management, European Financial Management Association, vol. 13(1), pages 135-158, January.
    2. Xin Ruan & Zhiyi Yin & Dan M. Frangopol, 2015. "Risk Matrix Integrating Risk Attitudes Based on Utility Theory," Risk Analysis, John Wiley & Sons, vol. 35(8), pages 1437-1447, August.
    3. Georgios K. Koulinas & Olympia E. Demesouka & Konstantinos A. Sidas & Dimitrios E. Koulouriotis, 2021. "A TOPSIS—Risk Matrix and Monte Carlo Expert System for Risk Assessment in Engineering Projects," Sustainability, MDPI, vol. 13(20), pages 1-14, October.
    4. Stefan Creemers & Erik Demeulemeester & Stijn Vonder, 2014. "A new approach for quantitative risk analysis," Annals of Operations Research, Springer, vol. 213(1), pages 27-65, February.
    5. Jianping Li & Chunbing Bao & Dengsheng Wu, 2018. "How to Design Rating Schemes of Risk Matrices: A Sequential Updating Approach," Risk Analysis, John Wiley & Sons, vol. 38(1), pages 99-117, January.
    6. Keith Kuester & Stefan Mittnik & Marc S. Paolella, 2006. "Value-at-Risk Prediction: A Comparison of Alternative Strategies," Journal of Financial Econometrics, Oxford University Press, vol. 4(1), pages 53-89.
    7. Michael Krisper, 2021. "Problems with Risk Matrices Using Ordinal Scales," Papers 2103.05440, arXiv.org.
    8. E.S. Levine, 2012. "Improving risk matrices: the advantages of logarithmically scaled axes," Journal of Risk Research, Taylor & Francis Journals, vol. 15(2), pages 209-222, February.
    9. Louis Anthony (Tony) Cox & Djangir Babayev & William Huber, 2005. "Some Limitations of Qualitative Risk Rating Systems," Risk Analysis, John Wiley & Sons, vol. 25(3), pages 651-662, June.
    10. Giot, Pierre & Laurent, Sebastien, 2003. "Market risk in commodity markets: a VaR approach," Energy Economics, Elsevier, vol. 25(5), pages 435-457, September.
    11. Shabnam Vatanpour & Steve E. Hrudey & Irina Dinu, 2015. "Can Public Health Risk Assessment Using Risk Matrices Be Misleading?," IJERPH, MDPI, vol. 12(8), pages 1-14, August.
    12. David J. Ball & John Watt, 2013. "Further Thoughts on the Utility of Risk Matrices," Risk Analysis, John Wiley & Sons, vol. 33(11), pages 2068-2078, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jianping Li & Chunbing Bao & Dengsheng Wu, 2018. "How to Design Rating Schemes of Risk Matrices: A Sequential Updating Approach," Risk Analysis, John Wiley & Sons, vol. 38(1), pages 99-117, January.
    2. Roger C. Jensen & Royce L. Bird & Blake W. Nichols, 2022. "Risk Assessment Matrices for Workplace Hazards: Design for Usability," IJERPH, MDPI, vol. 19(5), pages 1-23, February.
    3. Alex de Lima Teodoro da Penha & Samuel Vinícius Bonato & Joana Baleeiro Passos & Eduardo da Silva Fernandes & Cínthia Kulpa & Carla Schwengber ten Caten, 2024. "Navigating the Urgency: An Open Innovation Project of Protective Equipment Development from a Quadruple Helix Perspective," Sustainability, MDPI, vol. 16(4), pages 1-32, February.
    4. Laura Garcia‐Jorcano & Alfonso Novales, 2021. "Volatility specifications versus probability distributions in VaR forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 189-212, March.
    5. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
    6. Anna Kosovac & Brian Davidson & Hector Malano, 2019. "Are We Objective? A Study into the Effectiveness of Risk Measurement in the Water Industry," Sustainability, MDPI, vol. 11(5), pages 1-13, February.
    7. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    8. Žužek Tena & Rihar Lidija & Berlec Tomaž & Kušar Janez, 2020. "Standard Project Risk Analysis Approach," Business Systems Research, Sciendo, vol. 11(2), pages 149-158, October.
    9. Braione, Manuela & Scholtes, Nicolas K., 2014. "Construction of value-at-risk forecasts under different distributional assumptions within a BEKK framework," LIDAM Discussion Papers CORE 2014059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Gulsum Kubra Kaya & James Ward & John Clarkson, 2019. "A Review of Risk Matrices Used in Acute Hospitals in England," Risk Analysis, John Wiley & Sons, vol. 39(5), pages 1060-1070, May.
    11. Roger C. Jensen & Haley Hansen, 2020. "Selecting Appropriate Words for Naming the Rows and Columns of Risk Assessment Matrices," IJERPH, MDPI, vol. 17(15), pages 1-17, July.
    12. Haugom, Erik & Ray, Rina & Ullrich, Carl J. & Veka, Steinar & Westgaard, Sjur, 2016. "A parsimonious quantile regression model to forecast day-ahead value-at-risk," Finance Research Letters, Elsevier, vol. 16(C), pages 196-207.
    13. Timotheos Angelidis & Stavros Degiannakis, 2007. "Backtesting VaR Models: An Expected Shortfall Approach," Working Papers 0701, University of Crete, Department of Economics.
    14. Kulp-Tåg, Sofie, 2007. "An Empirical Investigation of Value-at-Risk in Long and Short Trading Positions," Working Papers 526, Hanken School of Economics.
    15. Dimitrios P. Louzis & Spyros Xanthopoulos‐Sisinis & Apostolos P. Refenes, 2013. "The Role of High‐Frequency Intra‐daily Data, Daily Range and Implied Volatility in Multi‐period Value‐at‐Risk Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 561-576, September.
    16. Vicki Bier, 2020. "The Role of Decision Analysis in Risk Analysis: A Retrospective," Risk Analysis, John Wiley & Sons, vol. 40(S1), pages 2207-2217, November.
    17. Huang, Dashan & Yu, Baimin & Fabozzi, Frank J. & Fukushima, Masao, 2009. "CAViaR-based forecast for oil price risk," Energy Economics, Elsevier, vol. 31(4), pages 511-518, July.
    18. Timotheos Angelidis & Alexandros Benos & Stavros Degiannakis, 2007. "A robust VaR model under different time periods and weighting schemes," Review of Quantitative Finance and Accounting, Springer, vol. 28(2), pages 187-201, February.
    19. Alan J. Card & James R. Ward & P. John Clarkson, 2014. "Trust‐Level Risk Evaluation and Risk Control Guidance in the NHS East of England," Risk Analysis, John Wiley & Sons, vol. 34(8), pages 1469-1481, August.
    20. Shabnam Vatanpour & Steve E. Hrudey & Irina Dinu, 2015. "Can Public Health Risk Assessment Using Risk Matrices Be Misleading?," IJERPH, MDPI, vol. 12(8), pages 1-14, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-03180-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.