IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v253y2022ics0925527322002080.html
   My bibliography  Save this article

Impact of aleatoric, stochastic and epistemic uncertainties on project cost contingency reserves

Author

Listed:
  • Curto, D.
  • Acebes, F.
  • González-Varona, J.M.
  • Poza, D.

Abstract

In construction projects, contingency reserves have traditionally been estimated based on a percentage of the total project cost, which is arbitrary and, thus, unreliable in practical cases. Monte Carlo simulation provides a more reliable estimation. However, works on this topic have focused exclusively on the effects of aleatoric uncertainty, but ignored the impacts of other uncertainty types. In this paper, we present a method to quantitatively determine project cost contingency reserves based on Monte Carlo Simulation that considers the impact of not only aleatoric uncertainty, but also of the effects of other uncertainty kinds (stochastic, epistemic) on the total project cost. The proposed method has been validated with a real-case construction project in Spain. The obtained results demonstrate that the approach will be helpful for construction Project Managers because the obtained cost contingency reserves are consistent with the actual uncertainty type that affects the risks identified in their projects.

Suggested Citation

  • Curto, D. & Acebes, F. & González-Varona, J.M. & Poza, D., 2022. "Impact of aleatoric, stochastic and epistemic uncertainties on project cost contingency reserves," International Journal of Production Economics, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:proeco:v:253:y:2022:i:c:s0925527322002080
    DOI: 10.1016/j.ijpe.2022.108626
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527322002080
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2022.108626?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. Fan, Miao & Lin, Neng-Pai & Sheu, Chwen, 2008. "Choosing a project risk-handling strategy: An analytical model," International Journal of Production Economics, Elsevier, vol. 112(2), pages 700-713, April.
    2. Anna Corinna Cagliano & Sabrina Grimaldi & Carlo Rafele, 2015. "Choosing project risk management techniques. A theoretical framework," Journal of Risk Research, Taylor & Francis Journals, vol. 18(2), pages 232-248, February.
    3. Mario Vanhoucke, 2018. "The Data-Driven Project Manager," Springer Books, Springer, number 978-1-4842-3498-3, July.
    4. Trietsch, Dan & Mazmanyan, Lilit & Gevorgyan, Lilit & Baker, Kenneth R., 2012. "Modeling activity times by the Parkinson distribution with a lognormal core: Theory and validation," European Journal of Operational Research, Elsevier, vol. 216(2), pages 386-396.
    5. Helton, J.C. & Johnson, J.D. & Oberkampf, W.L. & Sallaberry, C.J., 2006. "Sensitivity analysis in conjunction with evidence theory representations of epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1414-1434.
    6. Seyed Mohammad Seyedhoseini & Siamak Noori & Mohammad Ali Hatefi, 2009. "An Integrated Methodology for Assessment and Selection of the Project Risk Response Actions," Risk Analysis, John Wiley & Sons, vol. 29(5), pages 752-763, May.
    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. David Curto & Fernando Acebes & Jose M Gonzalez-Varona & David Poza, 2024. "Impact of aleatoric, stochastic and epistemic uncertainties on project cost contingency reserves," Papers 2406.03500, arXiv.org.
    2. Dengsheng Wu & Xiaoqian Zhu & Jie Wan & Chunbing Bao & Jianping Li, 2019. "A Multiobjective Optimization Approach for Selecting Risk Response Strategies of Software Project: From the Perspective of Risk Correlations," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 339-364, January.
    3. Zuo, Fei & Zio, Enrico & Xu, Yue, 2023. "Bi-objective optimization of the scheduling of risk-related resources for risk response," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. Xiaoyan Jiang & Sai Wang & Jie Wang & Sainan Lyu & Martin Skitmore, 2020. "A Decision Method for Construction Safety Risk Management Based on Ontology and Improved CBR: Example of a Subway Project," IJERPH, MDPI, vol. 17(11), pages 1-23, June.
    5. Mahsa Parsaei Motamed & Shahrooz Bamdad, 2022. "A multi-objective optimization approach for selecting risk response actions: considering environmental and secondary risks," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 266-303, March.
    6. Junguang Zhang & Xiwei Song & Hongyu Chen & Ruixia (Sandy) Shi, 2016. "Determination of critical chain project buffer based on information flow interactions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(9), pages 1146-1157, September.
    7. Ellinas, Christos & Allan, Neil & Johansson, Anders, 2016. "Project systemic risk: Application examples of a network model," International Journal of Production Economics, Elsevier, vol. 182(C), pages 50-62.
    8. Marcel Ioan BOLOS & Diana Claudia SABĂU-POPA & Emil SCARLAT & Ioana-Alexandra BRADEA & Camelia DELCEA,, 2016. "A Business Intelligence Instrument for Detection and Mitigation of Risks Related to Projects Financed from Structural Funds," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(2), pages 165-178.
    9. Matthias Thürer & Nuno O. Fernandes & Mark Stevenson & Cristovao Silva & Silvio Carmo-Silva, 2019. "POLC-A: an assessment of POLCA’s authorization element," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2435-2447, August.
    10. Liu, Zhixue & Ding, Ronggui & Wang, Lei & Song, Rui & Song, Xinyi, 2023. "Cooperation in an uncertain environment: The impact of stakeholders' concerted action on collaborative innovation projects risk management," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    11. Limbourg, Philipp & de Rocquigny, Etienne, 2010. "Uncertainty analysis using evidence theory – confronting level-1 and level-2 approaches with data availability and computational constraints," Reliability Engineering and System Safety, Elsevier, vol. 95(5), pages 550-564.
    12. Zhang, Z. & Jiang, C. & Wang, G.G. & Han, X., 2015. "First and second order approximate reliability analysis methods using evidence theory," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 40-49.
    13. Vanhoucke, Mario & Colin, Jeroen, 2016. "On the use of multivariate regression methods for longest path calculations from earned value management observations," Omega, Elsevier, vol. 61(C), pages 127-140.
    14. Lemos, R.F. & Ronconi, D.P., 2015. "Heuristics for the stochastic single-machine problem with E/T costs," International Journal of Production Economics, Elsevier, vol. 168(C), pages 131-142.
    15. Martens, Annelies & Vanhoucke, Mario, 2019. "The impact of applying effort to reduce activity variability on the project time and cost performance," European Journal of Operational Research, Elsevier, vol. 277(2), pages 442-453.
    16. Song, Jie & Martens, Annelies & Vanhoucke, Mario, 2022. "Using Earned Value Management and Schedule Risk Analysis with resource constraints for project control," European Journal of Operational Research, Elsevier, vol. 297(2), pages 451-466.
    17. McFarland, John & DeCarlo, Erin, 2020. "A Monte Carlo framework for probabilistic analysis and variance decomposition with distribution parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    18. Vaseghi, Forough & Martens, Annelies & Vanhoucke, Mario, 2024. "Analysis of the impact of corrective actions for stochastic project networks," European Journal of Operational Research, Elsevier, vol. 316(2), pages 503-518.
    19. Juhasz, Peter & Varadi, Kata & Vidovics-Dancs, Agnes & Szaz, Janos, 2017. "Measuring Path Dependency," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 8(1), pages 29-37.
    20. Bi, Kexin & Huang, Ping & Ye, Hui, 2015. "Risk identification, evaluation and response of low-carbon technological innovation under the global value chain: A case of the Chinese manufacturing industry," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 238-248.

    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:eee:proeco:v:253:y:2022:i:c:s0925527322002080. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.