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Probability distribution fitting of schedule overruns in construction projects

Author

Listed:
  • P E D Love

    (Curtin University, Perth, Australia)

  • C-P Sing

    (Curtin University, Perth, Australia)

  • X Wang

    (Curtin University, Perth, Australia)

  • D J Edwards

    (Birmingham City University, Birmingham, UK)

  • H Odeyinka

    (University of Ulster, Belfast, UK)

Abstract

The probability of schedule overruns for construction and engineering projects can be ascertained using a ‘best fit’ probability distribution from an empirical distribution. The statistical characteristics of schedule overruns occurring in 276 Australian construction and engineering projects were analysed. Skewness and kurtosis values revealed that schedule overruns are non-Gaussian. Theoretical probability distributions were then fitted to the schedule overrun data; including the Kolmogorov–Smirnov, Anderson–Darling and Chi-Squared non-parametric tests to determine the ‘Goodness of Fit’. A Four Parameter Burr probability function best described the behaviour of schedule overruns, provided the best overall distribution fit and was used to calculate the probability of a schedule overrun being experienced. The statistical characteristics of contract size and schedule overruns were also analysed, and the Wakeby (AU$101 m) models provided the best distribution fits and were used to calculate schedule overrun probabilities by contract size.

Suggested Citation

  • P E D Love & C-P Sing & X Wang & D J Edwards & H Odeyinka, 2013. "Probability distribution fitting of schedule overruns in construction projects," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(8), pages 1231-1247, August.
    • Handle: RePEc:pal:jorsoc:v:64:y:2013:i:8:p:1231-1247
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    Cited by:

    1. Daekyoung Yi & Eul-Bum Lee & Junyong Ahn, 2019. "Onshore Oil and Gas Design Schedule Management Process Through Time-Impact Simulations Analyses," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
    2. Andy Lewin & Ekundayo Shittu & Thomas Mazzuchi & Rene Dorp, 2021. "The correlation of cost and schedule variance in satellite programs: level of effort versus discrete cost accounts," Environment Systems and Decisions, Springer, vol. 41(2), pages 248-266, June.
    3. Liu, Wenli & Li, Ang & Fang, Weili & Love, Peter E.D. & Hartmann, Timo & Luo, Hanbin, 2023. "A hybrid data-driven model for geotechnical reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. He, Chusu & Milne, Alistair & Ataullah, Ali, 2023. "What explains delays in public procurement decisions?," Economic Modelling, Elsevier, vol. 121(C).
    5. Giuseppe F Gori & Patrizia Lattarulo & Marco Mariani, 2017. "Understanding the procurement performance of local governments: A duration analysis of public works," Environment and Planning C, , vol. 35(5), pages 809-827, August.
    6. Francisco Pinheiro Catalão & Carlos Oliveira Cruz & Joaquim Miranda Sarmento, 2023. "The entanglement of time and cost deviations in public projects," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 94(1), pages 241-272, March.
    7. Kim, Byung-Cheol, 2022. "Multi-factor dependence modelling with specified marginals and structured association in large-scale project risk assessment," European Journal of Operational Research, Elsevier, vol. 296(2), pages 679-695.
    8. Palit, Niladri & Brint, Andrew, 2020. "The effect of risk aversion on the optimal project resource rate," European Journal of Operational Research, Elsevier, vol. 287(3), pages 1092-1104.

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