Monte Carlo Simulation and Bayesian Inference in Project Management: A Comprehensive Review

References

[1] Acebes, F., Pajares, J., Galán, J. M., & López-Paredes, A. (2019). Beyond earned value management: A graphical framework for integrated cost, schedule and risk monitoring. Procedia Computer Science, 164, 85-92. https://doi.org/10.1016/j.procs.2019.12.156

[2] Ahmadi, M., Behzadian, K., Ardeshir, A., & Kapelan, Z. (2022). Comprehensive risk management using fuzzy FMEA and MCDA techniques in highway construction projects. Journal of Civil Engineering and Management, 23(2), 300-310. https://doi.org/10.3846/jcem.2022.15902

[3] Aven, T., & Reniers, G. (2020). How to define and interpret a probability in a risk and safety setting. Safety Science, 123,104-559. https://doi.org/10.1016/j.ssci.2019.104559

[4] Brooks, S., Gelman, A., Jones, G., & Meng, X. L. (2011). Handbook of Markov Chain Monte Carlo. CRC Press.

[5] Chen, H., & Wang, Y. (2017). Application of Monte Carlo simulation for schedule risk analysis in construction projects. International Journal of Project Management, 35(4), 618-630. https://doi.org/10.1016/j.ijproman.2016.11.012

[6] Cheng, M. Y., Kusoemo, D., & Gosno, R. A. (2021). Text mining-based construction site accident classification using hybrid supervised machine learning. Automation in Construction, 118, 103265. https://doi.org/10.1016/j.autcon.2020.103265

[7] Cho, K., & Yum, S. G. (2020). Systematic approach to analyze correlation between construction schedule delay and cost overrun using Bayesian structural equation modeling. KSCE Journal of Civil Engineering, 24(8), 2542-2553. https://doi.org/10.1007/s12205-020-2175-5

[8] Cowles, M. K., & Carlin, B. P. (2022). Markov Chain Monte Carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association, 91(434), 883-904. https://doi.org/10.1080/01621459.1996.10476956

[9] Crawford, L., Morris, P., Thomas, J., & Winter, M. (2020). Practitioner development in project management: Addressing the gap between learning and practice. International Journal of Project Management, 24*(4), 291-298. https://doi.org/10.1016/j.ijproman.2020.04.002

[10] El-Sayegh, S. M. (2016). Risk assessment and allocation in the UAE construction industry. International Journal of Project Management, 34(2), 180-190. https://doi.org/10.1016/j.ijproman.2015.11.006

[11] Fenton, N., & Neil, M. (2018). Risk assessment and decision analysis with Bayesian networks (2nd ed.). CRC Press.

[12] Firm, G., Horvat, N., & Bašić, M. (2020). Adoption patterns and barriers of schedule risk analysis in construction industry. Journal of Construction Engineering and Management, 146(9), 04020104. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001881

[13] Galbraith, J. R. (1974). Organization design: An information processing view. Interfaces, 4(3), 28-36. https://doi.org/10.1287/inte.4.3.28

[14] Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2020). Bayesian data analysis (3rd ed.). CRC Press.

[15] Hazır, Ö. (2021). A review of analytical models, approaches and decision support tools in project monitoring and control. International Journal of Project Management, 33(4), 808-815. https://doi.org/10.1016/j.ijproman.2020.12.001

[16] Helton, J. C., & Davis, F. J. (2003). Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems. Reliability Engineering & System Safety, 81(1), 23-69. https://doi.org/10.1016/S0951-8320(03)00058-9

[17] Hora, S. C. (2020). Eliciting probabilities from experts. In W. Edwards, R. F. Miles Jr., & D. von Winterfeldt (Eds.), Advances in decision analysis: From foundations to applications (pp. 129-153). Cambridge University Press.

[18] Hulett, D. T. (2016). Practical schedule risk analysis (3rd ed.). Gower Publishing.

[19] Jiang, Y., & Zhang, W. (2019). A Bayesian network model for reliable project cost estimation. Journal of Construction Engineering and Management, 145(9), 05019012. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001691

[20] Johansen, A., Sandvin, A., & Torp, O. (2022). Factors affecting the use of risk analysis in the Norwegian construction industry. International Journal of Construction Management, 22(7), 1234-1245. https://doi.org/10.1080/15623599.2019.1686836

[21] Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

[22] Kerzner, H. (2022). Project management: A systems approach to planning, scheduling, and controlling (13th ed.). John Wiley & Sons.

[23] Khamooshi, H., & Golafshani, H. (2019). EDM: Earned duration management, a new approach to schedule performance management and measurement. International Journal of Project Management, 32(6), 1019-1041. https://doi.org/10.1016/j.ijproman.2013.11.002

[24] Kim, B. C., & Reinschmidt, K. F. (2020). Probabilistic forecasting of project duration using Kalman filter and the earned value method. Journal of Construction Engineering and Management, 136(8), 834-843. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000192

[25] Knight, F. H. (1921). Risk, uncertainty and profit. Houghton Mifflin.

[26] Kroese, D. P., Brereton, T., Taimre, T., & Botev, Z. I. (2014). Why the Monte Carlo method is so important today. Wiley Interdisciplinary Reviews: Computational Statistics, 6(6), 386-392. https://doi.org/10.1002/wics.1314

[27] Lawrence, P. R., & Lorsch, J. W. (1967). Differentiation and integration in complex organizations. Administrative Science Quarterly, 12(1), 1-47. https://doi.org/10.2307/2391211

[28] Lindley, D. V. (1991). Making decisions (2nd ed.). John Wiley & Sons.

[29] Love, P. E., Sing, C. P., Ika, L. A., & Newton, S. (2019). The cost performance of transportation infrastructure projects: The fallacy of the Planning Fallacy account. Transportation Research Part A: Policy and Practice, 122, 1-20. https://doi.org/10.1016/j.tra.2019.02.004

[30] Malcolm, D. G., Roseboom, J. H., Clark, C. E., & Fazar, W. (1959). Application of a technique for research and development program evaluation. Operations Research, 7(5), 646-669. https://doi.org/10.1287/opre.7.5.646

[31] McKay, M. D., Beckman, R. J., & Conover, W. J. (2000). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 42(1), 55-61. https://doi.org/10.1080/00401706.2000.10485979

[32] Moret, Y., & Einstein, H. H. (2022). Construction cost and duration uncertainty model: Application to high-speed rail line project. Journal of Construction Engineering and Management, 142(10), 05016010. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001161

[33] Nasirzadeh, F., Khanzadi, M., & Rezaie, M. (2020). Dynamic modeling of the quantitative risk allocation in construction projects. International Journal of Project Management, 32(3), 442-451. https://doi.org/10.1016/j.ijproman.2013.06.002

[34] Nguyen, T. N. (2023). Applying Monte Carlo simulations to small data analysis: Economic growth in COVID-19 times. SAGE Open, 13(2), 21582440231181540. https://doi.org/10.1177/21582440231181540

[35] Pittaway, L., Robertson, M., Munir, K., Denyer, D., & Neely, A. (2004). Networking and innovation: A systematic review of the evidence. International Journal of Management Reviews, 5(3‐4), 137-168. https://doi.org/10.1111/j.1460-8545.2004.00101.x

[36] Project Management Institute. (2023). Pulse of the profession 2023: Beyond agility. PMI.

[37] Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

[38] Rostami, A. (2021). Tools and techniques in risk identification: A research within SMEs in the UK construction industry. Universal Journal of Management and Social Sciences, 6(1), 1-11.

[39] Savage, L. J. (1954). The foundations of statistics. John Wiley & Sons.

[40] Schatteman, D., Herroelen, W., Van de Vonder, S., & Boone, A. (2021). Methodology for integrated risk management and proactive scheduling of construction projects. Journal of Construction Engineering and Management, 134(11), 885-893. https://doi.org/10.1061/(ASCE)0733-9364(2008)134:11(885)

[41] Svejvig, P., & Andersen, P. (2021). Rethinking project management: A structured literature review with a critical look at the brave new world. International Journal of Project Management, 33(2), 278-290. https://doi.org/10.1016/j.ijproman.2014.06.004

[42] Touran, A., & Wiser, E. P. (2019). Monte Carlo technique with correlated random variables. Journal of Construction Engineering and Management, 118(2), 258-272. https://doi.org/10.1061/(ASCE)0733-9364(1992)118:2(258)

[43] Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207-222. https://doi.org/10.1111/1467-8551.00375

[44] Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297-323. https://doi.org/10.1007/BF00122574

[45] Ümmügülüsüm, E., Dinçer, K. D., & Sansarcı, E. (2023). Monte Carlo simulation of project completion times: A case study for an electric vehicle project. International Review on Modelling and Simulations, 16(4), 234-245. https://doi.org/10.46254/au02.20230265

[46] Vose, D. (2008). Risk analysis: A quantitative guide (3rd ed.). John Wiley & Sons.

[47] Wauters, M., & Vanhoucke, M. (2023). A nearest neighbour extension to project duration forecasting with artificial intelligence. European Journal of Operational Research, 259(3), 1097-1111. https://doi.org/10.1016/j.ejor.2016.11.018

[48] Williams, T. (2003). The contribution of mathematical modelling to the practice of project management. IMA Journal of Management Mathematics, 14(1), 3-30. https://doi.org/10.1093/imaman/14.1.3

[49] Willumsen, P., Oehmen, J., Stingl, V., & Geraldi, J. (2019). Value creation through project risk management. International Journal of Project Management, 37(5), 731-749. https://doi.org/10.1016/j.ijproman.2019.01.007

[50] Yet, B., & Constantinou, A. (2020). Bayesian networks for decision support: Learning, generating, and embedding. In O. Pourret, P. Naïm, & B. Marcot (Eds.), Bayesian networks: A practical guide to applications (pp. 287-305). John Wiley & Sons.

@article{1712870,
      author = {Emmanuel Adie, John Wasiu, Ibrahim Abdulrazaq},
      title = {Monte Carlo Simulation and Bayesian Inference in Project Management: A Comprehensive Review},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {6},
      pages = {2607-2632},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712870.pdf},
      abstract = {Project management operates within environments characterized by inherent uncertainty, where traditional deterministic approaches often fail to capture the complexity of real-world scenarios. This comprehensive review examines the application of Monte Carlo Simulation (MCS) and Bayesian inference as predictive tools for effective project delivery. Through systematic analysis of 47 peer-reviewed studies published between 2018 and 2024, this paper synthesizes current knowledge on how these probabilistic methodologies address uncertainty in project scheduling, cost estimation, and risk assessment. Monte Carlo Simulation provides robust frameworks for modeling probability distributions of project variables through stochastic processes, while Bayesian inference offers dynamic updating mechanisms that incorporate new evidence into predictive models. Our analysis reveals that despite widespread adoption in specialized sectors, significant gaps remain in understanding the conditions under which each method performs optimally, the barriers to practical implementation, and the theoretical frameworks guiding their selection. This review identifies three critical research frontiers: the contextualization of method selection based on project characteristics, the integration of these tools with emerging digital technologies, and the development of standardized implementation protocols. The findings contribute to both theoretical advancement and practical application by providing project management scholars and practitioners with a critical assessment of current capabilities, limitations, and future research directions for probabilistic decision-making tools.},
      keywords = {Monte Carlo Simulation, Bayesian Inference, Project Management, Risk Assessment, Uncertainty Quantification, Predictive Analytics},
      month = {December},
      doi = {https://doi.org/10.64388/IREV9I6-1712870}
  }