Estimating Project Completion Time with Monte Carlo Simulation
Keywords:Monte Carlo simulation, project scheduling, critical path method, precedence diagram method
Risk and uncertainty are factors that construction project managers have been increasingly had to deal with. Project completion time is one of the areas where the expected time is often underestimated or shorter than the actual completion time. Monte Carlo simulation is a widely used simulation technique in modeling a process that is difficult to predict due to its random variables. This study provides a practical way to use Monte Carlo simulation to simulate a project using functions available in a spreadsheet application. A project with five activities was simulated 2000 times using minimum, maximum, and expected duration. The mean, mode, and median simulation results were then plugged into their respective precedence diagram networks to compare them. The precedence diagram computations found that mean, mode, and median project completion times were longer than the initially expected completion time. The mean, mode, and median were 50, 53, and 48 days, respectively, which were 8, 11, and 6 days longer, respectively, than the 42 days initially expected. The study showed that the Monte Carlo simulation could assist the project manager in planning a project schedule that deals with risk and uncertainty more realistically.
S.A. Mubarak, Construction Project Scheduling and Control, Hoboken N.J.: John Wiley and Sons, Inc., 2010.
H. Kerzner, Project Management: A Systems Approach to Planning, Scheduling, and Controlling, Hoboken N.J: John Wiley and Sons, 2003.
J.R. Turner, The Handbook of Project-Based Management: Leading Strategic Change in Organizations, New York: McGraw Hill, 2009.
L.S. Riggs, “Risk Management in CPM Networks,” Computer-Aided Civil and Infrastructure Engineering, vol. 4, no. 3, p. 229, 1989.
C.S. Goh, H.A. Rahman, and Z.A. Samad, “Applying Risk Management Workshop for a Public Construction Project: Case Study,” Journal of Construction Engineering and Management, vol. 139, no. 5, p. 572, 2013.
A.S. Akintoye and M.J. MacLeod, “Risk analysis and management in construction,” International Journal of Project Management, vol. 15, no. 1, p. 31, 1997.
J.H.M. Tah and V. Carr, “Towards a Framework for Project Risk Knowledge Management in the Construction Supply Chain,” Advances in Engineering Software, vol. 12, no. 10, p. 12, 2001.
D. Forbes, S. Smith, and M. Horner, “Tools for Selecting Appropriate Risk Management Techniques in the Built Environment,” Construction Management and Economics, vol. 26, no. 11, p. 1241, 2008.
Z. Kong, J. Zhang, C. Li, X. Zheng, and Q. Guan, Risk Assessment of Plan Schedule by Monte Carlo Simulation, Shenzhen: Maxwell, 2015.
A. Laufer and G.A. Howell, “Construction Planning: Revising the Paradigm PMI.” https://www.pmi.org/learning/library/construction-planning-revising-paradigm-2110 (accessed Jul. 31, 2022).
N.H. Kadume and H. I. Naji, “Building Schedule Risks Simulation by Using BIM with Monte Carlo Technique,” IOP Conference Series: Earth and Environmental Science, vol. 856, no. 1, p. 012059, 2021.
J. Sobieraj and D. Metelski, “Project Risk in the Context of Construction Schedules-Combined Monte Carlo Simulation and Time at Risk (TaR) Approach: Insights from the Fort Bema Housing Estate Complex,” Applied Sciences, vol. 12, no. 3, p. 1044, 2022.
X. Chen, L. Cheng, G. Deng, S. Guan, and L. Hu, “Project duration-cost-quality prediction model based on Monte Carlo simulation,” Journal of Physics: Conference Series, vol. 1978, no. 1, p. 012048, 2021.
Y.H. Kwak and L. Ingall, “Exploring Monte Carlo Simulation Applications for Project Management,” Risk Management, vol. 9, no. 1, p. 44, 2007.
A. Namazian, S.H. Yakhchali, V. Yousefi, and J. Tamosaitiene, “Combining Monte Carlo Simulation and Bayesian Networks Methods for Assessing Completion Time of Projects under Risk,” International Journal of Environmental Research and Public Health, vol. 16, no. 24, p. 5024, 2019.
PMI, A Guide to the Project Management Body of Knowledge, Newtown Square: Project Management Institute, 2017.
M. Lu and S.A. Rizk, “Simplified CPM/PERT Simulation Model,” Journal of Construction Engineering & Management., vol. 126, no. 3, p. 219, 2000.
D. Vose, Quantitative Risk Analysis: A Guide to Monte Carlo Simulation Modelling, New York: John Wiley & Sons, 1996.
H.A. Taha, Operations Research An Introduction, Tenth edition. Boston: Pearson, 2017.
S. Manikandan, “Measures of Central Tendency: Median and Mode,” Journal of Pharmacology and Pharmacotherapeutics, vol. 2, no. 3, p. 214, 2011.
S. Manikandan, “Measures of Central Tendency: The Mean,” Journal of Pharmacology and Pharmacotherapeutics, vol. 2, no. 2, p. 140, 2011.
How to Cite
Copyright (c) 2022 REKONSTRUKSI TADULAKO: Civil Engineering Journal on Research and Development
This work is licensed under a Creative Commons Attribution 4.0 International License.