Skip to main content
SpringerLink
Log in

Exploring Monte Carlo Simulation Applications for Project Management

  • Article
  • Published:
Risk Management Aims and scope Submit manuscript

Abstract

Monte Carlo simulation is a useful technique for modeling and analyzing real-world systems and situations. This paper is a conceptual paper that explores the applications of Monte Carlo simulation for managing project risks and uncertainties. The benefits of Monte Carlo simulation are using quantified data, allowing project managers to better justify and communicate their arguments when senior management is pushing for unrealistic project expectations. Proper risk management education, training, and advancements in computing technology combined with Monte Carlo simulation software allow project managers to implement the method easily. In the field of project management, Monte Carlo simulation can quantify the effects of risk and uncertainty in project schedules and budgets, giving the project manager a statistical indicator of project performance such as target project completion date and budget.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Balcombe, K. and Smith, L. (1999). Refining the User of Monte Carlo Techniques for Risk Analysis in Project Planning. The Journal of Development Studies. 36 (2), p. 113.

    Article  Google Scholar 

  • Berney, C. and Danuser, G. (2003). FRET or No FRET: A Quantitative Comparison. Biophysical Journal. 84 (6), p. 3992.

    Article  Google Scholar 

  • Bhanot, G., Gara, A., Heidelberger, P., Lawless, E., Sexton, J.C. and Walkup, R. (2005). Optimizing Task Layout on the Blue Gene/L Supercomputer. IBM Journal of Research and Development. 49 (2/3), p. 489.

    Article  Google Scholar 

  • Boinske, C. (2003). How Much Can I Spend? Journal of Financial Service Professionals. 57 (1), p. 33.

    Google Scholar 

  • Button, S. (2003). Project Duration Prediction Using a Monte Carlo Simulation of the Periodic Output of the Project Resources. Monte Carlo Methods and Applications. 9 (3), p. 217.

    Article  Google Scholar 

  • Charette, N. (1996). Large-Scale Project Management is Risk Management. IEEE Software. 13 (4), pp. 110–117.

    Article  Google Scholar 

  • Cho, J.-G. and Yum, B.-J. (2004). Functional Estimation of Activity Criticality Indices and Sensitivity Analysis of Expected Project Completion Time. Journal of the Operational Research Society. 55 (8), p. 850.

    Article  Google Scholar 

  • Eckhardt, R. (1987). Stan Ulam, John von Neumann, and the Monte Carlo Method. Los Alamos Science. Special Issue (15), pp. 131–137.

    Google Scholar 

  • El-Ramly, H., Morgenstern, N. and Cruden, R. (2002). Probabilistic Slope Stability Analysis for Practice. Canadian Geotechnical Journal. 39 (3), p. 665.

    Article  Google Scholar 

  • Gebremichael, M., Krajewski, W.F., Morrissey, M., Langerud, D., Huffman, G.J. and Adler, R. (2003). Error Uncertainty Analysis of GPCP Monthly Rainfall Products: A Data-Based Simulation Study. Journal of Applied Meteorology. 42 (12), p. 1837.

    Article  Google Scholar 

  • Gilchrist, A., Allouche, E. and Cowan, D. (2003). Prediction and Mitigation of Construction Noise in an Urban Environment. Canadian Journal of Civil Engineering. 30 (4), p. 659.

    Article  Google Scholar 

  • Graves, R. (2001). Open and Closed: The Monte Carlo Model. PM Network. 15 (12), pp. 37–41.

    Google Scholar 

  • Hahl, J., Simell, T., Kupila, A., Keskinen, P., Knip, M., Ilonon, J. and Simell, O. (2003). A Simulation Model for Estimating Direct Costs of Type 1 Diabetes Prevention. Pharmacoeconomics. 21 (5), p. 295.

    Article  Google Scholar 

  • Hurley, W. (1998). On the Use of Martingales in Monte Carlo Approaches to Multiperiod Parameter Uncertainty in Capital Investment Risk Analysis. The Engineering Economist. 43 (2), p. 169.

    Article  Google Scholar 

  • Javid, M. and Seneviratne, P. (2000). Investment Risk Analysis in Airport Parking Facility Development. Journal of Construction Engineering and Management. 126 (4), p. 298.

    Article  Google Scholar 

  • Korol, A.B., Ronin, Y.I., Nevo, E. and Hayes, P.M. (1998). Multi-Interval Mapping of Correlated Trait Complexes. Heredity. 80 (3), p. 273.

    Article  Google Scholar 

  • Kwak, Y.H. and Stoddard, J. (2004). Project Risk Management: Lessons Learned from Software Development Environment. Technovation: An International Journal of Technical Innovation, Entrepreneurship and Technology Management. 24 (11), pp. 915–920.

    Article  Google Scholar 

  • LeBlanc, B., Braunschweig, B., Toulhoat, H. and Lutton, E. (2003). Improving the Sampling Efficiency of Monte Carlo Molecular Simulations: An Evolutionary Approach. Molecular Physics. 101 (22), p. 3293.

    Article  Google Scholar 

  • Lei, F., Ferguson, C., Bird, J.A., Lockley, J.J. and Dean, J.A. (1999). The INTEGRAL Mass Model – TIMM. Astrophysical Letters and Communications. 39 (1–6), p. 841.

    Google Scholar 

  • Lorterapong, P. and Moselhi, O. (1996). Project-Network Analysis Using Fuzzy Sets Theory. Journal of Construction Engineering and Management. 122 (4), p. 308.

    Article  Google Scholar 

  • Metropolis, N. and Ulam, S. (1949). The Monte Carlo Method. Journal of the American Statistical Association. 44 (247), pp. 335–341.

    Article  Google Scholar 

  • Monte Carlo Method (2005). [Online]. [1 December 2005]. Available from World Wide Web: http://www.riskglossary.com/link/monte_carlo_method.htm.

  • Phillips, C. (2001). The Economics of ‘More Research is Needed’;. International Journal of Epidemiology. 30 (4), p. 771.

    Article  Google Scholar 

  • Project Management Institute (2004). ANSI Standard #ANSI/PMI 99-001-2004; 2004. A Guide to the Project Management Body of Knowledge: PMBOK Guide, (3rd ed.) Newton Square, Pennsylvania; Project Management Institute.

  • Salamin, N., Hodkinson, T. and Savolainen, V. (2005). Towards Building the Tree of Life: A Simulation Study for All Angiosperm Genera. Systematic Biology. 54 (2), p. 183.

    Article  Google Scholar 

  • Santos, T. and Guedes Soares, C. (2005). Monte Carlo Simulation of Damaged Ship Survivability. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment. 219 (1), p. 25.

    Google Scholar 

  • Skitmore, M. and Ng, S. (2002). Analytical and Approximate Variance of Total Project Cost. Journal of Construction Engineering and Management. 128 (5), p. 456.

    Article  Google Scholar 

  • Smith, D. (1994). Incorporating Risk into Capital Budgeting Decisions Using Simulation. Management Decision. 32 (9), p. 20.

    Article  Google Scholar 

  • Williams, T. (2003). The Contribution of Mathematical Modeling to the Practice of Project Management. IMA Journal of Management Mathematics. 14 (1), p. 3.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Decision Sciences, School of Business, The George Washington University, Washington, DC, USA

    Young Hoon Kwak

  2. IBM Systems Technology Group, Silver Spring, MD, USA

    Lisa Ingall

Authors
  1. Young Hoon Kwak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lisa Ingall
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Young Hoon Kwak.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kwak, Y., Ingall, L. Exploring Monte Carlo Simulation Applications for Project Management. Risk Manag 9, 44–57 (2007). https://doi.org/10.1057/palgrave.rm.8250017

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/palgrave.rm.8250017

Keywords

Search

Navigation