Open Access Open Access  Restricted Access Subscription or Fee Access

Using Monte Carlo Simulation to Support Project Investment Decisions Under Uncertainty: Case of Jordan Phosphate Mines Company


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/iremos.v16i4.23517

Abstract


Large projects involving substantial investment encounter numerous risks related to cost and time estimation due to the multitude of variables in play when making the decision to invest. The significance of these decisions is highlighted by their ramifications, which include determining the project's feasibility, margin, and profitability—crucial factors, particularly when such projects are bank-financed. Concurrently, establishing the timeline for project completion is pivotal as it aligns with the contracts agreed upon with buyers. To address uncertainties and mitigate investment risks, decision-makers employ diverse methods. This paper offers a meticulous literature analysis focusing on the merits and drawbacks of utilizing the Monte Carlo simulation for budget and schedule estimations, considering it evaluates the probability distributions of all alterations affecting project evaluation, exemplified through a study on a phosphate processing plant. The paper elucidates the evaluation and risk analysis criteria of this investment project and illustrates the relevance of the Monte Carlo simulation in this scenario. The simulation, developed and executed via "@Risk" (Palisade), serves as a valuable tool in comprehensively understanding and mitigating the inherent risks and uncertainties in large-scale, significant investment projects.
Copyright © 2023 Praise Worthy Prize - All rights reserved.

Keywords


Estimation; Investment Decision-Making; Monte Carlo Simulation; Project Risk Management

Full Text:

PDF


References


M. J. Liberatore and B. Pollack-Johnson, Improving Project Management Decision Making by Modeling Quality, Time, and Cost Continuously, IEEE Transactions on Engineering Management, vol. 60, no. 3, pp. 518-528, Aug. 2013.
https://doi.org/10.1109/TEM.2012.2219586

Zhaoni Kong, Jianping Zhang, Chao Li, Xiaoying Zheng and Qingqing Guan, Risk Assessment of Plan Schedule by Monte Carlo Simulation, International Conference on Information Technology and Management Innovation (ICITMI 2015). ISBN-9781510814479, (2015).
https://doi.org/10.2991/icitmi-15.2015.83

Al Khasawneh, K., Al-Samamah, Y., Analytical Study of the Stagnation Point Properties and Impact Forces for Non-Continuum Flow Out of Planar Exit Impinge on Inclined Flat Plate Surface, (2022) International Review of Aerospace Engineering (IREASE), 15 (5), pp. 283-293.
https://doi.org/10.15866/irease.v15i5.22475

Solwa, S., Elmezughi, M., Almaktoof, A., Abo-Al-Ez, K., Genetic Algorithms: Tuning of Parameter K for the Labeling Diversity Problem in Wireless Communications, (2022) International Journal on Communications Antenna and Propagation (IRECAP), 12 (4), pp. 228-236.
https://doi.org/10.15866/irecap.v12i4.21788

Sarrut, D., Baudier, T., Borys, D., Etxebeste, A., Fuchs, H., Gajewski, J., ... & Maigne, L. (2022). The OpenGATE ecosystem for Monte Carlo simulation in medical physics. Physics in Medicine & Biology, 67(18), 184001.
https://doi.org/10.1088/1361-6560/ac8c83

Li, J., Zhang, J., Yang, X., Zhang, A., & Yu, M. (2023). Monte Carlo simulations of deformation behaviour of unbound granular materials based on a real aggregate library. International Journal of Pavement Engineering, 24(1), 2165650.
https://doi.org/10.1080/10298436.2023.2165650

Andrie Pasca Hendradewa. Schedule Risk Analysis by Different Phases of Construction Project Using CPM-PERT and Monte-Carlo Simulation. IOP Conference Series: Materials Science and Engineering. 528 012035. (2019).
https://doi.org/10.1088/1757-899X/528/1/012035

Ali, Mohammed Mubashir; Elazouni, Ashraf, Finance-based CPM/LOB scheduling of projects with repetitive non-serial, Construction Management And Economics Volume 27 (Issue 9): 839-856, Oct 2009.
https://doi.org/10.1080/01446190903191764

OGC, Management of Risk: Guidance for Practitioners, TSO, London 2007.

Hulett, D. T. Project schedule risk analysis: Monte Carlo simulation or PERT? PM Network, 14(2), 43-47, Journal of Building Construction and Planning Research, 6: 234-250, 2000.

Wyrozębski P & Wyrozębska A, Benefits of Monte Carlo simulation as the extension to the Programe Evaluation and Review Technique, The 2nd Electronic International Interdisciplinary Conference : 95-99, September 2013.

W. Na, P. Wuliang & G. Hua, A Robustness Simulation Method of Project Schedule based on the Monte Carlo Method, The Open Cybernetics & Systemics Journal, 8 :254-258, 2014.
https://doi.org/10.2174/1874110X01408010254

Mertcan Karabulut, Application of Monte Carlo simulation and PERT/CPM techniques in planning of construction projects: A Case Study, Periodicals of Engineering and Natural Scinces ISSN 2303-4521 Vol.5, No.3,: 408-420, December 2017.
https://doi.org/10.21533/pen.v5i3.152

W. Tysiak, Risk management in projects: The Monte Carlo approach versus PERT, Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems, pp. 906-910, 2011.
https://doi.org/10.1109/IDAACS.2011.6072904

K. A. Kirytopoulos, V. N. Leopoulos, and V. K. Diamantas, PERT vs. Monte Carlo Simulation along with the suitable distribution effect, International Journal of Project Organisation and Management Vol. 1, No. 1, 24-46, August 22, 2008.
https://doi.org/10.1504/IJPOM.2008.020027

Duran, O., Rodriguez, N. & Consalter, L. Neural networks for cost estimation of shell and tube heat exchangers. Expert Systems with Applications, Volume 36, p. 7435-7440, 2009.
https://doi.org/10.1016/j.eswa.2008.09.014

Caputo, A. C. & Pelagagge, P. M. 2008. Parametric and neural methods for cost estimation of process vessels. International Journal of Production Economics, 112, 934-954.
https://doi.org/10.1016/j.ijpe.2007.08.002

Cavalieri, S., Maccarrone, P. & Pinto, R. 2004. Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry. International Journal of Production Economics, 91, 165-177.
https://doi.org/10.1016/j.ijpe.2003.08.005

Layer A et al. Recent and future trends in cost estimation. Int J Comput Integr Manuf 15(6):499- 510, 2002.
https://doi.org/10.1080/09511920210143372

Oberlender, G. D., & Trost, S. M. Predicting accuracy of early cost estimates based on estimate quality. Journal of Construction Engineering and Management, 127(3), 173-182, 2001.
https://doi.org/10.1061/(ASCE)0733-9364(2001)127:3(173)

Dursun, O., & Stoy, C. Conceptual estimation of con- struction costs using the multistep ahead approach. Journal of Construction Engineering and Management, 142(9), 04016038. 2016.
https://doi.org/10.1061/(ASCE)CO.1943-7862.0001150

Hashemi, S. T., Ebadati E, O. M., & Kaur, H. A hybrid conceptual cost estimating model using ANN and GA for power plant projects. Neural Computing and Applications, 31(7), 2143-2154.
https://doi.org/10.1007/s00521-017-3175-5

Petroutsatou, K., Georgopoulos, E., Lambropoulos, S., & Pantouvakis, J. P. (2012). Early cost estimating of road tunnel construction using neural networks. Journal of Construction Engineering and Management, 138(6), 679-687.
https://doi.org/10.1061/(ASCE)CO.1943-7862.0000479

Chou, J.-S., Peng, M., Persad, K. R., & O'Connor, J. T. Quantity-based approach to preliminary cost estimates for highway projects. Transportation Research Record, Journal of the Transportation Research Board, 1946(1), 22-30, 2006.
https://doi.org/10.1177/0361198106194600103

Kwak, Y. H., & Watson, R. J. Conceptual estimating tool for technology-driven projects: exploring parametric estimating technique. Technovation, 25(12), 1430-1436, 2005.
https://doi.org/10.1016/j.technovation.2004.10.007

Yu, W. D. PIREM: a new model for conceptual cost estimation. Construction Management and Economics, 24(3), 259-270, 2006.
https://doi.org/10.1080/01446190500183735

Kim, G.-H., An, S.-H., & Kang, K.-I. Comparison of con- struction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Building and En- vironment, 39(10), 1235-1242, 2004.
https://doi.org/10.1016/j.buildenv.2004.02.013

Sonmez, R., Ergin, A., & Birgonul, M. T. Quantitative methodology for determination of cost contingency in international projects. Journal of Management in Engineering, 23(1), 35-39, 2007.
https://doi.org/10.1061/(ASCE)0742-597X(2007)23:1(35)

Choi, S., Kim, D. Y., Han, S. H., & Kwak, Y. H. (2014). Conceptual cost-prediction model for public road planning via rough set theory and case-based reasoning. Journal of Construction Engineering and Management, 140(1), 04013026.
https://doi.org/10.1061/(ASCE)CO.1943-7862.0000743

Koo, C., Hong, T., & Hyun, C. The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach. Expert Systems with Applications, 38(7), 8597-8606, 2011.
https://doi.org/10.1016/j.eswa.2011.01.063

Kim, K. J., Kim, K., & Kang, C. S. Approximate cost estimating model for PSC Beam bridge based on quantity of standard work. KSCE Journal of Civil Engineering, 13(6), 377-388, 2009.
https://doi.org/10.1007/s12205-009-0377-0

Smith, S., raham, A., Hernandez, S., & Kent, J. Devel- opment o a cost estimation framework for potential trans- load facilities Transportation Research Record: Journal of the Transportation Research Board, 2672(9), 24-34, 2018.
https://doi.org/10.1177/0361198118774690

Chou, J.-S., Yang, I.-T., & Chong, W. K. Probabilistic simulation for developing likelihood distribution of engineer- ing project cost. Automation in Construction, 18(5), 570-577, 2009.
https://doi.org/10.1016/j.autcon.2008.12.001

Kang, H.-W., & Kim, Y.-S. A model for risk cost and bidding price prediction based on risk information in plant construction projects. KSCE Journal of Civil Engineering, 22(11), 4215-4229, 2018.
https://doi.org/10.1007/s12205-018-0587-4

Zhu, B., Yu, L.-A., & Geng, Z.-Q. Cost estimation method based on parallel Monte Carlo simulation and market investigation for engineering construction project. Cluster Computing, 19(3), 1293-1308. 2016.
https://doi.org/10.1007/s10586-016-0585-6

Kim, S.-B., & Cho, J.-H. Development of the approximate cost estimating model for PSC box girder bridge based on the breakdown of standard work. Journal of the Korean Society of Civil Engineers, 33(2), 791800, 2013.
https://doi.org/10.12652/Ksce.2013.33.2.791

Staub-French, S., Fischer, M., Kunz, J., & Paulson, B. A generic feature-driven activity-based cost estimation process. Advanced Engineering Informatics, 17(1), 23-39, 2003.
https://doi.org/10.1016/S1474-0346(03)00017-X

Cheung, F. K., Rihan, J., Tah, J., Duce, D., & Kurul, E. Early stage multi-level cost estimation for schematic BIM models. Automation in Construction, 27, 67-77, 2012.
https://doi.org/10.1016/j.autcon.2012.05.008

Fazeli, A., Dashti, M. S., Jalaei, F., & Khanzadi, M. An integrated BIM-based approach for cost estimation in construction projects. Engineering, Construction and Architectural Management, 28(9), 2828-2854, 2020.
https://doi.org/10.1108/ECAM-01-2020-0027

El Mouden, M., Chahbouni, M., Boutahari, S., An Efficient Method for Statistical and Deterministic Tolerances Synthesis Using the Jacobian Torsor Model, (2022) International Review of Mechanical Engineering (IREME), 16 (10), pp. 555-563.
https://doi.org/10.15866/ireme.v16i10.22505

Choi, J. O., O'Connor, J. T., Kwak, Y. H., & Shrestha, B. K., Modularization business case analysis model for industrial projects. Journal of Management in Engineering, 35(3), 04019004, 2019a.
https://doi.org/10.1061/(ASCE)ME.1943-5479.0000683

G. Kłosowski and A. Gola, Risk-based estimation of manufacturing order costs with artificial intelligence, 2016 Federated Conference on Computer Science and Information Systems (FedCSIS), 2016, pp. 729-732.
https://doi.org/10.15439/2016F323

I. J. Schwarz, I. P. M. Sánchez, 29 Implementation of artificial intelligence into risk management decision-making processes in construction projects, 2015, pp. 361-362.

Humphreys, K., Project and cost engineers. 4th ed. (2004).
https://doi.org/10.1201/9780849390388

Richard E. Westney, The Engineer's Cost Handbook Tools for Managing Project Costs, MARCEL DEKKER, INC. 1997.
https://doi.org/10.1201/9780203910016

Marjuki, M. Computerized building cost estimating system, Malaysia: University Teknologi Malaysia, 2006.

M. G. Cox and B. R. L. Siebert, The use of a Monte Carlo method for evaluating uncertainty and expanded uncertainty, Metrologia, 43 S178-S188, 2006.
https://doi.org/10.1088/0026-1394/43/4/S03

Barraza G. Probabilistic estimation and allocation of project time contingency, Journal of Construction Engineering and Management, 137(4) pp 259-265, 2011
https://doi.org/10.1061/(ASCE)CO.1943-7862.0000280

Loizou, P. and French, N. (2012), Risk and uncertainty in development: A critical evaluation of using the Monte Carlo simulation method as a decision tool in real estate development projects, Journal of Property Investment & Finance, Vol. 30 No. 2, pp. 198-210.
https://doi.org/10.1108/14635781211206922

RICS (2003), The management of risk - yours, mine and ours, RICS Project Management, London, 2012.

Garg, N., Yadav, S. & Aswal, D.K. Monte Carlo Simulation in Uncertainty Evaluation: Strategy, Implications and Future Prospects. MAPAN 34, 299-304 (2019).
https://doi.org/10.1007/s12647-019-00345-5

Saidi, P., Sadeghi, M., & Tenreiro, C. Variance Reduction of Monte Carlo Simulation in Nuclear Engineering Field. In (Ed.), Theory and Applications of Monte Carlo Simulations. IntechOpen, 2013.
https://doi.org/10.5772/53384

Qiu Ling Guo B.Eng., MPhil, Development Of Risk Analysis Models For Decision-Making In Project Management, A thesis submitted to Napier University in partial fulfilment of the requirements for the Degree of Doctor of Philosophy, School of the Built Environment, Napier University, Edinburgh. UK, 2001.

Platon, Victor, and Andreea Constantinescu, Monte Carlo Method in risk analysis for investment projects, Procedia Economics and Finance 15 :393-400, 2014.
https://doi.org/10.1016/S2212-5671(14)00463-8

Wilson, A. Experiments in probabilistic cost modelling. In Building Cost Techniques: New Directions, Edited by: Brandon, P. S. 169-180. London: E. & F.N. Spon. 1982.

Raftery, J. Principles of Building Economics: An Introduction, Oxford: BSP Professional Books, 1991.

Newton, S. Methods of analysing risk exposure in the cost estimates of high quality offices. Construction Management and Economics, 10: 431-449, 1992.
https://doi.org/10.1080/01446199200000040


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize