An Assessment of the Causes of Schedule and Cost Overruns in Megaprojects using a Z-BWM-MARCOS Integrated Method

Document Type : Original Article

Authors

1 Ph.D. Candidate, Industrial Engineering Department, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran.

2 Assistant Professor, Industrial Engineering Department, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran.

3 Associate Professor, Industrial Engineering Department, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran.

10.48308/jimp.15.1.73

Abstract

Introduction: Megaprojects are defined as complex and large-scale investments, and their development and construction take many years. Schedule and cost overruns are among the most common challenges in megaprojects worldwide. Exceeding planned budgets and schedules are key factors contributing to the failure of many megaprojects. The aim of this research is to provide an integrated framework for evaluating the causes of schedule and cost overruns in megaprojects.
Methods: In this research, for the first time, an integrated approach combining the Failure Mode and Effects Analysis (FMEA) method with the Best-Worst Method (BWM) and MARCOS, developed based on Z-number theory, has been used in three phases to analyze the causes of schedule and cost overruns in megaprojects. In the first phase, based on the literature, the causes and risks are identified using the FMEA method, and the factors determining the Risk Priority Number (RPN) are quantified. In the second phase, using the Z-BWM method, the weights of the criteria are calculated. In the third phase, based on the outputs of the previous phases, the causes are prioritized using the Z-MARCOS method. In addition to assigning different weights to the criteria and considering uncertainty, reliability is also incorporated in this approach through the theory of Z-numbers.
Results and discussion: This study presents a novel methodology for assessing the schedule and cost overruns causes in megaprojects, utilizing the Z-BWM-MARCOS method to evaluate and prioritize 17 identified causes. The findings indicate that poor planning and scheduling, inadequate performance design, and a weak supplier network are the primary contributors to these overruns. To validate the proposed methodology, its results were compared with conventional methods, including FMEA, Fuzzy MARCOS, and Z-MOORA. The FMEA method exhibited significant shortcomings, notably, its failure to account for quality grades and conditions of uncertainty, which resulted in incomplete prioritization of failure causes. This limitation led to confusion among decision-makers regarding failure-related decisions. Similarly, while Fuzzy MARCOS addressed uncertainty, it still fell short in providing complete prioritization. In contrast, the Z-MOORA and Z-MARCOS methods effectively overcame these limitations by leveraging the advantages of Z-number theory, which incorporates both reliability and uncertainty. The implementation of the proposed approach demonstrated a strong correlation of 0.909 with the results of the Z-MOORA method, underscoring its effectiveness in delivering comprehensive rankings of causes.
Conclusions: Megaprojects are highly susceptible to significant cost overruns and schedule delays, posing major challenges for project management. The results of this study demonstrate the capability and superiority of the proposed approach compared to traditional methods such as FMEA and Fuzzy MARCOS in identifying the primary causes of cost and schedule overruns in megaprojects.

Keywords

Main Subjects

References

  1. Aboutorab, H., Saberi, M., Asadabadi, M. R., Hussain, O., Chang, E. (2018). ZBWM: The Z-number extension of Best Worst Method and its application for supplier development. Expert Systems with Applications. 107, 115-125.
  2. Aiyetan, A. O., Das, D. K. (2022). Factors and strategies for improving construction management on sites in mega-projects in South Africa: An explorative survey. Infrastructures, 7(2), 19.
  3. Alhammadi, Y., Al-Mohammad, M. S., Rahman, R. A. (2024). Modeling the causes and mitigation measures for cost overruns in building construction: The case of higher education projects. Buildings, 14(2), 487.
  4. Aljohani, A., Ahiaga-Dagbui, D., Moore, D. (2017). Construction projects cost overrun: What does the literature tell us?. International Journal of Innovation, Management and Technology, 8(2), 137.
  5. AlKhatib, M., Altarazi, S. (2019). A customized root cause analysis approach for cost overruns and schedule slippagein paper-machine-building projects. Management and Production Engineering Review.
  6. Alvand, A., Mirhosseini, S. M., Ehsanifar, M., Zeighami, E., Mohammadi, A. (2023). Identification and assessment of risk in construction projects using the integrated FMEA-SWARA-WASPAS model under fuzzy environment: a case study of a construction project in Iran. International journal of construction management, 23(3), 392-404.
  7. Amini, A., Alinezhad, A., Gharakhani, D. (2023). A New Rough BWM Approach for Evaluating and Selecting a Sustainable Supplier in Supply Chain Management. Journal of Industrial Management Perspective, 13(3), 9-38. (In Persian).
  8. Baloyi, L., Bekker, M. (2011). Causes of construction cost and time overruns: The 2010 FIFA World Cup stadia in South Africa. Acta Structilia: Journal for the Physical and Development Sciences, 18(1), 51-67.
  9. Boateng, P., Chen, Z., Ogunlana, S. O. (2015). An Analytical Network Process model for risks prioritisation in megaprojects. International journal of project management, 33(8), 1795-1811.
  10. Castelblanco, G., Fenoaltea, E. M., De Marco, A., Demagistris, P., Petruzzi, S., Zeppegno, D. (2024). Combining stakeholder and risk management: Multilayer network analysis for complex megaprojects. Journal of Construction Engineering and Management, 150(2), 04023161.
  11. Corrente, S., Greco, S., Rezaei, J. (2024). Better decisions with less cognitive load: The Parsimonious BWM. Omega, 126, 103075.
  12. Del Cerro Santamaria, G. (2019). Megaprojects, development and competitiveness: Building the infrastructure for globalization and neoliberalism. Athens journal of social sciences, 6(4), 263-290.
  13. Denicol, J., Davies, A., Krystallis, I. (2020). What are the causes and cures of poor megaproject performance? A systematic literature review and research agenda. Project management journal, 51(3), 328-345.
  14. Famiyeh, S., Amoatey, C. T., Adaku, E., Agbenohevi, C. S. (2017). Major causes of construction time and cost overruns: A case of selected educational sector projects in Ghana. Journal of Engineering, Design and Technology, 15(2), 181-198.
  15. Flyvbjerg, B. (2017). Introduction: The iron law of megaproject management. Bent Flyvbjerg, 1-18.
  16. Flyvbjerg, B. (2021). Make megaprojects more modular. Harvard Business Review, 58-63.
  17. Flyvbjerg, B. (2014). What you should know about megaprojects and why: An overview. Project management journal, 45(2), 6-19.
  18. Ghiaci, A. M., Ghoushchi, S. J. (2023). Assessment of barriers to IoT-enabled circular economy using an extended decision-making-based FMEA model under uncertain environment. Internet of Things, 22, 100719.
  19. Memarpour Ghiaci, A., Abbasi, M., Piri, M., Akhavan, P. (2024). Barriers to blockchain adoption in humanitarian logistics in an uncertain environment. Business Intelligence Management Studies, 12(47), 153-184. (In Persian).
  20. Ghoushchi, S. J., Yousefi, S., Khazaeili, M. (2019). An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures. Applied soft computing, 81, 105505.
  21. Ghoushchi, S. J., Jalalat, S. M., Bonab, S. R., Ghiaci, A. M., Haseli, G., Tomaskova, H. (2022). Evaluation of wind turbine failure modes using the developed SWARA-CoCoSo methods based on the spherical fuzzy environment. Ieee Access, 10, 86750-86764.
  22. Ghoushchi, S. J., Bonab, S. R., Ghiaci, A. M., Haseli, G., Tomaskova, H., Hajiaghaei-Keshteli, M. (2021). Landfill site selection for medical waste using an integrated SWARA-WASPAS framework based on spherical fuzzy set. Sustainability, 13(24), 13950.
  23. Gómez-Cabrera, A., Gutierrez-Bucheli, L., Muñoz, S. (2024). Causes of time and cost overruns in construction projects: a scoping review. International Journal of Construction Management, 24(10), 1107-1125.
  24. Goodarzi, N., Nazari, A. (2024). Evaluation of Human Resource Productivity Risks, Fuzzy DEMATEL and System Dynamics Approach (Case Study: High-Rise Building Projects). Journal of Industrial Management Perspective, 14(3), 141-168. (In Persian)
  25. Gul, M., Ak, M. F. (2021). A modified failure modes and effects analysis using interval-valued spherical fuzzy extension of TOPSIS method: case study in a marble manufacturing facility. Soft Computing, 25(8), 6157-6178.
  26. Guo, S., Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-based systems, 121, 23-31.
  27. Hamad, S. (2023). Cause of delay and cost overrun in infrastructure projects. Journal of Global Economics and Business, 4(15), 11-24.
  28. Jafarzadeh Ghoushchi, S., Shaffiee Haghshenas, S., Memarpour Ghiaci, A., Guido, G., Vitale, A. (2023). Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment. Neural computing and applications, 35(6), 4549-4567.
  29. Jafarzadeh Ghoushchi, S., Memarpour Ghiaci, A., Rahnamay Bonab, S., Ranjbarzadeh, R. (2022). Barriers to circular economy implementation in designing of sustainable medical waste management systems using a new extended decision-making and FMEA models. Environmental science and pollution research, 29(53), 79735-79753.
  30. Jafarzadeh Ghoushchi, S., Bonab, S. R., Ghiaci, A. M. (2023). A decision-making framework for COVID-19 infodemic management strategies evaluation in spherical fuzzy environment. Stochastic Environmental Research and Risk Assessment, 37(4), 1635-1648.
  31. Karamoozian, A., Wu, D. (2020). A hybrid risk prioritization approach in construction projects using failure mode and effective analysis. Engineering, Construction and Architectural Management, 27(9), 2661-2686.
  32. Kardes, I., Ozturk, A., Cavusgil, S. T., Cavusgil, E. (2013). Managing global megaprojects: Complexity and risk management. International business review, 22(6), 905-917.
  33. Khalilzadeh, M., Ghasemi, P., Afrasiabi, A., Shakeri, H. (2021). Hybrid fuzzy MCDM and FMEA integrating with linear programming approach for the health and safety executive risks: a case study. Journal of modelling in management, 16(4), 1025-1053.
  34. Kiani, M., Andalib Ardakani, D., Mirfakhredini, S. H., Zare Ahmadabadi, H. (2023). An analysis of the Barriers to the implementation of the circular economy and Industry 4.0 in the supply chain: the Meta-Synthesis approach and Fuzzy DANP. Journal of Industrial Management Perspective, 13(4), 9-45. (In Persian).
  35. Ma, H., Zeng, S., Lin, H., Chen, H., Shi, J. J. (2017). The societal governance of megaproject social responsibility. International Journal of Project Management, 35(7), 1365-1377.
  36. Mabaso, M. (2022). Risk identification in megaprojects and its impacts on project management constraints in civil engineering and construction companies.
  37. McKinsey & Company (2023). Increasing transparency in megaproject execution. Available from: https://www.mckinsey.com/capabilities/operations/our-insights/increasing-transparency-in-megaproject-execution.
  38. Ghiaci, A. M., Gheidar-Kheljani, J. (2024). A hybrid model of extended FMEA model based on F-PIPRECIA and Z-EDAS methods with Bow Tie to evaluate cybersecurity risks in Industry 4.0. Engineering Management and Soft, Computing, 9(2), 149-176. (In Persian).
  39. Memarpour Ghiaci, A., Karimi Gavareshki, M. H. (2024). An integrated Z-SWARA-MARCOS approach based on SWOT analysis to select infectious disease vaccination strategies. Modern Research in Decision Making, 9(2), 130-162. (In Persian).
  40. Memarpour Ghiaci, A., Garg, H., Jafarzadeh Ghoushchi, S. (2022). Improving emergency departments during COVID-19 pandemic: a simulation and MCDM approach with MARCOS methodology in an uncertain environment. Computational and Applied Mathematics, 41(8), 368.
  41. Olatunji, O. A., Rotimi, J. O. B., Rotimi, F. E., Silva, C. C. (2024). Causal relationship between project financing and overruns in major dam projects in Africa. Engineering, Construction and Architectural Management.
  42. Oyegoke, A. S., Al Kiyumi, N. (2017). The causes, impacts and mitigations of delay in megaprojects in the Sultanate of Oman. Journal of Financial Management of Property and Construction, 22(3), 286-302.
  43. Park, J., Park, C., Ahn, S. (2018). Assessment of structural risks using the fuzzy weighted Euclidean FMEA and block diagram analysis. The International Journal of Advanced Manufacturing Technology, 99, 2071-2080.
  44. Platoni, S., Timpano, F. (2020). The economics of mega-projects. Megaproject Management: A Multidisciplinary Approach to Embrace Complexity and Sustainability, 43-54.
  45. Rezaei, J., Arab, A., Mehregan, M. (2024). Analyzing anchoring bias in attribute weight elicitation of SMART, Swing, and best‐worst method. International Transactions in Operational Research, 31(2), 918-948.
  46. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
  47. Rostami, A., Oduoza, C. F. (2017). Key risks in construction projects in Italy: contractors’ perspective. Engineering, Construction and Architectural Management, 24(3), 451-462.
  48. Saaty, T.L. (2013). Analytic hierarchy process. Encyclopedia of operations research and management science. Encyclopedia of Operations Research and Management Science, US: Springer, p. 52-64.
  49. Söderlund, J., Sankaran, S., Biesenthal, C. (2017). The past and present of megaprojects. SAGE Publications Sage CA: Los Angeles, CA. p. 5-16.
  50. Stević, Ž., Pamučar, D., Puška, A., Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS). Computers & industrial engineering, 140, 106231.
  51. Tshidavhu, F., Khatleli, N. (2020). An assessment of the causes of schedule and cost overruns in South African megaprojects: A case of the critical energy sector projects of Medupi and Kusile. Acta Structilia, 27(1), 119-143.
  52. Van Marrewijk, A., Clegg, S. R., Pitsis, T. S., Veenswijk, M. (2008). Managing public–private megaprojects: Paradoxes, complexity, and project design. International journal of project management, 26(6), 591-600.
  53. Walker, P., Walsh, A. J., Ellis, M. (2021). The underestimation of cultural risk in the execution of megaprojects. International Journal of Civil and Environmental Engineering, 15(1).
  54. Wang, S., Chong, H. Y., Zhang, W. (2024). The impact of BIM-based integration management on megaproject performance in China. Alexandria Engineering Journal, 94, 34-43.
  55. Watermeyer, R., Phillips, S. (2020). Public infrastructure delivery and construction sector dynamism in the South African economy. Background Paper. NPC’s Economy Series. Pretoria: NPC.
  56. Zadeh, L. A. (2011). A note on Z-numbers. Information sciences, 181(14), 2923-2932.

Files

Share

How to cite

Statistics