Cost and Time Management Efficiency Assessment for Large Road Projects Using Data Envelopment Analysis

Abstract

Upon the completion of national road projects, their cost and time deviations are often reported. These deviations from the projected values are a result of complications in the time and cost management of such projects. Controlling the cost and time overrun of projects is important for successful implementation and efficient project management. However, few studies have attempted to measure the project cost and time management efficiency in civil engineering. Thus, this issue requires further investigation. In this study, large road projects that had poor cost and time management were selected. The chosen projects were configured as Decision Making Units (DMUs) in a Data Envelopment Analysis (DEA). DEA is a non-parametric modern mathematical tool for measuring relative managerial performance and determining efficient DMUs. A list containing the causes of cost and time deviations and the percentage deviation for each DMU was prepared. The cost and time management efficiency of the DMUs was calculated using DEA, and the resulting values were ordered according to importance. It is believed that this process will contribute to better cost and time management. 

Keywords

• data envelopment analysis,Management Performance Evaluation,Project Management Efficiency,Project Management,Cost and Time Overrun

Cost and Time Management Efficiency Assessment for Large Road Projects Using Data Envelopment Analysis

Abstract

Upon the completion of national road projects, their cost and time deviations are often reported. These deviations from the projected values are a result of complications in the time and cost management of such projects. Controlling the cost and time overrun of projects is important for successful implementation and efficient project management. However, few studies have attempted to measure the project cost and time management efficiency in civil engineering. Thus, this issue requires further investigation. In this study, large road projects that had poor cost and time management were selected. The chosen projects were configured as Decision Making Units (DMUs) in a Data Envelopment Analysis (DEA). DEA is a non-parametric modern mathematical tool for measuring relative managerial performance and determining efficient DMUs. A list containing the causes of cost and time deviations and the percentage deviation for each DMU was prepared. The cost and time management efficiency of the DMUs was calculated using DEA, and the resulting values were ordered according to importance. It is believed that this process will contribute to better cost and time management. 

Keywords

• Data Envelopment Analysis,Management Performance Evaluation,Project Management Efficiency,Project Management,Cost and Time Overrun

References

1. Ahbab, C. & Celik, T., 2012. An investigation on delay, cost overrun, quality, and health and safety problems in construction projects. Ankara, Middle East Technical University, pp. 1-6.
2. Ahsan, K. & Gunawan, I., 2010. Analysis of cost and schedule performance of international development projects. International Journal of Project Management, pp. 68-78.
3. Arditi, D., Akan, G. T. & Gurdamar, S., 1985. Cost overruns in public projects. International Journal of Project Management, pp. 218-224.
4. Assaf, S. A. & Al-Hejji, S., 2006. Causes of delay in large construction projects. International Journal of Project Management, pp. 349-357.
5. Banker, R. D., Charnes, A. & Cooper, W. W., 1984. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, pp. 1078-1092.
6. Besner, C. & Hobbs, B., 2006. The Perceived Vaeue And Potentiae Contribution Oe Project Manacement Practices To Project Success. Project Management Journal, pp. 37-48.
7. Charnes, A., Cooper, W. W. & Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research, pp. 429-444.
8. Cheng, Y.-M., 2014. An exploration into cost-influencing factors on construction projects. International Journal of Project Management, pp. 850-860.

9. Chou, J.-S. & Yang, J.-G., 2012. Project Management Knowledge and Effects on Construction Project Outcomes: An Empirical Study. Project Management Journal, pp. 47-67.
10. Cooper, W. W., Seiford, L. . M. & Tone, K., 2007. Data Envelopment Analysis. s.l.:Springer US.
11. Daneshvar, S., Izbirak, G. & Javadi, A., 2014. Sensitivity analysis on modified variable returns to scale model in Data Envelopment Analysis using facet analysis. Computers & Industrial Engineering, pp. 32-39.
12. de Carvalho, M. . M., Patah, L. . A. & Bido, D. . d. S., 2015. Project management and its effects on project success:Cross-country and cross-industry comparisons. International Journal of Project Management, pp. 1509-1522.
13. Eliasson, J. & Fosgerau, M., 2013. Cost overruns and demand shortfalls – Deception or selection?. Transportation Research Part B, pp. 105-113.
14. El-Mashaleh, M. . S., Rababeh, S. M. & Hyari, K. H., 2010. tilizing data envelopment analysis to benchmark safety performance of construction contractors. International Journal of Project Management, pp. 61-67.
15. Emrouznejad, Ali; Thanassoulis, Emmanuel;, 2017. PIM-DEA. [Online] Available at: http://www.deasoftware.co.uk/
16. Flyvbjerg, B., Holm, M. K. & Buhl, S. L., 2004. What Causes Cost Overrun in Transport Infrastructure Projects. Transport Reviews, pp. 3-18.
17. Flyvbjerg, B., Skamris Holm, M. K. & Buhl, S. L., 2003. How common and how large are cost overruns in transport infrastructure projects?. Transport Reviews, pp. 71-88.
18. Independent Evaluation Department- Asian Development Bank, 2016. Guidelines for the Evaluation of Public Sector Operations. [Online] Available at: http://www.adb.org/sites/default/files/institutional-document/32516/guidelines-evaluation-public-sector.pdf [Accessed 01 August 2016].
19. Iyer, K. C. & Banerjee, P. S., 2016. Measuring and benchmarking managerial efficiency of project execution schedule performance. International Journal of Project Management, pp. 219-236.
20. Joslin, R. & Müller, R., 2015. Relationships between a project management methodology and project success in different project governance contexts. International Journal of Project Management, pp. 1377-1392.
21. Lock, D., 2007. Project Management. Hampshire: Gower Publishing Limited.
22. Love, P. E. et al., 2014. Overruns in transportation infrastructure projects. Structur and Infrastructure Engineering, pp. 156-157.
23. Markham, S., 2016. Parametric versus non-parametric. [Online] Available at: http://www.csse.monash.edu.au/~smarkham/resources/param.htm
24. Mester, L. . J., 2003. Wharton Financial Institutions Center. [Online] Available at: http://fic.wharton.upenn.edu/fic/papers/03/0325.pdf
25. Montoneri, B., Lin, T. T., Lee, C.-C. & Huang, S.-L., 2012. Application of data envelopment analysis on the indicators contributing to learning and teaching performance. Teaching and Teacher Education, pp. 382-395.
26. Munns , A. K. & Bjeirmi, B. F., 1996. The role of project management in achieving project success. International Journal of Project Management, pp. 81-87.
27. Olsen, R. P., 1971. Can project management be defined ?. Project Management Quarterly. Project Management Institute, 2010. The Value of Project Management. [Online] Available at: http://www.pmi.org/-/media/pmi/documents/public/pdf/white-papers/value-of-project-management.pdf
28. Serrador, P. & Turner, R., 2015. The Relationship Between Project Success and Project Efficiency. Project Management Journal, pp. 30-39.
29. Shane, J. S., Molenaar, K. R. & Schexnayder, C., 2009. Construction project cost escalation factors. Journal of Management in Engineering, pp. 221-229.
30. Shehu, Z., Endut, I. R., Akintoye, A. & Holt, G. D., 2014. Cost overrun in the Malaysian construction industry projects: A deeper insight. International Jouranl of Project Management, pp. 1477-1480.
31. Thakor, A. V. & Boot, A. W., 2008. Handbook of Financial Intermediation and Banking. s.l.:North Holland- Elsevier.
32. Trick, M., 1998. Michael Trick's Operations Research Page. [Online] Available at: http://mat.gsia.cmu.edu/classes/QUANT/NOTES/chap12.pdf
33. Vartanian, T. P., 2011. Secondary Data Analysis. New York: Oxford University Press.