Project risk management of the construction industry enterprises based on fuzzy set theory
-
DOIhttp://dx.doi.org/10.21511/ppm.17(4).2019.17
-
Article InfoVolume 17 2019, Issue #4, pp. 203-213
- Cited by
- 919 Views
-
169 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
The construction industry is a crucially important element of the Ukrainian economy, since its development and performance affect other industries. The economic recession consequences and the unforeseen recent events, caused by different types of risks, have adversely affected the construction industry development and necessitated the search for modern methods of risk management. The study is based on a sample of five projects from five construction industry enterprises and covered the period of 2010–2018. A set of project risks, investigated by the group of experts, was analyzed based on fuzzy set theory, and included seven phases of the fuzzy set model construction to assess project risks of construction industry enterprises. Based on the identified elements of a fuzzy set model and a set of significant project risks, a value classifier of significant project risks for construction industry enterprises was developed. This allowed to estimate the current values of project risk indicators and to identify them by levels of their fuzzy subset membership. Besides, a classifier for the quantitative assessment of the total project risks level for investment projects was developed, which allowed estimating the value of the aggregate indicator. In order to improve the existed methodology, the study suggested introducing probabilistic values for the risk of project failure depending on the significance of the overall project risks. Accordingly, the paper identifies the probability of significant project risks simultaneous occurring during the project implementation. However, the higher the likelihood of risk, the higher the probability of investment project failure.
- Keywords
-
JEL Classification (Paper profile tab)G32, D81, L74
-
References37
-
Tables6
-
Figures1
-
- Figure 1. The system of trapezoidal membership functions Fі(x) on the 01 carrier
-
- Table 1. Indicators of the construction companies’ significant project risks according to the reduced impact degree on investment projects
- Table 2. The classifier of indicator values for significant project risks Xij of construction industry enterprises according to a single quantification metric
- Table 3. The classifier of the aggregate project risk quantification assessment for investment projects at the construction industry enterprises
- Table 4. A quantification system for assessing the level of threat of the investment project failure of construction industry enterprises
- Table 5. The membership level matrix of indicator carriers of construction enterprises’ significant project risks in fuzzy subsets of linguistic variable values of the project risk {RPRі} term-set
- Table 6. Quantification estimate results for the level of integrated project risks for Kyivmiskbud-1’s investment projects
-
- Akintoye, A. S., & Macleod, M. J. (1997). Risk analysis and management in Construction. International Journal of Project Management, 15(1), 1-38.
- Alekseev, A. V. (1979). Interpretation and definition of membership functions of fuzzy sets. Decision Methods and Systems, 42-50.
- Barton, T. L., Shenkir, W. G., & Walker, P. L. (2003). Making enterprise risk management pay off. Financial Executives Research Foundation, 215.
- Besner, C., & Hobbs, B. (2006). The perceived value and potential contribution of project management practices to project success. Project Management Journal, 37(3), 37-48.
- Bojadziev, G. (1997). Fuzzy Logic for Business, Finance and Management. Advances in Fuzzy Systems, 12, 252.
- Buckley, J. (1992). Solving fuzzy equations in economics and finance. Fuzzy Sets & Systems, 48, 289-296.
- Couturier, A., & Fioleau, B. (2002). Debt Level and Company Efficiency: Independence or Implication? An Evaluation of Fuzzy Implication. European Journal of Economic and Social Systems, 14(1), 17-25.
- De Bakker, K., Boonstra, A., & Wortmann, H. (2010). Does risk management contribute to IT project success? A metaanalysis of empirical evidence. International Journal of Project Management, 28(5), 493-503.
- Dukhanina, E. V., & Chudaykina, T. N. (2014). The main forming of identifying risks investment companies – building complex (on the example of Penza).
- Fishburn, P. C. (1978). Utility theory for decision making.
- Gavrysh, O. A., & Kavun, V. A. (2017). Critical analysis of regulatory framework of project risks management. Economic Bulletin of National Technical University of Ukraine “Kyiv Polytechnical Institute”, 14.
- Hassanein, A. A., & Afify, H. M. (2007). A risk identification procedure for construction contracts – a case study of power station projects in Egypt. Civil Engineering and Environmental Systems, 24, 3-14.
- Holton, G. A. (2003). Value-at-Risk. Theory and Practice. Academic Press, 297.
- Ilyashenko, S. M. (2006). Management of innovative development, 234.
- Iqbal, S., Choudhry, R. M., Holschemacher, K., Ali, A., & Tamošaitienė, J. (2015). Risk management in construction projects. Technological and Economic Development of Economy, 21, 65-78.
- João M. Pinto (2017). What is project finance? Investment Management and Financial Innovations, 14(1-1), 200-210.
- Kapliński, O. (2013). Risk Management of Construction Works by Means of the Utility Theory: A Case Study. Procedia Engineering, 57, 533-539.
- Kaufmann, A., & Gupta, M. (1991). Introduction to Fuzzy Arithmetic: Theory and Applications. Van Nostrand Reinhold, ASIN: 0442008996.
- Kolodiziev, O., Tyschenko, V., & Azizova, K. (2017). Project finance risk management for public-private partnership. Investment Management and Financial Innovations, 14(4), 171-180.
- Kovalev, V. V. (1997. Sbornik zadach po finansovomu analizy [A book of financial analysis problems] (128 p.). Moscow: Finance and Statistics (in Russian).
- Melnykova, V. A. (2019). Cluster analysis of project risks of enterprises in the construction industry. East European Science Journal, 9(49), 4-9.
- Nedosekin, A. O. (2003). Metodologicheskie osnovy modelirovaniya finansovoy deyatelnosti s ispolzovaniyem nechetko-mnozhestvennykh opisaniy [Methodological foundations of modeling financial activities using fuzzy set descriptions] (Doctoral Dissertation) (in Russian).
- Nedosekin, A. O., & Maksimov, O. B. (1999). Application of the fuzzy sets theory to the financial analysis of enterprises.
- Perminova, O., Gustafsson, M., & Wikstrom, K. (2008). Defining uncertainty in projects – a new perspective. International Journal of Project Management, 26, 73-79.
- Serpella, A. F., Ferrada, X., Howard, R., & Rubio, L. (2014). Risk management in construction projects: a knowledge-based approach. Procedia – Social and Behavioral Sciences, 119, 653-62.
- Shenhar, A. J., & Dvir, D. (2010). Reinventando gerenciamento de projetos – A abordagem diamante ao crescimento e inovação bem-sucedidos. São Paulo: M. Books, Harvard Business School Press.
- SIGEF Association official website.
- Taylan, O., Bafail, A. O., Abdulaal, R. M., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105-16.
- Trukhaev, R. (1981). Decision making models in the face of uncertainty, 258.
- Wang, S. Q., Dulaimi, M. F., & Aguria, M. Y. (2004). Risk management framework for construction projects in developing countries. Construction Management and Economics, 22, 237-52.
- Ward, S., & Chapman, C. (2003). Transforming project risk management into project uncertainty management. International Journal of Project Management, 21(2), 97-105.
- Williams, C. A., & Heins, R. M. (1989). Risk Management and Insurance (6th ed.) 836 p.
- Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 3, 338-353.
- Zadeh, L. A. (1971). Quantitative fuzzy semantics. Information Sciences, 3, 159-l76.
- Zadeh, L. A. (1976). The concept of a linguistic variable and its application to making approximate decisions (pp. 199-249).
- Zadeh, L. A. (1978). Fuzzy Sets as a Basis for a Theory of Possibility. Fuzzy Sets and Systems, 1(1).
- Zimmerman, H. J. (2001). Fuzzy Sets Theory – and Its Applications. Kluwer Academic Publishers, 514.