Project risk management of the construction industry enterprises based on fuzzy set theory
| Autor: | Valeriia Melnykova, Oleg Gavrysh |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Information Systems and Management
Index (economics) Sociology and Political Science Public Administration Strategy and Management Fuzzy set 02 engineering and technology lcsh:Business 0502 economics and business 0202 electrical engineering electronic engineering information engineering Business and International Management Publication 050208 finance construction industry business.industry Project risk management 05 social sciences General Business Management and Accounting fuzzy sets Engineering management Construction industry quantitative assessment 020201 artificial intelligence & image processing Business lcsh:HF5001-6182 risks Law management |
| Zdroj: | Problems and Perspectives in Management, Vol 17, Iss 4, Pp 203-213 (2019) |
| ISSN: | 1810-5467 1727-7051 |
| DOI: | 10.21511/ppm.17(4).2019.17 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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