Abstract
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.
Highlights
The construction sector has always played a key role in the structure of the entire Ukrainian industry
The purpose of this study is to manage the project certainty degree, is determined to be sufrisks of construction industry enterprises based ficient to substantiate a distribution law, from any on fuzzy set theory, which results in the creation given point of view, in probabilistic or any other of classifiers and matrix schemes for project risk manner
(5) fij means that the carriers of significant project risk indicators of construction industry enterprises belonging to fuzzy subsets of the linguistic term-set variables of the project risks level {RPRі} are determined in accordance with the Table 5 data, r are levels of the project risk significance that
Summary
The construction sector has always played a key role in the structure of the entire Ukrainian industry. The market conditions, where the construction industry operates, set many factors that can simultaneously cause a complex number of diverse and various-directional risks This makes it difficult to develop a method for aggregate and discrete estimation of project risks based on the theory of classical statistics, since it is not possible to obtain a sample of statistically homogeneous data/events from their general population under constant external observation conditions. Zadeh (1965, 1971, 1976, 1978) is the founder of Bakker et al (2012) stress the importance of the fuzzy set theory; his follower Nedosekin (1999, risk identification process, followed by a project 2003) improved the methodology by using a marisk report, registration, distribution, analysis and trix method of risk assessment.
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