Issue |
SHS Web Conf.
Volume 107, 2021
9th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2021)
|
|
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Article Number | 12002 | |
Number of page(s) | 9 | |
Section | Risk Management Models in Emergent Economy | |
DOI | https://doi.org/10.1051/shsconf/202110712002 | |
Published online | 24 May 2021 |
Economic-mathematical model for complex risk assessment of the enterprise investment project using fuzzy logic
1
Leonid Yuzkov Khmelnytskyi University of Management and Law, 8 Heroiv Maidanu Str., Khmelnytskyi, 29000, Ukraine
2
Khmelnytskyi National University, 11 Instytutska Str., Khmelnytskyi, 29016, Ukraine
* e-mail: inna.chaikovska@gmail.com
** e-mail: violete@ukr.net
*** e-mail: larumlab@gmail.com
The article proposes an economic-mathematical model for determining a comprehensive risk assessment of the investment project of the enterprise which are based on the approaches of A. Nedosekin. The model is built using fuzzy logic and takes into account the probability of occurrence of each of the identified risks and the level of impact of each of them on the project. The probability of risk is set by experts in the form of points and converted into linguistic terms, and the level of influence of each of them on the project – the ratio of benefits and is determined using Fishburne scales. The proposed Project Risk Model consists of the following stages: formation of initial data using expert opinions; construction of a hierarchical project risk tree; determination of weight coefficients (Fishburne weights) of project risks; selection and description of membership function and linguistic variables; conversion of input data provided by experts from a score scale into linguistic terms; recognition of qualitative input data on a linguistic scale; determination of a complex indicator of investment project risks; interpretation of a complex indicator. The developed model allows managing the risks of the project to maximize the probability of its successful implementation, to compare alternative projects and choose less risky, to minimize the level of unforeseen costs of the project.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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