Abstract
The article proposes a methodology for assessing the level of risk of a technical re-equipment project. This technique involves the range of possible threats at different stages of the project life cycle and a fuzzy model for appraisal of the level of risk. This assessment is performed based on two risk components: the likelihood of the risk occurring and the level of hazard that the risk poses to the enterprise. Further, the assessment is performing by types of risk. Define which chance has the highest level and which type of risk has the lowest level. Basing on the results of the analysis, the management of the enterprise can take timely measures to minimize the highest risks from the entire risk population and management impacts to prevent the growth of acceptable risks. Risk assessment at the stages of a technical re-equipment project allows determining controlling influences at these phases. The proposed methodology allows drawing the attention of enterprise management to the most possible and severe risks. Risk probability and the level of its severity for the enterprise are determining by experts. For this, the external and internal environment of the enterprise, the market for technical equipment, the demand, and the supply of the products manufactured by the enterprise are thoroughly studying. Before specifying estimates, experts identify the factors that are the root cause of risks. Experts can be both employees of the enterprise itself and specialists involved from outside. Expert assessments are subject to mandatory consistency checks. Fuzzy logic elements fit well into the concept of risk estimation since risks and their general properties, namely, probability and severity, are characterized by some fuzziness. They are usually describing verbally using linguistic variables.
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Mutovkina, N. (2022). Methodology for Assessing the Risk Level of a Technical Re-equipment Project Based on Fuzzy Logic. In: Hu, Z., Gavriushin, S., Petoukhov, S., He, M. (eds) Advances in Intelligent Systems, Computer Science and Digital Economics III. CSDEIS 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 121. Springer, Cham. https://doi.org/10.1007/978-3-030-97057-4_2
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