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Formation of Fuzzy Estimates of Initial Economic Indicators in Enterprise Management
Kotsyuba O. S., Laba M. S.

Kotsyuba, Oleksiy S., and Laba, Maksym S. (2025) “Formation of Fuzzy Estimates of Initial Economic Indicators in Enterprise Management.” Business Inform 3:174–182.
https://doi.org/10.32983/2222-4459-2025-3-174-182

Section: Economic and Mathematical Modeling

Article is written in Ukrainian
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UDC 658:330.4:519.86

Abstract:
The article is dedicated to the problem of forming fuzzy estimates of initial economic indicators, the need for which arises in enterprise management. In formal terms, defining fuzzy estimates of initial indicators in a certain problematic situation represents the task of constructing membership functions of fuzzy sets. The study analyzes methods for finding membership functions that are used or can be recommended for use in estimating initial economic indicators that are part of enterprise management objectives: the quasi-statistics-based method; the pairwise comparison method; the landmark method; the anchor interval method. Specifically, their constructive features and practical applicability characteristics are considered. In terms of practical applicability characteristics, for each method, the possibility of purposefully obtaining a membership function corresponding to a fuzzy number, as well as the level of complexity in overcoming the unresolved information deficit, are analyzed. The latter is proposed to be understood as the complexity of overcoming the lack of information during the direct use of the method, which persists after collecting the data necessary for forecasting the considered economic indicator. Additionally, it has been identified that the bottleneck in obtaining a fuzzy estimate of the forecasted economic indicator in the form of a fuzzy number is ensuring its convexity. In the case of using the pairwise comparison method, ensuring the convexity property for the desired fuzzy estimate requires the introduction of special constraints. Based on the comparison results of the level of complexity in overcoming the unresolved information deficit for the analyzed methods, it has been identified that the least complex method in this aspect be the quasi-statistics-based method, and the most complex being the pairwise comparison method.

Keywords: uncertainty, fuzziness, fuzzy estimation, membership function, quasi-statistics, pairwise comparisons, reference points, reference intervals.

Tabl.: 1. Formulae: 3. Bibl.: 31.

Kotsyuba Oleksiy S. – Doctor of Sciences (Economics), Associate Professor, Professor, Department of Business Economics and Entrepreneurship, Kyiv National Economic University named after Vadym Hetman (54/1 Beresteiskyi Ave., Kyiv, 03057, Ukraine)
Email: [email protected]
Laba Maksym S. – Postgraduate Student, Department of Business Economics and Entrepreneurship, Kyiv National Economic University named after Vadym Hetman (54/1 Beresteiskyi Ave., Kyiv, 03057, Ukraine)
Email: [email protected]

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