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Variance Analysis in the Implementation of Fuzzy Enterprise Budgeting
Kotsyuba O. S., Rud Y. A.

Kotsyuba, Oleksiy S., and Rud, Yuliia A. (2025) “Variance Analysis in the Implementation of Fuzzy Enterprise Budgeting.” Business Inform 11:235–246.
https://doi.org/10.32983/2222-4459-2025-11-235-246

Section: Economic and Mathematical Modeling

Article is written in Ukrainian
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UDC 65.012.8:003.26

Abstract:
The article addresses the issue of analyzing deviations in the implementation of enterprise budgets, the quantitative values of which are described by fuzzy estimates (numbers). When the initial values of budget indicators are set as fuzzy estimates, and the actual achieved values are ordinary numbers, the differences between them are also fuzzy estimates. Therefore, in the case of fuzzy budgets, it may be impossible to unambiguously determine the nature of the deviation (adverse/favorable) for a specific budget indicator. Using a simulation example, the study implements a fuzzy version of the model for determining deviations in the implementation of enterprise budgets, which involves adjusting the initial (baseline) budgets according to the actual volume of activity. Based on a special indicator that reflects the degree of unfavorability/favorability of the discrepancy between a fuzzy planned estimate and a precise actual value for an individual budget item, a corresponding generalized analytical model is formulated in this study. The defining characteristics of this model are as follows: it considers budget indicators with both negative and positive optimization directions; it also provides, accordingly, for separate assessment of the degree of unfavorability and the degree of favorability of deviations. The results of approbating this model using a simulation example demonstrated its effectiveness. As another analytical characteristic of fuzzy deviations in achieving the planned level of budget indicators, it is proposed to use their representative values. When determining these values, it is recommended to give priority to methods such as the center of gravity method, the centroid method, the maximum (modal) value method, and the midpoint of the average interval method. The two aspects (or directions) of analyzing discrepancies between planned and actual budget figures examined in the study complement each other – while the indicators reflecting the first aspect show the intensity (magnitude) of deviations in terms of a particular direction or orientation (adverse/favorable), the representative values capture their generalized or average magnitude.

Keywords: uncertainty, vagueness, budgeting, fuzzy budget, budget control, variance analysis.

Tabl.: 8. Formulae: 3. Bibl.: 26.

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]
Rud Yuliia A. – 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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