| Annotation: | Methods. Formation of methodological bases for evaluating the financial efficiency of a business model of a business enterprise was carried out on the basis of methodology and mathematical apparatus of fuzzy sets theory, as well as taxonomic analysis. Results. The stages formation system estimation of financial efficiency business model of industrial enterprise are defined in the article, and also two approaches of estimation of efficiency business model of industrial enterprises are suggested. In order to evaluate the financial efficiency of the business model an industrial enterprise, there is formed a system of single indicators for assessing the financial condition of the enterprise by such components as financial stability, liquidity and solvency, business activity and profitability. Fishburne's rule is proposed to use while weighing the main components of the integral index of financial efficiency of the business model of the enterprise. Novelty. Given the ambiguous conclusions about the financial efficiency of the business model of an industrial enterprise, which are carried out on the basis of the calculation of single indicators, the methodology and mathematical apparatus of fuzzy sets theory are proposed. In addition, to determine the direction of development of the problem under study, we propose to use two methods of constructing integral indicators. It is proposed to model the integral indicators of the financial performance of the business model using fuzzy sets and taxonomic analysis, which will help to evaluate the financial performance level of the business model more objectively. When constructing integral indicators, it is proposed to normalize the indicators using a method of aggregation of features based on the theory of «additive value», according to which the value of the whole is equal to the sum of the values of its constituents for the construction of the function of desirability. In constructing the taxonomic indicators of each of the categories characterizing the financial efficiency of the business model of an industrial enterprise, it is proposed to use the method of determining stimulants and stimulators of features in normalizing the individual indicators. Practical value. The se of method of constructing an integral indicator using the function of Harrington's desirability allows to determine the level of achievement of the desired index of financial efficiency of a business model of an industrial enterprise, and a taxonomic method of construction of an integral indicator will allow to determine and evaluate the level of development of financial efficiency of a business model of an industrial enterprise and to justify the reserves of its increase. |
| Literature: | - 1. Nuri Kayaoglu, M.A. aus Gumushane, ‘A Generic Approach for Dynamic Business Model Evaluation’, genehmigte Dissertation, Berlin (2014). Retrieved from https://depositonce.tu- berlin.de/handle/11303/4036?mode=full M. Ben Rabha, M.F. Boujmil, M. Saadoun, B. Bessaïs, Eur. Phys. J. Appl. Phys. (to be published)
- 2. Tesch, J.F., & Brillinger, A.S. (2019). The Evaluation Aspect of Digital Business Model Innovation. In: Tesch J. (eds) Business Model Innovation in the Era of the Internet of Things. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-319-98723-1_4.
- 3. Batocchio, A., Ghezzi, A., & Rangone, A. (2016). A method for evaluating business models implementation process. Business Process Management Journal, 22(4), 712-735. doi:10.1108/bpmj-08-2015-0117
- 4. Marius ALEXA. (December 2014), «Business Model Evaluation - A Conceptual Approach,» Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 14, pp. 245-260.
- 5. Horal, L., Shyiko, V., & Yaroshenko, O. (2019). Modeling Break-even Zone Using the Integral Methods. Proceedings of the 6th International Conference on Strategies, Models and Technologies of Economic Systems Management (SMTESM 2019). doi:10.2991/smtesm-19.2019.34
- 6. Bakhrushyn, V.Ie. (2011). Metody analizu danykh. Zaporizhzhia: KPU.
- 7. Pliuta, V. (1989). Sravnitelnyy mnogomernyy analiz v ekonomicheskikh issledovaniiakh. Moskva: Finansy i statistika.
- 8. Ofitsiynyy sayt PAT «Ukrtransnafta». Retrieved from www.ukrtransnafta.com
- 9. Byrd, R., Calzolari, N., Chodorow, M., Neff, M., & Risk, O. (1987). «Tools and Methods for Computational Lexicography» Computational Linguistics, vol. 13, n. 3-4.
- 10. Maedche, A., Pekar, V., & Staab, S. (2003). Ontology Learning Part One - on Discovering Taxonomic Relations from the Web. Web Intelligence,
- 301-319. doi:10.1007/978-3-662-05320-1_14
- 11. D. Zhang, D., & Lee, W.S. (2004). «Web Taxonomy Integration through CoBootstrapping», SIGIR-04, July 25-29, Sheffield UK.
- 12. Cimiano, P., Hotho, A., & Staab, S. (2004). «Comparing Conceptual, Divisive and Agglomerative Clustering for learning Taxonomies from Text», in 16th ECAI 2004, Valencia, Spain.
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