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Liburkina L. M.

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Application of Machine Learning Methods in the Algorithm for Finding Partners for Collaboration on the Example of the Retail Sector
Chernova N. L., Chernov O. O., Pyrohova S. Y.

Chernova, Natalia L., Chernov, Oleksandr O., and Pyrohova, Svitlana Ye. (2024) “Application of Machine Learning Methods in the Algorithm for Finding Partners for Collaboration on the Example of the Retail Sector.” Business Inform 9:153–161.
https://doi.org/10.32983/2222-4459-2024-9-153-161

Section: Economic and Mathematical Modeling

Article is written in Ukrainian
Downloads/views: 13

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UDC 334.7.01, 330.4

Abstract:
Retail companies play an important role in the global economy by meeting the daily needs of consumers. During the general economic crises, these companies demonstrate relatively smaller «setbacks» compared to the market, but, on the other hand, such a factor of stability is also a certain limiter of growth. Therefore, it is very common for companies to collaborate with each other, creating business collaborations to expand their market and achieve joint growth and success. The aim of the study is to develop and implement an algorithm for finding partners for collaboration. The proposed algorithm contains the following steps: formation of the research information base; preliminary statistical analysis of the generated dataset; classification of objects in the multifactorial feature space; assessment of the quality of classification; substantive analysis of the obtained classification; ranking objects within the cluster group and selecting candidates to create a collaboration. The algorithm is implemented for the output dataset of companies in the retail sector, which were part of the SP500 index as of the beginning of August 2024. The initial dataset contained information on the values of such financial and economic indicators as: dividend income, price/net profit ratio, return on assets, return on equity, profit margin, debt-to-equity ratio, price/revenue multiplier, price/money flow multiplier, price/book value multiplier, share of equity capital, current liquidity ratio. The implementation of the algorithm allows you obtaining a quantitative assessment of the suitability of the analyzed company for participation in the collaboration. Such a quantitative assessment is obtained as a result of the implementation of machine learning algorithms, namely, the k-medoids algorithm, which allows classifying the objects of study into relatively homogeneous groups, as well as identifying a representative of each group, whose coordinates play the role of a cluster centroid.

Keywords: collaboration strategy, partner, algorithm, machine learning, classification, representative.

Fig.: 2. Tabl.: 5. Formulae: 4. Bibl.: 14.

Chernova Natalia L. – Candidate of Sciences (Economics), Associate Professor, Associate Professor, Department of Software Engineering and Intelligent Control Technologies, National Technical University «Kharkiv Polytechnic Institute» (2 Kyrpychova Str., Kharkіv, 61002, Ukraine)
Email: [email protected]
Chernov Oleksandr O. – Postgraduate Student, Department of Entrepreneurship, Trade and Logistics, National Technical University «Kharkiv Polytechnic Institute» (2 Kyrpychova Str., Kharkіv, 61002, Ukraine)
Email: [email protected]
Pyrohova Svitlana Ye. – Senior Lecturer, Department of Higher Mathematics, Kharkіv National University of Radioelectronics (14 Nauky Ave., Kharkіv, 61166, Ukraine)
Email: [email protected]

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