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Application of Machine Learning Techniques in the FMCG Company

Student: Arina Zubkova

Supervisor: Tatiana Yakushkina

Faculty: Graduate School of Business

Educational Programme: Business Informatics (Bachelor)

Year of Graduation: 2020

Sales of FMCG companies directly depend on the completeness of the assortment presented in offline sales channels, convenient layout and window design. The assortment that is mandatory for display in the window is regulated, but from control, it is a high-cost task both in terms of time and the need for additional training of employees. The purpose of this paper is to analyze the application of machine learning methods in the company in the market of consumer goods and services to optimize processes in the company. To achieve this goal, the following tasks were set and solved: the scientific literature in the field was studied, the goals and structure of the company were described, the strategy of the sales Department was analyzed, the structure of data organization in the company was considered, the case of using machine learning methods for data collection was reviewed, the effectiveness of this solution was evaluated, and recommendations were made for further analytical work with data. The structure of this work consists of four sections: introduction, main part, conclusion and list of references. The main part contains three chapters: the first chapter reviews the literature, and the second and third chapters analyze the strategy and case of applying machine learning methods for data collection. The first chapter examines the FMCG market, examines data analysis methods, including machine learning methods in business to optimize processes, and also assess the risks of implementation. In the second chapter, we analyze the strategy of the Sales Department in the company in question. we consider both the structure of the organization as a whole and the structure of data organization in the company. It also describes the importance of tracking product representation on the shelves and describes a strategy to improve the effectiveness of monitoring this indicator in retail outlets. The third chapter provides an overview of the integration case of a tool based on machine learning methods for collecting information on retail outlets, and at the end provides an assessment of the effectiveness of this tool and recommendations.

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