The Improvement of Planning Processes in a Manufacturing Company

The final qualifying work consists of an introduction, three chapters, conclusion, a list of references, and an appendix. In the introduction, the relevance of the chosen topic is justified, the purpose and objectives of the study are set. The first chapter, "Analysis of the economic activity of the company "Unilever Rus", presents an analysis of the company's position in the FMCG market, as well as the logistics structure of the enterprise and describes the planning process adopted by the company. This chapter also describes the identified problems in demand planning and their impact on the efficiency of the business. In the second chapter, "Analysis of the structure of the planning process in a manufacturing company", the main aspects of the planning process in a manufacturing company that use the Sales & Operations Planning approach are analyzed, as well as the demand planning process is considered in more detail. In addition, we analyzed the forecasting models that are most often used to generate demand forecasts, as well as their application in the framework of machine learning technology. The third chapter, "Improving the planning process in "Unilever Rus", presents recommendations for changing the organizational structure of the demand planning division, as well as for supplementing the S&OP process. The implementation of demand forecasting using automated machine learning based on Microsoft Azure Machine Learning is presented on the example of one of the key clients of the company "Unilever Rus", and the economic efficiency of the proposed solutions is analyzed using the Dupont model. In conclusion, the main results that were obtained in the study are formulated. The final qualifying work consists of an introduction, three main chapters, a conclusion, and a list of references. The main text is presented on 84 pages, including 16 tables, 36 figures and 1 appendix.