Machine Learning And Optimization Technique - Application In Demand Planning & Supply Planning - A Case Of Nestlé – Ice Cream Products

Abstract

One of the most important processes that determines the entire success of the supply chain is Supply Chain Planning, which includes demand planning and supply planning. Specifically, the new century of big data gathered from intricate client behavior and intense. Competition between participants in similar areas has encouraged many businesses to engage in improving the supply chain planning process. The potential expansion of machine learning algorithms and optimization approaches has also enabled professionals and researchers to use these methods to sales forecasting and to create the proper inventory plans to meet market demand. Two key objectives are fulfilled by this essay. The first thing this study does is present a Gradient Boosting approach for sales prediction, which emphasizes the profitability and perishability of items during the feature engineering process. The next stage is to develop the optimum inventory ordering plan while taking into consideration discount quantity restrictions and consumer expectations for the product's remaining shelf life. In addition to technical solutions, a variety of business-smart solutions will be suggested in order to give a comprehensive picture of supply chain management for perishable commodities. The proposed solution is used in this paper for Nestlé Company, the biggest participant in the FMCG industry, with a desire to find a better solution that helps save costs as much as possible