| dc.description.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 | en_US |
