| dc.description.abstract | One of the most important steps that determines the overall performance of the supply
chain is Planning, which includes demand planning and supply planning. In particular, the
new century's big data, which is derived from the intricate customer behaviors, and the
fierce competition between players in the same industries have encouraged many
companies to invest in the process of supply chain planning. The potential growth of
optimization and machine learning algorithms has also enabled experts and researchers to
apply these methods to sales forecasting and to create the necessary inventory plans to
meet market demand. Three primary objectives are pursued in this essay. This thesis first
proposes a sales prediction model using the Gradient Boosting Regression technique,
focusing on the perishability and profitability of products in feature. Second, an ideal
inventory ordering strategy must be developed while taking discount quantity rules and
consumer demands for remaining shelf life into account. To provide a complete picture of
supply chain risk management for perishable goods, some solutions from the perspectives
of business acumen will be proposed in addition to engineering solutions. In this study, the
suggested solution is used for the case of CloudEats Company, a significant player in the
F&B sector, to help them boost the sales of their powder. | en_US |
