Demand Forecasting Models for Food Industry by Utilizing Machine Learning Approaches

Continued global economic instability and uncer-tainty is causing difficulties in predicting sales. As a result, many sectors decision-makers are facing new, pressing challenges. In supply chain management, the food industry key sector which sales movement demand forecasting for products more difficult to predict. Accurate helps minimize stored expired items across individual stores and, thus, reduces potential loss of these products. To help companies adapt rapid changes manage their effectively, it necessary utilize machine learning (ML) approaches because ML’s ability process evaluate large amounts data efficiently. This research compares two models confectionery from one largest distribution Saudi Arabia order improve company’s predict using algorithms. achieve this goal, Support Vectors Machine (SVM) Long Short-Term Memory (LSTM) algorithms were utilized. addition, evaluated based on performance quarterly time series. Both provided strong results when measured against model, but overall LSTM outperformed SVM.