A Proposed Demand Forecasting Model by Using Machine Learning for Food Industry

Abstract

Demand forecasting is one of the biggest challenges in supply chain management, especially in the food industry, due to the various variables that affect people's needs, continuous changes in prices, and overall economic factors. Many variables affect product sales and demands, such as promotional offers, seasons, holidays, and cultural events, among many others. Despite the difficulty, supply chain processes can be enhanced by using machine learning, which typically produces better predictions than conventional approaches. This paper proposes a model to improve demand forecasting accuracy for the food industry. More specifically, the model focuses on chocolate products, using data from a local chocolate distributor in Saudi Arabia. The proposed model will take data from sales at normal times of the year and promotional sales, in holiday times for example, and use cutting edge machine learning techniques to accurately forecast supply and demand levels in the chocolate industry.