Demand Forecasting Model using Deep Learning Methods for Supply Chain Management 4.0

In the context of Supply Chain Management 4.0, costumers' demand forecasting has a crucial role within an industry in order to maintain the balance between the demand and supply, thus improve the decision making. Throughout the Supply Chain (SC), a large amount of data is generated. Artificial Intelligence (AI) can consume this data in order to allow each actor in the SC to gain in performance but also to better know and understand the customer. This study is carried out in order to improve the performance of the demand forecasting system of the SC based on Deep Learning methods, including Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) using historical transaction record of a company. The experimental results enable to select the most efficient method that could provide better accuracy than the tested methods.