Machine Learning Applications in Supply Chain Management: A Deep Learning Model Using an Optimized LSTM Network for Demand Forecasting

The covid 19 has caused a strong health and economic crisis, where the business environment is turning into a great uncertainty and customer demand has become more and more fluctuating. As a result, demand forecasting as well as the task of predictive analysis, holds a great attention in the supply chain, in order to meet the needs of customers, avoid any out of stock, and at the same time avoid a waste of resources. In this paper, we propose a deep learning method based on long-term memory multilayer networks (LSTM) for demand forecasting. Using the grid search method, our method has the ability to automatically select the most optimal forecasting model, considering different combinations of LSTM hyperparameters of a time series. The proposed model has shown strength in capturing the existing nonlinear features in time series data compared to some known time series forecasting methods derived from statistical and machine learning approaches, using historical sales data of a Moroccan pharmaceutical manufacturing company. These methods include exponential smoothing (ETS), autoregressive integrated moving average (ARIMA), recurrent neural network (RNN). The evaluation of the proposed method and the comparison methods was performed using the root mean square error (RMSE) and Symmetric mean absolute percentage error (SMAPE). The comparison of the test results showed that the proposed method is the best performing with RMSE of 4487.32 and SMAPE of 0.026 much better than those obtained by the other models © 2022, International Journal of Intelligent Engineering and Systems.All Rights Reserved.