An Enhanced Method on Using Deep Learning Techniques in Supply Chain Management

Abstract

One of the most significant issues that supply networks are currently facing is accurately estimating the level of demand for their products. Along with improving stock management and reducing overhead costs, some of the goals of the plan included growing sales, earnings, and customer base. The evaluation of historical data with the purpose of improving demand forecasting can be accomplished with the assistance of several different methods, some of which include methodologies based on machine learning, time series analysis, and deep learning models. This can be done to improve the accuracy of demand forecasting. The purpose of this investigation is to design an insightful strategy for forecasting future demand. In this paper, we develop an enhanced model to support the supply chain management and it uses a deep learning model to improve the process of supply chain management. The deep learning model is trained, tested and validated to improve the process of supplying the products via supply chain. The simulation is carried out in python for a set of objects that to be tracked and the results show that the model achieves higher accuracy of sending the products.