DLSTM-SCM– A Dynamic LSTM-Based Framework for Smart Supply Chain Management

In the retail industry, Supply Chain Management (SCM) holds significant importance as it ensures the efficient movement of goods from suppliers to customers. In this intricate and fast-paced environment, the availability of accurate information and data is crucial. The purpose of this paper is to develop a framework that enhances forecasting accuracy and efficiency in Supply Chain (SC) operations within the retail industry. By analyzing the latest research and advancements in the field, thi
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paper seeks to contribute valuable insights into the potential of deep learning for SCM. The ultimate goal is to provide retailers with a reliable tool that empowers them to make informed decisions based on accurate predictions, thereby optimizing their SC operations and better meeting customer demands in the dynamic retail landscape. Furthermore, DLSTM-SCM framework updates dynamically the deployed Long Short-Term Memory (LSTM) models to predict sales for the upcoming day sales, utilizing historical sales data and incorporating statistical features such as lagging and shifting to enhance forecasting precision. The efficacy of DLSTM-SCM is demonstrated through its performance on real benchmarks, where it yielded significant improvements compared to existing methods.