Optimizing Digital Supply Chain Management Based On ERP Systems Using Artificial Intelligence With Deep Learning Model

Abstract

With the growth and progress of information technology, competition turned out to be more exhaustive on a global scale. Numerous enterprises have predicted that the future of process and supply chain management (SCM) might modify vividly, from scheduling, planning, optimisation, to transportation, with an occurrence of artificial intelligence (AI). Nowadays, people are more involved in machine learning (ML), AI, and other intellectual technologies, with regard to SCM. AI-driven SCM optimization and Enterprise Resource Planning (ERP) systems integration. As industries attempt for active excellence, the convergence of artificial intelligence (AI) and SCM arises as a transformative force in dynamic agility, efficacy, and competitiveness. Over a complete analysis, this abstract inspects the synergistic relationship among AI-driven SCM optimization and the combination of ERP systems, clarifying their collective impact on manufacturing efficiency. Therefore, the study present a new Optimizing Digital Supply Chain Management Based on ERP Systems using Artificial Intelligence (ODSCM-ERPAI) method. The proposed ODSCM-ERPAI technique depends upon the optimization algorithm and DL based prediction process for digital SCM. To achieve this, the ODSCM-ERPAI system originally completes data preprocessing to measure the input signals into a uniform format. Next, the bidirectional long short term memory (Bi-LSTM) model is used for prediction process with the inclusion of improved grey wolf optimization (IGWO) algorithm as a hyperparameter optimizer. To exhibit the improved performance of the ODSCM-ERPAI approach, a wide range of experiments are performed and the outcomes are examined under several aspects. The experimental outcomes reported the enhanced performances of the ODSCM-ERPAI model over the other techniques.