Predictive Insights: Real-Time Decision Making in Supply Chain Management

The supply chain management (SCM) is an important aspect to maintain the flow of business activities; it involves good management and coordination of the network comprising complex and interconnected parties who are involved in the provision of products and services to the final users the production, transportation, and delivery of the ready goods. The global integration of supply chains and the quickening stage of complexity and dynamic-environment requires the use of advanced tools and techniques to improve the processes of decision-making and to find the best operational routes. This has made the implementation of advanced machine learning algorithms relatively more important in supply chain management (SCM) for improving decision-making processes and optimizing operations. In this work, we propose a new model, called Hybrid LeNet-Xception Model (HLXM), which is a hybrid model of LeNet-5 and Xception to achieve a quick and smooth SCM in real-time. These performance comparisons were conducted with both traditional, and state-of-the-art machine learning models, resulting in improved demand forecasting, inventory handling, logistics optimization, and decision-making productivity in general. Results demonstrate the ability of HLXM to materially improve business operations, lower costs, and improve customer satisfaction