Deep Learning Applications In Materials Management For Pharmaceutical Supply Chains

Abstract

The application of deep learning can help in augmenting the smart materials management in the pharmaceutical supply chain. Deep learning can potentially elevate the current operational efficiency of supply chains using machine learning-based analytic techniques. With the intelligent decision-making capabilities of deep learning applications, the overall performance of the supply chain with respect to inventory management, demand prediction, order management, and warehouse operations can be optimized. Currently, these decision-making processes in the majority of pharmaceutical supply chains are performed by using intuition, consultants, machine learning algorithms, or analytics services based on datasets. Although these techniques are showing improved results in demand forecasting, inventory management, prescriptive analytics, classification, and warehouse optimization.
With the abundant existence of big data, faster computation, more sophisticated machine learning algorithms, and auto-piloted intelligent systems, there is great hope in incorporating deep learning in the pharmaceutical supply chain with respect to demand forecasting, inventory management, and warehouse operations. Embedded research in developing deep learning systems in materials management for analyzing vast datasets has not been reported yet in particular application research, neither in urgent drugs nor in the pharmaceutical supply chain domain due to the high non-linearities. Further practical implementation of deep learning systems toward the operational performance of the pharmaceutical supply chain has not been reported. Therefore, more research should be conducted to define the best methods of deep learning applications in pharmaceutical supply chains. Furthermore, deep learning can be utilized to enhance the procedures of purchasing, processing orders, and forecasting demand; and proper inventory rules. These applications will further reduce costs, improve the level of customer service, and create a feasible competitive advantage.