Developing Artificial Neural Network Based Model for Backorder Prediction in Supply Chain Management

In today’s golden era of digitization, every industry has been focused on the adaptation of new technologies to boost their market dominance and company revenue. For every industry, supply chain management is the main area of concentration in order to develop brand value and seize local and global markets. Supply chain management (SCM) is progressively becoming a pillar for any firm. It is the top-down and bottom-up centralized control of the flow of goods, information, services, and money. It encompasses all processes, from acquiring raw materials to manufacturing, marketing, regulating supply and demand, and creating final products in response to market needs. Industries may be able to reduce excess costs associated with their products and deliver the products to customers in a more effective and efficient manner by managing proper supply chain operations. The primary customer demand is for a high-quality product at a reasonable price and with prompt delivery. To satisfy customer demand and enhance profit by capturing market share, companies have to focus on digitization in supply chain management. Digitization aids in forecasting demand fluctuations, reducing unnecessary inventory management and managing back orders. Backorders, improper data handling, unexpected delays in delivering products, not being able to forecast demand properly, and the bullwhip effect are all major threats to the overall supply chain profitability. In this study, the main focus is the usage of artificial neural networks to predict backorder in supply chain management to overcome demand fluctuations.