Track: Supply Chain and Logisitcs Competition

Abstract

The movement of products, commodities, and information from manufacturers to consumers is part of the supply chain, which is a dynamic and complicated process. Recent technological developments, notably in the area of machine learning (ML), have created new possibilities for raising supply chain productivity. It is possible for devices to study from information and generate predictions or judgements using machine learning (ML), a subset of artificial intelligence (AI), without having to be explicitly programmed. Demand forecasting, inventory management, transportation planning, and supply chain risk management are just a few of the supply chain-related tasks that may be handled by ML algorithms. In addition, ML can be used to optimize transportation routes and schedules, which can help reduce transportation costs and improve delivery times. Overall, by enabling businesses to make better decisions, streamline their operations, and lower risks, ML has the possibility of significantly improving the efficiency of the supply chain.