AI-Driven Predictive Supply Chain Optimization Using Reinforcement Learning Algorithms

Abstract

In a dynamic market which is getting more and more uncertain, efficient supply chain management has emerged as a challenge of utmost importance to organizations. Conventional optimization methods are not usually capable of adapting to the changing demands in real-time and complicated operational constraints. The paper introduces a predictive supply chain optimization model that uses AI and is developed on the basis of a new Hybrid Deep Reinforcement Learning algorithm (HDRL-SCO). The proposed solution will combine the application of Long Short-Term Memory (LSTM) networks to make precise demand forecasts with a Deep Q-Network (DQN) to make smart decisions. The system is able to learn the best policies to manage inventory, transportation planning, and order fulfillment, by constantly interacting with the environment by modelling the supply chain as a Markov Decision Process. As the experimental outcomes show, the suggested model will result in a considerable decrease in the overall operating costs, a higher level of services, increased inventory turnover as well as a decrease in the delays in the fulfillment process as compared to the traditional approaches. The framework is highly adaptive, scalable, and resilient to uncertain and dynamic environments and thus can be applied in the real-life supply chain.