Enhancing Supply Chain Visibility through AI: Implementing Neural Networks and Reinforcement Learning Algorithms

Abstract

This research paper explores the transformative potential of artificial intelligence (AI) in enhancing supply chain visibility, with a focus on the implementation of neural networks and reinforcement learning algorithms. Supply chain visibility is crucial for optimizing operations, reducing costs, and improving customer satisfaction. However, achieving comprehensive visibility remains a challenge due to complex, multifaceted supply chain networks. This study investigates AI-driven methodologies to address these challenges, emphasizing the integration of neural networks for data processing and reinforcement learning for dynamic decision-making. The paper begins by examining existing visibility issues and the limitations of traditional approaches. It then details the development of an AI framework that leverages convolutional neural networks (CNNs) for real-time data extraction and analysis, facilitating more accurate demand forecasting and inventory management. Additionally, the research introduces a reinforcement learning model to optimize routing and logistics operations, utilizing policy gradient methods to adapt to changing conditions and uncertainties in supply chain networks. The proposed AI system was tested using simulated supply chain scenarios, demonstrating significant improvements in visibility metrics, such as reduced lead times and enhanced accuracy in inventory assessments. Results indicate that businesses adopting these AI technologies can achieve more adaptive and resilient supply chains. The paper concludes by discussing the implications of AI-driven visibility enhancements on supply chain strategies and the broader impact on industry practices, while also highlighting future research directions to explore the integration of emerging AI technologies.