The Role of Artificial Intelligence and Machine Learning in Supply Chain Optimization: A Systematic Review

Abstract

This research paper aims to conduct a systematic review to explore and synthesize the existing literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in supply chain optimization. The primary purpose is to gain insights into the current state of research, identify key trends, and evaluate the effectiveness of AI and ML in enhancing supply chain efficiency and performance. The study adopts a theoretical framework rooted in the principles of supply chain management and operations research. It leverages the theories of optimization, decision-making, and automation to examine the role of AI and ML in addressing supply chain complexities and challenges. A systematic literature review approach is employed to identify and analyze relevant articles from various databases and reputable sources. The selected studies undergo a rigorous screening process, including assessment based on predefined criteria, ensuring the inclusion of high-quality and credible research. The review highlights a substantial body of literature concerning the application of AI and ML in supply chain optimization. The findings demonstrate that AI and ML techniques, such as machine learning algorithms, optimization models, predictive analytics, and natural language processing, have demonstrated promising results in enhancing inventory management, demand forecasting, logistics, and overall supply chain performance. The incorporation of AI and ML in supply chain optimization presents several implications. From a research perspective, this review consolidates and organizes the existing knowledge, thereby offering researchers valuable insights for future investigations. On a practical level, supply chain professionals can leverage the findings to implement and integrate AI and ML technologies into their operations, thereby improving efficiency, cost-effectiveness, and customer satisfaction. Moreover, the adoption of AI and ML can contribute to the reduction of environmental impacts through optimized resource allocation and streamlined logistics. This research paper provides a comprehensive and up-to-date systematic review of the role of AI and ML in supply chain optimization. The synthesis of various studies and identification of trends adds significant value to the literature on supply chain management and AI applications. Additionally, the paper sheds light on potential research gaps, encouraging further exploration in this evolving field.