Reviewing Optimization Techniques in Supply Chains: AI and Blockchain Perspectives

Abstract

The integration of Artificial Intelligence (AI) and Big Data Analytics (BDA) into Supply Chain Management (SCM) has transformed the industry by enhancing efficiency, accuracy, and responsiveness. This review paper provides a comprehensive analysis of current AI applications in SCM, focusing on demand forecasting, inventory management, logistics, and transportation. Various AI techniques, including time-series forecasting, clustering, neural networks, SARIMA, and LSTM models, are discussed in detail. The impact of cutting-edge technologies on supply chain traceability and efficiency, such as blockchain and the Internet of Things (IoT), is also examined in this article. Despite the significant advancements, challenges such as gaps in closed-loop supply chains, terminology inconsistencies, and the need for better technical-managerial alignment persist. This review recognizes future research directions to address and solve these challenges and highlights the potential for AI to drive further innovations in SCM. Through case studies and bibliometric analyses, this paper underscores the significance of a comprehensive strategy for supply chain redesign, integrating physical, facilities, and information management to enhance sustainability and market responsiveness. Keywords— Supply chain management , Artificial Intelligence (AI), Big Data Analytics (BDA), Demand forecasting, Inventory management, Logistics optimization, Blockchain , Internet of Things (IoT), Smart Transportation, Tactical planning, Strategic planning, Advanced available-to-promise (AATP), Sustainability in supply chain.