Transforming Operations and Supply Chain Management with AI-Driven Digital Applications

The rapid advancement of artificial intelligence (AI) has led to its increasing adoption in digital applications for operations and supply chain management. Businesses leverage AI-driven digital applications for various purposes, including better demand forecasting, increased production efficiency, and improved inventory management. However, this adoption faces challenges such as high implementation costs, data privacy concerns, and workforce skill gaps, especially for businesses that are new to AI technologies. While past studies have explored AI adoption in business, gaps remain in understanding the holistic perspective of the different factors contributing to successful adoption. This study fills these gaps by using the Driver-Pressure-State-Impact-Response (DPSIR) framework to examine primary empirical studies on AI-driven digital applications for operations and supply chain management. Through a thematic analysis of 28 studies, this study identified a new theme called “enablers” in addition to the existing DPSIR themes of drivers, pressures, states, impacts, and responses and revealed new relationships between some of these themes. The study’s findings contribute to a deeper understanding of how AI applications can be leveraged in operations and supply chain management, offering practical insights for businesses embarking on their digital transformation journey and laying the groundwork for discussions of theoretical and managerial implications.