Impact of Artificial Intelligence on Supply Chain Optimization - Kelly, Alma (Journal of Technology and Systems, 2024)
Purpose: The general objective of the study was to investigate the impact of Artificial Intelligence on supply chain optimization. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of Artificial Intelligence on supply chain optimization. Preliminary empirical review revealed that AI significantly improved various aspects of supply chain management, including forecasting, inventory management, logistics, and risk management. It was found that AI technologies enhanced operational efficiency by providing more accurate demand predictions, optimizing logistics operations, and improving risk management capabilities. Additionally, AI contributed to greater sustainability in supply chains by reducing resource waste and supporting environmental goals, thus demonstrating its critical role in modernizing and optimizing supply chain practices. Unique Contribution to Theory, Practice and Policy: The Technology Acceptance Model (TAM), Resource-Based View (RBV) and Dynamic Capabilities Theory may be used to anchor future studies on Artificial Intelligence. The study recommended that future research should focus on developing theoretical models that integrate AI with traditional supply chain theories and that companies should adopt AI-driven tools for improved supply chain performance. It suggested that policymakers create guidelines for ethical AI use and data management to ensure responsible implementation. Additionally, it was recommended that collaboration between academia, industry, and technology providers be fostered to share best practices and address sector-specific needs. Lastly, it was advised that the long-term impacts and adaptability of AI technologies be evaluated to ensure their continued effectiveness and relevance.