Abstract:
This study investigates the impact of Artificial Intelligence (AI) on supply chain forecasting accuracy, with a particular focus on the mediating roles of data dependency and data quality. Rooted in the Conservation of Resources (COR) theory, the research posits that while AI holds great potential for improving forecasting performance, its efficacy is contingent upon the quality and availability of supporting data. Using a positivist, deductive, and quantitative methodology, data was collected from 384 supply chain professionals across Pakistan’s retail and manufacturing sectors through a structured questionnaire. Structural Equation Modeling (SEM) was applied to test the proposed model. The results confirm that AI positively affects forecasting accuracy both directly and indirectly via the sequential mediation of data dependency and data quality. These findings provide both theoretical contributions by extending COR theory into the domain of digital supply chains and practical insights for organizations aiming to maximize AI effectiveness through robust data governance. The study highlights the importance of aligning technological investments with data infrastructure to achieve superior forecasting outcomes in emerging market contexts.
