This study aims to examine the influence of commitment management on supply chain integration, supply chain transparency, and operational decision-making in manufacturing companies in Indonesia. Amidst the increasing complexity of supply chains and the demands for rapid, data-driven decision-making, companies need to build integrated and transparent systems, supported by strong commitment from top management. This study used a quantitative approach with a survey method of 128 respondents from manufacturing companies in Java, and the data were analyzed using Partial Least Square (PLS) techniques. The results showed that commitment management significantly influenced supply chain integration, supply chain transparency, and operational decision-making. Supply chain integration was also shown to influence supply chain transparency, but not significantly on operational decision-making. Meanwhile, supply chain transparency significantly influenced operational decision-making. A mediation test showed that the indirect influence of commitment management on operational decision-making through supply chain integration and transparency was not significant. This indicates that operational decision-making still relies heavily on the direct involvement of top management. This study provides a theoretical contribution in enriching the understanding of the role of management commitment in supply chain-based operational decision-making systems. Practically, the results of this study recommend strengthening the role of middle managers, decision-making training, and the implementation of integrated information systems to improve the effectiveness of operational decisions in real time.

This paper analyzes the effect of Artificial Intelligence (AI) implementation projects on the operational performance of manufacturing companies in Indonesia, considering the mediating role of quality internal integration, quality external integration, and supply chain responsiveness. This study uses a quantitative research approach, utilizing a survey method, with 109 manufacturing companies in Java and Bali that have adopted AI technology in their production processes. Data processing was carried out using the Partial Least Squares (PLS) approach. The results indicate that the AI implementation project has a significant impact on both internal quality integration and external quality integration but does not directly affect supply chain responsiveness or operational performance. Quality internal integration is proven to be a key variable that significantly affects quality external integration, supply chain responsiveness, and operational performance. Meanwhile, quality external integration has a significant effect on supply chain responsiveness but not on operational performance directly. Additionally, supply chain responsiveness has been proven to positively contribute to improving operational performance. The conceptual model developed in this study successfully demonstrates a multi-layered influence path from AI implementation project to operational performance through quality integration and supply chain responsiveness. This study highlights the importance of synergy between AI technology and internal and externally integrated quality management systems in achieving operational excellence in the manufacturing sector. These findings expand the theoretical understanding of the strategic role of AI in the context of supply chain and operational quality.