Business Intelligence Capabilities and AI-Driven Logistics Innovation: The Roles of Decision Support and Supply Chain Visibility

Leveraging Artificial Intelligence in Supply Chain Project Management: Evidence from an Empirical Study on Operational Excellence 

This study examines the role of Artificial Intelligence (AI) in enhancing supply chain project management and operational performance in a dynamic business environment. As supply chains become increasingly complex, data-intensive, and disruption-prone, organizations are adopting AI-driven tools to improve forecasting accuracy, optimize inventory, streamline logistics, and strengthen decision-making. The purpose of this research is to assess the level of AI adoption, identify key application areas, and examine the relationship between AI familiarity and AI adoption while considering the broader roles of organizational readiness and governance mechanisms. A quantitative research design was employed using a structured questionnaire administered to 42 respondents, including supply chain professionals, project managers, data/AI analysts, students, and other business or technology-related participants. Data were analyzed using descriptive statistics, correlation analysis, and regression techniques. The findings indicate that approximately 57% of respondents reported current AI adoption within their organizations, while the mean AI familiarity score was 3.6 on a five-point scale, reflecting moderate awareness. Correlation analysis revealed a positive relationship between AI familiarity and AI adoption (r = 0.61), suggesting that increased knowledge supports adoption behavior. The results also highlight the perceived importance of AI training, organizational preparedness, and governance frameworks in maximizing implementation benefits. This study contributes to business analytics, operations management, and decision sciences by providing empirical insight into AI-enabled supply chain transformation. The findings offer practical implications for managers, policymakers, and industry stakeholders seeking to strengthen AI readiness, improve operational efficiency, and promote responsible AI adoption for sustainable supply chain excellence.

Artificial Intelligence Utilization and Perceived Firm Performance in Chinese Logistics Firms: The Roles of Innovation Capability and Logistics Efficiency

Artificial intelligence (AI) is used in logistics, but the mechanisms linking AI utilization to firm performance remain insufficiently differentiated. Drawing on the information technology business value perspective and dynamic capabilities theory, this study examines whether managers’ perceptions of logistics-oriented AI utilization are associated with perceived firm performance through innovation capability and logistics efficiency, with managerial support treated as a secondary boundary condition. Cross-sectional survey data from 254 middle- and senior-level managers in Chinese logistics firms were analyzed using IBM SPSS Statistics 27 and IBM SPSS Amos 29 (IBM Corp., Armonk, NY, USA), and the PROCESS macro version 4.2 (Andrew F. Hayes, Calgary, AB, Canada), with Model 83 and 5000 bootstrap samples. Perceived AI utilization was positively associated with innovation capability, logistics efficiency, and perceived firm performance. Both mediators showed significant indirect effects, and their sequential indirect effect was also significant. The two individual indirect effects did not differ significantly, but both exceeded the sequential indirect effect. The proposed sequential, reverse-sequence, and parallel-mediation models produced identical fit indices, whereas the restricted direct-effects model showed weaker fit. Neither the AI utilization–managerial support interaction nor the moderated mediation indices was significant. Exploratory item-level analyses showed differentiated associations for demand forecasting and order allocation and for AI infrastructure; the pattern remained stable among 194 respondents involved in AI- or digital transformation-related activities. Innovation capability and logistics efficiency appear to function as complementary mechanisms, with a smaller capability-to-process pathway. Their relative ordering cannot be determined from the cross-sectional data. As the data are self-reported, the findings represent associations among managerial perceptions rather than objective causal effects. Sustainability implications are limited to operational efficiency because environmental outcomes were not directly measured.

AI-enabled decision intelligence for supply chain resilience: The roles of Human–AI collaboration and dynamic capabilities

Artificial intelligence (AI) is increasingly embedded in supply chain decision-making, yet its contribution to resilience depends on organizations’ ability to combine AI-generated intelligence with human expertise and adaptive organizational capabilities. This study examines how AI-enabled decision intelligence (AIDI) enhances supply chain resilience through human–AI collaboration and dynamic capabilities. Drawing on dynamic capabilities theory, a research model is developed in which AIDI strengthens human–AI collaboration and dynamic capabilities, while dynamic capabilities enable organizations to anticipate, respond to, and recover from supply chain disruptions. The model further proposes that human–AI collaboration and dynamic capabilities sequentially mediate the relationship between AIDI and supply chain resilience. Using survey data from 294 managers and professionals involved in AI-supported supply chain decision-making in Iranian organizations, the proposed relationships were tested using partial least squares structural equation modeling (PLS-SEM). The results support all hypothesized relationships, showing that AIDI positively influences human–AI collaboration, dynamic capabilities, and supply chain resilience. Dynamic capabilities significantly enhance supply chain resilience and mediate the AIDI–resilience relationship, while human–AI collaboration and dynamic capabilities sequentially mediate this relationship. These findings provide a capability-based explanation of how technological intelligence, human expertise, and organizational adaptability jointly contribute to resilient supply chain outcomes. Keywords: AI-Enabled Decision Intelligence, Human–AI Collaboration, Dynamic Capabilities, Supply Chain Resilience, Artificial Intelligence, Supply Chain Management.

Big Data Analytics Capabilities and Supply-chain Decision Agility in Multinational Enterprises: The Moderating Role of Managerial AI Literacy

This study investigates how big data analytics capabilities and managerial AI literacy jointly shape supply‐chain decision agility in multinational firms. Using 511 valid questionnaires from senior executives of high-tech electronics multinationals headquartered in Taiwan, we find that both managerial and technical aspects of big data analytics significantly boost decision agility. All three dimensions of AI literacy likewise show positive effects, with descriptive and diagnostic analytics exerting the most pronounced influence. Interaction tests further demonstrate that higher AI literacy magnifies the positive impact of analytics capabilities on agility. The findings suggest that firms seeking agile supply chains must not only upgrade data-management processes and technology platforms but also cultivate executives’ understanding of AI model logic, bias detection, and results application—enabling rapid sensing, swift decision-making, and timely execution in volatile environments. The study extends the integrated lens of dynamic capabilities and technology acceptance theory and provides a practical organisational-cognitive framework for multinational digital transformation.

From Data to Decisions: The Impact of Big Data Analytics Capability on Supply Chain Resilience and Agility

This paper analyses how Big Data Analytics Capability (BDAC) can enhance supply chain resilience and agility in more challenging and uncertain business contexts. The study aims at the application of the data-driven decision-making to the operational responsiveness and to improve the supply chain performance. The research approach will be qualitative research design with thematic analysis of scholarly articles based on peer-reviewed journals, academic books, and conference papers on the topic of big data analytics and the supply chain management. The results show that BDAC enhances the visibility of supply chain, predictive decisions, and responsiveness of an organization. The analytics features allow companies to predict disruptions, minimize the response time, and enhance the supply chain operation flexibility. Practical Implications: The study outlines the significance of digital transformation strategies, data infrastructure advancement, and analytics-based capabilities with regard to supply chain managers aiming to increase agility and resilience.  The research paper has value in the supply chain literature since it combines the lenses of dynamic capability and information processing theories. Future research can be done to examine industry-specific applications and empirical support on BDAC effects.

The Contribution of Business Intelligence to Decision-Making in International Companies