An Artificial Intelligence Integrated IT Management Framework for Analytics-Driven Operational Decision-Making in U.S. Supply Chain and Logistics Systems

Abstract

Artificial intelligence is increasingly embedded in supply chain and logistics operations, yet many organizations still manage predictive analytics, IT infrastructure, operational monitoring, and optimization as fragmented capabilities, limiting the consistency, speed, and analytical quality of operational decisions. This study aimed to develop and quantitatively evaluate an AI-integrated IT management framework for Analytics-Driven Operational Decision-Making Performance in U.S. supply chain and logistics systems. A quantitative, cross-sectional, case-based design was adopted across selected digitally enabled enterprise environments involving supply chain, logistics, transportation, warehousing, distribution, manufacturing, and related cloud-supported information systems. Using purposive sampling, data were collected from professionals with relevant expertise, including supply chain and logistics managers, IT and analytics specialists, operations and warehouse managers, transportation professionals, and AI or digital-transformation practitioners. Of 320 questionnaires distributed, 300 were returned and 288 were retained as usable cases, yielding a 90.0% valid response rate. The study examined four independent variables: AI-Enabled Predictive Analytics and Forecasting, Intelligent IT Infrastructure and Data Integration, AI-Assisted Supply Chain Monitoring and Decision Support, and Real-Time Analytics and Automated Operational Optimization, with Analytics-Driven Operational Decision-Making Performance as the dependent variable. Data analysis incorporated descriptive statistics, Cronbach’s alpha reliability testing, KMO and Bartlett’s validity assessment, Pearson correlation, multiple regression, multicollinearity diagnostics, Durbin-Watson testing, and hypothesis evaluation. Results showed a high mean for decision-making performance, M = 4.20, SD = 0.53, while Real-Time Analytics and Automated Operational Optimization recorded the strongest correlation, r = .75, p < .001, and the largest standardized regression effect, β = .32, p < .001. The overall model explained 70.4% of the variance, R² = .704, Adjusted R² = .700, F(4, 283) = 168.27, p < .001. These findings indicate that integrated predictive, infrastructural, monitoring, and real-time optimization capabilities can substantially strengthen operational decision quality, responsiveness, and evidence-based coordination in digitally connected enterprise supply chains in practice.