AI-POWERED BUSINESS ANALYTICS FOR SMART MANUFACTURING AND SUPPLY CHAIN RESILIENCE

Abstract

This study quantitatively examined the relationships among AI-powered business analytics capability, smart manufacturing integration, and supply chain resilience within manufacturing organizations operating in data-intensive supply chain environments. Using a cross-sectional research design, data were collected from 312 manufacturing firms that had adopted analytics-supported operational systems. Descriptive analysis indicated moderate-to-high levels of analytics capability (mean = 3.71, SD = 0.62), smart manufacturing integration (mean = 3.58, SD = 0.66), and supply chain resilience (mean = 3.64, SD = 0.61), suggesting a sample characterized by partial to advanced digital maturity. Correlation analysis revealed strong positive associations between analytics capability and manufacturing integration (r = 0.62), analytics capability and supply chain resilience (r = 0.55), and manufacturing integration and resilience (r = 0.59), supporting the coherence of the proposed model and construct distinctiveness. Reliability and validity assessments confirmed strong measurement quality, with Cronbach’s alpha values ranging from 0.88 to 0.91 and average variance extracted values exceeding 0.60 for all constructs. Regression analysis demonstrated that analytics capability significantly predicted smart manufacturing integration (β = 0.62, p < 0.001) and supply chain resilience (β = 0.33, p < 0.001). When smart manufacturing integration was included in the resilience model, its effect was significant (β = 0.41, p < 0.001), while the analytics coefficient decreased in magnitude, indicating partial mediation. The mediated model explained 52% of the variance in supply chain resilience, compared to 39% in the direct-effects model. Collinearity diagnostics confirmed stable estimation, with variance inflation factors below 2.0 across all predictors. Moderation analysis showed no significant interaction effect for environmental turbulence, indicating that the estimated relationships were stable across varying contextual conditions. Overall, the findings provided robust quantitative evidence that AI-powered business analytics capability contributed to supply chain resilience both directly and indirectly through smart manufacturing integration, reinforcing the role of analytics-enabled integration as a central mechanism linking digital capability development to resilient operational performance.