Enhancing Supply Chain Resilience Through Predictive Strategies and Innovation: A Path Toward Achieving Sdg9 (Industry, Innovation, and Infrastructure)

This study aims to investigates how organizations can strengthen supply chain resilience in the face of disruptions while contributing to the realization of SDG 9 (Industry, Innovation, and Infrastructure). It aims to identify and evaluate predictive, data-driven, and innovative strategies that enhance operational continuity and industrial sustainability. Grounded in the Resource-Based View (RBV), the Dynamic Capabilities Perspective (DCP), and Contingency Theory, the study explains how strategic resources, adaptive capabilities, and context-specific approaches collectively drive resilience in supply chains. A quantitative research design utilizing secondary data from 501 operational records with 13 variables was adopted. Descriptive, inferential, and logistic regression analyses were conducted to explore the relationship between disruptions, resilience-enhancing strategies, and performance outcomes. The study findings reveal that preventive maintenance and inventory redundancy significantly improve delivery efficiency and resilience but involve cost trade-offs. Predictive modeling achieved 79% accuracy, demonstrating the value of analytics-driven forecasting in maintaining sustainable industrial infrastructure. The study underscores that integrating preventive maintenance, redundancy, and predictive analytics can advance resilient industrial systems, supporting SDG 9 targets for sustainable innovation and infrastructure. This research bridges theoretical and empirical insights, offering practical guidance for managers and policymakers on embedding resilience, innovation, and data intelligence into industrial and supply chain systems aligned with the SDGs.