Data - Driven Optimization of Production Efficiency and Resilience in Global Supply Chains

Abstract

Our study presents a data-driven framework designed to simultaneously enhance supply chain resilience and optimize operational efficiency. By addressing key gaps in existing research, particularly the integration of risk management and resource optimization across the entire supply chain, this work offers a comprehensive approach to improving supply chain robustness. The framework was empirically tested within the context of Company A's global product management operations, where we quantified the economic impact of underutilized production capacities and assessed the benefits of strategic resource reallocation. Our analysis demonstrated that by optimizing idle production lines, resource utilization could be improved by 18%, resulting in annual cost savings of approximately $1.2 million. Additionally, the framework enhanced overall supply chain resilience by 25%, as evidenced by reduced recovery times and improved operational continuity during disruptions. These findings not only provide empirical support for the framework's effectiveness but also offer practical insights for businesses seeking to strengthen their supply chains in the face of increasing global uncertainties. The research contributes to the theoretical advancement of supply chain resilience and operational efficiency while offering actionable strategies for industry practitioners. The proposed framework serves as a scalable model adaptable to various industry contexts, thereby enhancing the resilience and competitiveness of enterprises in an increasingly volatile market environment.

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