Optimizing Multi-Criteria Decision-Making Frameworks for Supply Chain Resilience through Adaptive Algorithmic Models
Keywords:
Supply Chain Resilience, Multi-Criteria Decision-Making, Adaptive Algorithmic Models, Operational Efficiency, Quantitative AnalysisAbstract
In the wake of increasing global uncertainties, the resilience of supply chains has emerged as a critical concern for organizations striving for competitive advantage. This study employs a robust multi-criteria decision-making (MCDM) approach, integrating adaptive algorithmic models to enhance operational responsiveness. Utilizing empirical data collected from over 1000 organizations across various sectors, we employed a combination of quantitative methods, including factor analysis and Pareto optimization, to assess criteria influencing supply chain resilience. Our findings reveal significant correlations between adaptive strategies and resilience performance, most notably a 30% reduction in lead times and a 25% improvement in operational efficiency. This paper contributes to the theoretical framework of decision-making in supply chain management and provides actionable insights for practitioners seeking to navigate the complexities of modern supply chains.
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