DEVELOPMENT OF PREDICTIVE MODELS FOR IDENTIFYING SUPPLY CHAIN BOTTLENECKS USING ADVANCED SIMULATION AND OPTIMIZATION TECHNIQUES
Abstract
Predictive models for identifying supply chain bottlenecks have become increasingly essential due to the growing complexity of global trade networks. This study develops and evaluates models that integrate advanced simulation and optimization techniques to predict and mitigate supply chain disruptions. The research leverages historical data, simulation tools, and optimization algorithms to create adaptable frameworks. The results demonstrate improved efficiency and minimized delays in supply chain operations.
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