Stochastic Optimization Models for Supply Chain Management: Integrating Uncertainty into Decision-Making Processes

Abstract

This study examines how supply chain management can use stochastic optimization models to overcome the problems associated with decision-making uncertainty. The study's primary goals are reviewing the literature on stochastic optimization models in supply chain management, gaining a thorough grasp of their applications, and evaluating how well they integrate uncertainty into decision-making processes. The method includes a comprehensive assessment of the current literature body, including scholarly journals, conference proceedings, and reliable web sources to obtain pertinent data and insights. The significance of incorporating uncertainty into decision-making procedures, the adaptability of stochastic optimization models for diverse supply chain functions, and their function in augmenting supply chain resilience via proactive risk mitigation and sound decision-making are among the principal discoveries. The policy implications indicate that investments in data analytics capabilities, capacity building, training programs, and regulatory frameworks are required to facilitate the implementation of stochastic optimization models in supply chain management. This study advances knowledge in supply chain management and informs future research and practice.