基于概率语言术语集的体育用品行业供应链风险管理战略决策支持系统：扩展CRADIS方法

The global sports supplies industry, characterized by its complex and geographically dispersed supply chains, presents companies with a multitude of risk factors. These range from disruptions in raw material sourcing to geopolitical instability. This paper explores the challenges and opportunities associated with implementing effective supply chain risk management (SCRM) strategies within this industry. The study delve into the various types of risks faced by companies and propose strategies for mitigating them. One such strategy is Multi-Criteria Decision Making (MCDM). MCDM empowers companies to objectively evaluate and prioritize risks based on a range of factors and facilitates data-driven decision-making regarding supplier selection and risk mitigation approaches, ultimately leading to a more resilient and competitive supply chain. The paper highlights that the sports supplies industry can significantly benefit from the application of Probabilistic Linguistic Term Sets (PLTS) for enhanced risk assessment and management. By integrating PLTS into the decision-making process, companies gain a more nuanced understanding of supplier reliability and can implement proactive mitigation strategies. This translates to an overall increase in supply chain resilience and competitiveness. This paper proposes an advanced approach that leverages PLTS to address uncertainties inherent in decision-making. The study introduces innovative PLTS meticulously designed using the CRADIS method. CRADIS is a robust tool that aids in selecting optimal goals within the decision-making process. It combines various MCDM strategies, offering a powerful approach to navigate complex decision scenarios with multiple criteria. To demonstrate the adaptability and effectiveness of this proposed methodology, a case study is presented, showcasing its practical application and potential impact. To validate the effectiveness and real-world applicability of our improved MCDM CRADIS method, a comparative analysis with existing decision-making methods is conducted. This comparison highlights the strengths and advancements offered by the proposed approach.