摘要：This issue of tweets will introduce the 2.1. resilient supply chain of the intensive reading journal paper "A fuzzy group decision
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今天小编为大家带来“精读期刊论文《衡量食品供应链弹性的模糊群体决策模型：西班牙案例研究》的2.1.供应链弹性”。
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Today, the editor brings the "the 2.1. resilient supply chain of the journal paper 'A fuzzy group decision-making model to measure resiliency in a food supply chain: A case study in Spain'".
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一、内容摘要（Content summary）
本期推文将从思维导图、精读内容、知识补充三个方面介绍精读期刊论文《衡量食品供应链弹性的模糊群体决策模型：西班牙案例研究》的2.1.供应链弹性。
This issue of tweets will introduce the 2.1. resilient supply chain of the intensive reading journal paper "A fuzzy group decision-making model to measure resiliency in a food supply chain: A case study in Spain" from three aspects: mind mapping, intensive reading content, and knowledge supplement.
二、思维导图（Mind Mapping）
三、精读内容（Detailed Reading Content）
供应链及其弹性是现代企业管理中的重要概念。供应链管理负责管理产品和服务的整个流动过程，对全球贸易业务至关重要。而供应链弹性（SCR）作为应对供应链中断的有效手段，其重要性也日益得到认可。研究者们对SCR提出了不同的定义，但都强调了其在维护供应链稳定运行中的关键作用。通过增强供应链的复原力，企业可以更好地应对各种突发干扰，确保供应链的持续稳定运行。
supply chain and its elasticity are important concepts in modern enterprise management. Supply chain management is responsible for managing the entire flow of products and services and is critical to global trade operations. The importance of supply chain resilience (SCR) as an effective means to deal with supply chain disruptions has been increasingly recognized. Researchers have proposed different definitions of SCR, but all emphasize its critical role in maintaining the stable operation of the supply chain. By enhancing the resilience of the supply chain, enterprises can better cope with various sudden disturbances and ensure the continuous and stable operation of the supply chain.
供应链弹性管理的核心目标在于识别与产品和供应链运营相关的潜在中断风险源。通过识别这些风险因素，管理者和决策者可以准备相应的计划和政策来应对可能发生的中断。这些风险被分为宏观风险、经营风险以及内部风险。通过识别这些风险，管理人员可以制定策略和建议，以最小化中断造成的成本，并加快中断后的恢复。助于提高供应链的弹性，确保组织在面对挑战时能快速有效地响应，维护业务连续性和客户满意度。
The core objective of supply chain resilience management is to identify potential sources of disruption risk associated with product and supply chain operations. By identifying these risk factors, managers and decision makers can prepare appropriate plans and policies to deal with possible disruptions. These risks are classified into macro risks, operational risks and internal risks. By identifying these risks, managers can develop strategies and recommendations to minimize the costs of outages and accelerate recovery after outages. Helps improve the resilience of the supply chain, ensuring that organizations can respond quickly and effectively to challenges, maintaining business continuity and customer satisfaction.
下表列出了SCR文献中大多数近期研究中考虑的风险因素和子因素及其相应定义的综合列表。宏观风险包括自然灾害、疾病、政府规章、劳工罢工、政治冲突、恐怖袭击、水系统故障。经营风险涉及需求波动、客户口味变化、供应质量风险、供应商交货延迟、生产和制造故障、运输和物流失败、环境风险。内部风险包括内部操作风险、库存过多、系统和IT风险、与供应商的沟通失败和供应商的破产、备份供应商。
The following table presents a comprehensive list of risk factors and subfactors considered in most recent studies in the SCR literature and their corresponding definitions. Macro risks include natural disasters, disease, government regulations, labor strikes, political conflicts, terrorist attacks, and water system failures. Operational risks involve fluctuating demand, changing customer tastes, supply quality risks, supplier delivery delays, production and manufacturing failures, transportation and logistics failures, and environmental risks. Internal risks include internal operational risks, excess inventory, system and IT risks, failure to communicate with suppliers and supplier bankruptcies, and backup vendors.
最后，作者总结了供应链弹性管理中的常用模型。常用的数学模型包括数学模型，如线性规划（LP），混合整数规划（MIP），多目标规划（MOP），动态规划（DP），目标规划（GP），贝叶斯网络建模（BNM），马尔可夫链过程（MCP）。下表简要介绍了解决供应链弹性问题常用的模型与方法。
Finally, the author summarizes the common models of supply chain elasticity management. Commonly used mathematical models include mathematical models such as linear programming (LP), mixed integer programming (MIP), multi-objective programming (MOP), dynamic programming (DP), objective programming (GP), Bayesian network modeling (BNM), and Markov chain processes (MCP). The following table briefly introduces the commonly used models and methods to solve the supply chain elasticity problem.
四、知识补充——马尔可夫链过程（Knowledge Replenishment -- Markov Chain Process）
马尔可夫链过程，或称马尔可夫链，是一种随机过程，它描述了一个系统在给定当前状态下，未来状态的概率分布仅依赖于当前状态，而与过去的状态无关。这种性质被称为马尔可夫性质或无记忆性。在马尔可夫链中，系统的状态随时间变化，且每个状态转移的概率仅取决于当前状态。具体来说，如果状态能在离散时间上移动，则称为离散时间马尔可夫链；如果状态转移可以发生在任何时间点，则称为连续时间马尔可夫链。
A Markov chain process, or Markov chain, is a random process that describes how the probability distribution of future states of a system, given its current state, depends only on the current state and is independent of past states. This property is called Markov property or memorylessness. In a Markov chain, the state of the system changes with time, and the probability of each state transition depends only on the current state. Specifically, if the state can move in discrete time, it is called a discrete time Markov chain; If a state transition can occur at any point in time, it is called a continuous-time Markov chain.
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参考资料：ChatGPT、百度百科
参考文献：
Yazdani Morteza, Torkayesh Ali Ebadi, Chatterjee Prasenjit, et al. A fuzzy group decision-making model to measure resiliency in a food supply chain: A case study in Spain [J]. Socio-Economic Planning Sciences, 2022, 82(1): 101257-101271.
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来源：LearningYard学苑
