ABSTRACT

Frequent occurrence of disruptions in the supply chain due to the Covid-19 pandemic has increased the supply chain vulnerability (SCU), which affects the performance and revenue generation of firms. If the commodity we are dealing with in a supply chain is of a perishable nature, then the situation becomes more complicated, as such products need restrictive storage and transportation facilities. The dairy supply chain is one such perishable product supply chain. This paper, thus, proposes a method to identify key drivers of SCU in a dairy SC followed by establishing a model using interpretive structural modeling (ISM) and a graph theory approach (GTA) to calculate the SCU Index. It is important to quantify the SCU for identifying major factors affecting it and then developing techniques to mitigate it. In order to quantify the SCU, first, the ISM model is used to identify the interrelation between drivers, and then an adjacency matrix is formed by using the interdependence, thereby adding inheritance of each driver. A variable permanent matrix is formed to calculate the SCU Index for the SC. This proposed approach will help managers in mitigating the adverse effects of COVID-19 on the dairy supply chain.

摘要

由于Covid-19大流行而导致供应链中断频繁发生，这增加了供应链脆弱性（SCU），从而影响了公司的绩效和创收。如果我们在供应链中处理的商品易腐，那么情况就变得更加复杂，因为此类产品需要限制性的存储和运输设施。乳制品供应链就是这样的易腐产品供应链。因此，本文提出了一种在乳品SC中识别SCU关键驱动因素的方法，然后使用解释性结构建模（ISM）和图论方法（GTA）建立模型以计算SCU指数。量化SCU以识别影响SCU的主要因素，然后开发缓解SCU的技术非常重要。为了量化SCU，首先，ISM模型用于识别驱动程序之间的相互关系，然后利用相互依赖关系形成邻接矩阵，从而增加每个驱动程序的继承。形成一个可变的永久矩阵，以计算SC的SCU索引。提议的方法将帮助管理人员减轻COVID-19对乳制品供应链的不利影响。