Sourcing in the face of supply chain disruptions has been one of the most challenging tasks in supply chain management, particularly when such disruptions occur due to natural calamities, such as flood, fire, and earthquake, affecting both the primary and the backup suppliers. Invariably, such disruptions lead to reduced supply from the primary supplier, encouraging the supplier to place fresh orders with the backup suppliers. In order to mitigate the adverse effect of supply disruption, in this article we use the concepts underlying the well-known Duckworth–Lewis–Stern method, used in cricket, to revise the supply target of the primary supplier and to decide a target for the backup supplier. We simulated the supply disruption scenarios in an experimental setting by conducting a two-round questionnaire survey among 300 purchase managers. The means and variances of the participants’ estimates of probabilities of meeting the revised targets within the scheduled time for various model-generated supply scenarios were used to find the participants’ risk preferences. In the second-round survey, the participants, clustered in groups of 10, ranked their own risk preferences. These ranks were used to find the optimal portfolio choices. Finally, we validated the theoretical predictions for the risk options using two approaches—one, at the group level by estimating the within- and the between-group risk preferences of buyers, and, two, at the aggregate level, by considering all the participants, fitting quantile regression model to the experimental results, and estimating the risk preference structures for different quantiles of the relative risk–return trade-off distributions.

中文翻译：

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在供应中断期间拆分订单的最佳组合选择：体育原则在日常采购中的应用

面对供应链中断的采购一直是供应链管理中最具挑战性的任务之一，尤其是当此类中断是由于洪水、火灾和地震等自然灾害而发生时，主要供应商和备用供应商都会受到影响。这种中断总是会导致主要供应商的供应减少，从而鼓励供应商向备用供应商下新订单。为了减轻供应中断的不利影响，在本文中，我们使用板球运动中使用的著名 Duckworth-Lewis-Stern 方法的基础概念来修改主要供应商的供应目标并确定供应商的目标备用供应商。我们通过对 300 名采购经理进行两轮问卷调查，模拟了实验环境中的供应中断情景。参与者对在预定时间内针对各种模型生成的供应情景实现修订目标的概率的估计值的均值和方差用于确定参与者的风险偏好。在第二轮调查中，参与者以 10 人为一组，对他们自己的风险偏好进行排名。这些排名用于寻找最佳投资组合选择。最后，我们使用两种方法验证了风险选择的理论预测——一种是在群体层面，通过估计买家的群体内和群体间风险偏好，另两种是在总体层面，通过考虑所有参与者，将分位数回归模型拟合到实验结果，并估计相对风险回报权衡分布的不同分位数的风险偏好结构。