The resilience of on-time delivery to capacity and material shortages: An empirical investigation in the automotive supply chain

供应链 弹性(材料科学) 汽车工业 业务 供应链风险管理 生产(经济) 产业组织 供应链管理 运营管理 服务管理 工程类 营销 经济 微观经济学 热力学 物理 航空航天工程
作者
Helena Carvalho,Bardia Naghshineh,Kannan Govindan,V. Cruz‐Machado
出处
期刊:Computers & Industrial Engineering [Elsevier BV]
卷期号:171: 108375-108375 被引量:12
标识
DOI:10.1016/j.cie.2022.108375
摘要

Supply chains around the globe are susceptible to disturbances that negatively impact their performance. Generally, supply chain disturbances lead to failure modes that impact the ability of the supply chain to deliver the promised goods and services on time. Therefore, companies operating in different supply chains are willing to become resilient to disturbances and their ensuing failure modes to be able to deliver on time and remain competitive. In light of this willingness, this study aims to propose an index that enables companies to assess their resilience of on-time delivery to supply chain failure modes based on the resilience practices they deploy. To this end, drawing on the knowledge derived from case study data analysis and literature, eight propositions and an explanatory framework are put forward that theorize the identified relationships between supply chain disturbances, failure modes, resilience practices, and on-time delivery as the primary indicator for measuring supply chain performance. Next, considering the resilience practices companies tend to deploy, an index capable of assessing the companies’ resilience of on-time delivery to two prevalent supply chain failure modes, namely capacity shortage and material shortage is modelled and tested using a case study in an upstream automotive supply chain in Portugal. The results indicate high resilience levels of on-time delivery to the aforementioned failure modes, mainly due to the high cost of production halt in the automotive industry. Additionally, a set of supply chain capabilities and their related resilience practices and supply chain state variables are identified that can be deployed and controlled to improve supply chain resilience.

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