Resilience Index: Proposal and Application in the Automotive Supply Chain

Carvalho, H. Resilience Index: Proposal and Application in the Automotive Supply Chain. Cambridge, {UK}, 2011.

Type of Work:

working paper presented at 10th EurOMA Doctoral Seminar


Purpose –This paper aims to propose a resilience index to assess the company ability to avoid and minimize the negative effects of supply chain disturbances.
Design/methodology/approach – An inductive research approach was used to develop a resilience assessment model. In a first stage, to collect empirical data, an exploratory case study was performed in seven companies’ belong to different positions in the Portuguese automotive supply chain. Next, a resilience index was derived from the case study main findings. In a final stage, the index was then tested using a case study approach in the automotive supply chain.
Findings – It was found that managers do not associate supply chain disturbances to a particular type of events, but with the negative effects that events provoke. The results also suggest that the resilience strategies they used are dependent on the type of supply chain disturbances negative effects. These empirical findings were used to develop a supply chain resilience assessment model and two resilience indexes: resilience index of “on time delivery” to “capacity shortage” and resilience index of “on time delivery” to “materials shortage”.
Research limitations/implications – This paper has the limitations common to all case studies, such as the subjectivity of the analysis and results generalization. Since only the automotive sector was studied, the findings are not universally applicable across different industry sectors in various countries.
Originality/value – The study contributes to the existing literature by empirically investigating the main effects of supply chain disturbances and how companies can increase supply chain resilience. It also suggests a way to evaluate companies’ resilience after a disturbance occurrence and identifies a set of supply chain state variables to improve supply chain resilience.