Using interpretive structural modeling to identify and ranking performance measures: an application in the automotive supply chain

Purpose - The main purpose of this paper is to identify and rank a set of performance measures using the approach of interpretive structural modelling (ISM) to support the evaluation of automotive supply chain performance. Design/methodology/approach - The paper develops a framework to analyze the interactions among a suggested set of performance measures using the ISM approach. To identify the contextual relationships among the suggested measures, five experts from the automotive industry were consulted. Findings - Using the ISM approach the performance measures were clustered according to their driving power and dependence power. Inventory level and lead time are the two performance measures at the bottom level of the hierarchy, implying higher driving power. Operational costs, business wastage, environmental costs, delivery time and customer satisfaction are identified as autonomous measures. This means that they are relatively disconnected from the other suggested performance measures. It is also observed that the cash-to-cash cycle is a weak driver but strongly dependent on the other performance measures. Practical implications - The proposed approach gives managers a better understanding of the performance measures that have most influence on others (driving performance measures) and those measures which are most influenced by others (dependent performance measures). This kind of information is strategic for managers who can use it to identify which performance measures they should concentrate on, and how they can manage the trade-offs between measures. Originality/value - This paper highlights the deployment of ISM as a management decision support tool in the identification and ranking of a set of performance measures to make part of a system for the measurement of supply chain performance.