@article {azevedo_using_2013, title = {Using interpretive structural modeling to identify and ranking performance measures: an application in the automotive supply}, journal = {Baltic Journal of Management}, volume = {in press}, year = {2013}, note = {

n/a

}, abstract = {

Purpose - The main objective of this paper is to identify and rank a set of performance measures using the Interpretive Structural Modeling (ISM) approach to support the automotive supply chain performance evaluation.
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 attending to their driving and dependence power. Inventory level and lead time are the two performance measures at the bottom level of the hierarchy, implying higher driving power. Also, 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 on mutual influences of performance measures (driving performance measures) and also those measures which are most influenced by others (dependent performance measures). Using {ISM} managers can get insight on the order and direction of complex relations among the suggested performance measures. This kind of information is strategic for managers could identify which performance measures they should concentrate on, and how they can manage the tradeoffs between them.
Originality/value - This paper highlights the deployment of {ISM} as a manager{\textquoteright} decision support tool in the identification and ranking of a set of performance measures to make part of a supply chain performance measurement system.

}, author = {Azevedo, S. G. and Carvalho, H. and Cruz-Machado, V.} }