Analysis of interactions among the variables of supply chain performance measurement system implementation

Abstract

PurposeThe purpose of this paper is to determine the key supply chain performance measurement system (SCPMS) implementation variables, on which the top management should focus, so as to improve the effectiveness and efficiency of supply chain (SC).Design/methodology/approachIn this paper, an interpretive structural modeling (ISM)‐based approach has been employed to model the SCPMS implementation variables. These variables have been categorized under “enablers” and “results.” The enablers are the variables that help boost the SCPMS implementation variables, while results variables are the outcome of good SCPMS implementation.FindingsThe paper highlights the variables associated with implementation of SCPMS. A key finding of this modeling is that awareness about performance measurement system (PMS) in SC is a very significant enabler. For better results, top management should focus on improving the high‐driving power enablers such as awareness of PMS in SC, commitment by the top management, consistency with strategic goals, funding for PMS implementation, and effective information systems.Originality/valueIn this paper, an interpretation of SCPMS implementation variables in terms of their driving and dependence powers has been carried out. Those variables possessing higher driving power in the ISM need to be taken care of on a priority basis because there are a few other dependent variables being affected by them.