To determine the key reverse logistics variables, which the top management should focus so as to improve the productivity and performance of computer hardware supply chains.
In this paper, an interpretive structural modeling (ISM) based approach has been employed to model the reverse logistics variables typically found in computer hardware supply chains. These variables have been categorized under “enablers” and “results”. The enablers are the variables that help boost the reverse logistics variables, while results variables are the outcome of good reverse logistics practices.
A key finding of this modeling is that environmental concern is the primary cause of the initiation of reverse logistics practices in computer hardware supply chains. For better results, top management should focus on improving the high driving power enabler variables such as regulations, environmental concerns, top management commitment, recapturing value from used products, resource reduction, etc.
In this research, an interpretation of reverse logistics 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 on a priority basis because there are a few other dependent variables being affected by them. Variables emerging with high dependence contribute to productivity and performance of green supply chain.
