Manufacturing performance measurement and target setting: A data envelopment analysis approach

Manufacturing decision makers have to deal with a large number of reports and metrics for evaluating the performance of manufacturing systems. Since the metrics provide different and at times conflicting assessments, it is hard for the manufacturing decision makers to track and improve overall manufacturing system performance. This research presents a data envelopment analysis (DEA) based approach for performance measurement and target setting of manufacturing systems. The approach is applied to two different manufacturing environments. The performance peer groups identified using DEA are utilized to set performance targets and to guide performance improvement efforts. The DEA scores are checked against past process modifications that led to identified performance changes. Limitations of the DEA based approach are presented when considering measures that are influenced by factors outside of the control of the manufacturing decision makers. The potential of a DEA based generic performance measurement approach for manufacturing systems is provided.

► We use DEA to analyze two real scenarios, assembly line and wafer manufacturing.
► We select apt models for each from a set of input and output oriented DEA models.
► The models are validated with input from manufacturing personnel.
► The results help manufacturing management in decision making.
► A framework for performance measurement is proposed for manufacturing facilities.