Multi-Stage data envelopment analysis congestion model

This paper develops a multi-stage data envelopment analysis congestion model to measure the efficiency and congestion of supply chain. Congestion is said to occur when the output that is maximally possible can be increased by reducing one or more inputs without improving any other input or output. There are no models dealing with supply chain congestion, however we modify one stage Fare, Grosskopf and Lovell approach that proceeds in two stages into multi-stage data envelopment analysis models. We first examine closed loop serial supply chain processes where each stage has its own inputs and at the same time uses carryover inputs from the previous stage. This is the case for all the intermediate stages but not for the polar stages. For instance, the first stage that constitute the supplier stage do not have any carryover inputs as it is the polar stage of the closed loop supply chain. We show that breaking down the production processes of supply networks for evaluation can generate more practical insights in how to eliminate inefficiency in the supply.