Frontier-based performance analysis models for supply chain management: State of the art and research directions

Effective supply chain management relies on information integration and implementation of best practice techniques across the chain. Supply chains are examples of complex multi-stage systems with temporal and causal interrelations, operating multi-input and multi-output production and services under utilization of fixed and variable resources as well as potentially environmental exposure. Acknowledging the lack of system's view, the need to identify system-wide as well as individual effects, as well as the incorporation of a coherent set of performance metrics, the recent literature reports on an increasing, but yet limited, number of applications of frontier analysis models (e.g. DEA) for the performance assessment of supply chains or networks. The relevant models in this respect are multi-stage models with various assumptions on the intermediate outputs and inputs, enabling the derivation of metrics for technical and cost efficiencies for the system as well as the autonomous links. This paper reviews the state of the art in multi-stage or network DEA modeling, along with a critical review of the advanced applications that are reported in terms of the consistency of the underlying assumptions and the results derived. Consolidating the current work in this range using a unified notation and by comparing the properties of the models presented, the paper is closed with recommendations for future research in terms of both theory and application.