Supply chain performance measurement system using DEA modeling

Purpose – This paper aims to illustrate the use of data envelopment analysis (DEA) in measuring internal supply chain performance. Design/methodology/approach – Two DEA models were developed – the technical efficiency model and the cost efficiency model. The models are further enhanced with scenario analysis to derive more meaningful business insights for managers in making resources planning decisions. Findings – The information obtained from the DEA models helps managers to identify the inefficient operations and take the right remedial actions for continuous improvement. More importantly, the opportunity cost (forgone profit) calculated serves as a good reference to managers to make efficient decisions on resource allocations. Research limitations/implications – Results are based on the deterministic data set. Future enhancement of the study would be to look into the possibility of modeling DEA in a stochastic supply chain environment (non‐deterministic) due to the fact that supply chain operates in a dynamic environment. Practical implications – The proposed DEA‐based approach provides useful managerial implications in the measurement of supply chain efficiency. The study proves the usefulness of DEA as a decision‐making tool in supply chain. Originality/value – This paper provides useful insights into the use of DEA as a modeling tool to aid managerial decision making in measuring supply chain efficiency.