Sustainability-oriented efficiency of retail supply chains: A combination of Life Cycle Assessment and dynamic network Data Envelopment Analysis.

Assessing the efficiency of retail supply chains (RSCs) requires analytical tools that address the different activities involved in these chains. In this sense, dynamic network Data Envelopment Analysis (DEA) arises as a suitable method to evaluate the operational performance of RSCs over a period of time. However, its use for sustainability-oriented efficiency assessment constitutes a knowledge gap that limits its applicability for thorough decision-making processes, e.g. at the retail company level. This article fills this gap through the combination of Life Cycle Assessment (LCA) and dynamic network DEA. A novel five-step LCA + DEA approach is proposed and applied to a case study of 30 RSCs in Spain for the period 2015–2017. In this case, the supply chain structure involves three divisions: central distribution, operation of retail stores, and home delivery. Both overall- and term-efficiency scores were found to widely range from 0.38 to 1.00, with only 1 RSC deemed efficient. Regarding divisional efficiency, store operation was found to generally show significantly higher efficiency scores than the distribution divisions. The link between long distribution distances and low efficiency stresses the relevance of integrating a network perspective into the efficiency assessment. In addition to efficiency scores, the LCA + DEA approach enriches the assessment by providing environmental, operational and socio-economic benchmarks to further support the management of RSCs from a sustainability perspective. Unlabelled Image • Life Cycle Assessment and dynamic network Data Envelopment Analysis were combined. • The method was proven through a case study of 30 retail supply chains with 3 divisions. • Efficiency scores, sustainability benchmarks and economic savings were estimated. • Unlike store operation, central distribution and home delivery bring inefficiency. • The network perspective enriches the results from previous unidivisional studies. [ABSTRACT FROM AUTHOR]