Distribution network rationalisation through benchmarking with DEA

Purpose – The purpose of this paper is to discuss the use of data envelopment analysis (DEA) to benchmark store performance for the purpose of rationalising retail distribution network. Design/methodology/approach – As an illustration of the approach, DEA is applied to a sample of front stores of a major retailer in Australia to compare their relative efficiency in distribution. Together with other techniques such as customer segmentation and spatial distribution of demand, this paper shows that DEA can provide an objective basis for distribution network rationalisation and be a suitable analytical tool to facilitate continuous improvement. Findings – Based on the DEA results, it is concluded that overall distribution efficiency of the part of the retail network under study can be improved by either closing the less efficient stores or merging them with the others in the same service areas to streamline the network. Such rationalisation will help aggregate demand and improve vehicle utilisation for distribution with minor impact on current level of customer service. Research limitations/implications – This study lends insight into the use of DEA, together with other analyses, for distribution network rationalisation. This approach is less data hungry and relatively easy to implement than full‐fledged optimisation through integer programming. To serve mainly as a proof of concept and an illustration of the approach, the scope of the study is limited to six stores in the retail network with relative performance in distribution evaluated on a single input and a single output variables. Practical implications – Managers can use DEA to benchmark the distribution performance of their stores against the best performers in the retail network so as to identify areas for improvement. The approach can also assist in the adoption of best practice and facilitate more effective allocation of resources across the entire retail network. Social implications – Retail network rationalisation through benchmarking with DEA can facilitate continuous improvement in distribution efficiency. This will help reduce fuel consumption, carbon emission, as well as other pollutions such as noise and traffic congestion. Originality/value – Research in retail network performance using DEA to date is mainly on comparative performance of supermarkets within or between chains. The focus is mainly placed on the relationship between floor area, workforce, and sales. This paper fills the gap in the literature by applying DEA in distribution network rationalisation instead of mere performance comparison of individual stores. It focuses on distribution costs rather than store attributes and supplements DEA with other techniques to obtain a fuller picture of the overall network efficiency in terms of distribution. It also contributes to a better understanding of how demand management can affect distribution efficiency of the retail network.