Benchmarking Using Data Envelopment Analysis: Application to Stores of a Post and Banking Business

Data Envelopment Analysis (DEA) is a non-parametric, optimisation-based benchmarking technique first introduced by Charnes et al. (European Journal of Operational Research, 2(6), pp. 429–444, 1978), later extended by Banker et al. (Management Science 30(9), pp. 1078–1092, 1984), with many variations of DEA models proposed since. DEA measures the production efficiency of a so-called Decision Making Unit (DMU) which consumes inputs to produce outputs. DEA is a particularly useful tool when there are multiple measures to be analysed in terms of DMU (or organisation) performance, allowing it to benchmark and identify comparable peers. DEA can incorporate different measures of multi-dimensional activities thus allowing for DMU complexity and is particularly useful for more ingrained analyses when investigating the effects of contextual or environmental factors on organisations’ performance. DEA has been applied in numerous areas including banking, education, health, transport, justice, retail stores, auditing, fighter jet design, research and development to name a few. DEA is based around a production model which assesses the efficiency of DMUs in turning inputs into outputs. This is done by comparing units with each other to identify the most efficient DMUs that define a frontier of best performance, which is used to measure the performance of non-efficient DMUs. This efficient frontier represents “achieved best performance” based on actual outputs produced and inputs consumed and thus provides a useful practical reference set for benchmarking and performance improvement. There are very few assumptions required in DEA and its non-parametric form avoids the need to consider alternative distribution properties. In this chapter we first describe the case of a Post and Banking Business, and then introduce DEA in the context of our case. Different DEA models and additional features are discussed. We give a brief outline of an open-source software tool for DEA and finally apply three different DEA models to the case study and discuss the results. The Learning Outcomes of This Chapter Are: Develop an intuitive understanding of DEA Understand basic linear programming models for DEA Be aware of common DEA modelling techniques Be able to conduct a DEA analysis supported by pyDEA software Be able to interpret the DEA results and explain them to a non-technical audience