Abstract
Data Envelopment Analysis (DEA) is a relative measure of efficiency applied to a set of decision units and is being used more and more frequently in the supermarket sector. Nonetheless, given how strongly the sector’s financials depend on demand, companies need to combine this measurement with trade area information to best manage corporate efficiency. In this paper, the proposal consists of integrating DEA with a clearly articulated, structural typology so that supermarkets, based on their particular characteristics, can determine which variables are most critical for improving their efficiency. This methodology has been validated in the case of one of Spain’s five largest supermarket chains. A principal component analysis and a classification analysis were carried out on a series of internal management variables from 61 locations for which DEA had been used to calculate efficiency and to which multiple trade area variables were added using GIS. Some of them are related to the loyalty scheme membership programme. These latter variables described the implantation of the loyalty scheme member programme and were revealed as key elements for the efficiency of the supermarket. This methodology provides marketing profiles that are more adapted to local circumstances, thus allowing companies to set better internal benchmarking objectives.
Highlights
Data Envelopment Analysis (DEA) has been increasingly used as a method to determine the relative efficiencies of a set of organisational units when there are multiple inputs and outputs (Charnes et al, 1978; Emrouznejad & Yang, 2018)
Given the peculiar characteristics of the food distribution sector, DEA requires an additional analysis that accounts for each individual trade area, such as demographic features and the proximity of competitors
Combining a structural typology based on current overall management with the trade area’s particular features, distinctions can be made to fine-tune the study for improved efficiency
Summary
Data Envelopment Analysis (DEA) has been increasingly used as a method to determine the relative efficiencies of a set of organisational units when there are multiple inputs and outputs (Charnes et al, 1978; Emrouznejad & Yang, 2018). DEA is a data-dependent tool that identifies the most efficient units within a set. The final score classifies the units based on the various results obtained and the available resources (Nitkiewicz et al, 2014). Org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Baviera-Puig et al Internal benchmarking in retailing with DEA and GIS: the case of
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