Sensitivity of operational and environmental benchmarks of retail stores to decision-makers' preferences through Data Envelopment Analysis

Sci Total Environ. 2020 May 20:718:137330. doi: 10.1016/j.scitotenv.2020.137330. Epub 2020 Feb 14.

Abstract

Within the framework of multi-criteria decision analysis (MCDA), weighting methods are typically used to capture decision-makers' preferences. In this regard, the increasing use of the combined LCA (Life Cycle Assessment) + DEA (Data Envelopment Analysis) methodology as an MCDA tool requires an in-depth analysis of how the preferences of decision-makers could affect the outcomes of LCA + DEA studies. This work revisits a case study of 30 retail stores/supply chains located in Spain by applying alternative weighted DEA approaches to evaluate the influence of decision-makers' preferences (weights) on the final outcomes, with a focus on efficiency scores and operational and environmental benchmarks. The ultimate goal is to effectively capture the view of stakeholders when applying LCA + DEA for the sound, sustainability-oriented management of multiple similar entities. Different weight vectors are separately applied to three types of DEA elements: operational inputs, time terms, and divisions. Besides, preferences from three alternative standpoints are considered: company manager through direct rating, and environmental policy-maker and local community through AHP (analytic hierarchy process). A significant influence on efficiency scores and sustainability benchmarks was found when weighting decision-makers' preferences on operational inputs. Additionally, a moderate influence was observed when weighting divisions according to a policy-maker or local community perspective. Although the results are case-specific, they lead to the general recommendation to enrich LCA + DEA studies by following not only an equal-weight approach but also approaches that include the preferences of the stakeholders effectively involved in the study.

Keywords: Efficiency; Life cycle assessment; Multi-criteria decision analysis; Retail supply chain; Weighting.