Benchmarking and analysis of store performance in a retail group

• Autor(a) principal: Mendonça, Ana Catarina Lourenço
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.14/28381
• topic: Store performance; Store efficiency; Retail analytics; Retail performance benchmarking; Data envelopment analysis; Tobit regression model; Desempenho da loja; Eficiência da loja; Análise de retalho; Comparação de desempenho no retalho; Domínio/Área Científica::Ciências Sociais::Economia e Gestão

An everyday need of retail firms operating in saturated markets, where they face fierce competition, is the accurate and unbiased analysis of store performance. The aim of this work is to analyse and benchmark store efficiency and propose targets for store performance improvement in a Portuguese Retail Group. Panel data for 27 stores in the period 2015 to 2017 has been used to allow the assessment of store efficiency and the setting of improvement goals for the inefficient units, while identifying adequate efficiency drivers. The methodologies and literature review allowed the identification of the techniques to be applied in the study: 1) Data Envelopment Analysis, to measure stores relative efficiency and to set improvement targets to the stores under analysis; 2) Tobit Regression Model, to identify the predominant factors leading to efficiency. Literature review and analysis of the available dataset enabled the selection of the variables to be used when applying each of the above techniques. The work undertaken led to a number of important conclusions, regarding different aspects of this real life multivariable problem: 1) identification of efficient and inefficient stores; 2) store efficiency distribution by geographical location; 3) evolution of store efficiency over the time; 4) definition of performance targets for the inefficient stores; 5) benchmark highest performing units against lowest performing units with different indicators and 6) identification and quantification of the environmental factors influencing store efficiency.