DEVELOPMENT OF AN INDICATOR OF PROPENSITY TO ENERGY COMMERCIAL LOSSES USING GEOSPATIAL STATISTICAL TECHNIQUES AND SOCIO-ECONOMIC DATA: THE CASE OF AES ELETROPAULO

• Autor(a) principal: Francisco, Eduardo
• Data de Publicação: 2010
• Outros Autores: Fagundes, Eduardo Bortotti, Ponchio, Mateus Canniatti, Zambaldi, Felipe
• Tipo de documento: Artigo
• Idioma: eng; por
• Título da fonte: RAM. Revista de Administração Mackenzie
• Texto Completo: https://editorarevistas.mackenzie.br/index.php/RAM/article/view/1597
• topic: Operations management; Energy distribution; Loss management; Geographically weighted regression; Social vulnerability.; Gestão de operações; Distribuição de energia; Gestão de perdas; Geographically weighted regression; Vulnerabilidade social.

Given the growing importance of integrating marketing and operations indicators to enhance business performance, and the availability of sophisticated geospatial statistical techniques, this paper draws on these concepts to develop an indicator of propensity to energy commercial losses. Loss management is a strategic topic among energy distribution companies, in particular for AES Eletropaulo. In such context, this work’s objectives are: (i) to appropriate spatial auto-regressive models and geographically weighted regression in measuring the cultural influence of neighborhood in customer behavior in the energy fraud act; (ii) to replace slum coverage areas by a regional social vulnerability index; and (iii) to associate energy loss with customer satisfaction indicators, in a spatial-temporal approach. Spatial regression techniques are revised, followed by a discussion on social vulnerability and customer satisfaction indicators. Operational data obtained from AES Eletropaulo’s geographical information systems were combined with secondary data in order to generate predictive regression models, having energy loss as the response variable. Results show that the incorporation of market and social oriented data about customers substantially contribute to explicate energy loss – the coefficient of determination in the regression models rose from 17.76% to 63.29% when the simpler model was compared to the more complex one. Suggestions are made for future work and opportunities for the replication of the methodology in comparable contexts are discussed.