Formative evaluation of electricity distribution utilities using data envelopment analysis

The use of Data Envelopment Analysis (DEA) in the electricity distribution sector has been prolific in the number of papers published in research journals. However, while numerous studies have been documented, they have mostly been summative. Their aim has been predominantly descriptive and classificatory. This paper argues that evaluations of a formative nature are more effective than summative studies in promoting a better understanding of the structures and processes of electricity distribution utilities and, consequently, are more appropriate to contribute to performance improvement. To illustrate the use of DEA for formative evaluation, and highlight some of the difficulties of using DEA in practice, this paper compares the cost-efficiency of the Portuguese electricity distribution companies from 2002 to 2006. A dynamic analysis using Malmquist Indices is also conducted in order to evaluate the changes in productivity over this period. Our analysis shows that the application of DEA for formative purposes meets some difficulties. In particular it shows that while the modelling of productivity/efficiency scores using DEA is relatively straightforward, it is comparatively more difficult to develop models that are economically valid and that produce results with face validity. On the basis of the insights derived from this analysis, the paper provides some recommendations regarding the successful application of DEA for performance improvement

Using data envelopment analysis to support the design of process improvement interventions in electricity distribution

A significant number of studies have documented the use of Data Envelopment Analysis (DEA) for efficiency measurement in the context of electricity distribution, particularly at the level of the distribution utilities. However, their aim has been predominantly descriptive and classificatory, without any attempt to ‘open’ the black box of the transformation process. In contrast, our aim is to explore the potential of DEA to contribute to the design of effective process improvement interventions within a distribution utility. In particular, in this paper, we study an important question within the context of DEA analysis: that is, to investigate whether differences in efficiency can be attributed to a particular managerial programme or design feature. We use two different methodologies to undertake this type of analysis. Firstly, we apply Mann–Whitney rank statistics to the scores obtained from DEA in order to evaluate the statistical significance of the differences observed between an intervention programme and its control group programme. Secondly, we undertake dynamic analysis with the Malmquist Productivity Index in order to study the impact of the introduction of a new technology on a group of units. Our case study focuses on the performance evaluation of medium-voltage power lines belonging to one of the service areas in the Public Electricity Distribution System in Portugal. The results from our case study show that the application of DEA for process improvement interventions has great potential and should be explored in other contexts

On single-stage DEA models with weight restrictions

The literature on data envelopment analysis (DEA) often employs multiplier models that incorporate very small (theoretically infinitesimal) lower bounds on the input and output weights. Computational problems arising from the solution of such programs are well known. In this paper we identify an additional theoretical problem that may arise if such bounds are used in a multiplier model with weight restrictions. Namely, we show that the use of small lower bounds may lead to the identification of an efficient target with negative inputs. We suggest a corrected model that overcomes this problem

Integrating the data envelopment analysis and the balanced scorecard approaches for enhanced performance assessment

This article presents the development of a conceptual framework which aims to assess Decision Making Units (DMUs)from multiple perspectives. The proposed conceptual framework combines the Balanced Scorecard(BSC)method with the non-parametric technique known as Data Envelopment Analysis (DEA) by using various interconnected models which try to encapsulate four perspectives of performance (financial, customers, internal processes,learning and growth). The practical relevance of the conceptual model has been tested by using it to assess the performance of DMUs in a multinational company which operates in two business areas.Various models were developed with the collaboration of the directors of the company in order to conceive an appropriate and consensual framework, which may provide useful information for the company.The application of the conceptual framework provides structured information regarding the performance of each DMU(from multiple perspectives)and ways to improve it.By integrating the BSC and the DEA approaches this research helps to identify where there is room for improving organisational performance and points out opportunities for reciprocal learning between DMUs.In doing so,this article provides a set of recommendations relating to the successful application of DEA and its integration with the BSC,in order to promote a continuous learning process and to bring about improvements in performance

Formative evaluation of sustainability in rural electrification programs from a management perspective: A case study from Venezuela

Nowadays, around 1.2 billion people remain without access to electricity in rural areas of Africa, Asia and Latin America. In order to achieve universal access, a wide range of institutions must be involved in long-term rural electrification programs within a regional and national scope. In this context, the main objective of this research is to propose an evaluation methodology which aims to promote continual improvement of the programs underway, by undertaking the appropriate modifications in terms of their design and implementation. In this regard, a formative approach is sought, as the programs underway require from elements for continual improvement. In addition, a management perspective is also sought, as the results focus on program leaders for the implementation of suitable modifications, as and when needed. The proposed evaluation methodology considers 4 sustainability dimensions: environmental, technical, socioeconomic and institutional, in turn composed of 15 criteria that allow the design and implementation of the program to be analyzed in detail. The criteria are assessed by means of indicators dealing with the specific conditions of each program. As a case study, the Venezuelan programPeer ReviewedPostprint (author's final draft

DEA models with production trade-offs and weight restrictions

There is a large literature on the use of weight restrictions in multiplier DEA models. In this chapter we provide an alternative view of this subject from the perspective of dual envelopment DEA models in which weight restrictions can be interpreted as production trade-offs. The notion of production trade-offs allows us to state assumptions that certain simultaneous changes to the inputs and outputs are technologically possible in the production process. The incorporation of production trade-offs in the envelopment DEA model, or the corresponding weight restrictions in the multiplier model, leads to a meaningful expansion of the model of production technology. The efficiency measures in DEA models with production trade-offs retain their traditional meaning as the ultimate and technologically realistic improvement factors. This overcomes one of the known drawbacks of weight restrictions assessed using other methods. In this chapter we discuss the assessment of production trade-offs, provide the corresponding theoretical developments and suggest computational methods suitable for the solution of the resulting DEA models

Consistent weight restrictions in data envelopment analysis

It has recently been shown that the incorporation of weight restrictions in models of data envelopment analysis (DEA) may induce free or unlimited production of output vectors in the underlying production technology, which is expressly disallowed by standard production assumptions. This effect may either result in an infeasible multiplier model with weight restrictions or remain undetected by normal efficiency computations. The latter is potentially troubling because even if the efficiency scores appear unproblematic, they may still be assessed in an erroneous model of production technology. Two approaches to testing the existence of free and unlimited production have recently been developed: computational and analytical. While the latter is more straightforward than the former, its application is limited only to unlinked weight restrictions. In this paper we develop several new analytical conditions for a larger class of unlinked and linked weight restrictions

Manutenção preventiva ou manutenção corretiva em linhas aéreas de média tensão? Utilização do data envelopment analysis como auxiliar de gestão dos ativos técnicos de manutenção

O uso de Data Envelopment Analysis (DEA) no setor de distribuição de energia elétrica tem dado origem à publicação de vários artigos científicos. No geral, estes artigos focam-se na comparação da eficiência das empresas de distribuição de eletricidade. Na generalidade dos artigos, o tratamento da informação tem sido predominantemente descritivo e classificatório, sem focar no processo de transformação. Em contraste, o trabalho que se apresenta aqui pretende mostrar as potencialidades do DEA na análise de variáveis do processo de transformação e procura explorar o seu potencial para a identificação dos programas e intervenções que contribuem para a melhoria efetiva no processo de distribuição de eletricidade. É nossa convicção que as avaliações de natureza formativa, com fins de aprendizagem, são mais eficazes do que os estudos sumativos porque contribuem para uma melhor compreensão das estruturas e processos, sendo portanto mais adequadas para contribuir para a melhoria do desempenho. Neste trabalho, apresenta-se uma questão importante no contexto da análise DEA: a de investigar, se as diferenças de eficiência são devidas a um programa específico de gestão ou às características de conceção. Para o efeito, o estudo recorre a dois métodos diferentes para realizar este tipo de análise. Em primeiro lugar, aplicamos a estatística de rank de Mann-Whitney aos scores do DEA, a fim de avaliar a significância estatística das diferenças observadas entre um programa de tratamento e o programa de controlo. Em segundo lugar, procedemos a uma análise dinâmica com o Índice de Produtividade de Malmquist, a fim de estudar o impacto da introdução de uma nova tecnologia num grupo de unidades. O estudo de caso desenvolvido centra-se na avaliação do desempenho de linhas de média tensão afetas a uma das regiões de serviço de uma empresa de distribuição de energia elétrica, regulada pelo Sistema Público de Distribuição de Energia em Portugal (ERSE). Os resultados do estudo de caso mostram que a aplicação do DEA tem um grande potencial para contribuir para a melhoria dos processos e deve ser explorado noutros contextos

DEA Models with Production Trade-offs and Weight Restrictions

The final publication is available at Springer via http://dx.doi.org/10.1007/978-1-4899-7553-9There is a large literature on the use of weight restrictions in multiplier DEA models. In this chapter we provide an alternative view of this subject from the perspective of dual envelopment DEA models in which weight restrictions can be interpreted as production trade-offs. The notion of production trade-offs allows us to state assumptions that certain simultaneous changes to the inputs and outputs are technologically possible in the production process. The incorporation of production trade-offs in the envelopment DEA model, or the corresponding weight restrictions in the multiplier model, leads to a meaningful expansion of the model of production technology. The efficiency measures in DEA models with production trade-offs retain their traditional meaning as the ultimate and technologically realistic improvement factors. This overcomes one of the known drawbacks of weight restrictions assessed using other methods. In this chapter we discuss the assessment of production trade-offs, provide the corresponding theoretical developments and suggest computational methods suitable for the solution of the resulting DEA models