Considerations About Adding Aggregated Variables to the DEA Model

  • Filipe Giovani Bonin BisoffiEmail author
  • Renata Maria Ganselli Stevaux
  • Victor Henrique Duarte de Oliveira
  • Sérgio Ricardo Barbosa
Part of the Telecommunications and Information Technology book series (TIT)


Even in a scenario where Big Data has increasingly become a part of company structure, it is not uncommon to come across statistical or mathematical analysis processes lacking sufficient available observations to consider all the business variables of interest. As a consequence, it affects, in different levels, all analytic methods whether statistical or mathematical. Specifically in the case of the benchmarking model known as DEA (Data Envelopment Analysis), the convergence of involved algorithms is directly dependent on the relation between the quantity of observations and the quantity of variable considered. Any attempt to circumvent the conditions imposed by these methodologies will result in loss of information. The scope of this study is to propose an alternative method to deal with this problem when the DEA model is applied and minimize information loss.


DEA Confidence interval Nonparametric regression analysis 


  1. 1.
    Montgomery DC, Peck EA, Vining GG (2012) Introduction to linear regression analysis. WileyGoogle Scholar
  2. 2.
    Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444Google Scholar
  3. 3.
    Tone K, Tsutsui M (2009) Network DEA: a slacks-based measure approach. Eur J Oper Res 197)(1):243–252Google Scholar
  4. 4.
    Jenkins L, Anderson M (2003) A multivariate statistical approach to reducing the number of variables in data envelopment analysis. Eur J Oper Res 147(1):51–61Google Scholar
  5. 5.
    Senra LFADC et al (2007) Estudo sobre métodos de seleção de variáveis em DEA. Pesqui Oper 27(2):191–207Google Scholar
  6. 6.
    Berg SV (2010) Water utility benchmarking: measurement, methodologies, and performance incentives. IWA PublishingGoogle Scholar
  7. 7.
    Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 30(9):1078–1092Google Scholar
  8. 8.
    Agarwal S, Yadav SP, Singh SP (2011) A new slack DEA model to estimate the impact of slacks on the efficiencies. Eur J Oper Res 12(3):241–256Google Scholar
  9. 9.
    Farrell MJ (1957) The measurement of productive efficiency. J R Stat Soc Ser A (Gener):253–290Google Scholar
  10. 10.
    Bogetoft P, Otto L (2010) Benchmarking with DEA, SFA, and R. SpringerGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Filipe Giovani Bonin Bisoffi
    • 1
    Email author
  • Renata Maria Ganselli Stevaux
    • 1
  • Victor Henrique Duarte de Oliveira
    • 2
  • Sérgio Ricardo Barbosa
    • 2
  1. 1.Decision Management Support & Applications DepartmentCPqD Research and Development Center in TelecommunicationsCampinasBrazil
  2. 2.CEMIG Distribuição S.A.Belo HorizonteBrazil

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