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A linear relational DEA model to evaluate two-stage processes with shared inputs

Abstract

Two-stage data envelopment analysis (DEA) efficiency models identify the efficient frontier of a two-stage production process. In some two-stage processes, the inputs to the first stage are shared by the second stage, known as shared inputs. This paper proposes a new relational linear DEA model for dealing with measuring the efficiency score of two-stage processes with shared inputs under constant returns-to-scale assumption. Two case studies of banking industry and university operations are taken as two examples to illustrate the potential applications of the proposed approach.

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References

  • Agrell PJ, Hatami-Marbini A (2013) Frontier-based performance analysis models for supply chain management: state of the art and research directions. Comput Ind Eng 66(3):567–583

  • Akther S, Fukuyama H, Weber WL (2013) Estimating two-stage network Slacks-based inefficiency: an application to Bangladesh banking. Omega 41(1):88–96

    Article  Google Scholar 

  • Amirteimoori A, Kordrostami S (2005) Multi-component efficiency measurement with imprecise data. Appl Math Comput 162:1265–1277

    MathSciNet  MATH  Google Scholar 

  • Amirteimoori A, Emrouznejad A, Khoshandam L (2013) Classifying flexible measures in data envelopment analysis: a slacks-based measure. Measurement 46(10):4100–4107

    Article  Google Scholar 

  • Banker RD, Charnes A, Cooper WW (1984) Some models for estimating technical and scale inefficiency in data envelopment analysis. Manag Sci 30:1078–1092

    Article  MATH  Google Scholar 

  • Banker RD, Kauffman RJ, Morey RC (1990) Measuring gains in operational efficiency from information technology: a study of the positran deployment at Hardee’s Inc. J Manag Inf Syst 7(2):29–54

    Article  Google Scholar 

  • Barros CP, Managi S, Matousek R (2012) The technical efficiency of the Japanese banks: non-radial directional performance measurement with undesirable output. Omega 40(1):1–8

    Article  Google Scholar 

  • Beasley JE (1990) Comparing university departments. Omega 18(2):171–183

    Article  Google Scholar 

  • Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444

    Article  MathSciNet  MATH  Google Scholar 

  • Chen Y, Zhu J (2004) Measuring information technology’s indirect impact on firm performance. Inf Technol Manag J 5(1–2):9–22

    Article  Google Scholar 

  • Chen Y, Liangb L, Yangb F, Zhu J (2006) Evaluation of information technology investment: a data envelopment analysis approach. Comput Oper Res 33:1368–1379

    Article  Google Scholar 

  • Chen Y, Du J, David H, Zhu SJ (2010) DEA model with shared resources and efficiency decomposition. Eur J Oper Res 207(1):339–349

    Article  MathSciNet  MATH  Google Scholar 

  • Cook WD, Hababou M, Tuenter HJH (2000) Multicomponent efficiency measurement and shared inputs in data envelopment analysis: an application to sales and service performance in bank branches. J Product Anal 14:209–224

    Article  Google Scholar 

  • Cook WD, Zhu J (2007) Classifying inputs and outputs in DEA. Eur J Oper Res 180:692–699

    Article  MATH  Google Scholar 

  • Cook WD, Liang L, Zhu J (2010) Measuring performance of two-stage network structures by DEA: a review and future perspective. Omega 38:423–430

    Article  Google Scholar 

  • de Mello Soares JCCB, Gomes EG, Angulo Meza L, Soares de Mello MHC, Soares de Mello AJR (2006) Engineering post-graduate programmes: a quality and productivity analysis. Stud Edu Eval 32:136–152

  • Ebrahimnejad A, Tavana M, Lotfi FH, Shahverdi R, Yousefpour M (2014) A three-stage data envelopment analysis model with application to banking industry. Measurement 49:308–319

    Article  Google Scholar 

  • Emrouznejad A, Anouze AL (2010) DEA/C&R: DEA with classification and regression tree: a case of banking efficiency. Expert Syst 27(4):231–246

    Article  Google Scholar 

  • Emrouznejad A, Anouze AL (2009) A note on the modeling the efficiency of top Arab banks. Expert Syst Appl 36 (3, part 1):5741–5744

  • Emrouznejad A, Parker BR, Tavares G (2008) Evaluation of research in efficiency and productivity: a thirty years survey of the scholarly literature in DEA. Soc Econ Plan 42(3):151–157

  • Fare R, Grosskopf S (2000) Network DEA. Soc Econ Plan Sci 34(1):35–49

    Article  MATH  Google Scholar 

  • Jahanshahloo GR, Amirteimoori AR, Kordrostami S (2004) Measuring the multi-component efficiency with shared inputs and outputs in data envelopment analysis. Appl Math Comput 155:283–293

    MathSciNet  MATH  Google Scholar 

  • Kao C, Hwang S-N (2008) Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. Eur J Oper Res 185:418–429

    Article  MATH  Google Scholar 

  • Kao C (2009) Efficiency decomposition in network data envelopment analysis: a relational model. Eur J Oper Res 192:949–962

    Article  MATH  Google Scholar 

  • Kao C, Hwang S-N (2010) Efficiency measurement for network systems: IT impact on firm performance. Decis Support Syst 48(3):437–446

    Article  Google Scholar 

  • Kao C (2014) Network data envelopment analysis: a review. Eur J Oper Res 239:1–16

    Article  MathSciNet  MATH  Google Scholar 

  • Kwimbere FJ (1987) Measuring efficiency in not-for-profit organisations: an attempt to evaluate efficiency in selected UK university departments using data envelopment analysis (DEAl. MSC thesis). School of Management, University of Bath, Claverton Down. Bath BA2 7AY, UK

  • Paradi JC, Rouatt S, Zhu H (2011) Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega 39(1):99–109

    Article  Google Scholar 

  • Paradi JC, Zhu H (2013) A survey on bank branch efficiency and performance research with data envelopment analysis. Omega 1(1):61–79

    Article  Google Scholar 

  • Premachandra IM, Chen Y, Watson J (2011) DEA as a tool for predicting corporate failure and success: a case of bankruptcy assessment. Omega 39(6):620–626

    Article  Google Scholar 

  • Tavassoli M, Faramarzi GR, Farzipoor Saen R (2014) Efficiency and effectiveness in airline performance using a SBM-NDEA model in the presence of shared input. J Air Trans Manag 34:146–153

    Article  Google Scholar 

  • Toloo M (2009) On classifying inputs and outputs in DEA: a revised model. Eur J Oper Res 198:358–360

    Article  MathSciNet  MATH  Google Scholar 

  • Toloo M, Sohrabi B, Nalchigar S (2009) A new method for ranking discovered rules from data mining by DEA. Expert Syst Appl 36(4):8503–8508

    Article  Google Scholar 

  • Toloo M (2012) Alternative solutions for classifying inputs and outputs in data envelopment analysis. Comput Math Appl 63:1104–1110

    Article  MathSciNet  MATH  Google Scholar 

  • Toloo M (2013) The most efficient unit without explicit inputs: an extended MILP-DEA model. Measurement 46(9):3628–3634

    Article  Google Scholar 

  • Toloo M (2014a) Notes on classifying inputs and outputs in data envelopment analysis: a comment. Eur J Oper Res 235(3):810–812

  • Toloo M (2014b) An epsilon-free approach for finding the most efficient unit in DEA. Appl Math Model 38:3182–3192

  • Tomkins C, Green R (1988) An experiment in the use of data envelopment analysis for evaluating the efficiency of UK university departments of accounting. Fin Account Mgmt 4(2):147–164

    Article  Google Scholar 

  • Wang CH, Gopal R, Zionts S (1997) Use of data envelopment analysis in assessing information technology impact on firm performance. Ann Oper Res 73:191–213

    Article  MATH  Google Scholar 

  • Yu M, Fan C (2009) Measuring the performance of multimode bus transit: a mixed structure network DEA model. Trans Res Part E 45:501–515

    Article  Google Scholar 

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Acknowledgments

The research was supported by the Czech Science Foundation (GACR project 14-31593S) and through European Social Fund within the project CZ.1.07/2.3.00/20.0296.

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Correspondence to Mehdi Toloo.

Additional information

Communicated by Antonio José Silva Neto.

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Toloo, M., Emrouznejad, A. & Moreno, P. A linear relational DEA model to evaluate two-stage processes with shared inputs. Comp. Appl. Math. 36, 45–61 (2017). https://doi.org/10.1007/s40314-014-0211-2

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  • DOI: https://doi.org/10.1007/s40314-014-0211-2

Keywords

  • Data envelopment analysis (DEA)
  • Shared inputs
  • Two-stage processes
  • IT investment
  • Research income

Mathematics Subject Classification

  • 90C05
  • 90C30
  • 90C90