Log in

Measuring performance with common weights: network DEA

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In conventional data envelopment analysis (DEA), a production system has been seen as a black box for measuring the efficiency without any attention to what is happening inside the system. However, in practice, performance improvement often requires observing the internal structure of the producing system in order to find the sources of inefficiencies. In addition, weight flexibility as a key property of the multiplier DEA models allows a system to totally disregard an assessment factor, either input or output, from the evaluation process by assigning a value of zero or epsilon to its weight. This paper contributes to the existing literature by proposing a common-weights DEA model when the production system includes a number of interrelated processes. To this end, we propose an aggregate DEA model to calculate the most favourable common weights for determining the efficiency of all production systems and their processes at the same time. Our proposed aggregate model not only is linear for equitably evaluating the producing units on the same scale, but also enables us to deal with the mixed network structures. Furthermore, the network system is decomposed into a series system to build a relational network DEA model that emphasises separate relatedness. This greatly reduces the computational complexities for enormous volumes of data in many real applications and treat difficulties in network DEA models including the zero value and fluctuating weights and multiple solutions. Managerially speaking, this paper aims to provide the top management team of a production system with an integrated framework to shape a better strategic decision process about firm performance, which is to treat the sources of inefficiencies and ultimately take corrective actions over the long run. Put differently, the proposed framework helps top managers make proper decisions in complex situations with a view of improving a firm’s efficiency in all production divisions, which can be identified as a core competency leading to competitive advantages of the organisation. In the context of performance management, our study is equipped with a simple numerical example and a case study of the non-life insurance companies to demonstrate the applicability of the proposed common-weights network model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. CRS means the input fluctuation would result in the same proportional the output fluctuation, and VRS implies a disproportionate increase or decrease in outputs when inputs have uplifted.

References

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

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  3. Charnes A, Cooper WW, Lewin AY, Seiford LM (1994) Data envelopment analysis: theory, methodology, and applications. Kluwer Academic Publishers, Norwell

    Book  Google Scholar 

  4. Cook WD, Roll Y, Kazakov A (1990) A DEA model for measuring the relative efficiency of highway maintenance patrols. INFOR 28(2):113–124

    Google Scholar 

  5. Roll Y, Cook WD, Golany B (1991) Controlling factor weights in data envelopment analysis. IIE Trans 23(1):2–9

    Article  Google Scholar 

  6. Doyle JR, Green RH (1995) Cross-evaluation in DEA: improving discrimination among DMUs. INFOR 33(3):205–222

    MATH  Google Scholar 

  7. Liu FHF, Peng HH (2008) Ranking of units on the DEA frontier with common weights. Comput Oper Res 35(5):1624–1637

    Article  Google Scholar 

  8. Saati S, Hatami-Marbini A, Agrell PJ, Tavana M (2012) A common set of weight approach using an ideal decision making unit in data envelopment analysis. J Ind Manag Optim 8(3):623–637

    Article  MathSciNet  Google Scholar 

  9. Hosseinzadeh Lotfi F, Hatami-Marbini A, Agrell PJ, Aghayi N, Gholami K (2013) Allocating fixed resources and setting targets using a common-weights DEA approach. Comput Ind Eng 64(2):631–640

    Article  Google Scholar 

  10. Hatami-Marbini A, Tavana M, Agrell PJ, Hosseinzadeh Lotfi F, Ghelej Beigi Z (2015) A common-weights DEA model for centralized resource reduction and target setting. Comput Ind Eng 79:195–203

    Article  Google Scholar 

  11. Wang YM, Chin KS (2010) A neutral DEA model for cross-efficiency evaluation and its extension. Expert Syst Appl 37(5):3666–3675

    Article  Google Scholar 

  12. Färe R, Grosskopf S (1996) Intertemporal production frontiers: with dynamic DEA. Kluwer, New York

    Book  Google Scholar 

  13. Färe R, Grosskopf S (2000) Network DEA. Socio Econ Plan Sci 34:35–49

    Article  Google Scholar 

  14. 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  Google Scholar 

  15. Seiford L, Zhu J (1999) Profitability and marketability of the top 55 US commercial banks. Manag Sci 45(9):1270–1288

    Article  Google Scholar 

  16. Chen Y, Zhu J (2004) Measuring information technology’s indirect impact on firm performance. Inform Technol Manag 5(12):9–22

    Article  Google Scholar 

  17. Tone K, Tsutsui M (2009) Network DEA: a slacks-based measure approach. Eur J Oper Res 197(1):243–252

    Article  Google Scholar 

  18. Castelli L, Pesenti R, Ukovich W (2010) A classification of DEA models when the internal structure of the decision-making units is considered. Ann Oper Res 173(1):207–235

    Article  MathSciNet  Google Scholar 

  19. Cook WD, Liang L, Zhu J (2010) Measuring performance of two-stage network structures by DEA: a review and future perspective. Omega 38(6):423–430

    Article  Google Scholar 

  20. 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

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  22. Castelli L, Pesenti R (2014) Network, shared flow and multi-level DEA models: a critical review. In: Cook WD, Zhu J (eds) Data envelopment analysis. Springer, New York, pp 329–376

    Chapter  Google Scholar 

  23. Kao C, Hwang SN (2008) Efficiency decomposition in two-stage data envelopment analysis: an application to non-life insurance companies in Taiwan. Eur J Oper Res 185(1):418–429

    Article  Google Scholar 

  24. Kao C (2009) Efficiency decomposition in network data envelopment analysis: a relational model. Eur J Oper Res 192(3):949–962

    Article  Google Scholar 

  25. Yang C, Liu HM (2012) Managerial efficiency in Taiwan bank branches: a network DEA. Econ Model 29(2):450–461

    Article  Google Scholar 

  26. Zimmermann HJ (1978) Fuzzy programming and linear programming with several objective functions. Fuzzy Set Syst 1:45–55

    Article  MathSciNet  Google Scholar 

  27. Dewispelare AR, Sage AP (1981) On combined multiple objective optimization theory and multiple attribute utility theory for evaluation and choice making. Large Scale Syst Inf Decis Technol 2:1–19

    MATH  Google Scholar 

  28. Charnes A, Cooper WW (1977) Goal programming and multiple objective optimizations: part 1. Eur J Oper Res 1(1):39–54

    Article  Google Scholar 

  29. Sakawa M (1982) Interactive multiobjective decision making by the sequential proxy optimization technique: SPOT. Eur J Oper Res 9(4):386–396

    Article  MathSciNet  Google Scholar 

  30. Sakawa M (1993) Fuzzy sets and interactive multiobjective optimization. Plenum Press, New York

    Book  Google Scholar 

  31. Toloo M (2014) An epsilon-free approach for finding the most efficient unit in DEA. Appl Math Model 38(13):3182–3192

    Article  MathSciNet  Google Scholar 

  32. Fecher F, Kessler D, Perelman S, Pestieau P (1993) Productive performance of the French insurance industry. J Prod Anal 4(1–2):77–93

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers and the associate editor for their insightful comments and suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Adel Hatami-Marbini.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hatami-Marbini, A., Saati, S. Measuring performance with common weights: network DEA. Neural Comput & Applic 32, 3599–3617 (2020). https://doi.org/10.1007/s00521-019-04219-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04219-4

Keywords

Navigation