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Data Envelopment Analysis as a Kaizen Tool: SBM Variations Revisited
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Data Envelopment Analysis as a Kaizen Tool: SBM Variations Revisited

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Abstract:

Slacks-based measure (SBM) (Tone (2001), Pastor et al. (1999)) has been widely utilized as a representative non-radial DEA model. In Tone (2010), I developed four variants of the SBM model where main concerns are to search the nearest point on the efficient frontiers of the production possibility set. However, in the worst case, a massive enumeration of facets of polyhedron associated with the production possibility set is required. In this paper, I will present a new scheme, called SBM-Max, for this purpose which requires a limited number of additional linear program solutions for each inefficient DMU. Although the point thus obtained is not always the nearest point, it is acceptable for practical purposes and from the point of computational loads. Inefficient DMUs can be improved to the efficient status with less input-reductions and less output-enlargement. Thus, this model proposes a Kaizen (improvement) tool by DEA.

Info:

Periodical:
Bulletin of Mathematical Sciences and Applications (Volume 16)
Pages:
49-61
DOI:
https://doi.org/10.18052/www.scipress.com/BMSA.16.49
Citation:
K. Tone "Data Envelopment Analysis as a Kaizen Tool: SBM Variations Revisited", Bulletin of Mathematical Sciences and Applications, Vol. 16, pp. 49-61, 2016
Online since:
August 2016
Authors:
Kaoru Tone
Keywords:
DEA, Kaizen, Nearest Point, SBM, SBM-Max
Export:
RIS, BibTeX, Text
Distribution:
Open Access

Creative Commons License
This work is licensed under a
Creative Commons Attribution 4.0 International License

References:

[1] W.W. Cooper, L.M. Seiford, K. Tone, Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, Second Edition, Springer, (2007).

[2] H. Fukuyama, H. Masaki, K. Sekitani, J. Shi, Distance optimization approach to ratio-form efficiency measures in data envelopment analysis, Journal of Productivity Analysis. 42 (2014) 175-186.

DOI: https://doi.org/10.1007/s11123-013-0366-7

[3] A. Hadi-Vencheh, J. Jablonsky, A. Esmaeilzadeh, The slack-based measure model based on supporting hyperplanes of production possibility set, Expert Systems with Applications. 42(19) (2015) 6522-6529.

DOI: https://doi.org/10.1016/j.eswa.2015.03.032

[4] J.T. Pastor, J.L. Ruiz, I. Sirvent, An enhanced DEA Russell graph efficiency measure, European Journal of Operational Research. 115 (1999) 596-607.

DOI: https://doi.org/10.1016/s0377-2217(98)00098-8

[5] K. Tone, A slacks-based measure of efficiency in data envelopment analysis, European Journal of Operational Research. 130 (2001) 498-509.

DOI: https://doi.org/10.1016/s0377-2217(99)00407-5

[6] K. Tone, A slacks-based measure of super-efficiency in data envelopment analysis, European Journal of Operational Research. 143 (2002) 32-41.

DOI: https://doi.org/10.1016/s0377-2217(01)00324-1

[7] K. Tone, Variations on the theme of slacks-based measure of efficiency in DEA, European Journal of Operational Research. 200 (2010) 901-907.

DOI: https://doi.org/10.1016/j.ejor.2009.01.027

[8] K. Tone, M. Tsutsui, Network DEA: A slacks-based measure approach, European Journal of Operational Research. 197 (2009) 243-252.

DOI: https://doi.org/10.1016/j.ejor.2008.05.027

[9] K. Tone, M. Tsutsui, Dynamic DEA: A slacks-based measure approach, Omega. 38(3) (2010) 145-156.

DOI: https://doi.org/10.1016/j.omega.2009.07.003

[10] K. Tone, M. Tsutsui, Dynamic DEA with network structure: A slacks-based measure approach, Omega. 42(1) (2014) 124-131.

DOI: https://doi.org/10.1016/j.omega.2013.04.002
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