Data Envelopment Analysis as a Kaizen Tool: SBM Variations Revisited

Tone, Kaoru

doi:10.18052/www.scipress.com/BMSA.16.49

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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

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Distribution:

Open Access

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

References:

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

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Cited By:

[1] K. Tone, T. Chang, C. Wu, "Handling Negative Data in Slacks-Based Measure Data Envelopment Analysis Models", European Journal of Operational Research, 2019

DOI: https://doi.org/10.1016/j.ejor.2019.09.055

[2] Ł. Brzezicki, "Effectiveness of higher education didactic activity", Wiadomości Statystyczne. The Polish Statistician, Vol. 62, p. 56, 2017

DOI: https://doi.org/10.5604/01.3001.0014.1066