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Inverse DEA with frontier changes for new product target setting

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  • Lim, Dong-Joon

Abstract

Inverse data envelopment analysis (DEA) is a reversed optimization problem that can serve as a useful planning tool for managerial decisions by providing information such as how much resources (or outcomes) should be invested (or produced) to achieve a desired level of competitiveness whereas the conventional DEA focuses mainly on a post-hoc assessment of the organizational performance. Inverse DEA studies however are based on an assumption that the efficiency level of observed decision making units (DMUs) will not change within the period of interest, which in fact confines the use of inverse DEA to a sensitivity analysis by simply addressing what alternative levels of input and/or output would have been possible to result in the same efficiency score obtained. In this paper, we discuss an inverse DEA problem considering expected changes of the production frontier in the future by integrating the inverse optimization problem with a time series application of DEA so that it can be an ex-ante decision support tool for the new product target setting practices. We use an example of the vehicle engine development case to demonstrate the proposed method.

Suggested Citation

  • Lim, Dong-Joon, 2016. "Inverse DEA with frontier changes for new product target setting," European Journal of Operational Research, Elsevier, vol. 254(2), pages 510-516.
    • Handle: RePEc:eee:ejores:v:254:y:2016:i:2:p:510-516
    DOI: 10.1016/j.ejor.2016.03.059
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    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. Hadi-Vencheh, Abdollah & Foroughi, Ali Asghar & Soleimani-damaneh, Majid, 2008. "A DEA model for resource allocation," Economic Modelling, Elsevier, vol. 25(5), pages 983-993, September.
    3. Wei, Quanling & Zhang, Jianzhong & Zhang, Xiangsun, 2000. "An inverse DEA model for inputs/outputs estimate," European Journal of Operational Research, Elsevier, vol. 121(1), pages 151-163, February.
    4. S. Ruzika & M. M. Wiecek, 2005. "Approximation Methods in Multiobjective Programming," Journal of Optimization Theory and Applications, Springer, vol. 126(3), pages 473-501, September.
    5. Yan, Hong & Wei, Quanling & Hao, Gang, 2002. "DEA models for resource reallocation and production input/output estimation," European Journal of Operational Research, Elsevier, vol. 136(1), pages 19-31, January.
    6. Dominique Deprins & Léopold Simar & Henry Tulkens, 2006. "Measuring Labor-Efficiency in Post Offices," Springer Books, in: Parkash Chander & Jacques Drèze & C. Knox Lovell & Jack Mintz (ed.), Public goods, environmental externalities and fiscal competition, chapter 0, pages 285-309, Springer.
    7. Jahanshahloo, G.R. & Soleimani-damaneh, M. & Ghobadi, S., 2015. "Inverse DEA under inter-temporal dependence using multiple-objective programming," European Journal of Operational Research, Elsevier, vol. 240(2), pages 447-456.
    8. Saeid Ghobadi & Saeid Jahangiri, 2015. "Inverse DEA: Review, Extension and Application," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(04), pages 805-824.
    9. Lim, Dong-Joon & Anderson, Timothy R. & Shott, Tom, 2015. "Technological forecasting of supercomputer development: The March to Exascale computing," Omega, Elsevier, vol. 51(C), pages 128-135.
    10. Lim, Dong-Joon & Anderson, Timothy R. & Inman, Oliver Lane, 2014. "Choosing effective dates from multiple optima in Technology Forecasting using Data Envelopment Analysis (TFDEA)," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 91-97.
    11. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    12. Bougnol, M.-L. & Dulá, J.H., 2009. "Anchor points in DEA," European Journal of Operational Research, Elsevier, vol. 192(2), pages 668-676, January.
    13. Rolf Färe & Shawna Grosskopf & Dimitris Margaritis, 2011. "Malmquist Productivity Indexes and DEA," International Series in Operations Research & Management Science, in: William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), Handbook on Data Envelopment Analysis, chapter 0, pages 127-149, Springer.
    14. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2006. "Introduction to Data Envelopment Analysis and Its Uses," Springer Books, Springer, number 978-0-387-29122-2, November.
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