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DEA-based pre-merger planning tool

Author

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  • S Lozano

    (Universidad de Sevilla)

  • G Villa

    (Universidad de Sevilla)

Abstract

Data envelopment analysis (DEA) can be used as a pre-merger planning tool to estimate expected cost and profit efficiency gains. Specifically, in this paper, two alternative DEA models are presented, one to minimize post-merger input cost and the other to maximize post-merger profit. The first model assumes that input prices are known, whereas the second assumes that output prices are known. As both models explicitly consider the possibility of closing existing units, they are especially apt for in-market horizontal mergers, in which considerable overlap may exist among the branches of the merging firms. Indicative efficiency ratios are proposed based on the results of the models. The proposed approach is, in addition, rather flexible, allowing the optional inclusion of a variety of features and constraints, such as incompatibility among units, employment guarantees, etc.

Suggested Citation

  • S Lozano & G Villa, 2010. "DEA-based pre-merger planning tool," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(10), pages 1485-1497, October.
    • Handle: RePEc:pal:jorsoc:v:61:y:2010:i:10:d:10.1057_jors.2009.106
    DOI: 10.1057/jors.2009.106
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    Cited by:

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    2. Chen, Zhongfei & Wanke, Peter & Tsionas, Mike G., 2018. "Assessing the strategic fit of potential M&As in Chinese banking: A novel Bayesian stochastic frontier approach," Economic Modelling, Elsevier, vol. 73(C), pages 254-263.
    3. Lozano, S., 2013. "DEA production games," European Journal of Operational Research, Elsevier, vol. 231(2), pages 405-413.
    4. Jianhui Xie & Xiaoxuan Zhu & Liang Liang, 2020. "A multiplicative method for estimating the potential gains from two-stage production system mergers," Annals of Operations Research, Springer, vol. 288(1), pages 475-493, May.
    5. Sebastián Lozano & Belarmino Adenso-Díaz, 2021. "A DEA approach for merging dairy farms," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(6), pages 209-219.
    6. Gholam R. Amin & Ali Emrouznejad & Said Gattoufi, 2017. "Modelling generalized firms’ restructuring using inverse DEA," Journal of Productivity Analysis, Springer, vol. 48(1), pages 51-61, August.
    7. Xiao Shi & Yongjun Li & Ali Emrouznejad & Jianhui Xie & Liang Liang, 2017. "Estimation of potential gains from bank mergers: A novel two-stage cost efficiency DEA model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(9), pages 1045-1055, September.
    8. Mattsson, Pontus & Tidanå, Claes, 2019. "Potential efficiency effects of merging the Swedish district courts," Socio-Economic Planning Sciences, Elsevier, vol. 67(C), pages 58-68.
    9. Hai-Liu Shi & Ying-Ming Wang & Sheng-Qun Chen & Yi-Xin Lan, 2017. "An approach to two-sided M&A fits based on a cross-efficiency evaluation with contrasting attitudes," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(1), pages 41-52, January.
    10. Gholam R. Amin & Mustapha Ibn Boamah, 2020. "A new inverse DEA cost efficiency model for estimating potential merger gains: a case of Canadian banks," Annals of Operations Research, Springer, vol. 295(1), pages 21-36, December.
    11. Chang, Tsung-Sheng & Lin, Ji-Gang & Ouenniche, Jamal, 2023. "DEA-based Nash bargaining approach to merger target selection," European Journal of Operational Research, Elsevier, vol. 305(2), pages 930-945.
    12. Lozano, S., 2012. "Information sharing in DEA: A cooperative game theory approach," European Journal of Operational Research, Elsevier, vol. 222(3), pages 558-565.
    13. An, Qingxian & Wen, Yao & Ding, Tao & Li, Yongli, 2019. "Resource sharing and payoff allocation in a three-stage system: Integrating network DEA with the Shapley value method," Omega, Elsevier, vol. 85(C), pages 16-25.
    14. Mohammad Khoveyni & Robabeh Eslami, 2022. "Merging two-stage series network structures: A DEA-based approach," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 273-302, March.

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