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Optimal Paths And Costs Of Adjustment In Dynamic DEA Models: With Application To Chilean Department Stores

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  • Filadelfo Mateo
  • Tim Coelli
  • Chris O'Donnell

Abstract

In this paper we propose a range of dynamic data envelopment analysis (DEA) models which allow information on costs of adjustment to be incorporated into the DEA framework. We first specify a basic dynamic DEA model predicated on a number of simplifying assumptions. We then outline a number of extensions to this model to accommodate asymmetric adjustment costs, non-static output quantities, non-static input prices, and non-static costs of adjustment, technological change, quasi-fixed inputs and investment budget constraints. The new dynamic DEA models provide valuable extra information relative to the standard static DEA models—they identify an optimal path of adjustment for the input quantities, and provide a measure of the potential cost savings that result from recognising the costs of adjusting input quantities towards the optimal point. The new models are illustrated using data relating to a chain of 35 retail department stores in Chile. The empirical results illustrate the wealth of information that can be derived from these models, and clearly show that static models overstate potential cost savings when adjustment costs are non-zero. Copyright Springer Science+Business Media, LLC 2006

Suggested Citation

  • Filadelfo Mateo & Tim Coelli & Chris O'Donnell, 2006. "Optimal Paths And Costs Of Adjustment In Dynamic DEA Models: With Application To Chilean Department Stores," Annals of Operations Research, Springer, vol. 145(1), pages 211-227, July.
    • Handle: RePEc:spr:annopr:v:145:y:2006:i:1:p:211-227:10.1007/s10479-006-0034-7
    DOI: 10.1007/s10479-006-0034-7
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    Cited by:

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    3. Aparicio, Juan & Kapelko, Magdalena, 2019. "Accounting for slacks to measure dynamic inefficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 278(2), pages 463-471.
    4. Wei-Kang Wang & Irene Wei Kiong Ting & Kuo-Cheng Kuo & Qian Long Kweh & Yan-Heng Lin, 2018. "Corporate diversification and efficiency: evidence from Taiwanese top 100 manufacturing firms," Operational Research, Springer, vol. 18(1), pages 187-203, April.
    5. Hampf, Benjamin, 2016. "Rational Inefficiency, Adjustment Costs and Sequential Technologies," VfS Annual Conference 2016 (Augsburg): Demographic Change 145796, Verein für Socialpolitik / German Economic Association.
    6. Magdalena Kapelko, 2019. "Measuring productivity change accounting for adjustment costs: evidence from the food industry in the European Union," Annals of Operations Research, Springer, vol. 278(1), pages 215-234, July.
    7. Mónika-Anetta Alt, 2012. "Measuring Romanian do-it-yourself retail chain’s efficiency during the economic crisis," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 24(1), pages 85-102.
    8. Chang, Young-Tae & (Kevin) Park, Hyosoo & Zou, Bo & Kafle, Nabin, 2016. "Passenger facility charge vs. airport improvement program funds: A dynamic network DEA analysis for U.S. airport financing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 88(C), pages 76-93.
    9. Hampf, Benjamin, 2017. "Rational inefficiency, adjustment costs and sequential technologies," European Journal of Operational Research, Elsevier, vol. 263(3), pages 1095-1108.
    10. Philipp Geymueller, 2009. "Static versus dynamic DEA in electricity regulation: the case of US transmission system operators," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 17(4), pages 397-413, December.

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