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Rough data envelopment analysis and its application to supply chain performance evaluation

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  • Xu, Jiuping
  • Li, Bin
  • Wu, Desheng

Abstract

Supply chain (SC) performance evaluation is a complex decision-making problem involving various criteria under uncertainty situations. This paper studies the supply chain performance evaluation of a furniture manufacture industry in the southwest of China. We identify the main uncertainty factors affecting evaluation process, and then model and analyze them using our rough data envelopment analysis (RDEA) models. We create rough DEA by integrating classical DEA and rough set theory. The solution approach of rough DEA is discussed and employed to evaluate the supply chain network operation efficiency of the furniture manufacture industry. Then we give a practical example to illustrate the efficacy and efficiency of the rough DEA model and show how decision-making is improved by the insights the rough DEA model provides.

Suggested Citation

  • Xu, Jiuping & Li, Bin & Wu, Desheng, 2009. "Rough data envelopment analysis and its application to supply chain performance evaluation," International Journal of Production Economics, Elsevier, vol. 122(2), pages 628-638, December.
    • Handle: RePEc:eee:proeco:v:122:y:2009:i:2:p:628-638
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    2. Holden, R. & Xu, B. & Greening, P. & Piecyk, M. & Dadhich, P., 2016. "Towards a common measure of greenhouse gas related logistics activity using data envelopment analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 105-119.
    3. Garcia, Fernanda A. & Marchetta, Martin G. & Camargo, Mauricio & Morel, Laure & Forradellas, Raymundo Q., 2012. "A framework for measuring logistics performance in the wine industry," International Journal of Production Economics, Elsevier, vol. 135(1), pages 284-298.
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    5. Somayeh Soheilirad & Kannan Govindan & Abbas Mardani & Edmundas Kazimieras Zavadskas & Mehrbakhsh Nilashi & Norhayati Zakuan, 2018. "Application of data envelopment analysis models in supply chain management: a systematic review and meta-analysis," Annals of Operations Research, Springer, vol. 271(2), pages 915-969, December.
    6. Ke Wang, 2013. "Efficiency evaluation of multistage supply chain with data envelopment analysis models," CEEP-BIT Working Papers 48, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    7. Akber Aman Shah & Desheng Wu & Vladmir Korotkov, 2019. "Are Sustainable Banks Efficient and Productive? A Data Envelopment Analysis and the Malmquist Productivity Index Analysis," Sustainability, MDPI, vol. 11(8), pages 1-19, April.
    8. Mohamed Dia & Amirmohsen Golmohammadi & Pawoumodom M. Takouda, 2020. "Relative Efficiency of Canadian Banks: A Three-Stage Network Bootstrap DEA," JRFM, MDPI, vol. 13(4), pages 1-25, April.
    9. Shiva Moslemi & Abolfazl Mirzazadeh & Gerhard-Wilhelm Weber & Mohammad Ali Sobhanallahi, 2022. "Integration of neural network and AP-NDEA model for performance evaluation of sustainable pharmaceutical supply chain," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 1116-1157, September.
    10. Rashed Khanjani Shiraz & Hirofumi Fukuyama, 2018. "Integrating geometric programming with rough set theory," Operational Research, Springer, vol. 18(1), pages 1-32, April.
    11. Johnson, Andrew & Carnovale, Steven & Song, Ju Myung & Zhao, Yao, 2021. "Drivers of fulfillment performance in mission critical logistics systems: An empirical analysis," International Journal of Production Economics, Elsevier, vol. 237(C).
    12. Pournader, Mehrdokht & Kach, Andrew & Fahimnia, Behnam & Sarkis, Joseph, 2019. "Outsourcing performance quality assessment using data envelopment analytics," International Journal of Production Economics, Elsevier, vol. 207(C), pages 173-182.
    13. Pan Liu & Shuping Yi, 2016. "New Algorithm for Evaluating the Green Supply Chain Performance in an Uncertain Environment," Sustainability, MDPI, vol. 8(10), pages 1-21, September.
    14. Wang, Ying-Ming & Chin, Kwai-Sang, 2010. "Some alternative models for DEA cross-efficiency evaluation," International Journal of Production Economics, Elsevier, vol. 128(1), pages 332-338, November.
    15. Wang, Shiying & Chen, Huimiao & Wu, Desheng, 2019. "Regulating platform competition in two-sided markets under the O2O era," International Journal of Production Economics, Elsevier, vol. 215(C), pages 131-143.
    16. Ramanathan, Ramakrishnan & Ramanathan, Usha & Zhang, Yubo, 2016. "Linking operations, marketing and environmental capabilities and diversification to hotel performance: A data envelopment analysis approach," International Journal of Production Economics, Elsevier, vol. 176(C), pages 111-122.
    17. Ewa Chodakowska & Joanicjusz Nazarko, 2020. "Assessing the Performance of Sustainable Development Goals of EU Countries: Hard and Soft Data Integration," Energies, MDPI, vol. 13(13), pages 1-26, July.
    18. Huo, Baofeng & Gu, Minhao & Jiang, Bin, 2018. "China-related POM research: Literature review and suggestions for future research," International Journal of Production Economics, Elsevier, vol. 203(C), pages 134-153.

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