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A DEA-based approach for competitive environment analysis in global operations strategies

Author

Listed:
  • Jiawen Liu

    (EM - EMLyon Business School)

  • Yeming Gong
  • Joe Zhu
  • Jinlong Zhang

Abstract

While competitive environment analysis is critical to a global retailing operations strategy, there exist research gaps from perspectives of operational performance, retailing industrial environment, and nondiscretionary factors. Therefore, our research objective is to propose a new approach to conduct competitive environment analysis for a global operations strategy in retailing, by examining relationships between discretionary inputs of the supply chain, nondiscretionary inputs of the environment, and performance of retailing. We develop a nondiscretionary data envelopment analysis model to assess performance in retailing and integrate it with econometric analysis. Using multisource data of 124 organizations in the global retailing industry, it is interesting to find: while nondiscretionary factors significantly influence the operational performance of global retailers, firms in an environment with a higher market concentration, larger consumer spending per capita, and smaller inhabitants' population are more likely to achieve a higher operational efficiency in retailing. Another interesting finding with practical implication is: inputs relevant to outside environment (e.g., suppliers in upstream and outlets in downstream supply chain) can influence operational efficiency more than inputs in internal supply chain (e.g., warehouses).

Suggested Citation

  • Jiawen Liu & Yeming Gong & Joe Zhu & Jinlong Zhang, 2018. "A DEA-based approach for competitive environment analysis in global operations strategies," Post-Print hal-02312151, HAL.
  • Handle: RePEc:hal:journl:hal-02312151
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    Cited by:

    1. Costa Melo, Isotilia & Alves Junior, Paulo Nocera & Callefi, Jéssica Syrio & da Silva, Karoline Arguelho & Nagano, Marcelo Seido & Rebelatto, Daisy Aparecida do Nascimento & Rentizelas, Athanasios, 2023. "Measuring the performance of retailers during the COVID-19 pandemic: Embedding optimal control theory principles in a dynamic data envelopment analysis approach," Operations Research Perspectives, Elsevier, vol. 10(C).
    2. Almohri, Haidar & Chinnam, Ratna Babu & Colosimo, Mark, 2019. "Data-driven analytics for benchmarking and optimizing the performance of automotive dealerships," International Journal of Production Economics, Elsevier, vol. 213(C), pages 69-80.
    3. Tsionas, Mike G. & Patel, Pankaj C., 2023. "Tinkering or orchestrating? The value of country-level asset management capability and entrepreneurship outcomes," International Journal of Production Economics, Elsevier, vol. 255(C).
    4. Veiga, Gabriela Lobo & Pinheiro de Lima, Edson & Frega, José Roberto & Gouvea da Costa, Sérgio Eduardo, 2021. "A DEA-based approach to assess manufacturing performance through operations strategy lenses," International Journal of Production Economics, Elsevier, vol. 235(C).
    5. Gafner, Andreas & Loske, Dominic & Klumpp, Matthias, 2021. "Efficiency measurement of grocery retail warehouses with DEA," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.), Adapting to the Future: Maritime and City Logistics in the Context of Digitalization and Sustainability. Proceedings of the Hamburg International Conf, volume 32, pages 317-348, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    6. Piran, Fabio Sartori & Lacerda, Daniel Pacheco & Camanho, Ana S. & Silva, Maria C.A., 2021. "Internal benchmarking to assess the cost efficiency of a broiler production system combining data envelopment analysis and throughput accounting," International Journal of Production Economics, Elsevier, vol. 238(C).
    7. Sueyoshi, Toshiyuki & Ryu, Youngbok, 2022. "Performance assessment on technology transition from small businesses to the U.S. Department of Defense," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    8. Ayoub Al-Zabidi & Ateekh Ur Rehman & Mohammed Alkahtani, 2021. "An Approach to Assess Sustainable Supply Chain Agility for a Manufacturing Organization," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    9. Zhou, Lianjie & Hu, Junhui & Liu, Dongshuang & He, Mengwei, 2023. "Studying the role of fiscal policy to utilize natural resources development: Leads to sustainable development goals," Resources Policy, Elsevier, vol. 84(C).
    10. Xue, Longfei & Gong, Yeming & Yang, Bingnan & Xu, Xianhao, 2024. "Resilience, efficiency fluctuations, and regional heterogeneity in disaster: An empirical study on logistics," Socio-Economic Planning Sciences, Elsevier, vol. 93(C).
    11. Qingxian An & Ping Wang & Honglin Yang & Zongrun Wang, 2021. "Fixed cost allocation in two-stage system using DEA from a noncooperative view," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(4), pages 1077-1102, December.
    12. Hahn, G.J. & Brandenburg, M. & Becker, J., 2021. "Valuing supply chain performance within and across manufacturing industries: A DEA-based approach," International Journal of Production Economics, Elsevier, vol. 240(C).
    13. Christian Hoyer & Indra Gunawan & Carmen Haule Reaiche, 2020. "The Implementation of Industry 4.0 – A Systematic Literature Review of the Key Factors," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 557-578, July.

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