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Zara Uses Operations Research to Reengineer Its Global Distribution Process

Author

Listed:
  • Felipe Caro

    (UCLA Anderson School of Management, University of California, Los Angeles, Los Angeles, California 90095)

  • Jérémie Gallien

    (MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142)

  • Miguel Díaz

    (Zara, 15142 Arteixo, La Coruña, Spain)

  • Javier García

    (Zara, 15142 Arteixo, La Coruña, Spain)

  • José Manuel Corredoira

    (Zara, 15142 Arteixo, La Coruña, Spain)

  • Marcos Montes

    (Zara, 15142 Arteixo, La Coruña, Spain)

  • José Antonio Ramos

    (Carrefour, 28028 Madrid, Spain)

  • Juan Correa

    (Dell Computers, Austin, Texas 78759)

Abstract

Overcoming significant technical and human difficulties, Zara recently deployed a new process that relies extensively on sophisticated operations research models to determine each inventory shipment it sends from its two central warehouses to its 1,500 stores worldwide. By taking a retail size-assortment view of a store's inventory, the model incorporates the link between stock levels and demand to select store replenishment quantities. Through a rigorous, controlled field experiment, we estimate that this new process has increased sales by 3--4 percent; this corresponds to estimated profits of approximately $233 million and $353 million in additional revenues for 2007 and 2008, respectively.

Suggested Citation

  • Felipe Caro & Jérémie Gallien & Miguel Díaz & Javier García & José Manuel Corredoira & Marcos Montes & José Antonio Ramos & Juan Correa, 2010. "Zara Uses Operations Research to Reengineer Its Global Distribution Process," Interfaces, INFORMS, vol. 40(1), pages 71-84, February.
    • Handle: RePEc:inm:orinte:v:40:y:2010:i:1:p:71-84
    DOI: 10.1287/inte.1090.0472
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    References listed on IDEAS

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    1. Stephen A. Smith & Dale D. Achabal, 1998. "Clearance Pricing and Inventory Policies for Retail Chains," Management Science, INFORMS, vol. 44(3), pages 285-300, March.
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    Cited by:

    1. Shin Woong Sung & Young Jae Jang & Jung Hoon Kim & Juyeong Lee, 2017. "Business Analytics for Streamlined Assort Packing and Distribution of Fashion Goods at Kolon Sport," Interfaces, INFORMS, vol. 47(6), pages 555-573, December.
    2. Georgia Perakis & Donald Rosenfield, 2018. "The MIT Leaders for Global Operations Program," Interfaces, INFORMS, vol. 48(3), pages 189-203, June.
    3. Dilupa Nakandala & Henry Lau & Paul K.C. Shum, 2017. "A lateral transshipment model for perishable inventory management," International Journal of Production Research, Taylor & Francis Journals, vol. 55(18), pages 5341-5354, September.
    4. Sorescu, Alina & Frambach, Ruud T. & Singh, Jagdip & Rangaswamy, Arvind & Bridges, Cheryl, 2011. "Innovations in Retail Business Models," Journal of Retailing, Elsevier, vol. 87(S1), pages 3-16.
    5. Omar, Yamila M. & Minoufekr, Meysam & Plapper, Peter, 2019. "Business analytics in manufacturing: Current trends, challenges and pathway to market leadership," Operations Research Perspectives, Elsevier, vol. 6(C).
    6. Songtao Li & Ruoran Chen & Lijian Yang & Dinglong Huang & Simin Huang, 2020. "Predictive modeling of consumer color preference: Using retail data and merchandise images," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1305-1323, December.
    7. Wen, Xin & Choi, Tsan-Ming & Chung, Sai-Ho, 2019. "Fashion retail supply chain management: A review of operational models," International Journal of Production Economics, Elsevier, vol. 207(C), pages 34-55.
    8. Ying Ding & Yanping Tu & Jingchuan Pu & Liangfei Qiu, 2021. "Environmental Factors in Operations Management: The Impact of Air Quality on Product Demand," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 2910-2924, September.
    9. Marshall Fisher & Marcelo Olivares & Bradley R. Staats, 2020. "Why Empirical Research Is Good for Operations Management, and What Is Good Empirical Operations Management?," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 170-178, January.
    10. Marlene A. Smith & Murray J. Côté, 2022. "Predictive Analytics Improves Sales Forecasts for a Pop-up Retailer," Interfaces, INFORMS, vol. 52(4), pages 379-389, July.
    11. Pol Boada-Collado & Victor Martínez-de-Albéniz, 2020. "Estimating and Optimizing the Impact of Inventory on Consumer Choices in a Fashion Retail Setting," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 582-597, May.
    12. Shuyun Ren & Hau-Ling Chan & Pratibha Ram, 2017. "A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty," Annals of Operations Research, Springer, vol. 257(1), pages 335-355, October.
    13. Fernando Bernstein & Victor Martínez-de-Albéniz, 2017. "Dynamic Product Rotation in the Presence of Strategic Customers," Management Science, INFORMS, vol. 63(7), pages 2092-2107, July.
    14. João Reis, 2023. "Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python," Logistics, MDPI, vol. 7(4), pages 1-19, December.
    15. Christopher S. Tang, 2017. "OM Forum—Three Simple Approaches for Young Scholars to Identify Relevant and Novel Research Topics in Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 338-346, July.

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