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Distribution network deployment for omnichannel retailing

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  • Arslan, Ayşe N.
  • Klibi, Walid
  • Montreuil, Benoit

Abstract

This article studies the distribution problem of brick-and-mortar retailers aiming to integrate the online channel into their operations. The article presents an integrated modeling approach addressing the online channel-driven distribution network deployment (e-DND) problem under uncertainty. The e-DND involves decisions on operating fulfillment platforms and on assigning a fulfillment mission to those platforms, while anticipating the revenues and costs induced by order fulfillment, replenishment, delivery, and inventory holding. To model this problem while taking into account the uncertain nature of multi-item online orders, store sales, and capacities, a two-stage stochastic program with mixed-integer recourse is developed. Two alternative deployment strategies, characterized by allocation of orders, inventory positioning, delivery schema, and inbound flow pattern decisions, are investigated using this model. The first deployment strategy investigates the ship-from stores practice where the on-hand inventory is used for all sales channels. The second deployment strategy additionally considers the advanced positioning of inventory at a fulfillment center in the urban area where the online orders are requested. To solve the two-stage stochastic model with integer recourse, an exact solution approach combining scenario sampling and the integer L-shaped method is proposed. Numerical results, inspired by the case of a European retailer, are provided to evaluate the performance of the deployment strategies and the efficiency of the proposed solution approach.

Suggested Citation

  • Arslan, Ayşe N. & Klibi, Walid & Montreuil, Benoit, 2021. "Distribution network deployment for omnichannel retailing," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1042-1058.
  • Handle: RePEc:eee:ejores:v:294:y:2021:i:3:p:1042-1058
    DOI: 10.1016/j.ejor.2020.04.016
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    References listed on IDEAS

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    2. Qiu, Ruozhen & Ma, Lin & Sun, Minghe, 2023. "A robust omnichannel pricing and ordering optimization approach with return policies based on data-driven support vector clustering," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1337-1354.
    3. Millstein, Mitchell A. & Bilir, Canser & Campbell, James F., 2022. "The effect of optimizing warehouse locations on omnichannel designs," European Journal of Operational Research, Elsevier, vol. 301(2), pages 576-590.
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    5. Kim, Nayeon & Montreuil, Benoit & Klibi, Walid & Zied Babai, M., 2023. "Network inventory deployment for responsive fulfillment," International Journal of Production Economics, Elsevier, vol. 255(C).
    6. Ben Mohamed, Imen & Klibi, Walid & Sadykov, Ruslan & Şen, Halil & Vanderbeck, François, 2023. "The two-echelon stochastic multi-period capacitated location-routing problem," European Journal of Operational Research, Elsevier, vol. 306(2), pages 645-667.
    7. Omar, Haytham & Klibi, Walid & Babai, M. Zied & Ducq, Yves, 2023. "Basket data-driven approach for omnichannel demand forecasting," International Journal of Production Economics, Elsevier, vol. 257(C).
    8. Snoeck, André & Winkenbach, Matthias & Fransoo, Jan C., 2023. "On-demand last-mile distribution network design with omnichannel inventory," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    9. Hübner, Alexander & Hense, Jonas & Dethlefs, Christian, 2022. "The revival of retail stores via omnichannel operations: A literature review and research framework," European Journal of Operational Research, Elsevier, vol. 302(3), pages 799-818.
    10. Lin, Yun Hui & Wang, Yuan & Lee, Loo Hay & Chew, Ek Peng, 2022. "Omnichannel facility location and fulfillment optimization," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 187-209.
    11. Jia Guo & Burcu B. Keskin, 2023. "Designing a centralized distribution system for omni‐channel retailing," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1724-1742, June.
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