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Alibaba Realizes Millions in Cost Savings Through Integrated Demand Forecasting, Inventory Management, Price Optimization, and Product Recommendations

Author

Listed:
  • Yuming Deng

    (Alibaba Digital Supply Chain Divisio, Hangzhou, Zhejiang 311121, China)

  • Xinhui Zhang

    (Alibaba Digital Supply Chain Divisio, Hangzhou, Zhejiang 311121, China; Alibaba Freshippo Division, Hangzhou, Zhejiang 311121, China)

  • Tong Wang

    (Alibaba Digital Supply Chain Divisio, Hangzhou, Zhejiang 311121, China)

  • Lin Wang

    (Alibaba Digital Supply Chain Divisio, Hangzhou, Zhejiang 311121, China)

  • Yidong Zhang

    (Alibaba Digital Supply Chain Divisio, Hangzhou, Zhejiang 311121, China)

  • Xiaoqing Wang

    (Alibaba Digital Supply Chain Divisio, Hangzhou, Zhejiang 311121, China)

  • Su Zhao

    (Alibaba Digital Supply Chain Divisio, Hangzhou, Zhejiang 311121, China)

  • Yunwei Qi

    (Alibaba Digital Supply Chain Divisio, Hangzhou, Zhejiang 311121, China)

  • Guangyao Yang

    (Alibaba Freshippo Division, Hangzhou, Zhejiang 311121, China)

  • Xuezheng Peng

    (Alibaba Freshippo Division, Hangzhou, Zhejiang 311121, China)

Abstract

Alibaba, which operates one of the world’s largest e-commerce platforms, designed a comprehensive omnichannel retail infrastructure that enables both online and offline ordering of products, ranging from general merchandise to fresh produce. Intelligent decisions in the supply chain, such as demand forecasting, inventory management, and price optimization, are critical to the success of any retail business; however, many unique features in retail operations defy traditional operations research solutions and pose considerable challenges. Alibaba developed a series of models and algorithms, namely, deep-learning algorithms for demand forecasting, simulation optimization–based models for inventory management, price optimization for promotions, and markdown optimization for product recommendations. Alibaba has implemented these algorithms in almost all its retail businesses over the last three years and has generated, on an annual basis, $42 million of savings in shrinkage and inventory costs, $110 million in increased sales, and $13 million dollars in increased profit.

Suggested Citation

  • Yuming Deng & Xinhui Zhang & Tong Wang & Lin Wang & Yidong Zhang & Xiaoqing Wang & Su Zhao & Yunwei Qi & Guangyao Yang & Xuezheng Peng, 2023. "Alibaba Realizes Millions in Cost Savings Through Integrated Demand Forecasting, Inventory Management, Price Optimization, and Product Recommendations," Interfaces, INFORMS, vol. 53(1), pages 32-46, January.
    • Handle: RePEc:inm:orinte:v:53:y:2023:i:1:p:32-46
    DOI: 10.1287/inte.2022.1145
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    References listed on IDEAS

    as
    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Shipra Agrawal & Zizhuo Wang & Yinyu Ye, 2014. "A Dynamic Near-Optimal Algorithm for Online Linear Programming," Operations Research, INFORMS, vol. 62(4), pages 876-890, August.
    3. Salinas, David & Flunkert, Valentin & Gasthaus, Jan & Januschowski, Tim, 2020. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1181-1191.
    4. Haoxun Chen & Bo Dai & Yuan Li & Yidong Zhang & Xiaoqing Wang & Yuming Deng, 2022. "Stock allocation in a two-echelon distribution system controlled by (s, S) policies," International Journal of Production Research, Taylor & Francis Journals, vol. 60(3), pages 894-911, February.
    5. Xinhui Zhang & Doug Meiser & Yan Liu & Brett Bonner & Lebin Lin, 2014. "Kroger Uses Simulation-Optimization to Improve Pharmacy Inventory Management," Interfaces, INFORMS, vol. 44(1), pages 70-84, February.
    6. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    7. Pavithra Harsha & Shivaram Subramanian & Joline Uichanco, 2019. "Dynamic Pricing of Omnichannel Inventories," Service Science, INFORMS, vol. 21(1), pages 47-65, January.
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