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A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains

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  • Pereira, Marina Meireles
  • Frazzon, Enzo Morosini

Abstract

The integration of selling and fulfillment processes triggered by omni-channels is transforming the retailer’s operations management. In this context, there is a lack of research regarding the connection between digital and physical worlds in retail supply chains. This paper aims to propose a data-driven approach that combines machine-learning demand forecasting and operational planning simulation-based optimization to adaptively synchronize demand and supply in omni-channel retail supply chains. The findings are substantiated through the application of the approach in an omni-channel retail supply chain. The combination of clustering and neural networks improved demand forecast, supporting an assertive identification of demand volume and location. Simulation-based optimization allowed for the definition of which facility would serve identified demands most effectively. The approach reduced fulfillment lead time, mitigated backorders arising from incompatible product´s supply and demand, and lowered operational costs, which are key performance indicators in today’s competitive retail markets.

Suggested Citation

  • Pereira, Marina Meireles & Frazzon, Enzo Morosini, 2021. "A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains," International Journal of Information Management, Elsevier, vol. 57(C).
    • Handle: RePEc:eee:ininma:v:57:y:2021:i:c:s026840122030205x
    DOI: 10.1016/j.ijinfomgt.2020.102165
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    Citations

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    Cited by:

    1. Kumar Detwal, Pankaj & Soni, Gunjan & Kumar Jakhar, Suresh & Kumar Srivastava, Deepak & Madaan, Jitender & Kayikci, Yasanur, 2023. "Machine learning-based technique for predicting vendor incoterm (contract) in global omnichannel pharmaceutical supply chain," Journal of Business Research, Elsevier, vol. 158(C).
    2. Xiaoxia Chen & Xiaofeng Su & Zhongbin Li & Jingjing Wu & Manhua Zheng & Anxin Xu, 2022. "RETRACTED ARTICLE: The impact of omni-channel collaborative marketing on customer loyalty to fresh retailers: the mediating effect of the omni-channel shopping experience," Operations Management Research, Springer, vol. 15(3), pages 983-997, December.
    3. Chou, Yixuan & Tang, Wenjin, 2023. "Corporate advertising expense and share price synchronization," Finance Research Letters, Elsevier, vol. 56(C).
    4. Fildes, Robert & Kolassa, Stephan & Ma, Shaohui, 2022. "Post-script—Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1319-1324.

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