IDEAS home Printed from https://ideas.repec.org/a/cvr/ijisrt/202604ijisrt26apr477.html

Optimizing Last-Mile Delivery and Distribution Efficiency Using Predictive Analytics in U.S. Supply Chain Systems

Author

Listed:
  • Michael Oppong

  • Mathias Vera

  • Paul Onyekwuluje

Abstract

Last-mile delivery — the final leg from a Regional Distribution Center (RDC) to an individual store — represents the costliest and most operationally complex segment of the retail supply chain, accounting for an estimated 41% of total logistics expenditure in large-format retail. This study presents a predictive analytics and operations research framework, implemented across six RDC regions using over 54 million rows of operational data, to simultaneously optimize delivery routing, store-level product allocation, and compliance monitoring. The methodology integrates time-series forecasting, KMeans demand segmentation, linear programming (PuLP), and unsupervised anomaly detection (Isolation Forest, Z-score) within a Google BigQuery data infrastructure, with results surfaced through Tableau and Power BI executive dashboards.

Suggested Citation

  • Michael Oppong & Mathias Vera & Paul Onyekwuluje, 2026. "Optimizing Last-Mile Delivery and Distribution Efficiency Using Predictive Analytics in U.S. Supply Chain Systems," International Journal of Innovative Science and Research Technology (IJISRT), IJISRT Publication, vol. 11(04), pages 1532-1539, April.
    • Handle: RePEc:cvr:ijisrt:2026:04:ijisrt26apr477
    DOI: https://doi.org/10.38124/ijisrt/26apr477
    as

    Download full text from publisher

    File URL: https://www.ijisrt.com/optimizing-lastmile-delivery-and-distribution-efficiency-using-predictive-analytics-in-us-supply-chain-systems
    Download Restriction: no
    File URL: https://libkey.io/https://doi.org/10.38124/ijisrt/26apr477?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cvr:ijisrt:2026:04:ijisrt26apr477. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Rahul Goyel (email available below). General contact details of provider: https://www.ijisrt.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.