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

Michael Oppong (), Mathias Vera () and Paul Onyekwuluje ()

International Journal of Innovative Science and Research Technology (IJISRT), 2026, vol. 11, issue 04, 1532-1539

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.

Keywords: Last-Mile Delivery; Distribution Optimization; Linear Programming; K-Means Segmentation; Isolation Forest; Anomaly Detection; Supply Chain Compliance; U.S. Logistics Economics (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:cvr:ijisrt:2026:04:ijisrt26apr477

DOI: 10.38124/ijisrt/26apr477

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