A Predictive Data Analytics Model for Enhancing Last-Mile Delivery Efficiency in Urban Logistics Networks

• Author(s): Opeyemi Morenike Filani; John Oluwaseun Olajide; Grace Omotunde Osho; Patience Okpeke Paul
• Paper ID: 1709617
• Page: 181-192
• Published Date: 10-10-2019
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 3 September-2019

Abstract

Urban logistics networks have undergone rapid transformation due to increasing consumer demand, e-commerce expansion, and the growing complexity of last-mile delivery operations. Despite technological advancements, inefficiencies persist in the final leg of the delivery process, often leading to increased costs, environmental burdens, and diminished customer satisfaction. This paper proposes a Predictive Data Analytics Model (PDAM) to enhance last-mile delivery efficiency in urban logistics. By leveraging a literature-based methodology and analyzing over 100 peer-reviewed sources, the study synthesizes existing approaches in predictive analytics, logistics optimization, and urban freight systems. The model integrates real-time data, machine learning techniques, and geospatial intelligence to forecast delivery constraints, optimize routing, and improve service reliability. Structured into a detailed introduction, literature review, model design, discussion, and conclusion, this paper provides a strategic framework for logistics planners, policymakers, and technology implementers seeking to transform urban delivery ecosystems.

Keywords

last-mile delivery, predictive analytics, urban logistics, routing optimization, machine learning, delivery efficiency

Citations

IRE Journals:
Opeyemi Morenike Filani, John Oluwaseun Olajide, Grace Omotunde Osho, Patience Okpeke Paul "A Predictive Data Analytics Model for Enhancing Last-Mile Delivery Efficiency in Urban Logistics Networks" Iconic Research And Engineering Journals Volume 3 Issue 3 2019 Page 181-192

IEEE:
Opeyemi Morenike Filani, John Oluwaseun Olajide, Grace Omotunde Osho, Patience Okpeke Paul "A Predictive Data Analytics Model for Enhancing Last-Mile Delivery Efficiency in Urban Logistics Networks" Iconic Research And Engineering Journals, vol. 3, no. 3, Sep. 2019

APA:
Opeyemi Morenike Filani, John Oluwaseun Olajide, Grace Omotunde Osho, Patience Okpeke Paul (2019). A Predictive Data Analytics Model for Enhancing Last-Mile Delivery Efficiency in Urban Logistics Networks. Iconic Research And Engineering Journals, 3(3).

MLA:
Opeyemi Morenike Filani, John Oluwaseun Olajide, Grace Omotunde Osho, Patience Okpeke Paul "A Predictive Data Analytics Model for Enhancing Last-Mile Delivery Efficiency in Urban Logistics Networks" Iconic Research And Engineering Journals, vol. 3, no. 3, Sep. 2019.

BibTeX

@article{1709617,
author = {Opeyemi Morenike Filani, John Oluwaseun Olajide, Grace Omotunde Osho, Patience Okpeke Paul},
title = {A Predictive Data Analytics Model for Enhancing Last-Mile Delivery Efficiency in Urban Logistics Networks},
journal = {Iconic Research And Engineering Journals},
year = {2019},
volume = {3},
number = {3},
pages = {181-192},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709617.pdf},
abstract = {Urban logistics networks have undergone rapid transformation due to increasing consumer demand, e-commerce expansion, and the growing complexity of last-mile delivery operations. Despite technological advancements, inefficiencies persist in the final leg of the delivery process, often leading to increased costs, environmental burdens, and diminished customer satisfaction. This paper proposes a Predictive Data Analytics Model (PDAM) to enhance last-mile delivery efficiency in urban logistics. By leveraging a literature-based methodology and analyzing over 100 peer-reviewed sources, the study synthesizes existing approaches in predictive analytics, logistics optimization, and urban freight systems. The model integrates real-time data, machine learning techniques, and geospatial intelligence to forecast delivery constraints, optimize routing, and improve service reliability. Structured into a detailed introduction, literature review, model design, discussion, and conclusion, this paper provides a strategic framework for logistics planners, policymakers, and technology implementers seeking to transform urban delivery ecosystems.},
keywords = {last-mile delivery, predictive analytics, urban logistics, routing optimization, machine learning, delivery efficiency},
month = {September}
}