An integrated approach based on classification and forecasting intermittent demand model for urban pick-up: a case study of Moroccan carrier

Pick-up links play a crucial role in logistics chains. It is the most expensive and polluting part of urban logistics. Management and decision-making must be optimised to improve their performance, and develop urban logistics sustainably. Several factors make its management difficult. Due to that, this process produces intermittent demand series. The authors' aim in this paper is to improve the pick-up chain by anticipating customers' requests. Based on K-means clustering, the integrated approach proposes two novel estimation models for demand occurrence, followed by a forecasting model derived from benchmarking studies between three methods: SES, Croston, and SBA on a real dataset. The authors' approach demonstrates the value of the classification model and the outperformance of SBA over other methods. This area has not been researched. Thus, this study contributes to urban logistics durability and freight transportation. Consequently, carriers will be provided with new-and-improved benefits in the future based on this relevant context.

• Record URL:
  • https://doi.org/10.1504/IJLSM.2025.146062
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/17427967
• Supplemental Notes:
  • Copyright © 2025 Inderscience Enterprises Ltd.
• Authors:
  • Bourrich, Leila
  • Elhaq, Saâd Lissane
• Publication Date: 2025

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1-41
• Serial:
  • International Journal of Logistics Systems and Management
  • Volume: 51
  • Issue Number: 1
  • Publisher: Inderscience Enterprises Limited
  • ISSN: 1742-7967
  • EISSN: 1742-7945
  • Serial URL: http://www.inderscience.com/jhome.php?jcode=ijlsm

Subject/Index Terms

• TRT Terms: Forecasting; Machine learning; Pickup and delivery service; Shipper demand; Supply chain management; Urban goods movement
• Geographic Terms: Morocco
• Subject Areas: Data and Information Technology; Freight Transportation; Planning and Forecasting;

Filing Info

• Accession Number: 01959136
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 26 2025 11:42AM