The data-driven analytics for investigating cargo loss in logistics systems
International Journal of Physical Distribution & Logistics Management
ISSN: 0960-0035
Article publication date: 13 February 2017
Abstract
Purpose
Cargo loss has been a major issue in logistics management. However, few studies have tackled the issue of cargo loss severity via business analytics. Hence, the purpose of this paper is to provide guidance about how to retrieve valuable information from logistics data and to develop cargo loss mitigation strategies for logistics risk management.
Design/methodology/approach
This study proposes a research design of business analytics to scrutinize the causes of cargo loss severity.
Findings
The empirical results of the decision tree analytics reveal that transit types, product categories, and shipping destinations are key factors behind cargo loss severity. Furthermore, strategies for cargo loss prevention were developed.
Research limitations/implications
The proposed framework of cargo loss analytics provides a research foundation for logistics risk management.
Practical implications
Companies with logistics data can utilize the proposed business analytics to identify cargo loss factors, while companies without logistics data can employ the proposed cargo loss mitigation strategies in their logistics systems.
Originality/value
This pioneer empirical study scrutinizes the critical cargo loss issues of cargo damage, cargo theft, and cargo liability insurance through exploiting real cargo loss data.
Keywords
Acknowledgements
This work is partially supported by Ministry of Science and Technology, Taiwan, ROC under Grant No. MOST 103-2410-H-009-029-MY3. The authors also wish to thank the Editor-in-Chief and referees for their helpful comments and suggestions. Any errors or omissions remain the sole responsibility of the authors.
Citation
Wu, P.-J., Chen, M.-C. and Tsau, C.-K. (2017), "The data-driven analytics for investigating cargo loss in logistics systems", International Journal of Physical Distribution & Logistics Management, Vol. 47 No. 1, pp. 68-83. https://doi.org/10.1108/IJPDLM-02-2016-0061
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited