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The data-driven analytics for investigating cargo loss in logistics systems

Pei-Ju Wu (Department of Transportation Technology and Management, Feng Chia University, Taichung, Taiwan)
Mu-Chen Chen (Department of Transportation and Logistics Management, National Chiao Tung University, Taipei, Taiwan)
Chih-Kai Tsau (Degree Program of Transportation and Logistics, National Chiao Tung University, Taipei, Taiwan)

International Journal of Physical Distribution & Logistics Management

ISSN: 0960-0035

Article publication date: 13 February 2017

2504

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

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