Developing Classification Models for Cargo Loss Type and Severity ─ A Case of Electronics Products

• Main Authors: Tsau, Chih-Kai, 曹智凱
• Other Authors: Chen, Mu-Chen
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/7mj78q

碩士 === 國立交通大學 === 管理學院運輸物流學程 === 102 === Supply chain risk management has been a popular topic in recent years. Cargo loss in supply chain and logistics activities has been the major cause of delays and supply chain disruption; however, rarely do academic papers or examples in practice provide comprehensive studies focusing on types of cargo loss and loss prevention in various modes of transportation used by global companies. Therefore, this paper will mainly emphasize on the global electronics industry, gathering and analyzing all causes of its cargo losses in transit. Decision tree analysis, as generally used as one of the data mining technics, will be adapted to develop classification models for cargo loss type and severity. This paper also examines the effectiveness and value of these models in practice. The frame work of the study is based on the process of KDD (Knowledge Discovery from Database) and the classification models are produced by using Decision Tree Analysis in the step of data mining. The cargo loss classification model produced by the decision tree analysis helps the manager understand the classification rule and the combination of logistics conditions easily so as to become a reference tool to the result of cargo loss prediction and helps the manager to make a more effective plan on cargo loss prevention. Furthermore, the KDD and Decision Tree Analysis used in this study are expected to be spread out in different industries and products, to manage their cargo loss analysis and cargo loss prevention in the logistics activities.