TY - JOUR
T1 - Data-driven operational risk analysis in E-Commerce Logistics
AU - Xu, Gangyan
AU - Qiu, Xuan
AU - Fang, Meng
AU - Kou, Xiaofei
AU - Yu, Ying
N1 - Funding Information:
This work is supported by National Natural Science Foundation of China under Grant 71804034 , New Faculty Start-up Fund of Harbin Institute of Technology , Shenzhen under Grant FB45001022 .
Funding Information:
This work is supported by National Natural Science Foundation of China under Grant 71804034, New Faculty Start-up Fund of Harbin Institute of Technology, Shenzhen under Grant FB45001022.
Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/4
Y1 - 2019/4
N2 - The efficiency of E-Commerce Logistics (ECL) has become a major success factor for e-commerce companies in the competitive marketplace nowadays. However, the operation of ECL is complex and vulnerable to many risks, which would severely threaten its performance. A clear understanding of these risks would benefit a lot for conducting targeted measures to effectively mitigate their adverse effects. Therefore, this paper proposes a quantitatively analysis approach for operational risks in ECL based on extensive historical e-commerce transaction data. More specifically, the typical operation process of ECL is extracted through sequential analysis of key activities. After that, taking operation time as the key performance indicator, the performance patterns of different operation phases are analyzed. Then, considering the diverse distributions of operation time in different phases, especially the multimodal distribution of transportation time, a Gaussian Mixture Model (GMM) based risk analysis approach is proposed. Finally, an experimental case study is provided to measure the operational risks using real-life ECL data, and several managerial implications are also discussed based on the results.
AB - The efficiency of E-Commerce Logistics (ECL) has become a major success factor for e-commerce companies in the competitive marketplace nowadays. However, the operation of ECL is complex and vulnerable to many risks, which would severely threaten its performance. A clear understanding of these risks would benefit a lot for conducting targeted measures to effectively mitigate their adverse effects. Therefore, this paper proposes a quantitatively analysis approach for operational risks in ECL based on extensive historical e-commerce transaction data. More specifically, the typical operation process of ECL is extracted through sequential analysis of key activities. After that, taking operation time as the key performance indicator, the performance patterns of different operation phases are analyzed. Then, considering the diverse distributions of operation time in different phases, especially the multimodal distribution of transportation time, a Gaussian Mixture Model (GMM) based risk analysis approach is proposed. Finally, an experimental case study is provided to measure the operational risks using real-life ECL data, and several managerial implications are also discussed based on the results.
KW - Data analytics
KW - E-Commerce Logistics
KW - Gaussian mixture model
KW - Operational risks
KW - Risk analysis
UR - http://www.scopus.com/inward/record.url?scp=85062568217&partnerID=8YFLogxK
U2 - 10.1016/j.aei.2019.03.001
DO - 10.1016/j.aei.2019.03.001
M3 - Journal article
AN - SCOPUS:85062568217
SN - 1474-0346
VL - 40
SP - 29
EP - 35
JO - Advanced Engineering Informatics
JF - Advanced Engineering Informatics
ER -