Data-driven operational risk analysis in E-Commerce Logistics

Abstract 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.

[1]  Yan Shi,et al.  Monitoring and assessing fruit freshness in IOT-based e-commerce delivery using scenario analysis and interval number approaches , 2016, Inf. Sci..

[2]  Tsan-Ming Choi,et al.  Risk management of logistics systems , 2016 .

[3]  T. Comes,et al.  A critical review on supply chain risk – Definition, measure and modeling ☆ , 2015 .

[4]  George Q. Huang,et al.  Optimization based transportation service trading in B2B e-commerce logistics , 2016, Journal of Intelligent Manufacturing.

[5]  Riska Asriana Sutrisnowati,et al.  Bayesian network construction from event log for lateness analysis in port logistics , 2015, Comput. Ind. Eng..

[6]  George Q. Huang,et al.  Synchronizing e-commerce city logistics with sliding time windows , 2019, Transportation Research Part E: Logistics and Transportation Review.

[7]  R. Ramanathan.  The moderating roles of risk and efficiency on the relationship between logistics performance and customer loyalty in e-commerce , 2010 .

[8]  Gangyan Xu,et al.  Heuristic routing methods in multiple-block warehouses with ultra-narrow aisles and access restriction , 2019, Int. J. Prod. Res..

[9]  Rishee K. Jain,et al.  Understanding building occupant activities at scale: An integrated knowledge-based and data-driven approach , 2018, Adv. Eng. Informatics.

[10]  George Q. Huang,et al.  Efficient intermodal transportation auctions for B2B e-commerce logistics with transaction costs , 2015 .

[11]  Roy E. Welsch,et al.  Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring , 2016 .

[12]  Hao Luo,et al.  Physical assets and service sharing for IoT-enabled Supply Hub in Industrial Park (SHIP) , 2015 .

[13]  George Q. Huang,et al.  Efficient Multi‐Attribute Multi‐Unit Auctions for B2B E‐Commerce Logistics , 2017 .

[14]  John P. T. Mo,et al.  Quantitative lifecycle risk analysis of the development of a just-in-time transportation network system , 2018, Adv. Eng. Informatics.

[15]  Lily Rachmawati,et al.  Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology , 2016, IEEE Transactions on Intelligent Transportation Systems.

[16]  Douglas A. Reynolds,et al.  Gaussian Mixture Models , 2018, Encyclopedia of Biometrics.

[17]  Jie Chen,et al.  Supply chain operational risk mitigation: a collaborative approach , 2013 .

[18]  Christopher S. Tang.  Perspectives in supply chain risk management , 2006 .

[19]  Latifa Oukhellou,et al.  Powered Two-Wheelers Critical Events Detection and Recognition Using Data-Driven Approaches , 2018, IEEE Transactions on Intelligent Transportation Systems.

[20]  Nan Liu,et al.  Effects of e-commerce channel entry in a two-echelon supply chain: A comparative analysis of single- and dual-channel distribution systems , 2015 .

[21]  Pei-Ju Wu,et al.  Unstructured big data analytics for retrieving e-commerce logistics knowledge , 2018, Telematics Informatics.

[22]  Gangyan Xu,et al.  IoT-based tracking and tracing platform for prepackaged food supply chain , 2017, Ind. Manag. Data Syst..

[23]  David B. Dunson,et al.  Exploiting Big Data in Logistics Risk Assessment via Bayesian Nonparametrics , 2014, Oper. Res..

[24]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[25]  Yong Deng,et al.  Development of a predictive model for Clostridium difficile infection incidence in hospitals using Gaussian mixture model and Dempster–Shafer theory , 2017, Stochastic Environmental Research and Risk Assessment.

[26]  Xiande Zhao,et al.  Improving logistics outsourcing performance through transactional and relational mechanisms under transaction uncertainties: Evidence from China , 2016 .

[27]  Junwei Wang,et al.  Data-Driven Resilient Fleet Management for Cloud Asset-enabled Urban Flood Control , 2018, IEEE Transactions on Intelligent Transportation Systems.

[28]  Ming Li,et al.  Cloud-based fleet management for prefabrication transportation , 2019, Enterp. Inf. Syst..

[29]  Kuan-Yu Hu,et al.  An innovative automated storage and retrieval system for B2C e-commerce logistics , 2010 .

[30]  A. Azadeh,et al.  Performance optimization of an online retailer by a unique online resilience engineering algorithm , 2018, Enterp. Inf. Syst..

[31]  Zhiyuan Liu,et al.  Collaborative mechanisms for berth allocation , 2015, Adv. Eng. Informatics.