With fast growth of broadband internet connection, online shopping has become a popular channel for young people. It is necessary for e-retail to provide a reliable, efficient and powerful delivery system in order to send goods to customer fast and safely. For the past few years, TAOBAO is more and more important e-commerce for China and Taiwan. Risk management is regarded as the important issue in supply chain management, and the vulnerability is the new concept of risk analysis. From the point of view of risk management, the multinational delivery plays an important role more in the field of e-commerce, and logistics process is complicated and has a lot of risks. However, there are fewer studies focused on the vulnerability issue of the logistics process, especially for the case of TAOBAO. If managers understand the most vulnerable parts in all businesses, they could take actions and know how to allocate resources to avoid risks from happening. The objective of this study is to develop an evaluation model and discuss the risk of logistic delivery system via the Fuzzy Cognitive Map (FCM) and Sensitivity Model (SM). This study focuses on a theoretical model intended to capture the dynamic operation process of the multinational retailing delivery service system of TAOBAO. We establish an evaluation model to analyze and describe the vulnerability using the Fuzzy Cognitive Maps and different scenarios are implemented, observed, and appraised. Finally, we will provide some discussion and build management strategy to help managers reduce the risks proactively as well.

1. Aven, T. (2015). Risk analysis. New York, NY: John Wiley & Sons. https://doi.org/10.1002/9781119057819
   
2. Bueno, S., & Salmeron, J. L. (2008). Fuzzy modeling enterprise resource planning tool selection. Computer Standards & Interfaces, 30(3), 137-147. https://doi.org/10.1016/j.csi.2007.08.001
   
3. Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social vulnerability to environmental hazards. Social Science Quarterly, 84(2), 242-261. https://doi.org/10.1111/1540-6237.8402002
   
4. Hartmann, E., & de Grahl, A. (2012). Logistics outsourcing interfaces: The role of customer partnering behavior. International Journal of Physical Distribution & Logistics Management, 42(6), 526-543. https://doi.org/10.1108/09600031211250578
   
5. Huang, Y. K., Feng, C. M., Wang, I. W., & Jeng, H. Y. (2010). The factors affecting the 24-hour delivery through a fuzzy cognitive map: A case study of pchome dot com. Paper presented at the International Conference on Management Science and Engineering. Monash University, Melbourne, Australia.
   
6. Lado, A. A., Paulraj, A., & Chen, I. J. (2011). Customer focus, supply-chain relational capabilities and performance: Evidence from US manufacturing industries. The International Journal of Logistics Management, 22(2), 202-221. https://doi.org/10.1108/09574091111156550
   
7. Lambert, D. M., GarciaDastugue, S. J., & Croxton, K. L. (2005). An evaluation of processoriented supply chain management frameworks. Journal of Business Logistics, 26(1), 25-51. https://doi.org/10.1002/j.2158-1592.2005.tb00193.x
   
8. Lau, H. C., Jiang, Z. Z., Ip, W. H., & Wang, D. (2010). A credibility-based fuzzy location model with Hurwicz criteria for the design of distribution systems in B2C e-commerce. Computers & Industrial Engineering, 59(4), 873-886. https://doi.org/10.1016/j.cie.2010.08.018
   
9. Lee, K. C., Kim, J. S., Chung, N. H., & Kwon, S. J. (2002). Fuzzy cognitive map approach to web-mining inference amplification. Expert Systems with Applications, 22(3), 197-211. https://doi.org/10.1016/S0957-4174(01)00054-9
   
10. Oren, T. I., & Longo, F. (2008). Emergence, anticipation and multisimulation: Bases for conflict simulation. Paper presented at the proceedings of the European modeling & simulation symposium (pp. 17-19). Campora S. Giovanni (CS), Italy.
    
11. Peck, H. (2007). Drivers of supply chain vulnerability: An integrated framework. International Journal of Physical Distribution & Logistics Management, 35(4), 210-232. https://doi.org/10.1108/09600030510599904
    
12. Rodriguez-Repiso, L., Setchi, R., & Salmeron, J. L. (2007). Modelling IT projects success with fuzzy cognitive maps. Expert Systems with Applications, 32(2), 543-559. https://doi.org/10.1016/j.eswa.2006.01.032
    
13. Schneider, M., Shnaider, E., Kandel, A., & Chew, G. (1998). Automatic construction of FCMs. Fuzzy Sets and Systems, 93(2), 161-172. https://doi.org/10.1016/S0165-0114(96)00218-7
    
14. Sheffi, Y., & Rice Jr, J. B. (2005). A supply chain view of the resilient enterprise. MIT Sloan Management Review, 47(1), 41-49.
    
15. Tierney, K., & Bruneau, M. (2007). Conceptualizing and measuring resilience: A key to disaster loss reduction. TR News, 250, 14-17.
    
16. Tokar, T., Aloysius, J. A., Waller, M. A., & Williams, B. D. (2011). Retail promotions and information sharing in the supply chain: A controlled experiment. The International Journal of Logistics Management, 22(1), 5-25. https://doi.org/10.1108/09574091111127534
    
17. Tsadiras, A. K., Kouskouvelis, I., & Margaritis, K. G. (2003). Using fuzzy cognitive maps as a decision support system for political decisions. In Panhellenic Conference on Informatics (pp. 172-182). Springer Berlin Heidelberg, Berlin, Germany. https://doi.org/10.1007/3-540-38076-0_12
    
18. Verhulsta, E. (2014). Applying systems and safety engineering principles for antifragility. Paper presented at the Proceeding of Computer Science, 32, 842-849.
    
19. Wagner, S. M., & Bode, C. (2009). Dominant risks and risk management practices in supply chains. In Supply Chain Risk: A Handbook of Assessment, Management and Performance (pp. 271-290). New York, NY: Springer. https://doi.org/10.1108/13598540910995219

Huang, Y. K. (2017). Vulnerability of multinational retailing delivery service: A case study of TAOBAO. International Journal of Business and Administrative Studies, 3(2), 72-78.