Xin Lu

Session topic: Big Data in Smart Manufacturing

Session chairman: Xin Lu, an associate professor at the Department of Information Systems & Management at the National University of Defense Technology. He is the co-founder and chief analyst of Flowminder Foundation, which is devoted to improve public health and welfare in low- and middle-income countries with big data analytics. His research includes analytics for big data, social networks, and statistical sampling techniques, and was published in Nature, Phy. Rep, PLOS MED, PNAS, GEC, et al. Applications of his research include relief response in the Haiti earthquake in 2010, the Japanese earthquake and tsunami in 2011, the Bangladesh cyclone Mahasen in 2013, the Nepal earthquake and flooding in 2015 and 2016, etc. His research received a lot of media attention and were reported by BBC (2011, 2014, 2016), New York Times (2012), Science (2012), Santa Fe Institute (2015), MIT (2013, 2014), etc. In 2013, Dr. Lu's study was listed by MIT Technology Review "Ten breakthrough technologies 2013", and win the "2016 GLOMO Award" in the World Mobile Congress in Barcelona.

Title: Quantifying Traceability in Supply Chain Networks

Abstract: While recent work has focused on understanding the role of network structure on propagation dynamics, its impact on traceability, or the ability to identify the propagation source, has received less attention. We propose a novel quantity, network traceability entropy (NTE), to measure the intrinsic ability of a network structure to support traceability. Using food supply chain networks and varying a range of topological properties, we demonstrate how NTE can be used to systematically compare the traceability of various network configurations and yield insights into the influential role of specific parameters. Results from stylized networks as well as data on the Chinese pork supply chain demonstrate that NTE effectively measures the accuracy of source identification in scenarios of outbreaks. The proposed measure opens possibilities to quantify the traceability of any network involving a diffusion process and is useful in network design or optimization applications where traceability is desirable.