Optimizing cross-border logistics for ecommerce.
ABSTRACT: Cross-border ecommerce has expanded at an exceptional rate. It is projected that global shipping volume will reach 100 billion by 2020. Nowadays, millions of customized, small, individual packed parcels flow through a complicated logistic supply chain involving local, ground and air carriers from their original countries to the consumers in another destination country. Accomplished by the physical flow, billions of data points for these parcels move across countries digitally. This research review the end to end physical and digital cross-border logistics supply chain, and identify several data science use cases for parcel flow prediction, demand forecasting, network optimization and simulation. It also discussed the next generation of spatial data science tech stack and platform for global logistics supply chain optimization.
BIO: Dr. Zhuojie Huang is a distinguished engineer of data science in Pitney Bowes. He is an accomplished geospatial data science leader with 7 years of experience in data science and advanced analytics with great business acumen. He built 2 analytic-driven products from scratch with both reaching $100M annual revenue in 2019. He grew and coached a data science team focus on supply chain with expertise in machine learning, deep learning, optimization and simulation. He led capstone projects in machine learning and optimization in collaboration with Cornell University and Worcester Polytechnic University. He is a subject matter expert in supply chain optimization, geospatial data science and demographic analysis. He published 25 academic papers in the discipline of geography, network science, epidemiology, public health, remote sensing, computer science and database.
