Liang Xu

Assistant Professor
Supply Chain Management and Analytics
HLH 511 T
P.O. Box 884114
Lincoln, NE 68588-4114
(402) 472-3442
Liang Xu Photo

Liang (Leon) is an Assistant Professor of Supply Chain Management and Analytics in the College of Business at the University of Nebraska - Lincoln. Prior to joining UNL, he earned his Ph.D. in Supply Chain Management from Smeal College of Business, Pennsylvania State University in 2019. His research addresses the important and urgent issues facing the U.S. healthcare system, such as exorbitant drug prices, drug shortages, drug innnvoation and approval, and opioid epidemic. He leverages a versatile set of methods including analytical modeling, empirical methods, data analytics, and experimental methods to provide insights and policy implications to address these issues. His research has been published in top OM journals such as Manufacturing & Service Operations Management. He also serves as ad hoc reviewers for Production and Operations Management and Decision Sciences.

  • Jeanne and Charles Rider Graduate Fellowship, Smeal College of Business, Penn State University, 2017.
  • Jeanne and Charles Rider Graduate Fellowship, Smeal College of Business, Penn State University, 2018.
  • Ossian R. MacKenzie Doctoral Teaching Award, Smeal College of Business, Penn State University, 2017.
  • First prize, Structural Modeling Applications for Research in Technology (SMART) Workshop Competition, Carnegie Mellon University, 2015.

SCMA 350 Business Analytics/Information Analysis (taught at UNL): This course focuses on how to explore meaningful patterns in big data and how to communicate these findings through effective visuals. This is not an independent theoretical course, but relates to multiple disciplines including computer science, statistics and aesthetics. The goal is to expose students to data visualization and data analytics, and to develop business decision making skills and critical thinking. Students in this course will learn to use Excel and Tableau.

SCMA 450 Data Modeling and Computing (taught at UNL): This course is an introduction to advanced data and statistical modeling techniques for business analytics applications. The course first introduces statistical computing tools and techniques using the R programming language, and then presents the role of data analytics and modeling techniques in business applications. Students will also learn Bayesian-thinking concepts in relation to decision-making under uncertainty. The course is designed for students interested in advanced data modeling and computing skills in Business Analytics. 

SCM 421 Supply Chain Analytics (taught at Penn State): This course is focused on quantitative supply chain modeling and analysis. We discuss methods that are used extensively in business organizations to solve large, structured problems. Such methods generate results that support decision-making at all levels of the organization over various time horizons. Secondarily, these methodologies should improve your own problem solving skills. The teaching approach will include lectures, skill-building exercises, and cases with the support of several software packages available on the PC in Microsoft Excel.