Xin (David) Ding is an associate professor of Supply Chain Management and an interim vice chair for the Department of Supply Chain Management at Rutgers Business School. His research examines the demand-supply relationship in the healthcare context. His ongoing work examines how healthcare organizations develop operational excellence through service design, workflow optimization, and data analytics in competitive markets. Through working with national/state health agencies and hospital systems, Dr. Ding has published his research in leading academic journals including FT-50 journals, Decision Sciences, Management and Business Review, Journal of Service Research, and Industrial Marketing Management, among others. His research receives support from the Cancer Institute of New Jersey (PI), National Sciences Foundation (consultant), and Project Management Institute. David serves as an academic scholar with Cornell Institute for Healthy Futures, an Associate Editor for the Journal of Operations Management and the Decision Sciences Journal. Dr. Ding had taught subjects in healthcare services management, operations analysis, project management, and healthcare analytics in online, offline, and hybrid formats to students in specialty master, MBA, and undergraduate programs. He also taught multivariate analysis and empirical research methods to Ph.D. students and business intelligence to DBA students. His research, teaching, and service have also been recognized by NJ Bright Idea Award, Junior Faculty Research Award, Decision Sciences Institute Best Paper Award, University Teaching Award, Susan McFarland Award from Healthcare Data and Analytics Association, and Decision Sciences Best Reviewer Award, etc. He also provided consulting and training to leading hospitals, medical centers, and healthcare services organizations in NJ and NY.
Xin (David) Ding
Associate Professor, Interim Vice ChairSupply Chain Management
Rutgers Business School
Key topics
Value-based purchasing program, hospital operations and efficiency improvements, disparity in access to care, application of business intelligence, machine learning/artificial intelligence bias, healthcare supply chain and inventory management.