Fei Qin

Dr. Fei Qin serves as Associate Professor of Supply Chain Management in the Finance and Supply Chain Management Department at Shippensburg University's John L. Grove College of Business, with office located in Grove Hall 133. His research and teaching focus on advanced supply chain optimization and operations analytics.

Dr. Qin's educational journey includes:

• Post-Doc Research Fellow in Operations Management, Desautels Faculty of Management, McGill University (2014-2015)
• Ph.D. in Business Administration, Carl H. Lindner College of Business, University of Cincinnati (2014)
• Master of Engineering in Aerospace Engineering, Iowa State University (2006)
• Master of Science (2003) and Bachelor of Science (2000) from Fudan University, Shanghai, China

His research spans supply chain resilience under uncertainty, behavioral operations, and sustainable logistics. With expertise in mathematical modeling and optimization, he examines complex phenomena like fairness concerns in decentralized chains and capacity risk management. His interdisciplinary background bridges aerospace engineering and business analytics, enabling unique approaches to supply chain challenges.

Publication analysis reveals evolving research trends: early work focused on computational mechanics (2010), transitioning to supply chain risk (2012-2014), then incorporating behavioral economics (2016) and construction logistics (2017). Current work integrates capacity uncertainty with sustainability initiatives, reflecting industry's growing focus on circular economy principles.

Scientific Awards:

• No scientific awards documented in source materials

Dr. Qin brings extensive teaching experience across institutions including University of Cincinnati, Oakland University, and Soochow University in China. His industry-relevant research addresses critical gaps in supply chain coordination under uncertainty, with publications appearing in premier journals like European Journal of Operational Research and Decision Sciences. His current work examines human behavioral biases in trade-in programs and construction supply chain optimization.