Dr. Rodgers is an Assistant Professor in the Supply Chain Management Department of the Rutgers Business School, where he currently teaches Demand Planning. His research interests include power grid expansion planning, simulation-based optimization, and quantitative methods to assess the impact of supply chain disruptions and risk on operational performance.
Dr. Rodgers holds a PhD in Industrial & Systems Engineering from Rutgers University, MS degrees in Statistics and Industrial & Systems Engineering from Rutgers University, a MEng in Pharmaceutical Manufacturing Practices from Stevens Institute of Technology, and a BS in Ceramics and Materials Science Engineering from Rutgers University. Additionally, he has presented his research at many academic conferences, and was also awarded an NSF Fellowship via the Integrative Graduate Education and Research Traineeship (IGERT) program.
Prior to joining the Rutgers Business School’s faculty, Dr. Rodgers worked in the telecommunications, pharmaceutical, transportation, and management consulting industries in various business process improvement and analytics roles.
Data-Driven Analysis for Decision-Making
This course introduces data-driven model-building and analytic techniques for business applications. It covers key concepts of both deterministic and probabilistic models, including big data forecasting techniques, such as linear regression; inventory management; linear programming algorithms; and queuing (waiting line) analytics. Examples are drawn from various real-world applications such as production operations at Bristol-Myers Squibb, service operations at Verizon, and queuing systems at the Port Authority of NY & NY among many other illustrative examples.
Ph.D., Rutgers University – Industrial & Systems Engineering
M.S., Rutgers University – Industrial & Systems Engineering
M.S., Rutgers University – Applied & Mathematical Statistics
M.Eng., Stevens Institute of Technology – Pharmaceutical Manufacturing Practices
B.S., Rutgers University – Ceramic Engineering