Mark Rodgers
Assistant Professor
Assistant Professor
Dr. Mark Rodgers is an Assistant Professor of Supply Chain Management at Rutgers Business School, where he specializes in energy systems, sustainability, demand planning, and operations research. His academic journey is deeply intertwined with Rutgers University, beginning with his undergraduate studies in engineering and culminating in a PhD in Industrial & Systems Engineering. His research primarily focuses on developing decision-support tools that integrate renewable energy resources and enhance the resilience of supply chains, particularly within the contexts of energy and environmental sustainability.
Dr. Rodgers has authored several peer-reviewed articles in top-tier journals such as Applied Energy, Energy, and Computers & Industrial Engineering. His work has significantly impacted the understanding of power system expansion, energy security, and sustainable supply chain practices. His research is not only theoretical but also highly practical, influencing public policy and industry practices. Notably, Dr. Rodgers has secured over $1 million in research funding, working on projects with prestigious organizations such as the New Jersey Board of Public Utilities and the New Jersey Department of Environmental Protection.
In addition to his research, Dr. Rodgers is a dedicated educator recognized for his excellence in teaching. He has received the RBS-SCM Instructional Fellow Award (2022-2024) and the RBS Junior Faculty Teaching Award (2019). His courses emphasize mathematical modeling and analytical thinking, preparing students to tackle complex decision-making scenarios in supply chain management.
Dr. Rodgers' contributions extend beyond academia through his active involvement in service to the university and the broader community. He has played a pivotal role in advancing Rutgers' sustainability initiatives, including efforts to optimize the university's grid expansion planning and reduce its carbon footprint. His leadership in these areas has set a precedent for other institutions aiming to achieve sustainability goals.
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
Name: Culhan Kumcu, Gul
Graduation Date: 2023/October