Mike Sherwin is a full-time Assistant Professor of Supply Chain Management at Duquesne University, teaching courses in supply chain management, operations, and Lean Six Sigma. Before joining Duquesne University, he served as an Assistant Professor of Industrial Engineering and Assistant Graduate Program Director at the University of Pittsburgh. He has taught and developed courses in supply chain management, operations, quality, Lean/Six Sigma, and was responsible for the Industrial Engineering Senior Design Capstone Program. Before joining academia, Dr. Sherwin spent more than 15 years working in the nuclear power, defense, metals, and aerospace industries. He is President-elect of the Institute for Industrial and Systems Engineers (IISE) Logistics and Supply Chain Division Board of Directors, an ASQ Six Sigma Black Belt, a registered Professional Engineering in the Commonwealth of Pennsylvania, and a certified training partner for the American Society for Quality (ASQ).
Dr. Sherwin has a Ph.D. in Industrial and Systems Engineering (Mississippi State University), an M.S. in Industrial Engineering (Penn State University), an M.B.A. (Carnegie Mellon University), and a B.S. in Materials Science and Engineering (Penn State University). His research interests are applied decision analytics, reliability optimization, and risk mitigation within critical supply chains. He is also interested in teaching methodologies with a specific interest in Industry 4.0 applications.
“Any time you have an opportunity to make a difference in this world and you don’t, then you are wasting your time on earth.”
- Roberto Clemente
Research
I have observed and directly experienced the inefficiencies associated with decision-making that takes place in industry settings. Today, data is abundant, but not always efficiently utilized or converted into information. Decisions are often made qualitatively, reactively, and without consideration for the overall system. My primary interest is to develop mathematical models that solve these problems. Specifically, I am interested in quantifying reliability, identifying risks, and subsequently recommending mitigation strategies for complex systems. My dissertation research focused on developing such models for supply chains. I am actively working to extend my research both within and outside of supply chain network applications with a focus on multi-criteria decision-making and machine learning. I am interested in expanding my work to solve reliability problems facing other network systems that will benefit the global economy and ultimately the public good.
Teaching
My teaching philosophy is driven by a desire to share my passion for manufacturing and operations research topics and equip future leaders with the analytical, problem solving, and higher order thinking skills to make a meaningful contribution to society for years to come. With experience at various levels of organizations in both academic and industry settings, I believe I have a unique perspective to do just that. I view my students as customers and base my teaching style on a model of mutual trust, common respect, and continuous improvement. This style is integrated into each class discussion and by incorporating lessons learned from year-to-year. In all cases, I make an effort to see the lessons being taught through the eyes of the student while maintaining relevancy to current industry practice. Students have different learning styles and as a result, the curriculum being delivered should address those styles (visual, auditory, and tactile). Consequently, a mix of lecture, examples (through story-telling), discussion, demonstrations, and exercises are incorporated into every class.