Mike Sherwin is an Assistant Professor of Industrial Engineering at the University of Pittsburgh. He joined the University of Pittsburgh faculty full-time after almost 20-years of industry experience working in a variety of roles within manufacturing organizations. He has taught courses in supply chain management, senior capstone design, warehouse operations, facility layout and material handling, manufacturing operations, Lean/Six Sigma, quality assurance/control, optimization methods, and design of experiments. Mike's research interests include engineering education, supply chain reliability, and predictive analytics, focusing on applying numerical methods to solve real-world problems. He is a registered professional engineer in the Commonwealth of Pennsylvania, an ASQ Certified Six Sigma Black Belt, a certified ASQ Instructor, a member of the IISE Logistic and Supply Chain Division's Board of Directors, a published author, and has spoken at several industry conferences and trade organization events.
“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
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.
My service activities aim to grow the awareness of the Industrial Engineering profession and apply the Industrial Engineering Body of Knowledge to help make the world a better place. Currently, my service activities include serving on the Board for the Logistics and Supply Chain Division of the Institute of Industrial and Systems Engineers (IISE), as an advisor to undergraduate Industrial Engineering students at the University of Pittsburgh, in the capacity of Faculty Advisor to the Student Chapter of the IISE at the University of Pittsburgh, on the Undergraduate Program Committee, and in a variety of community service activities.
As an industry practitioner, I was responsible for hiring, managing, and developing engineers who graduated from a variety of universities and across a range of disciplines. At the core, engineers are problem solvers and need to be equipped with the tools to effectively address the challenges of the 21st century and make a positive impact on society. My research aims to define, inform, and improve engineering education and Industrial Engineering education in particular. This includes the design and study of education in project-based teams and environments that best mimic what students will experience as professionals. In addition, I am interested in advancing the profession of Industrial Engineering through this research and the promotion of the discipline to aid in solving the world's problems. I strongly believe that Industrial Engineers are uniquely equipped with the skills to integrate and improve systems of people, materials, information, and equipment. As the world embarks on the 4th Industrial Revolution (Industry 4.0), the skills of Industrial Engineers will be key to its success.
Improved Decision Making
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.