Scott Ferguson

Dr. Scott Ferguson arrived at NC State in the Fall of 2008 and has taught undergraduate courses in Dynamics (MAE 208), Introduction to Aerospace Engineering (MAE 250), Aerospace Vehicle Performance (MAE 251), ME Capstone Design (MAE 416), Fundamentals of Product Design (MAE 426), and Introduction to Space Flight (MAE 467). He also teaches graduate sections of Fundamentals of Product Design (MAE 526) and created Engineering Design Optimization (MAE 531).

Research Interest

Dr. Ferguson’s research answers questions about optimal product variety and optimal system architecture. His team conducts cutting-edge research into how product/system value can be maximized by modeling market-driven environments and considering uncertain and/or conflicting future requirements. This is done by applying design thinking principles and recognizing that design is inherently a decision-making process. The products of this research advance design automation and help engineers navigate the interdisciplinary challenges associated with designing consumer products and complex engineered systems.

Engineering design requires the incorporation of interdisciplinary perspectives, creating solutions by synthesizing various components, dealing with uncertainty, and considering the unintended consequences of design decisions. Dr. Ferguson’s research is motivated by the fact that even the most basic engineering design decision requires a value analysis. Fundamental questions driving this work include:

1. What information do product engineers need for making value-driven design decisions in a market-systems context?
2. How do we maximize the user experience with a product/system?
3. What is the right mix of products that should be offered?
4. What is the relationship between system configuration/architecture and needs/preferences that change over time?
5. How do we effectively manage change after a product/system has been fielded?

Dr. Ferguson and his team draw upon research advances in design automation, multiobjective/multidisciplinary optimization, customer preference modeling techniques (such as conjoint analysis and discrete choice theory), data analytics (including AI/ML), product customization, requirements definition, system architecture modeling, and change propagation techniques. Currently, the lab pursues four primary research directions:

1. Preference/demand modeling, product platforming, and product customization/personalization
2. Designing complex engineered systems capable of reconfigurability, evolvability, and resilience
3. Inverse engineering design using machine learning and AI
4. Advancing engineering design education

To learn more about this work, please visit his lab website: System Design Optimization Laboratory.

Education