Our Research

The Design SPACE Lab conducts research that looks into how designers can conceive, analyze, choose, and create more sustainable products and systems. This work is multi-disciplinary in nature, and it combines elements and models from engineering, systems modeling, optimization, economics, behavioral science, and management science to help designers, managers, consumers, and policy-makers make more sustainable decisions that account for their complex environments.

The four research areas are organized based on the complexity and application of the work. Regarding complexity, the left side of the diagram is about addressing complexity challenges external to the designed product, such as user behavior and multi-stakeholder interactions; the right side deals with complexity challenges internal to the product, such as multi-disciplinary engineering analysis coordination. Regarding application, research on the upper end of the diagram is about engineering, systems, and design education, whereas the bottom section focuses on supporting design and policy decisions in practice.

Research Areas

The current Design SPACE research includes the following projects: Design for market systems, multi-disciplinary design optimization, design approaches, and policy modeling and analysis.

Our design for market systems research looks into new ways to model the relationships among designers, corporations, consumers, and policy-makers in order to understand the impacts of different sustainability initiatives. For example, if we want to decrease our national or global fuel consumption and emissions from automobiles, we need to change the ways that cars are designed, the ways that companies market and price their vehicles, the ways that consumers purchase and use vehicles, and the ways that policy-makers regulate and incentivize the market. Understanding these interactions can help us anticipate market system reactions and improve the ways that decisions are made.

Multi-disciplinary design optimization is about designing complex products or systems where the models and tools used to analyze the system span a number of different disciplines. For example, when optimizing the design of an aircraft, we need to concurrently analyze the aerodynamics, structural integrity, and engine performance. This is particularly challenging because the design of each of these subsystems affects the performance of the other, and usually the subsystem models are understood by different individuals or departments within an organization. This research looks into how to support better decision-making throughout the design process in complex design situations and in other decision scenarios.

Regarding design education and training, we are looking into ways to improve the ways that engineering students learn and make sense of design processes, methods, and tools. One current project is investigating the ways that current students conceptualize design, both at the outset and immediately after a third-year, project-based design course. This is looking for changes in design concepts throughout the course, as well as correlations between design concepts and course project outcomes. A previous project in this space looked into the differences, strengths, and unique characteristics of the design approaches that are taught in different fields, including mechanical engineering, industrial engineering, systems engineering, software engineering, and business management. Part of this work looks specifically at approaches for sustainable design, seeking to improve environmental, social, and economic outcomes of products and systems.

Our strategic design and policy analysis research seeks to improve the ways that system models can help governments and corporations make more informed decisions on policy and strategy. By applying systems thinking and best practices in systems modeling, simulations can be tailored to meet the needs of strategic decision-makers that want to test the impact of different policy interventions in a virtual environment. Some of the trickiest decision scenarios arise when there are competing objectives among different stakeholders, and we look into how decisions and strategies are developed, as well as how they can be improved.