Co-design collects insights from multiple stakeholders collaboratively making it a powerful method to design with marginalized populations. In the latter context, stakeholders have varying levels of power causing asymmetry and possible suppression of one group over another. Such power differentials can hinder co-design’s effectiveness. Through thirteen semi-structured interviews with co-design facilitators who have worked with marginalized communities in 43 different countries, we discovered that despite efforts to mitigate power differentials, significant disparities in educational and cultural backgrounds, language barriers, and gender imbalances prevent true collaboration. Tools for prototyping, analysis and evaluation often require literacy, advanced training, and resources. When these are inaccessible, co-design fails to materialize in the design analysis, implementation, and evaluation phases. We found this failure occurred with marginalized groups. We also found that experienced facilitators were aware of their own privilege as well as the power differentials of outside stakeholders such as donors, and they prioritized strategies to address them ahead of time.

The value proposition for ground vehicle modularity in the U.S. Army and other services has been a topic of continuing debate. Studies to date have largely focused on individual system elements such as manufacturing or maintenance, lacking a holistic perspective of the implications of modularity for the entire fleet operation and life-cycle. The U.S. Army Science and Technology community has demonstrated the technical feasibility of large-scale, transformative ground vehicle modularity, but the business case for modularity remains incomplete. There are multiple criteria tradeoffs between modular and mission-specific (conventional) vehicle platforms, such as total life-cycle cost, mission utility, personnel requirements, and fleet adaptability. This paper presents a system-of-systems framework to address these tradeoffs to support high-level decisions on the strategic feasibility of ground vehicle modularity. We demonstrate this framework with a notional example and an application to the Joint Tactical Transport System (JTTS), a U.S. Army Tank Automotive Research, Development and Engineering Center demonstrator program. Under certain modeling assumptions with regards to the operation of a modular fleet, results for the JTTS study indicate that modularity can lead to significant cost savings at the expense of increased personnel requirements.

Aesthetic appeal is a primary driver of customer consideration for products such as automobiles. Product designers must accordingly convey design attributes (e.g., 'Sportiness'), a challenging proposition given the subjective nature of aesthetics and heterogeneous market segments with potentially different aesthetic preferences. We introduce a scalable deep learning approach that predicts how customers across different market segments perceive aesthetic designs and provides a visualization that can aid in product design. We tested this approach using a large-scale product design and crowdsourced customer data set with a Siamese neural network architecture containing a pair of conditional generative adversarial networks. The results show that the model predicts aesthetic design attributes of customers in heterogeneous market segments and provides a visualization of these aesthetic perceptions. This suggests that the proposed deep learning approach provides a scalable method for understanding customer aesthetic perceptions.