The capture and analysis of diverse data is widely recognized as being vital to the design of new products and services across the digital economy. We focus on its use to inspire the co-design of visitor experiences in museums as a distinctive case that reveals opportunities and challenges for the use of personal data. We present a portfolio of data-inspired visiting experiences that emerged from a 3-year Research Through Design process. These include the overlay of virtual models on physical exhibits, a smartphone app for creating personalized tours as gifts, visualizations of emotional responses to exhibits, and the data-driven use of ideation cards. We reflect across our portfolio to articulate the diverse ways in which data can inspire design through the use of ambiguity, visualization, and inter-personalization; how data inspire co-design through the process of co-ideation, co-creation, and co-interpretation; and how its use must negotiate the challenges of privacy, ownership, and transparency. By adopting a human perspective on data, we are able to chart out the complex and rich information that can inform design activities and contribute to datasets that can drive creativity support systems.

Machine learning (ML) is increasingly used to enhance intelligent products in the field of product design. However, ML has a never-ending lifecycle in which its capabilities and technical properties iteratively change as new annotated data are utilized. The never-ending lifecycle of ML (which includes data annotation, model training, and other steps) has led to challenges to the prototyping of ML-enhanced products and requires a high level of ML literacy in designers. To facilitate the prototyping of ML-enhanced products and improve the ML literacy of designers, we draw inspiration from a design method called Material Lifecycle Thinking (MLT), which regards ML as a continuously evolving design material. Based on the MLT, we proposed a cyclical prototype workflow and developed inML Kit, a toolkit enabling designers to make functional ML prototypes and improve ML literacy by involving them in the never-ending ML lifecycle. The toolkit was designed, iterated, and implemented through the participatory design process with experienced designers in this field. We evaluated inML Kit by conducting a controlled user study where our toolkit was compared with Google AIY. The evaluation results imply that our inML Kit helps designers to make functional ML prototypes while improving their ML literacy.

The main objective of this paper is to develop a novel approach for linear kitchen layout design which utilizes information from existing layouts via machine learning algorithms. With the growing popularity of large-scale virtual 3D environments for architectural visualization and the game industry, the manual interior design of virtual scenes becomes prohibitively expensive in terms of time and resources. In our approach, the machine learning model automatically generates layout suggestions. The proposed procedural kitchen generation (PKG) model is a pipeline of six Machine Learning (ML) classifiers that are trained and tested on a kitchen layout dataset created by interior designers. The performances of the model are evaluated for the following classifiers: Random forest, Decision tree, AdaBoost, Naive Bayes, MLP, SVM, and L2 Logistic regression. Random forest, as the best performing classifier is used in the final PKG model, and integrated into Unity Engine for automatic 3D kitchen generation and presentation. The PKG model is evaluated in the quantitative and perceptual study, showing better performance than the prior rule-based method. The perceptual study results demonstrate that our tool can be used to speed up designer's work, improve communication with clients, and educate interior design students.

With the emergence of Internet of Things (IoT) as a new source of “big” data and value creation, businesses encounter novel opportunities as well as challenges in IoT design. Although recent research argues that digital technology can enable new kinds of development processes that are distinctive from their counterparts in the 20th century, minimal attention has been focused on the IoT design process. In order to contextualize New Product Development (NPD) processes for IoT, this paper comprehensively interrogates existing, and emerging development approaches for products, services, software, and integrated products, and several factors that affect designing IoT. This discussion includes the generic development process, the commonalities and differences of different development approaches, and processes. The paper demonstrates that only a few existing approaches reflect vital characteristics of networked artifacts or the integration of data science within the development model, which is one of the key attributes of IoT design. From these investigations, we propose “The Mobius Strip Model of IoT Development ProcessI,” a conceptual process for IoT design, which is distinctive to others. The continuous loops of the IoT design integrate the attributes and phases of different processes and consist of two different development approaches and strategies. Understanding the particular attributes of the IoT NPD process can help novice and experienced researchers in both feeding and drawing insight from the broader design discourse.

The successful planning of future product generations requires reliable insights into the actual products’ problems and potentials for improvement. A valuable source for these insights is the product use phase. In practice, product planners are often forced to work with assumptions and speculations as insights from the use phase are insufficiently identified and documented. A new opportunity to address this problem arises from the ongoing digitalization that enables products to generate and collect data during their utilization. Analyzing these data could enable their manufacturers to generate and exploit insights concerning product performance and user behavior, revealing problems and potentials for improvement. However, research on analyzing use phase data in product planning of manufacturing companies is scarce. Therefore, we conducted an exploratory interview study with decision-makers of eight manufacturing companies. The result of this paper is a detailed description of the potentials and challenges that the interviewees associated with analyzing use phase data in product planning. The potentials explain the intended purpose and generic application examples. The challenges concern the products, the data, the customers, the implementation, and the employees. By gathering the potentials and challenges through expert interviews, our study structures the topic from the perspective of the potential users and shows the needs for future research.

Data-enabled design (DED) is a promising new methodology for designing with users from within their own context in an iterative and hands-on fashion. However, the agile and flexible qualities of the methodology do not directly translate to every context. In this article, we reflect on the design process of an intelligent ecosystem, called ORBIT, and a proposed evaluative study planned with it. This was part of a DED project in collaboration with a medical hospital to study the post-operative behavior in the (remote) context of bariatric patients. The design and preparation of this project and the process towards an eventual study rejection from the medical ethical committee (METC) provide rich insights into (1) what it means to conduct DED research in a clinical context, and (2) where the boundaries of the method might lie in this specific application area. We highlight insights from carefully designing the substantial infrastructure for the study, and how different aspects of DED translated less easily to the clinical context. We analyze the proposed study setup through the lenses of several modifications we made to DED and further reflect on how to expand and scale up the methodology and adapt the process for the clinical context.