Design Analytics: Capturing, Understanding, and Meeting Customer Needs Using Big Data

Abstract

The evolution of design thinking has seen numerous challenges and advances in transforming information into knowledge for engineers to design systems, products, and processes. These transformations occur in three stages throughout a design process. In simple form, the early, middle, and late stages of a design process serve to develop an understanding of the customer’s needs, arrive at the final concept of the design, and analyze and support the performance and usage profile of the deployed product, respectively. The quality and accuracy of the input information and the effectiveness of each transformation determine the success or failure of the product. Capturing good information and converting it to knowledge are two important tasks that have motivated a long history of research in design processes and tools. In this paper, we propose Design Analytics (DA) as a new paradigm for significantly enhancing the core information-to-knowledge transformations. The overall aim is to capture, store, and leverage digital information about artifacts, their performance, and their usage. The information is transformed into knowledge in each of the three stages using various analytics and cyber-enabled tools such as design repositories and concept generators. The ultimate result is better performing and functioning products. As web analytics has transformed how companies interact with consumers on the internet, we expect DA to transform how companies design products with and for consumers. An illustrative case study is performed to demonstrate some of the foundations of DA in the redesign of a refrigerator.

Visual Analytics Tools for Sustainable Lifecycle Design: Current Status, Challenges, and Future Opportunities

“…These issues make it challenging to quantitatively predict relationships between products’ attributes and their environmental impacts [13]. The growing trend towards product digitization and the environments in which they operate make it increasingly possible to address these challenges through the use of data-driven approaches [14]. Fully and semi-automated approaches based on techniques such as data mining, neural networks, automated concept generation, and expert systems have been developed [15–19].…”

Public Perception and Expectations of Biomimetics Technology: Empirical Survey of Museum Visitors in Japan

“…This movement is increasingly shifting to companies and society. The positive connotation of biomimetics offers new perspectives and possibilities in companies’ innovation processes and business models (Hacco and Shu ; Bar‐Cohen ; Van Horn et al ), as these technologies differ from other “techno‐sciences,” especially in emotional and normative content (Kennedy ). For example, in contrast to genetic engineering, the principle of emulating nature means that biomimetics is not regarded as harmful to humans or nature (Gleich et al.…”

Continuous Preference Trend Mining for Optimal Product Design With Multiple Profit Cycles

“…Recent progress in this area has garnered notable achievements in predictive product design, for example, merging predictive data mining and knowledge discovery methods with product portfolio design [1], predictive trend mining for product portfolio design [2], defining design analytics [3], and linking on-line reviews with product attributes [4,5], to name a few. The area of analytics is emerging as a promising area for the use of large-scale data generated by the users, original equipment manufacturers (OEMs), markets, and the public, which are often freely available.…”