Sustainable Smart Glasses with Text Mining, QFD and TRIZ

By Muhammad OsamaReviewed by Susha Cheriyedath, M.Sc.May 10 2024

A recent article published in the journal Scientific Reports introduced a novel methodology for sustainable product design, integrating quality function deployment (QFD), text mining, and the theory of inventive problem solving (TRIZ). These methodologies were applied to the design of smart glasses, a promising platform for augmented reality (AR) technology.

The researchers aimed to address the challenges of integrating customer feedback, technical innovation, and environmental sustainability into the product development process. It demonstrated that the proposed methodology effectively identified customer needs, translated them into technical specifications, and resolved design contradictions using inventive principles. Additionally, the research presented a conceptual design of next-generation smart glasses that aligned with consumer preferences and eco-innovation guidelines.

Background

AR technology seamlessly merges the physical world with digital elements, creating interactive environments where virtual and real components coexist together. This integration enhances surroundings by overlaying digital information and interfaces onto the physical environment.

Among the various platforms for AR, smart glasses stand out as a pivotal device poised to become the next leading in smart technology. These wearable gadgets feature a see-through optical display, strategically positioned within the user’s vision field. By doing so, smart glasses facilitate the seamless blending of the physical surroundings with virtual elements, offering users an immersive AR experience. However, the rapid development of smart devices poses challenges for sustainable product design, notably in extending product lifespan, reducing electronic waste, and minimizing environmental impact.

Traditional design methods often overlook valuable customer feedback, crucial for product quality, customer satisfaction, and market competitiveness. Moreover, they may struggle with technical contradictions and innovation challenges. Therefore, there is a need for an integrated approach leveraging customer insights, technical innovation, and sustainability principles for systematic smart device design.

About the Research

In this paper, the authors integrated text mining, TRIZ, and QFD to develop a new technique for sustainable product design, using smart glasses as a case study to validate their methodology. The primary research questions addressed were:

• How can practitioners identify and analyze customers' feedback from digital/online platforms to enhance the design of smart glasses?
• What are the factors affecting customer satisfaction, and how can these insights be applied to TRIZ and QFD to guide the design of advanced smart glasses for higher customer satisfaction?
• What does the design of advanced smart glasses look like based on the recent customer requirements?

Text mining techniques were employed to extract customer feedback from product reviews on smart glasses. The researchers collected data from Amazon using web scraping and utilized tools like the natural language toolkit (NLTK) and the TextRank algorithm to preprocess and analyze the data. This step aimed to identify frequently discussed product features, customer preferences, and commonly mentioned issues in the reviews.

Additionally, the study applied the QFD technique to translate customer feedback into technical specifications. A house of quality (HOQ) matrix was utilized to link customer requirements to product features and specifications, assessing the correlation and importance of each factor. This step identified critical product features such as long battery life, good display resolution, and wide field of view, along with corresponding technical specifications like battery capacity, optical components, and display resolution.

Finally, the TRIZ theory was applied to identify and resolve technical contradictions in the design process. Utilizing a contradiction matrix and inventive principles, the study categorized and addressed design conflicts, like balancing weight and battery life. This analysis generated eight eco-innovation suggestions based on TRIZ principles, such as self-service, taking out, and mechanics substitution.

Research Findings

The authors effectively demonstrated how text mining could provide valuable insights into customer preferences and expectations for smart glasses, and how QFD and TRIZ methodologies could translate these insights into technical specifications and innovative solutions.

Additionally, they presented a conceptual design of next-generation smart glasses incorporating eco-innovation suggestions derived from TRIZ principles. This conceptual design featured a lightweight and ergonomic build, a real-time AR display, a fast processor, and a durable battery. It also included adaptive lenses that adjusted to lighting conditions, as well as intuitive controls via hand gestures and voice commands.

Moreover, the paper claimed that the conceptual design not only met customer requirements and environmental standards but also set the stage for future advancements in smart glasses.

Applications

This novel methodology effectively identifies customer needs, translates them into technical specifications, and resolves design contradictions using inventive principles. It also integrates sustainability principles into the design process, promoting extended product lifespan, reduced electronic waste, and minimal environmental impact. These valuable insights and guidelines can significantly benefit future product development in the smart device industry, paving the way for advancements in smart glasses.

Conclusion

In summary, the novel approach proved effective for sustainable product design, specifically applied to smart glasses. The authors contributed to sustainable design and innovation literature, providing a systematic approach for designing technologically advanced, user-centric, and environmentally friendly smart devices. Moving forward, they recommended expanding data sources, investigating other smart products, and evaluating proposed designs in real-world scenarios.