Using Design Insights From Customer Feedback to Advance Design for Repairability

Abstract Design for repairability is an important design practice to increase the useful life of consumer products and decrease environmental impact. Current design for repairability guidelines include general practices that can be applied to a range of products across industries. However, these guidelines lack device-specific insights. This research proposes a methodology for extracting repairability design insights from customer feedback. This would help repair-conscious designers identify device components that may need redesign and/or prioritize components to offer as replacement parts. In this research, standalone computer keyboards are examined as a case study. This approach includes a repairability keyword list to extract repairability-related phrases from Amazon product reviews. Topic modeling is performed with Non-Negative Matrix Factorization and BERTopic to assess device failure modes for computer keyboards. The repairability-related phrases are used to manually compare the repairability of three different keyboards. The proposed method identifies several failure modes for computer keyboards, such as sticky keys, Bluetooth disconnection, keyboard leg breakage, and instability in the keyboard base. This research indicates that topic modeling is a promising approach for obtaining repairability-related design leads from customer feedback.