IoT-assisted AI guidance to increase consumer repair willingness in dishwashers

Abstract

Many consumers dispose of their electronics before their intended lifetimes, contributing to the growing amount of e-waste. Repair is seen as a promising strategy to extend product lifetimes,but many consumers encounter motivational, product and system barriers. Within the context of product design, emerging AI and IoT technologies may help mitigate these barriers to increase consumer willingness to repair.This Master’s Thesis explores the barriers consumers encounter when trying to self-repair dishwashers. In collaboration with ATAG, the DW60 dishwasher, their flagship model featuring the latest technologies and IoT connectivity, was used as a case study. ATAG’s professional and self-repair context, along with the DW60’s design, were analysed to identify key barriers and the potential of IoT and AI solutions throughout the consumer repair journey.The barriers start with a lack of effective triggers to encourage regular care activities, which can lead to product breakdown due to overdue maintenance. In case of product malfunction, error messages help identify the breakdown but offer little guidance for further fault diagnosis.Available documentation focuses on cause-oriented fault diagnosis but offers little help when a component is defective, leading to extensive trial and error with no assurance of finding the exact cause. Additional repair information is fragmented, difficult to access, incomplete, overly text heavy and can be difficult to understand for non-technical users.To address these barriers, this project introduces a concept that combines intuitive dishwasher design for repair with AI assistant support. When a malfunction occurs, users can scan a QR code displayed next to the error message to access a website-based AI assistant. This virtual agent guides them through fault diagnosis, repair decision-making, spare part sourcing, and component replacement. The system delivers adaptive, context-aware information and instructions, featuring clear descriptions, visuals, and safety warnings. Additionally, IoT-enabled sensors and cloud-based repair knowledge bases support data exchange and analysis. This enables real-time task verification and quick diagnostic testing. The system prioritises the most severe potential causes, leading to a more targeted and systematic approach to fault diagnosis and post-repair validation. These features make the repair process manageable for repairers while simultaneously building their confidence. This is further facilitated by a redesign of theDW60, incorporating intuitive disassembly and reassembly, including cues that also assist the AI assistant’s visual support.Qualitative user testing results show the potential of intuitive product design and IoT-supported AI guidance to increase consumers’ willingness to repair dishwashers, particularly during fault diagnosis and repair arrangement. Additionally, this system could be further developed and applied to the wider field of appliance care. Future research should therefore focus on further validation of desirability, feasibility and viability by both users and manufacturers.