Dynamic product redesign optimization: integrating Extenics and TRIZ with SO-PMI analysis for time-Dependent customer feedback from online reviews

This study proposes a dynamic product redesign framework that integrates Extenics, TRIZ, and SO-PMI analysis to address time-dependent customer feedback from online reviews. The Multimodal Importance Performance Competitor Analysis (MIPCA) framework advances prior models by incorporating multimodal data fusion, Kano-based asymmetric sentiment weighting, TRIZ for conflict resolution, and LLM-enhanced feature extraction. To mitigate concerns regarding framework complexity, a modular design is employed, enabling independent use of components with incremental benefits demonstrated through ablation studies. Python crawlers collected product reviews, which were processed to extract key attributes using the Apriori algorithm and evaluated via SO-PMI. Identified design conflicts, such as battery life limitations and camera performance gaps in smartphones, were resolved using TRIZ principles like property transformation and preliminary action. A case study comparing the Apple iPhone 16 Pro Max and Huawei Mate 60 Pro demonstrates the framework’s ability to derive comprehensive user feedback and support effective redesign, projecting 15–20% satisfaction improvements validated through sensitivity analyses, simulated user studies, and comparative benchmarking.