A lean-oriented Kano–QFD framework for hierarchical resource allocation during passive wearable wellness device development in the context of over-engineering risk
Abstract
To address over-engineering risk at the fuzzy front end of multi-attribute-coupled passive wearable wellness devices (PWWDs) development, this study develops a lean-oriented Kano–QFD hierarchical decision framework guided by the lean principles of value maximization and waste elimination. The Kano model classifies and screens customer requirements (CRs), QFD translates the retained requirement information into EC technical weights, and a distribution-referenced parameterized tiering rule converts these continuous weights into high-, intermediate-, and low-priority resource-allocation tiers. A multisensory aromatherapy wearable accessory serves as the empirical case. Distributional diagnostics, sensitivity analysis of the threshold coefficient k , and comparison with Pareto analysis are used to examine tier stability and allocation differences. The case excludes two Indifferent CRs and assigns 13 ECs to a 4–5–4 configuration. Shape–Style Compatibility, Overall Net Weight, Design Symbol Application Rate, and Sustainable Material Ratio form the high-priority tier. Compared with Pareto analysis, parameterized tiering retains the same lower-end EC subset while further dividing the Pareto priority set into high- and intermediate-priority tiers.
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BibTeX
@article{zhou2026leanoriented,
title = {{A lean-oriented Kano–QFD framework for hierarchical resource allocation during passive wearable wellness device development in the context of over-engineering risk}},
author = {Zijia Zhou and Jing Liu and Pohsun Wang},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-76968-x},
url = {https://www.nature.com/articles/s41598-026-76968-x},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A lean-oriented Kano–QFD framework for hierarchical resource allocation during passive wearable wellness device development in the context of over-engineering risk
AU - Zijia Zhou
AU - Jing Liu
AU - Pohsun Wang
PY - 2026
DA - 2026/10/22
JO - Scientific Reports
DO - 10.1038/s41598-026-76968-x
UR - https://www.nature.com/articles/s41598-026-76968-x
AB - To address over-engineering risk at the fuzzy front end of multi-attribute-coupled passive wearable wellness devices (PWWDs) development, this study develops a lean-oriented Kano–QFD hierarchical decision framework guided by the lean principles of value maximization and waste elimination. The Kano model classifies and screens customer requirements (CRs), QFD translates the retained requirement information into EC technical weights, and a distribution-referenced parameterized tiering rule converts these continuous weights into high-, intermediate-, and low-priority resource-allocation tiers. A multisensory aromatherapy wearable accessory serves as the empirical case. Distributional diagnostics, sensitivity analysis of the threshold coefficient k , and comparison with Pareto analysis are used to examine tier stability and allocation differences. The case excludes two Indifferent CRs and assigns 13 ECs to a 4–5–4 configuration. Shape–Style Compatibility, Overall Net Weight, Design Symbol Application Rate, and Sustainable Material Ratio form the high-priority tier. Compared with Pareto analysis, parameterized tiering retains the same lower-end EC subset while further dividing the Pareto priority set into high- and intermediate-priority tiers.
SN - 2045-2322
ER -