The framework begins with customer requirements for the wearable, which are screened through the Kano model. This step retains requirements that represent different forms of customer value before the information is transferred into technical priorities through QFD, or quality function deployment. In this case, two customer requirements categorized as indifferent were excluded. The retained information was then converted into weights for 13 engineering characteristics, providing a quantitative basis for deciding where development resources should be concentrated.

Because continuous engineering-characteristic weights do not by themselves define discrete resource tiers, the researchers used a distribution-referenced, parameterized rule. The rule divided the weighted characteristics into high-, intermediate- and low-priority groups. Shape–Style Compatibility, Overall Net Weight, Design Symbol Application Rate and Sustainable Material Ratio were placed in the high-priority tier, producing the reported 4–5–4 configuration. The study linked this hierarchy to the lean principles of maximizing value and eliminating waste at the fuzzy front end of product development.

The authors assessed the stability of the resulting tiers using distributional diagnostics and sensitivity analysis of the threshold coefficient, k. They also compared the allocation with a Pareto analysis. Parameterized tiering retained the same subset of engineering characteristics at the lower end of the priority distribution, while dividing the Pareto priority set into separate high- and intermediate-priority tiers. This comparison indicates that the proposed method preserves part of the selection produced by Pareto analysis while adding a more differentiated allocation structure.

The work is presented as a decision framework for a development case rather than as evidence that a particular wearable improves health outcomes. Its principal contribution is a way to turn mixed customer requirements into a transparent, tiered allocation of engineering resources when product attributes are tightly coupled. For passive wellness devices, where added functions may increase complexity without adding commensurate customer value, such a screening process could help development teams focus effort on the attributes identified as highest priorities. The case is limited to one multisensory aromatherapy wearable accessory, so broader performance across other device types and customer groups remains to be evaluated.

Subject of Research: Hierarchical resource allocation for passive wearable wellness device development using Kano modeling, QFD and lean principles

Article Title: A lean-oriented Kano–QFD framework for hierarchical resource allocation during passive wearable wellness device development in the context of over-engineering risk

Article References: Zhou, Z., Liu, J., & Wang, P. (2026). A lean-oriented Kano–QFD framework for hierarchical resource allocation during passive wearable wellness device development in the context of over-engineering risk. Scientific Reports. https://doi.org/10.1038/s41598-026-76968-x

Image Credits: AI Generated

DOI: 10.1038/s41598-026-76968-x

Keywords: wearable devices, Kano model, quality function deployment, lean product development, resource allocation, over-engineering, aromatherapy accessory, engineering priorities, lean-oriented, Kano, framework, hierarchical