Optimal new product design using reverse quality function deployment with nonlinear regression modeling

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Udgivet i:The International Journal of Quality & Reliability Management vol. 42, no. 2 (2025), p. 357-376
Hovedforfatter: Wang, Jian
Andre forfattere: Tan, Yi, Zhang, Jingzhi, Han, Yajuan
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Emerald Group Publishing Limited
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022 |a 0265-671X 
022 |a 1758-6682 
024 7 |a 10.1108/IJQRM-11-2021-0415  |2 doi 
035 |a 3161415839 
045 2 |b d20250101  |b d20251231 
084 |a 14215  |2 nlm 
100 1 |a Wang, Jian  |u Shanghai University, Shanghai, China 
245 1 |a Optimal new product design using reverse quality function deployment with nonlinear regression modeling 
260 |b Emerald Group Publishing Limited  |c 2025 
513 |a Journal Article 
520 3 |a PurposeQuality function deployment (QFD) has been widely applied in new product development, but existing research on QFD has some limitations. Primarily, QFD lacks the capability to provide feedback on the satisfaction degree of customer requirements (CRs) according to the actual values of engineering characteristics (ECs). In addition, QFD does not quantitatively consider the interrelationships among ECs. Reverse QFD (R-QFD) was introduced to implement the feedback process. On this basis, this paper quantitatively considers the interrelationships among ECs in the R-QFD model and extends these relationships to encompass combinations of multiple ECs, aiming to improve the inference accuracy of the model.Design/methodology/approachA nonlinear regression model was established between CRs and ECs, aiming to infer the satisfaction degree of CRs based on the implementation status of ECs. This model considers the interdependencies among ECs and extends the consideration of pairwise EC correlations from every two to every fifteen. Lingo Software is utilized to seek solutions for this program. To facilitate the implementation of the program, a directive to simplify the solution has been proposed.FindingsThe experimental results indicate that the interrelationships among ECs significantly affect the inference accuracy of the R-QFD model, thereby verifying the necessity of considering higher-order interrelationships among ECs within the R-QFD framework. Based on the results from data experiments, this paper also proposes research recommendations pertaining to ECs hierarchy for varying quantities of ECs.Originality/valueThe outcomes of this study have further refined the R-QFD model, addressing its limitations of ignoring the interrelationships among ECs. This transformation elevates the R-QFD model from a relatively simple linear model to a nonlinear model formed through modeling, thereby enhancing its accuracy and applicability. In practical terms, this study provides case support for the application of the R-QFD model in manufacturing industry. 
653 |a Accuracy 
653 |a Product development 
653 |a Design of experiments 
653 |a Feedback 
653 |a Regression models 
653 |a Product design 
653 |a Optimization 
653 |a Inference 
653 |a Linear programming 
653 |a Methods 
653 |a Linguistics 
653 |a Literature reviews 
653 |a Quality function deployment 
653 |a Fuzzy logic 
653 |a Competition 
700 1 |a Tan, Yi  |u Shanghai University, Shanghai, China 
700 1 |a Zhang, Jingzhi  |u Shanghai University, Shanghai, China 
700 1 |a Han, Yajuan  |u Shanghai University, Shanghai, China 
773 0 |t The International Journal of Quality & Reliability Management  |g vol. 42, no. 2 (2025), p. 357-376 
786 0 |d ProQuest  |t ABI/INFORM Global 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3161415839/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3161415839/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3161415839/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch