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Hypergraph and network flow-based quality function deployment
Quality function deployment (QFD) has been a widely-acknowledged tool for translating customer requirements into quality product characteristics based on which product development strategies and focus areas are identified. However, the QFD method considers the correlation and effect between developm...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768306/ https://www.ncbi.nlm.nih.gov/pubmed/36568657 http://dx.doi.org/10.1016/j.heliyon.2022.e12263 |
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author | Abonyi, János Czvetkó, Tímea |
author_facet | Abonyi, János Czvetkó, Tímea |
author_sort | Abonyi, János |
collection | PubMed |
description | Quality function deployment (QFD) has been a widely-acknowledged tool for translating customer requirements into quality product characteristics based on which product development strategies and focus areas are identified. However, the QFD method considers the correlation and effect between development parameters, but it is not directly implemented in the importance ranking of development actions. Therefore, the cross-relationships between development parameters and their impact on customer requirement satisfaction are often neglected. The primary objective of this study is to make decision-making more reliable by improving QFD with methods that optimize the selection of development parameters even under capacity or cost constraints and directly implement cross-relationships between development parameters and support the identification of interactions visually. Therefore, QFD is accessed from two approaches that proved efficient in operations research. 1) QFD is formulated as a network flow problem with two objectives: maximizing the benefits of satisfying customer needs using linear optimization or minimizing the total cost of actions while still meeting customer requirements using assignment of minimum cost flow approach. 2) QFD is represented as a hypergraph, which allows efficient representation of the interactions of the relationship and correlation matrix and the determination of essential factors based on centrality metrics. The applicability of the methods is demonstrated through an application study in developing a sustainable design of customer electronic products and highlights the improvements' contribution to different development strategies, such as linear optimization performed the best in maximizing customer requirements' satisfaction, assignment as minimum cost flow approach minimized the total cost, while the hypergraph-based representation identified the indirect interactions of development parameters and customer requirements. |
format | Online Article Text |
id | pubmed-9768306 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-97683062022-12-22 Hypergraph and network flow-based quality function deployment Abonyi, János Czvetkó, Tímea Heliyon Research Article Quality function deployment (QFD) has been a widely-acknowledged tool for translating customer requirements into quality product characteristics based on which product development strategies and focus areas are identified. However, the QFD method considers the correlation and effect between development parameters, but it is not directly implemented in the importance ranking of development actions. Therefore, the cross-relationships between development parameters and their impact on customer requirement satisfaction are often neglected. The primary objective of this study is to make decision-making more reliable by improving QFD with methods that optimize the selection of development parameters even under capacity or cost constraints and directly implement cross-relationships between development parameters and support the identification of interactions visually. Therefore, QFD is accessed from two approaches that proved efficient in operations research. 1) QFD is formulated as a network flow problem with two objectives: maximizing the benefits of satisfying customer needs using linear optimization or minimizing the total cost of actions while still meeting customer requirements using assignment of minimum cost flow approach. 2) QFD is represented as a hypergraph, which allows efficient representation of the interactions of the relationship and correlation matrix and the determination of essential factors based on centrality metrics. The applicability of the methods is demonstrated through an application study in developing a sustainable design of customer electronic products and highlights the improvements' contribution to different development strategies, such as linear optimization performed the best in maximizing customer requirements' satisfaction, assignment as minimum cost flow approach minimized the total cost, while the hypergraph-based representation identified the indirect interactions of development parameters and customer requirements. Elsevier 2022-12-09 /pmc/articles/PMC9768306/ /pubmed/36568657 http://dx.doi.org/10.1016/j.heliyon.2022.e12263 Text en © 2022 The Authors. Published by Elsevier Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Research Article Abonyi, János Czvetkó, Tímea Hypergraph and network flow-based quality function deployment |
title | Hypergraph and network flow-based quality function deployment |
title_full | Hypergraph and network flow-based quality function deployment |
title_fullStr | Hypergraph and network flow-based quality function deployment |
title_full_unstemmed | Hypergraph and network flow-based quality function deployment |
title_short | Hypergraph and network flow-based quality function deployment |
title_sort | hypergraph and network flow-based quality function deployment |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9768306/ https://www.ncbi.nlm.nih.gov/pubmed/36568657 http://dx.doi.org/10.1016/j.heliyon.2022.e12263 |
work_keys_str_mv | AT abonyijanos hypergraphandnetworkflowbasedqualityfunctiondeployment AT czvetkotimea hypergraphandnetworkflowbasedqualityfunctiondeployment |