Multiobjective Decision-Making for Technical Characteristics Selection in a House of Quality

Xie, Jinxing; Qin, Qin; Jiang, Maoting

doi:10.1155/2020/9243142

Cited by 8 publications

(6 citation statements)

References 46 publications

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“…To determine the best conceptual design in the process of concept evaluation, [45] introduced the customer evaluation and design specifications described by soft foundation theory and entropy theory into the TOPSIS framework to rank the pros and cons. On the basis of establishing the relationship between consumer demand and technical characteristics, [46] calculated the basic importance of technical characteristics and used the TOPSIS multiattribute decision model to modify the basic importance of technical characteristics to overcome the limitation of considering only consumer factors. Su et al used the TOPSIS method to prioritize new samples coupled with different weight design elements to explore the coupling characteristics of elements in product Kansei image design [47].…”

Section: Topsis Methodmentioning

confidence: 99%

Cognitive Matching of Design Subjects in Product Form Evolutionary Design

Zhang

Wang

Zhou

et al. 2021

Computational Intelligence and Neuroscience

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In actual product development, the cognitive differences between users and designers make it difficult for the designed products to be recognized by users. To reduce the cognitive differences between these two design subjects, this paper proposes a method of cognitive matching of the design subjects. First, we use the relevant methods of Kansei engineering to quantify the Kansei image cognition of the two design subjects and construct a cognitive matching model of the design subjects with information entropy and the technique for order preference by similarity to ideal solution (TOPSIS). Second, according to the Kansei image, the Kansei image prototype cluster is constructed, and the representative Kansei image prototype is obtained. Then, we combine an artificial neural network (ANN) with a cognitive matching model of the design subjects to construct a product Kansei image evaluation system; this is used to evaluate the evolved forms. Finally, a product Kansei image form evolution system is constructed based on the genetic algorithm (GA). To some extent, the system simulates the cognitive matching process between designers and users in product design, helps designers to more accurately understand the cognitive trends of the two design subjects, and provides a theoretical basis for the intelligent design of product forms through the cognitive balance of multiple design subjects. This paper takes a beverage bottle as an example to verify the feasibility of the model through a comparative study.

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Section: Topsis Methodmentioning

confidence: 99%

Cognitive Matching of Design Subjects in Product Form Evolutionary Design

Zhang

Wang

Zhou

et al. 2021

Computational Intelligence and Neuroscience

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“…Teknik pengembangan produk ini proses ini memungkinkan fitur dan spesifikasi penting untuk diidentifikasi diidentifikasi dan nilai target dari produk yang dirancang ukuran kinerja yang ingin dicapai (Angie et al, 2020). HoQ adalah alat inti dari QFD, dan merupakan bentuk intuitif dari ekspresi matriks (Xie et al, 2020). Kebutuhan pelanggan dianggap sebagai WHAT dan diberikan di sisi kiri diagram HOQ.…”

Section: Pelaksanaanunclassified

Pendampingan Peningkatan Produktivitas Usaha Mikro Kecil dan Menengah Mie

Supriyadi,

Widyarto,

Saputra

et al. 2023

JPMWP

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UMKM memiliki kendala terkait dengan peralatan penunjang produksi sehingga kurang dapat bersaing dengan industri. Peralatan yang masih bersifat manual selain membuat tingkat produktivitas rendah juga berdampak pada potensi sakit akibat kerja yang disebabkan pekerjaan yang bersifat manual. Kegiatan pengabdian ini bertujuan untuk membantu memperbaiki proses produksi mie melalui perancangan mesin pemipih dan pemotong adonan mie. Kegiatan pengabdian dilakukan pada UMKM Mie di daerah Cilegon. Perancangan peralatan berdasarkan tahapan house of quality untuk mendapatkan alat bantu sesuai dengan kebutuhan mitra. Hasil pengabdian menunjukkan alat bantu mampu meningkatkan produktivitas 3 sampai 5 kali dibandingkan peralatan yang digunakan sebelumnya. Alat bantu juga mampu memperbaiki posisi kerja sehingga dapat meminimalkan potensi sakit akibat kerja.

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“…Another significant difference is that the data-driven models do not distinguish between different customer profiles. Moreover, a model that cites the use of QFD and addresses product requirements in FFE is proposed by Xie, Qin, and Jiang [36]. However, technical design requirements are prioritized.…”

Section: A Product Development and The Requirements Involvedmentioning

confidence: 99%

Decision Method Focused on the Fuzzy Front-End Phase: A Study Applied to the Development of an Electronic Starting Block for Running Athletes

Dzulinski¹,

Braghini²,

Nascimento³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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In this work, we present a model to support multi-criteria decision-making in the selection of components for the initial proposals of a product or portfolio of products during the Fuzzy Front-End (FFE) phase of the Product Development Process (PD) to reduce risk and uncertainty and increase agility. The model is made of eight stages in which triangular-based fuzzy is employed to weigh customer requirements, and a direct numerical scale is used to weigh technical requirements. The main differences of this model are the identification and weighting of requirements based on different customer profiles and the identification of global customer requirements that have a direct or indirect relationship with all or most technical requirements. We applied the model in the development of an electronic starting block for running athletes with sensors that collected data to assist in training and performance improvement and were able to reduce the number of combinations of components in the FFE stage, and consequently, the development time, with the prioritization of roughly 30% of the components (10 parts of a total of 33). We highlight that there is still a need for further studies investigating the relationship of customer profiles and the impact on PDP and other ways to analyze how customer requirements impact technical requirements.

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Multiobjective Decision-Making for Technical Characteristics Selection in a House of Quality

Cited by 8 publications

References 46 publications

Cognitive Matching of Design Subjects in Product Form Evolutionary Design

Cognitive Matching of Design Subjects in Product Form Evolutionary Design

Pendampingan Peningkatan Produktivitas Usaha Mikro Kecil dan Menengah Mie

Decision Method Focused on the Fuzzy Front-End Phase: A Study Applied to the Development of an Electronic Starting Block for Running Athletes

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