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A Fuzzy Quality Function Deployment (FQFD) model for deriving optimum targets

Research output: Contribution to journal › Article › peer-review

Standard

A Fuzzy Quality Function Deployment (FQFD) model for deriving optimum targets. / Vanegas, L.; Labib, Ashraf.

In: International Journal of Production Research, Vol. 39, No. 1, 2001, p. 99-120.

Research output: Contribution to journal › Article › peer-review

Harvard

Vanegas, L & Labib, A 2001, 'A Fuzzy Quality Function Deployment (FQFD) model for deriving optimum targets', International Journal of Production Research, vol. 39, no. 1, pp. 99-120. https://doi.org/10.1080/00207540010005079

APA

Vanegas, L., & Labib, A. (2001). A Fuzzy Quality Function Deployment (FQFD) model for deriving optimum targets. International Journal of Production Research, 39(1), 99-120. https://doi.org/10.1080/00207540010005079

Vancouver

Vanegas L, Labib A. A Fuzzy Quality Function Deployment (FQFD) model for deriving optimum targets. International Journal of Production Research. 2001;39(1):99-120. https://doi.org/10.1080/00207540010005079

Author

Vanegas, L. ; Labib, Ashraf. / A Fuzzy Quality Function Deployment (FQFD) model for deriving optimum targets. In: International Journal of Production Research. 2001 ; Vol. 39, No. 1. pp. 99-120.

Bibtex

@article{8eaebb8be3324fe18844444baec3d667,
title = "A Fuzzy Quality Function Deployment (FQFD) model for deriving optimum targets",
abstract = "Quality Function Deployment (QFD) is a powerful tool that translates the Voice of the Customer (VoC) into the Engineering Characteristics (ECs), which are those that can be modified in order to meet the desires of the customer. A main objective of QFD is the determination of target values of ECs; however, the conventional QFD aims only empirically at finding these targets, which makes it difficult for the ECs to be optimum. This paper proposes a novel method for determining optimum targets in QFD. Fuzzy numbers are used to represent the imprecise nature of the judgements, and to de{\textregistered} ne more appropriately the relationships between ECs and Customer Attributes (CAs). Constraints such as cost, technical difficulty and market position are considered. An example of a car door is presented to show the application of the method.",
author = "L. Vanegas and Ashraf Labib",
year = "2001",
doi = "10.1080/00207540010005079",
language = "English",
volume = "39",
pages = "99--120",
journal = "International Journal of Production Research",
issn = "0020-7543",
publisher = "Taylor and Francis Ltd.",
number = "1",

}

RIS

TY - JOUR

T1 - A Fuzzy Quality Function Deployment (FQFD) model for deriving optimum targets

AU - Vanegas, L.

AU - Labib, Ashraf

PY - 2001

Y1 - 2001

N2 - Quality Function Deployment (QFD) is a powerful tool that translates the Voice of the Customer (VoC) into the Engineering Characteristics (ECs), which are those that can be modified in order to meet the desires of the customer. A main objective of QFD is the determination of target values of ECs; however, the conventional QFD aims only empirically at finding these targets, which makes it difficult for the ECs to be optimum. This paper proposes a novel method for determining optimum targets in QFD. Fuzzy numbers are used to represent the imprecise nature of the judgements, and to de® ne more appropriately the relationships between ECs and Customer Attributes (CAs). Constraints such as cost, technical difficulty and market position are considered. An example of a car door is presented to show the application of the method.

AB - Quality Function Deployment (QFD) is a powerful tool that translates the Voice of the Customer (VoC) into the Engineering Characteristics (ECs), which are those that can be modified in order to meet the desires of the customer. A main objective of QFD is the determination of target values of ECs; however, the conventional QFD aims only empirically at finding these targets, which makes it difficult for the ECs to be optimum. This paper proposes a novel method for determining optimum targets in QFD. Fuzzy numbers are used to represent the imprecise nature of the judgements, and to de® ne more appropriately the relationships between ECs and Customer Attributes (CAs). Constraints such as cost, technical difficulty and market position are considered. An example of a car door is presented to show the application of the method.

U2 - 10.1080/00207540010005079

DO - 10.1080/00207540010005079

M3 - Article

VL - 39

SP - 99

EP - 120

JO - International Journal of Production Research

JF - International Journal of Production Research

SN - 0020-7543

IS - 1

ER -

ID: 119672