Fuzzy Based Quality Function Deployment

Saurabh Kumar Gupta

ISBN 10: 6139922771 / ISBN 13: 9786139922772

Edité par LAP LAMBERT Academic Publishing, 2018

A propos de cet article

nach der Bestellung gedruckt Neuware - Printed after ordering - Quality function deployment (QFD) has been widely used to translate customer requirements to a product's technical attributes. Correctly rating the importance of every customer requirement is essential to the QFD process because it will largely affect the final target value of a product's technical attributes. The success of a product depends not only on whether it meets the customer requirements, but also on how it compares with competitor products. In design evaluation, alternatives are evaluated against several criteria, such as low cost, short lead time and other general minimum requirements to be satisfied. The specification of quantitative performance requirements is necessary in order that the evaluation can be carried out systematically. However, precise (crisp) performance requirements are normally not adequate, for example, it could be stated that the maximum cost of a design is Rs.2000, but Rs.2500 may be still acceptable. Because a requirement like this is imprecise (fuzzy), a fuzzy set or fuzzy number is appropriate for capturing it.

N° de réf. du vendeur 9786139922772

Détails bibliographiques

• Titre: Fuzzy Based Quality Function Deployment
• Éditeur: LAP LAMBERT Academic Publishing
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 6139922771
• ISBN à 13 chiffres: 9786139922772
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

Quality function deployment (QFD) has been widely used to translate customer requirements to a product’s technical attributes. Correctly rating the importance of every customer requirement is essential to the QFD process because it will largely affect the final target value of a product’s technical attributes. The success of a product depends not only on whether it meets the customer requirements, but also on how it compares with competitor products. In design evaluation, alternatives are evaluated against several criteria, such as low cost, short lead time and other general minimum requirements to be satisfied. The specification of quantitative performance requirements is necessary in order that the evaluation can be carried out systematically. However, precise (crisp) performance requirements are normally not adequate, for example, it could be stated that the maximum cost of a design is Rs.2000, but Rs.2500 may be still acceptable. Because a requirement like this is imprecise (fuzzy), a fuzzy set or fuzzy number is appropriate for capturing it.