Data Acquisition for Quality Loss Function Modelling | Zendy

Søren Nygaard Pedersen | Zendy; Thomas J. Howard | Zendy

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Data Acquisition for Quality Loss Function Modelling

Author(s) -

Søren Nygaard Pedersen,

Thomas J. Howard

Publication year - 2016

Publication title -

procedia cirp

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.683

H-Index - 65

ISSN - 2212-8271

DOI - 10.1016/j.procir.2016.02.032

Subject(s) - quality (philosophy) , measure (data warehouse) , product (mathematics) , function (biology) , quality function deployment , reliability engineering , computer science , focus (optics) , engineering , data mining , operations management , mathematics , philosophy , geometry , epistemology , evolutionary biology , biology , value engineering , physics , optics

Quality loss functions can be a valuable tool when assessing the impact of variation on product quality. Typically, the input for the quality loss function would be a measure of the varying product performance and the output would be a measure of quality. While the unit of the input is given by the product function in focus, the quality output can be measured and quantified in a number of ways. In this article a structured approach for acquiring stakeholder satisfaction data for use in quality loss function modelling is introduced.

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