ScholarGate
Pembantu

Bandingkan kaedah

Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.

Penyebaran Kualiti Bayesian (Bayesian Quality Function Deployment)×Quality Function Deployment×
BidangReka Bentuk EksperimenReka Bentuk Eksperimen
KeluargaProcess / pipelineProcess / pipeline
Tahun asalQFD: 1966–1972; Bayesian QFD extensions: 2000s–present1966 (Japan); popularised in the West ~1988
PengasasYoji Akao (QFD); Bayesian extension developed by multiple researchers including Fung, Tang, and colleaguesYoji Akao
JenisProbabilistic customer-driven design planning methodStructured quality planning and product design method
Sumber perintisTang, J., Fung, R. Y. K., Xu, B., & Wang, D. (2002). A new approach to quality function deployment planning with financial consideration. Computers & Operations Research, 29(11), 1447–1463. DOI ↗Akao, Y. (Ed.). (1990). Quality Function Deployment: Integrating Customer Requirements into Product Design. Productivity Press. ISBN: 978-0915299416
AliasBayesian QFD, Probabilistic QFD, Bayesian House of Quality, Bayesian Voice of the Customer AnalysisQFD, House of Quality, customer-driven engineering, voice of the customer matrix
Berkaitan54
RingkasanBayesian Quality Function Deployment (Bayesian QFD) integrates Bayesian probabilistic inference into the classical House of Quality framework to handle uncertainty in customer preference data and relationship matrices. By expressing relationship weights and importance ratings as probability distributions rather than point estimates, it propagates uncertainty through the planning process and yields more defensible engineering prioritization decisions under incomplete or conflicting customer information.Quality Function Deployment (QFD) is a structured method for translating customer needs — the voice of the customer — into specific technical requirements at every stage of product or service development. Originating in Japan in the 1960s, QFD uses a matrix-based tool called the House of Quality to make customer priorities visible, link them to engineering parameters, expose trade-offs, and maintain focus on what customers actually value throughout the design process.
ScholarGateSet data
  1. v1
  2. 2 Sumber
  3. PUBLISHED
  1. v1
  2. 2 Sumber
  3. PUBLISHED

Pergi ke carian Muat turun slaid

ScholarGateBandingkan kaedah: Bayesian Quality Function Deployment · Quality Function Deployment. Dicapai 2026-06-15 daripada https://scholargate.app/ms/compare