AI SearchLearning curve analyses of quality costs and productivity : case study of a consumer goods manufacturing company
Call Number: AIT Thesis no. IE-92-21 Material type:
TextSeries: Asian Institute of Technology. Thesis ; no. IE-92-21Publication details: Bangkok : Asian Institute of Technology, 1992Description: 109 leavesSubject(s): Online resources: Dissertation note: Thesis (M.Eng.) - Asian Institute of Technology, 1992 Summary: This study introduces an analytic framework for extending univariate learning curves (manufacturing progress functions) to multivariate models for quality costs and productivity analyses. Multivariate functions are needed to account for the multitude of factors that can influence learning in manufacturing operations. Two bivariate manufacturing progress function for a consumer goods manufacturing company is presented. This study is also concerned with predicting ultimate productivity and quality costs and the time scale in which it is approached. Direct Method, Iterative Method and Three Parameter Estimation Method are also presented to predict these parameters.
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Asian Institute of Technology Library AIT Publications | AIT Thesis no. IE-92-21 (Browse shelf(Opens below)) | 1 | Available | 30050003074522 | |||||||||||||
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20-AIT Publication
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Asian Institute of Technology Library AIT Publications | AIT Thesis no. IE-92-21 (Browse shelf(Opens below)) | 2 | Available | 30050003461927 | |||||||||||||
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Asian Institute of Technology Library Archives | AIT Thesis no. IE-92-21 (Browse shelf(Opens below)) | 1 | Available | 30050160075460 |
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Engineering and Technology
Thesis (M.Eng.) - Asian Institute of Technology, 1992
This study introduces an analytic framework for extending univariate learning curves (manufacturing progress functions) to multivariate models for quality costs and productivity analyses. Multivariate functions are needed to account for the multitude of factors that can influence learning in manufacturing operations. Two bivariate manufacturing progress function for a consumer goods manufacturing company is presented. This study is also concerned with predicting ultimate productivity and quality costs and the time scale in which it is approached. Direct Method, Iterative Method and Three Parameter Estimation Method are also presented to predict these parameters.
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