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A comparison of RoHS risk assessment using the Logistic Regression Model and Artificial Neural Network Model

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2 Author(s)
Cheng-Chang Chang ; Department of Industrial and Systems Engineering, Chung Yuan Christian University, Chung Li 32023, Taiwan ; Dah-Chuan Gong

Under the RoHS Directive enacted in the European Union, there exist a number of green quality uncertainties and risks at various stages during product lifecycle management. The green product management system designed in this study, consisting of green design management, supplier management and green production management, is mainly in charge of controlling quality uncertainties and risks to prevent from producing non-green products at various stages. There is a great deal of uncertainties associated with the introduction of green quality control at every stage, and risks will rise correspondingly, thereby causing goodwill and cost losses. Consequently, green quality should be controlled in advance. To assess the extent and severity of the impact of the risk on enterprises, to focus on risk factors with strong impacts based on the priority of risk control, and to reduce the probability of risk, this study uses two approaches - Artificial Neural Network Model and Logistic Regression Model - to integrate green quality control information flow among green design management, supplier management and green production management.

Published in:

2010 International Conference on Machine Learning and Cybernetics  (Volume:3 )

Date of Conference:

11-14 July 2010