TY - GEN
T1 - A comparison of rohs risk assessment using the Logistic Regression Model and Artificial Neural Network Model
AU - Chang, Cheng Chang
AU - Gong, Dah Chuan
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Artificial neural network model
KW - Logistic regression model
KW - Risk management
KW - RoHS
UR - http://www.scopus.com/inward/record.url?scp=78149327575&partnerID=8YFLogxK
U2 - 10.1109/ICMLC.2010.5580849
DO - 10.1109/ICMLC.2010.5580849
M3 - 会议稿件
AN - SCOPUS:78149327575
SN - 9781424465262
T3 - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
SP - 1396
EP - 1401
BT - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
T2 - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Y2 - 11 July 2010 through 14 July 2010
ER -