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

Cheng Chang Chang*, Dah Chuan Gong

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Pages1396-1401
Number of pages6
DOIs
StatePublished - 2010
Externally publishedYes
Event2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China
Duration: 11 07 201014 07 2010

Publication series

Name2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Volume3

Conference

Conference2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Country/TerritoryChina
CityQingdao
Period11/07/1014/07/10

Keywords

  • Artificial neural network model
  • Logistic regression model
  • Risk management
  • RoHS

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