%0 Journal Article %1 IJACSA.2013.040206 %A B.L. SONG W.LIAO, J.LEE %D 2013 %J International Journal of Advanced Computer Science and Applications(IJACSA) %K Collaborative Pattern Prediction; Supply Wind assurance; chain expert management; quality recognition; system; turbine %N 2 %T Intelligent Collaborative Quality Assurance System for Wind Turbine Supply Chain Management %U http://ijacsa.thesai.org/ %V 4 %X To determine the root causes or sources of variance of bad quality in supply chains is usually more difficult because multiple parties are involved in the current global manufacturing environment. Each component within a supply chain tends to focus on its own responsibilities and ignores possibilities for interconnectivity and therefore the potential for systematic quality assurance and quality tracing. Rather than concentrating on assigning responsibility for “recall” incidents, it would be better to expend that energy on constructing a collaborative system to assure product quality by employing a systematic view for the entire supply chain. This paper presents a systematic framework for intelligent collaborative quality assurance throughout an entire supply chain based on an expert system for implementing two levels of quality assurance: system level and component level. This proposed system provides intelligent functions for quality prediction, pattern recognition and data mining. A case study for wind turbines is given to demonstrate this approach. The results show that such a system can assure product quality improved in a continuous process.

@article{IJACSA.2013.040206, abstract = {To determine the root causes or sources of variance of bad quality in supply chains is usually more difficult because multiple parties are involved in the current global manufacturing environment. Each component within a supply chain tends to focus on its own responsibilities and ignores possibilities for interconnectivity and therefore the potential for systematic quality assurance and quality tracing. Rather than concentrating on assigning responsibility for “recall” incidents, it would be better to expend that energy on constructing a collaborative system to assure product quality by employing a systematic view for the entire supply chain. This paper presents a systematic framework for intelligent collaborative quality assurance throughout an entire supply chain based on an expert system for implementing two levels of quality assurance: system level and component level. This proposed system provides intelligent functions for quality prediction, pattern recognition and data mining. A case study for wind turbines is given to demonstrate this approach. The results show that such a system can assure product quality improved in a continuous process.}, added-at = {2014-02-21T08:00:08.000+0100}, author = {{B.L. SONG W.LIAO}, J.LEE}, biburl = {https://www.bibsonomy.org/bibtex/22f48aaa2ed54db89c5cbd365209ad74a/thesaiorg}, interhash = {ec972aed8a1e9f970b48d177422f88c9}, intrahash = {2f48aaa2ed54db89c5cbd365209ad74a}, journal = {International Journal of Advanced Computer Science and Applications(IJACSA)}, keywords = {Collaborative Pattern Prediction; Supply Wind assurance; chain expert management; quality recognition; system; turbine}, number = 2, timestamp = {2014-02-21T08:00:08.000+0100}, title = {{Intelligent Collaborative Quality Assurance System for Wind Turbine Supply Chain Management}}, url = {http://ijacsa.thesai.org/}, volume = 4, year = 2013 }