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dc.contributor.authorRidha Derrouicheen_US
dc.contributor.authorPongsak Holimchayachotikulen_US
dc.contributor.authorKomgrit Leksakulen_US
dc.date.accessioned2018-09-04T04:20:54Z-
dc.date.available2018-09-04T04:20:54Z-
dc.date.issued2011-08-03en_US
dc.identifier.other2-s2.0-79960920320en_US
dc.identifier.other10.1109/LOGISTIQUA.2011.5939435en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79960920320&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/49955-
dc.description.abstractThis paper proposes an integrated framework between B2B supply chains (B2B-SC) and performance evaluation systems. This framework is based on data mining techniques, enabling the development of a predictive collaborative performance evolution model and decision making which has forward-looking collaborative capabilities. The results are deployment for collaborative performance guidelines, which were validated by the domain experts in terms of its real practical usage efficiency. This framework enables managers to develop systematic manners to predict future collaborative performance and recognize latent problems in their relationship. Its usages and difficulties were also discussed. Furthermore, the final predictive results and rules contain vital information relating to SC improvement in the long term. © 2011 IEEE.en_US
dc.subjectEngineeringen_US
dc.titlePredictive performance model in collaborative supply chain using decision tree and clustering techniqueen_US
dc.typeConference Proceedingen_US
article.title.sourcetitle2011 4th International Conference on Logistics, LOGISTIQUA'2011en_US
article.stream.affiliationsESC Saint-Etienneen_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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