Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/55616
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dc.contributor.authorSiravat Teerasoponpongen_US
dc.contributor.authorApichat Sopadangen_US
dc.date.accessioned2018-09-05T02:58:38Z-
dc.date.available2018-09-05T02:58:38Z-
dc.date.issued2016-01-01en_US
dc.identifier.issn18684238en_US
dc.identifier.other2-s2.0-84964908540en_US
dc.identifier.other10.1007/978-3-319-33111-9_7en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84964908540&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/55616-
dc.description.abstract© IFIP International Federation for Information Processing 2016. The management of the supply chain in presence of uncertainty is a challenge task. This paper proposes a stochastic model for modeling both the structure and the operation of the supply chain. Existing approaches for this task are either deterministic or single level structure which might not be appropriate to capture the essences of the supply chain. The proposed method employs the Markov chain model as the foundation and incorporate the concept of multi-level. The levels are used to model both the internal events and the external events. In the proposed method, the product life cycle management is used as a guiding principle to identify each component of the supply chain.en_US
dc.subjectDecision Sciencesen_US
dc.titleRisk probability assessment model based on PLM’s perspective using modified markov processen_US
dc.typeBook Seriesen_US
article.title.sourcetitleIFIP Advances in Information and Communication Technologyen_US
article.volume467en_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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