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dc.contributor.authorAbolghasemi, Mahdi
dc.contributor.authorKhodakarami, Vahid
dc.contributor.authorTehranifard, Hamid
dc.date.accessioned2015-05-27T16:40:28Z
dc.date.available2015-05-27T16:40:28Z
dc.date.issued2015-04
dc.identifier.citationAbolghasemi,, Mahdi; Khodakarami, Vahid; Tehranifard, Hamid. A new approach for supply chain risk management: Mapping SCOR into Bayesian network. "Journal of Industrial Engineering and Management", Abril 2015, vol. 8, núm. 1, p. 280-302.
dc.identifier.issn2013-0953
dc.identifier.urihttp://hdl.handle.net/2099/16525
dc.description.abstractPurpose: Increase of costs and complexities in organizations beside the increase of uncertainty and risks have led the managers to use the risk management in order to decrease risk taking and deviation from goals. SCRM has a close relationship with supply chain performance. During the years different methods have been used by researchers in order to manage supply chain risk but most of them are either qualitative or quantitative. Supply chain operation reference (SCOR) is a standard model for SCP evaluation which have uncertainty in its metrics. In This paper by combining qualitative and quantitative metrics of SCOR, supply chain performance will be measured by Bayesian Networks. Design/methodology/approach: First qualitative assessment will be done by recognizing uncertain metrics of SCOR model and then by quantifying them, supply chain performance will be measured by Bayesian Networks (BNs) and supply chain operations reference (SCOR) in which making decision on uncertain variables will be done by predictive and diagnostic capabilities. Findings: After applying the proposed method in one of the biggest automotive companies in Iran, we identified key factors of supply chain performance based on SCOR model through predictive and diagnostic capability of Bayesian Networks. After sensitivity analysis, we find out that ‘Total cost’ and its criteria that include costs of labors, warranty, transportation and inventory have the widest range and most effect on supply chain performance. So, managers should take their importance into account for decision making. We can make decisions simply by running model in different situations. Research limitations/implications: A more precise model consisted of numerous factors but it is difficult and sometimes impossible to solve big models, if we insert all of them in a Bayesian model. We have adopted real world characteristics with our software and method abilities. On the other hand, fewer data exist for some of the performance metrics. Practical implications: Mangers often use simple qualitative metrics for SCRM. However, combining qualitative and quantitative metrics will be more useful. Industries can recognize the important uncertain metrics by predicting supply chain performance and diagnosing possible happenings. Originality/value: This paper proposed a Bayesian method based on SCOR metrics which has the ability to manage supply chain risks and improve supply chain performance. This is the only presented case study for measuring supply chain performance by SCOR metrics.
dc.format.extent23 p.
dc.language.isoeng
dc.publisherOmniaScience
dc.rightsAttribution-NonCommercial 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Economia i organització d'empreses::Direcció d’operacions::Modelització de transports i logística
dc.subjectÀrees temàtiques de la UPC::Economia i organització d'empreses::Gestió i direcció
dc.subject.lcshBusiness logistics--Risk management
dc.subject.lcshRisk management
dc.subject.lcshDecision-making
dc.subject.lcshPerformance--Measurement
dc.subject.lcshBayesian statistical decision theory
dc.subject.otherSCOR
dc.subject.otherBayesian networks
dc.titleA new approach for supply chain risk management: Mapping SCOR into Bayesian network
dc.typeArticle
dc.subject.lemacLogística (Indústria) -- Gestió del risc
dc.subject.lemacGestió del risc
dc.subject.lemacDecisió, Presa de
dc.subject.lemacEstadística bayesiana
dc.identifier.dlB-28744-2008
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
local.citation.authorAbolghasemi,, Mahdi; Khodakarami, Vahid; Tehranifard, Hamid
local.citation.publicationNameJournal of Industrial Engineering and Management
local.citation.volume8
local.citation.number1
local.citation.startingPage280
local.citation.endingPage302


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