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dc.contributor.authorRodgers, Mark
dc.contributor.authorSingham, Dashi I.
dc.date2019-05
dc.date.accessioned2019-07-31T21:09:55Z
dc.date.available2019-07-31T21:09:55Z
dc.date.issued2019-05
dc.identifier.citationRodgers, Mark, and Dashi Singham. "A Framework for Assessing Disruptions in a Clinical Supply Chain Using Bayesian Belief Networks." Journal of Pharmaceutical Innovation (2019): 1-15.
dc.identifier.urihttp://hdl.handle.net/10945/62648
dc.descriptionThe article of record may be found at https://doi.org/10.1007/s12247-019-09396-2
dc.description.abstractPurpose: Clinical trial study failures cause significant disruptions to supply chain operations, which lead to operational ineffi- ciencies and financial losses. Methods: In this paper, a framework to construct a Bayesian belief network (BBN) by leveraging subject matter expertise and probabilistic elicitation methods to quantify the probability of a disruption to a clinical supply chain is presented. Results: The effect of varying input factors on a disruption probability is studied, and new metrics are developed to evaluate the significance of a disruption. Conclusions: This framework allows practitioners to assess the probability of disruptions to their network, thus enabling targeted strategies to be developed and implemented.en_US
dc.language.isoen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleA Framework for Assessing Disruptions in a Clinical Supply Chain Using Bayesian Belief Networksen_US
dc.typeArticleen_US
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.contributor.departmentOperations Research (OR)
dc.subject.authorBayesian belief networksen_US
dc.subject.authorClinical trialsen_US
dc.subject.authorDisruptionen_US
dc.subject.authorRisk analysisen_US
dc.subject.authorSupply chain managementen_US
dc.subject.authorProbabilistic elicitationen_US


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