Leveraging a Bayesian Network approach to model and analyze supplier vulnerability to severe weather risk: A case study of the U.S. pharmaceutical supply chain following Hurricane Maria
Identifieur interne : 000386 ( Main/Exploration ); précédent : 000385; suivant : 000387Leveraging a Bayesian Network approach to model and analyze supplier vulnerability to severe weather risk: A case study of the U.S. pharmaceutical supply chain following Hurricane Maria
Auteurs : Jeanne-Marie Lawrence [États-Unis] ; Niamat Ullah Ibne Hossain [États-Unis] ; Raed Jaradat [États-Unis] ; Michael Hamilton [États-Unis]Source :
- International Journal of Disaster Risk Reduction [ 2212-4209 ] ; 2020.
Abstract
The United States government has identified the health care sector as part of the critical infrastructure for homeland security to protect citizens against health risks arising from terrorism, natural disasters, and epidemics. Citizens also have expectations about the role that health care plays in enjoying a good quality of life, by providing response systems to handle emergencies and other illness situations adequately. Among the systems required to support desired performance levels is a robust and resilient pharmaceutical supply chain that is free of disruption. Shortages of drugs place undue pressure on healthcare providers to devise alternative approaches to administer patient care. With climate change expected to result in increasingly severe weather patterns in the future, it is critical that logistics engineers understand the impact that a catastrophic weather event could have on supply chain disruption to facilitate the design of supply systems that are robust and resilient. This study investigates the main causal and intermediate events that led to risk propagation in, and disruption of, the U.S. pharmaceutical supply chain following Hurricane Maria. A causality Bayesian model is developed to depict linkages between risk events and quantify the associated cumulative risk. The quantification is further examined through different advanced techniques such as predictive inference reasoning and sensitivity analysis. The general interpretation of these analyses suggests that port resilience is imperative to pharmaceutical supply chain performance in the case of Puerto Rico.
Url:
DOI: 10.1016/j.ijdrr.2020.101607
PubMed: 32346504
PubMed Central: 7187851
Affiliations:
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last="Ibne Hossain">Niamat Ullah Ibne Hossain</name><affiliation wicri:level="1"><nlm:aff id="aff1">Department of Industrial and Systems Engineering, Mississippi State University, PO Box 9542, Mississippi State, 39762, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Industrial and Systems Engineering, Mississippi State University, PO Box 9542, Mississippi State, 39762</wicri:regionArea><wicri:noRegion>39762</wicri:noRegion></affiliation></author><author><name sortKey="Jaradat, Raed" sort="Jaradat, Raed" uniqKey="Jaradat R" first="Raed" last="Jaradat">Raed Jaradat</name><affiliation wicri:level="1"><nlm:aff id="aff1">Department of Industrial and Systems Engineering, Mississippi State University, PO Box 9542, Mississippi State, 39762, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Industrial and Systems Engineering, Mississippi State University, PO Box 9542, Mississippi State, 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Jeanne Marie" uniqKey="Lawrence J" first="Jeanne-Marie" last="Lawrence">Jeanne-Marie Lawrence</name><affiliation wicri:level="1"><nlm:aff id="aff1">Department of Industrial and Systems Engineering, Mississippi State University, PO Box 9542, Mississippi State, 39762, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Industrial and Systems Engineering, Mississippi State University, PO Box 9542, Mississippi State, 39762</wicri:regionArea><wicri:noRegion>39762</wicri:noRegion></affiliation></author><author><name sortKey="Ibne Hossain, Niamat Ullah" sort="Ibne Hossain, Niamat Ullah" uniqKey="Ibne Hossain N" first="Niamat Ullah" last="Ibne Hossain">Niamat Ullah Ibne Hossain</name><affiliation wicri:level="1"><nlm:aff id="aff1">Department of Industrial and Systems Engineering, Mississippi State University, PO Box 9542, Mississippi State, 39762, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Industrial and Systems Engineering, Mississippi State University, PO Box 9542, Mississippi State, 39762</wicri:regionArea><wicri:noRegion>39762</wicri:noRegion></affiliation></author><author><name sortKey="Jaradat, Raed" sort="Jaradat, Raed" uniqKey="Jaradat R" first="Raed" last="Jaradat">Raed Jaradat</name><affiliation wicri:level="1"><nlm:aff id="aff1">Department of Industrial and Systems Engineering, Mississippi State University, PO Box 9542, Mississippi State, 39762, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Industrial and Systems Engineering, Mississippi State University, PO Box 9542, Mississippi State, 39762</wicri:regionArea><wicri:noRegion>39762</wicri:noRegion></affiliation></author><author><name sortKey="Hamilton, Michael" sort="Hamilton, Michael" uniqKey="Hamilton M" first="Michael" last="Hamilton">Michael Hamilton</name><affiliation wicri:level="1"><nlm:aff id="aff2">Institute for Systems Engineering Research, Mississippi State University, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Institute for Systems Engineering Research, Mississippi State University</wicri:regionArea><wicri:noRegion>Mississippi State University</wicri:noRegion></affiliation></author></analytic><series><title level="j">International Journal of Disaster Risk Reduction</title><idno type="eISSN">2212-4209</idno><imprint><date when="2020">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>The United States government has identified the health care sector as part of the critical infrastructure for homeland security to protect citizens against health risks arising from terrorism, natural disasters, and epidemics. Citizens also have expectations about the role that health care plays in enjoying a good quality of life, by providing response systems to handle emergencies and other illness situations adequately. Among the systems required to support desired performance levels is a robust and resilient pharmaceutical supply chain that is free of disruption. Shortages of drugs place undue pressure on healthcare providers to devise alternative approaches to administer patient care. With climate change expected to result in increasingly severe weather patterns in the future, it is critical that logistics engineers understand the impact that a catastrophic weather event could have on supply chain disruption to facilitate the design of supply systems that are robust and resilient. This study investigates the main causal and intermediate events that led to risk propagation in, and disruption of, the U.S. pharmaceutical supply chain following Hurricane Maria. A causality Bayesian model is developed to depict linkages between risk events and quantify the associated cumulative risk. The quantification is further examined through different advanced techniques such as predictive inference reasoning and sensitivity analysis. The general interpretation of these analyses suggests that port resilience is imperative to pharmaceutical supply chain performance in the case of Puerto Rico.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Ashp" uniqKey="Ashp">ASHP</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="American Medical Association Ama" uniqKey="American Medical Association Ama">American Medical Association (AMA)</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pharmacycheckercom" uniqKey="Pharmacycheckercom">PharmacyCheckercom</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tang, C S" uniqKey="Tang C">C.S. Tang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="O Marrah, K" uniqKey="O Marrah K">K. 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Ritchie</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>États-Unis</li></country></list><tree><country name="États-Unis"><noRegion><name sortKey="Lawrence, Jeanne Marie" sort="Lawrence, Jeanne Marie" uniqKey="Lawrence J" first="Jeanne-Marie" last="Lawrence">Jeanne-Marie Lawrence</name></noRegion><name sortKey="Hamilton, Michael" sort="Hamilton, Michael" uniqKey="Hamilton M" first="Michael" last="Hamilton">Michael Hamilton</name><name sortKey="Ibne Hossain, Niamat Ullah" sort="Ibne Hossain, Niamat Ullah" uniqKey="Ibne Hossain N" first="Niamat Ullah" last="Ibne Hossain">Niamat Ullah Ibne Hossain</name><name sortKey="Jaradat, Raed" sort="Jaradat, Raed" uniqKey="Jaradat R" first="Raed" last="Jaradat">Raed Jaradat</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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