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Concurrent modeling of causality, temporality, and systemic risk in supply chains

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TitleInfo
Title
Concurrent modeling of causality, temporality, and systemic risk in supply chains
SubTitle
a hybrid approach
Name (type = personal)
NamePart (type = family)
Sun
NamePart (type = given)
Ke
NamePart (type = date)
1990-
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Ke Sun
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
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Luxhøj
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James T.
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James T. Luxhøj
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Advisory Committee
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chair
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Jafari
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Mohsen A.
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Mohsen A. Jafari
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Advisory Committee
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internal member
Name (type = personal)
NamePart (type = family)
Jeong
NamePart (type = given)
Myong K.
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Myong K. Jeong
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Advisory Committee
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internal member
Name (type = personal)
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Lawrence
NamePart (type = given)
Sheila M.
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Sheila M. Lawrence
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Advisory Committee
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outside member
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Rutgers University
Role
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degree grantor
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School of Graduate Studies
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school
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theses
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2017
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2017-10
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2017
Place
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xx
Language
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eng
Abstract (type = abstract)
The uncertainty of operations for supply chain involved companies is becoming more complex with the growth of globalized business collaboration. A supply chain is a complex system with dynamic flows of capital, goods, information and people. The temporal fluctuations of politics, economics, nature and technology on a supply chain process may potentially cause disruptions to the whole system. The malfunctions of supply chain systems around the world cost companies billions of dollars and months of recovery time every year. Prevention and mitigation of supply chain disruption risks are crucial for companies to maintain their competitive advantage. However, the performance of mitigation plans may be unsatisfactory due to a myriad of interactive impact factors in supply chains under uncertainty. This situation requires a direct and concise tool to monitor and control supply chain risks concurrently. Various qualitative and quantitative risk analysis tools are introduced to unveil the myth of uncertainty. A Bayesian Belief Network (BBN) is one of the risk modeling approaches that provides a systemic conditional probabilistic view on risk analysis. A Dynamic Bayesian Network (DBN) offers a solution that enables temporal factors in a BBN, which is in consonance with the characteristics of time-sensitive risks in supply chains. Inputs and outputs of DBNs are probability values. Supply chain practitioners may have difficulty to concretize the values into practical operations immediately because supply chain performance is measured with actual units of money or inventory. System Dynamics (SD) is a simulation tool for modeling complex socio-technical systems in feedbacks, stocks and their flows. However, SD has limitations in simulating conditional probabilities within the dynamic flows. By utilizing the essence of DBN and SD, this dissertation proposes a Dynamic Flow Bayesian Network (DFBN) to offer a comprehensive methodology for supply chain risk analysis. An Optimized Dynamic Flow Bayesian Network (ODFBN) method is developed with modifications based on the DFBNs by incorporating multi-objective optimization, multi-pricing strategy and Value-at-Risk. By applying the concept of Supply Chain Network Equilibrium, an Equilibrated Dynamic Flow Bayesian Network (EDFBN) method is developed to balance the needs of each stage and maximize the profitability of the entire supply chain. In this dissertation, mathematical integration of the models is presented and application to a supply chain case study inspired by the real-world is also conducted. Finally, a prototypical executable interface for industrial implementation is developed.
Subject (authority = RUETD)
Topic
Industrial and Systems Engineering
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
Identifier
ETD_8287
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xii, 208 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Business logistics
Subject (authority = ETD-LCSH)
Topic
Risk management
Note (type = statement of responsibility)
by Ke Sun
RelatedItem (type = host)
TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3BZ696J
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
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Sun
GivenName
Ke
Role
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Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2017-08-11 10:23:39
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Name
Ke Sun
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Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
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Copyright protected
Availability
Status
Open
Reason
Permission or license
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2017-08-11T10:21:34
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2017-08-11T10:21:34
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