Abstract
dc:description.abstractThis dissertation consists of three essays studying three closely related aspects of supply chain resilience. The first essay is "Value of Supply Disruption Information and Information Accuracy", in which we examine the factors that influence the value of supply disruption information, investigate how information accuracy influences this value, and provide managerial suggestions to practitioners. The study is motivated by the fact that fully accurate disruption information may be difficult and costly to obtain and inaccurate disruption information can decrease the financial benefit of prior knowledge and even lead to negative performance. We perform the analysis by adopting a newsvendor model. The results show that information accuracy, specifically information bias and information variance, plays an important role in determining the value of disruption information. However, this influence varies at different levels of disruption severity and resilience capacity. The second essay is "Quantifying Supply Chain Resilience: A Dynamic Approach", in which we provide a new type of quantitative framework for assessing network resilience. This framework includes three basic elements: robustness, recoverability and resilience, which can be assessed with respect to different performance measures. Then we present a comprehensive analysis on how network structure and other parameters influence these different elements. The results of this analysis clearly show that both researchers and practitioners should be aware of the possible tradeoffs among different aspects of supply chain resilience. The ability of the framework to support better decision making is then illustrated through a systemic analysis based on a real supply chain network. The third essay is "Network Characteristics and Supply Chain Disruption Resilience", in which we investigate the relationships between network characteristics and supply chain resilience. In this work, we first prove that investigating network characteristics can lead to a better understanding of supply chain resilience behaviors. Later we select key characteristics that play a critical role in determining network resilience. We then construct the regression and decision tree models of different supply chain resilience measures, which can be used to estimate supply chain network resilience given the key influential characteristics. Finally, we conduct a case study to examine the estimation accuracy.
Degree
thesis:*- Name thesis:degree_name
- Ph. D.
- Level thesis:degree_level
- doctoral
- Discipline thesis:degree_discipline
- Business, Business Information Technology
- Department dc:contributor.department
- Business Information Technology
- Grantor dc:publisher
- Virginia Tech
- Year dc:date.issued
- 2017
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Li, Yuhong
- Chair dc:contributor.committeechair
-
- Zobel, Christopher W.
- Committee members dc:contributor.committeemember
-
- Seref, Onur
- Russell, Roberta S.
- Rees, Loren P.
- Wang, Gang Alan
Subjects
dc:subject × 5Rights
dc:rights- Statement dc:rights
-
- In Copyright
- Licence dc:rights.uri
Identifiers
dc:identifier.*- Dc Identifier Other
- vt_gsexam:12376
- OAI identifier oai:identifier
- oai:vtechworks.lib.vt.edu:10919/78352