Journal Article10.1016/J.JMSY.2016.09.006
A general framework for assessing system resilience using Bayesian networks: A case study of sulfuric acid manufacturer
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TL;DR: In this paper, the authors explored the key drivers that contribute to the design of resilient supply chains based on the notion of absorptive, adaptive and restorative capacities and introduced a generic conceptual framework comprising five key phases: threat analysis, resilience capacity design, resilience cost evaluation, resilience quantification, and resilience improvement.
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About: This article is published in Journal of Manufacturing Systems. The article was published on 01 Oct 2016. The article focuses on the topics: Resilience (network) & Socio-ecological system.
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Citations
Review of quantitative methods for supply chain resilience analysis
TL;DR: This paper conceptualizes and comprehensively presents a systematic review of the recent literature on quantitative modeling the SCR while distinctively pertaining it to the original concept of resilience capacity.
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Resilient supplier selection and optimal order allocation under disruption risks
Seyed Mohsen Hosseini,Nazanin Morshedlou,Dmitry Ivanov,M.D. Sarder,Kash Barker,Abdullah Al Khaled +5 more
TL;DR: A stochastic bi-objective mixed integer programming model is proposed to support the decision-making in how and when to use both proactive and reactive strategies in supplier selection and order allocation and can benefit suppliers to find the optimal set of operational decisions that enhance their resilience capabilities.
336
Probabilistic framework to evaluate the resilience of engineering systems using Bayesian and dynamic Bayesian networks
TL;DR: A novel approach to assess the time-dependent resilience of engineering systems using resilience indicators using the Dynamic Bayesian Network (DBN), a mathematical probabilistic framework based on the DBN is developed to model the resilience of dynamic engineering systems.
217
Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review.
TL;DR: In this article, the authors present a survey on the application of Bayesian networks to supply chain resilience and risk analysis, and discuss the challenges of current research, identifying and proposing future research directions.
214
Ripple effect modelling of supplier disruption: integrated Markov chain and dynamic Bayesian network approach
TL;DR: A new model based on integration of Discrete-Time Markov Chain (DTMC) and a Dynamic Bayesian Network (DBN) is constructed and a metric that quantifies the ripple effect of supplier disruption on manufacturers in terms of total expected utility and service level is proposed.
References
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Generic metrics and quantitative approaches for system resilience as a function of time
TL;DR: Generic metrics and formulae for quantifying system resilience are proposed that are generic enough to be implemented in a variety of applications as long as appropriate figures-of-merit and the necessary system parameters, system decomposition and component parameters are defined.
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•Book
Risk Assessment and Decision Analysis with Bayesian Networks
Norman Fenton,Martin Neil +1 more
- 07 Nov 2012
TL;DR: Risk Assessment and Decision Analysis with Bayesian Networks explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide powerful insights and better decision making.
821
Understanding Responses to Supply Chain Disruptions: Insights from Information Processing and Resource Dependence Perspectives
TL;DR: In this article, the authors apply information processing and resource dependence perspectives to identify the repertoire of strategic responses to supply chain disruptions and to devise and test a model that explains the occurrence of the alternative responses.
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