2 Search Results for "Quigley, John"

Document
DOI: 10.4230/OASIcs.SCOR.2014.1
A Novel Framework for Quantification of Supply Chain Risks

Authors: Abroon Qazi, John Quigley, and Alex Dickson

Published in: OASIcs, Volume 37, 4th Student Conference on Operational Research (2014)

Abstract
Supply chain risk management is an active area of research and there is a research gap of exploring established risk quantification techniques in other fields for application in the context of supply chain management. We have developed a novel framework for quantification of supply chain risks that integrates two techniques of Bayesian belief network and Game theory. Bayesian belief network can capture interdependency between risk factors and Game theory can assess risks associated with conflicting incentives of stakeholders within a supply network. We introduce a new node termed ‘Game theoretic risks’ in Bayesian network that gets its qualitative and quantitative structure from the Game theory based analysis of the existing policies and partnerships within a supply network. We have applied our proposed risk modeling framework on the development project of Boeing 787 aircraft. Two different Bayesian networks have been modeled; one representing the Boeing’s perceived supply chain risks and the other depicting real time supply chain risks faced by the company. The qualitative structures of both the models were developed through cognitive maps that were constructed from the facts outlined in a case study. The quantitative parts were populated based on intuition and subsequently updated with the facts. The Bayesian network model incorporating quantification of game theoretic risks provides all the reasons for the delays and financial loss of the project. Furthermore, the proactive strategies identified in various case studies were verified through our model. Such an integrated application of two different quantification techniques in the realm of supply chain risk management bridges the mentioned research gap. Successful application of the framework justifies its potential for further testing in other supply chain risk quantification scenarios.
Cite as

Abroon Qazi, John Quigley, and Alex Dickson. A Novel Framework for Quantification of Supply Chain Risks. In 4th Student Conference on Operational Research. Open Access Series in Informatics (OASIcs), Volume 37, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@InProceedings{qazi_et_al:OASIcs.SCOR.2014.1,
  author =	{Qazi, Abroon and Quigley, John and Dickson, Alex},
  title =	{{A Novel Framework for Quantification of Supply Chain Risks}},
  booktitle =	{4th Student Conference on Operational Research},
  pages =	{1--15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-67-5},
  ISSN =	{2190-6807},
  year =	{2014},
  volume =	{37},
  editor =	{Crespo Del Granado, Pedro and Joyce-Moniz, Martim and Ravizza, Stefan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.SCOR.2014.1},
  URN =		{urn:nbn:de:0030-drops-46653},
  doi =		{10.4230/OASIcs.SCOR.2014.1},
  annote =	{Keywords: bayesian belief network, cognitive maps, conflicting incentives, game theory, supply chain risk management}
}
Document
DOI: 10.4230/OASIcs.SCOR.2012.21
Empirical Bayes Methods for Discrete Event Simulation Performance Measure Estimation

Authors: Shona Blair, Tim Bedford, and John Quigley

Published in: OASIcs, Volume 22, 3rd Student Conference on Operational Research (2012)

Abstract
Discrete event simulation (DES) is a widely-used operational research methodology facilitating the analysis of complex real-world systems. Although, generally speaking, simplicity is greatly desirable in DES modelling applications, in many cases the nature of the underlying system results in simulation models which are large in scale, complex, and expensive to run. As such, the careful design and analysis of simulation experiments is essential to ensure valid and efficient inference concerning DES model performance measures. It is envisaged that empirical Bayes (EB) methods, which enable data to be pooled across a set of populations to support inference of the parameters of a single population, may be of use within this context. Despite this potential, EB has so far been neglected within the DES literature. This paper presents a preliminary computational investigation into the efficacy of EB procedures in the estimation of DES performance measures. The results of this investigation, and their significance, are explored. Additionally, likely directions for future research are also addressed.
Cite as

Shona Blair, Tim Bedford, and John Quigley. Empirical Bayes Methods for Discrete Event Simulation Performance Measure Estimation. In 3rd Student Conference on Operational Research. Open Access Series in Informatics (OASIcs), Volume 22, pp. 21-30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InProceedings{blair_et_al:OASIcs.SCOR.2012.21,
  author =	{Blair, Shona and Bedford, Tim and Quigley, John},
  title =	{{Empirical Bayes Methods for Discrete Event Simulation Performance Measure Estimation}},
  booktitle =	{3rd Student Conference on Operational Research},
  pages =	{21--30},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-39-2},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{22},
  editor =	{Ravizza, Stefan and Holborn, Penny},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.SCOR.2012.21},
  URN =		{urn:nbn:de:0030-drops-35436},
  doi =		{10.4230/OASIcs.SCOR.2012.21},
  annote =	{Keywords: Discrete Event Simulation, Analysis Methodology, Empirical Bayes}
}
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