Professor John Quigley

Management Science

Personal statement

John is an Industrial Statistician with expertise in developing and applying statistical and stochastic methods to build decision support models. In particular, he has extensive experience in developing models for reliability growth analysis.  For example, with his colleague Professor Walls, they were actively leading activities in the DTI/aerospace industry funded project, Reliability Enhancement Methodology and Modelling (REMM) which was awarded the Simms Prize by the Royal Aeronautical Society.  He has been involved in consultancy and applied research projects for reliability growth with, for example, Aero-Engine Controls, Rolls Royce, Irving Aerospace, BAE SYSTEMS and the MOD. The model developed as part of the REMM project is included in the industry standard for reliability growth analysis methods, BS/IEC 61164 as well as contributing to the Strathclyde Business Schools impact cases for the Research Enhancement Framework.

Beyond defence, John has experience of developing decision support models for asset management for energy utilities (e.g. Scottish Power, SSE), water utilities (KTP with Scottish Water) and critical infrastructure (e.g. anchorage condition assessment of Forth Road Bridge).  Wider modelling has been in support of risk analysis (e.g. supplier risk analysis with Rolls Royce as part on a major ongoing EPSRC research project, risk of train derailments with Railway Safety and Standards Board). 

John has worked with the European Food Safety Agency (EFSA) training staff for elicitation and quantification of expert uncertainty as well as leading the COST Working Group on Processes and Procedures for eliciting expert judgment.  The COST project resulted in the book Elicitation: The Science and Art of Structuring Judgement.

John is an Associate of the Society of Actuaries, a Chartered Statistician, and a member of the Safety and Reliability Society.  He has a Bachelor of Mathematics in Actuarial Science from the University of Waterloo, Canada and a PhD in Management Science from the University of Strathclyde. 

Publications

Quantifying the benefit of SHM : can the VoI be negative?
Verzobio Andrea, Bolognani Denise, Zonta Daniele, Quigley John
13th Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019 Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019 (2019)
https://doi.org/10.1117/12.2518878
Quantifying the benefit of structural health monitoring : what if the manager is not the owner?
Bolognani Denise, Verzobio Andrea, Tonelli Daniel, Cappello Carlo, Glisic Branko, Zonta Daniele, Quigley John
Structural Health Monitoring (2018)
https://doi.org/10.1177/1475921718794506
A probabilistic design reuse index
Vasantha Gokula, Sherlock Andrew, Corney Jonathan, Quigley John
ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2018) (2018)
Sequential refined partitioning for probabilistic dependence assessment
Werner Christoph, Bedford Tim, Quigley John
Risk Analysis (2018)
https://doi.org/10.1111/risa.13162
Rationalising the use of Twitter by official organisations during risk events : operationalising the social amplification of risk framework through causal loop diagrams
Comrie EL, Burns C, Coulson AB, Quigley J, Quigley KF
European Journal of Operational Research (2018)
https://doi.org/10.1016/j.ejor.2018.07.034
The conditional value of information of SHM : what if the manager is not the owner?
Tonelli Daniel, Verzobio Andrea, Bolognani Denise, Cappello Carlo, Glisic Branko, Zonta Daniele, Quigley John
Health Monitoring of Structural and Biological Systems XII Health Monitoring of Structural and Biological Systems XII 2018 (2018)
https://doi.org/10.1117/12.2296614

more publications

Teaching

John provides specialist teaching for a number of programmes at various levels.  These have included teaching Management Science at all levels of undergraduate and postgraduate as well as Executive Education.  The postgraduate programmes for which he teaches include MSc in Operational Research and Business Analysis & Consulting as well as MBA.  Together with Professor Scholarios from the department of Human Resouce Management, he developed the Research Methods training module for all research students in the Strathclyde Business School.  John has taught in 10 different international centres across Europe, the Middle East and South East Asia, as well as Executive Education in Canada. 

 

John is committed to making effective use of technology to support teaching and learning.  He has been involved in managing, developing and teaching on pedagogically successful online and distance courses, as well as investigating the effectiveness of using virtual reality environments to support teaching.    

Professional activities

Workshops on Mathematical Methods in Reliability
Organiser
2011
Selex Gallileo
Visiting researcher
2011
Risk Governance
Organiser
2011
Canada School Contribution Agreement - Foundations of Risk
To be assigned
2010
Consultancy with Doosan Babcock Power Systems
Advisor
2010
International Conference on Reliability
Keynote/plenary speaker
2010

more professional activities

Projects

Towards Game-changer Service Operation Vessels for Offshore Windfarms (NEXUS) H2020-BG-2016-2017
Vassalos, Dracos (Principal Investigator) Bedford, Tim (Co-investigator) Boulougouris, Evangelos (Co-investigator) Bujorianu, Luminita (Co-investigator) Lazakis, Iraklis (Co-investigator) McMillan, David (Co-investigator) Puisa, Romanas (Co-investigator) Quigley, John (Co-investigator) Revie, Matthew (Co-investigator) Theotokatos, Gerasimos (Co-investigator) Walls, Lesley (Co-investigator)
01-Jan-2017 - 31-Jan-2020
TIC LCPE Repowering Hydro Plants - a Decision Support Tool
Walls, Lesley (Principal Investigator) Howick, Susan (Co-investigator) Quigley, John (Co-investigator) Revie, Matthew (Co-investigator)
01-Jan-2017 - 31-Jan-2018
ERDF Atlantic Area Programme 2014-2020 IN. 4.0 Project
Ates, Aylin (Co-investigator) Sminia, Harry (Co-investigator) Walls, Lesley (Co-investigator) Quigley, John (Co-investigator)
01-Jan-2017 - 31-Jan-2020
Design the Future 2: Enabling Design Re-use through Predictive CAD
Corney, Jonathan (Principal Investigator) Quigley, John (Co-investigator)
"Engineering Design work typically consists of reusing, configuring, and assembling of existing components, solutions and knowledge. It has been suggested that more than 75% of design activity comprises reuse of previously existing knowledge.

However in spite of the importance of design reuse activities researchers have estimated that 69% of companies have no systematic approaches to preventing the reinvention of the wheel. The major issue for supporting design re-use is providing solutions that partially re-use previous designs to satisfy new requirements. Although 3D Search technologies that aim to create a Google for 3D shapes have been increasing in capability and speed for over a decade they have not found widespread application and have been referred to as a solution looking for a problem! This project is motivated by the belief that, with a new type of user interface, 3D search could be the solutions to the design reuse problem.

The system this research is aiming to produce is analogous to the text message systems of mobile phones. On mobile phones 'Predictive text' systems complete words or phrases by matching fragments against dictionaries or phrases used in previous messages. Similarly a 'predictive CAD' system would complete 3D models using 'shape search' technology to interactively match partial CAD features against component databases. In this way the system would prompt the users with fragments of 3D components that complete, or extend, geometry added by the user. Such a system could potential increase design productivity by making the reuse of established designs an efficient part of engineering design.

Although feature based retrieval of components from databases of 3D components has been demonstrated by many researchers so far the systems reported have been relatively slow and unable to be components of an interactive design system. However recent breakthroughs in sub-graph matching algorithms have enabled the emergence of a new generation of shape retrieval algorithms, which coupled with multi-core hardware, are now fast enough to support interactive, predictive design interfaces. This proposal aims to investigate the hypothesis that a Predictive CAD system would allow engineers to more effectively design new components that incorporate established, or standard, functional or manufacturing geometries. This would find commercial applications within large or distributed engineering organizations.

This project is an example of how data mining could potentially be employed to increase design productivity because even small engineering companies will have many hundreds of megabytes of CAD data that a Predictive CAD system would effectively pattern match against."
01-Jan-2017 - 31-Jan-2020
Cognitive Geology Phase 2
Quigley, John (Principal Investigator) Shipton, Zoe (Co-investigator)
One of the innovative steps taken by Cognitive Geology in the area of Oilfield Evaluation area is to create the CDF of economic outcomes weighted by quality. This project proposes to improve the quality metric and in the process innovate the preservation and automatic use of expert Geological/Geophysical opinion.
Assessing model quality is of critical importance to the creation of the cumulative density function that is used by Cognitive Geology to evaluate likely economic outcomes. Beyond the statistical measures of the quality of fit, there are also expert opinions. Expert opinions are very expensive to obtain, often use tacit knowledge and mathematically are often not wholly supportable from the apparent evidence. Capturing and automating the application of the expert opinions is therefore an important objective within the established oil industry and also within the development of the cognitive technology aspects of the product range being developed by Cognitive Geology.
Traditional Oilfield evaluation practises use expert knowledge. The group of Geologists who are skilled in the art are rapidly reaching the end of their careers, and as they leave with them leaves the skill base and the knowledge that took decades to acquire. Evidence based Probabilistic Oilfield evaluation is proving itself increasingly valuable, but is traditionally mathematically derived with little consideration of the Geological expert’s opinions, the automatic inclusion of which has up to now been impossible.
This project proposes the creation of a rigorous method by which relevant knowledge possessed by an expert might be preserved for future use in an automated system.
01-Jan-2016 - 31-Jan-2017
KTP-Scottish Water
Walls, Lesley (Principal Investigator) Arulselvan, Ashwin (Co-investigator) Barlow, Euan (Co-investigator) Quigley, John (Co-investigator) Revie, Matthew (Co-investigator)
08-Jan-2016 - 07-Jan-2018

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