Professor John Quigley

Head Of Department

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.

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. 


Emulation of utility functions over a set of permutations : sequencing reliability growth tasks
Wilson Kevin J, Henderson Daniel A, Quigley John
Technometrics, (2017)
A methodology for constructing subjective probability distributions with data
Quigley John, Walls Lesley
ElicitationElicitation, (2017)
Elicitation in the classical model
Quigley John, Colson Abigail, Aspinall Willy, Cooke R.M.
ElicitationElicitation, (2017)
Elicitation : state of the art and science
Dias Luis C , Morton Alec, Quigley John
ElicitationElicitation, (2017)
Learning from mixed OR method practice : the NINES case study
Howick Susan, Ackermann Fran, Walls Lesley, Quigley John, Houghton Tom
Omega Vol 69, pp. 70-81, (2017)
Elicitation : The Science and Art of Structuring Judgement
Dias Luis C , Morton Alec, Quigley John

more publications


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

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

more professional activities


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)
Period 01-Sep-2017 - 31-Aug-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."
Period 01-Aug-2017 - 31-Jul-2020
EPSRC Doctoral Training Grant | Blair, Shona
Quigley, John (Principal Investigator) Bedford, Tim (Co-investigator) Blair, Shona (Research Co-investigator)
Period 01-Oct-2008 - 01-May-2016
EPSRC Doctoral Training Grant - DTA, University of Strathclyde | Purves, David
Walls, Lesley (Principal Investigator) Quigley, John (Co-investigator) Purves, David (Research Co-investigator)
Period 01-Jan-2014 - 01-Sep-2017
Evacuating the Halifax Peninsula: Multidisciplinary Analysis and Training to Improve Evacuation from Coastal Floods
Quigley, John (Researcher) Burns, Calvin (Researcher)
Our research will provide two principal outcomes. First, we will issue a publically available report that summarizes our findings and recommendations for improvement of evacuations during floods. Secondly, we will develop a prototype for a collaborative game that can be used to train emergency managers for different evacuation scenarios, focussing on interdependence, time constraint, unanticipated human reactions, judgement, cooperation and accountability. Experience can be difficult to obtain in the context of evacuations because they happen so rarely; our prototype will help to develop skills and judgement so emergency managers can be more aware of context and better prepared should an event occur.
Period 01-Jan-2016 - 30-Apr-2017
Impact Acceleration Account - University Of Strathclyde 2012 / RA9178
Walls, Lesley (Principal Investigator) Quigley, John (Co-investigator)
Period 01-Oct-2012 - 31-Mar-2017

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