Professor John Quigley
The appliance of management science takes the uncertainty out of decision making as Professor John Quigley explains.
Management science is a relatively newly recognised field; literally applying science to the study of management in a business context. Through developing plans and models, it allows organisations to successfully anticipate the consequences of courses of action, and avoid the need for expensive trials.
The department of management science at the University of Strathclyde works with industry to do exactly that, collaborating with major organisations on how to deal with complex decisions. Professor John Quigley, who heads up the department, explains: “For an organisation, experimenting can be expensive or even impossible. In reality, you can’t just test your ideas out in an unstructured way.
“What a model allows you to do is explore a lot of ‘what if’ scenarios prior to actually implementing your decision or your strategy. By doing this we expose weaknesses for an organisation and then develop more robust strategies. If someone has an idea and wants to go ahead with a particular idea, we run it through the model and say, ‘this is what we are anticipating is going to happen from this course of action, and these are your vulnerabilities.’”
Management science is implemented across all kinds of sectors and organisations, with decision support models helping to manage everything from staffing levels in hospitals, to assets and investment strategies for large companies.
“We engage in a lot of different activities,” says Quigley. “My colleagues are involved in a big project right now with Smart Mature Resilience, which is a multi-disciplinary research project creating more resilient cities in Europe.
“Within this project they are developing a big systematic risk model. It involves a number of cities such as Glasgow, San Sebastian, Kristianstad, Rome, Riga, and Bristol. The idea here is to work with cities and city planners to enhance cities’ capacity to resist or recover from hazardous effects such as climate change.”
But management science – also known as operational research – can be applied to any sector. While working on a project with Rolls Royce marine engines, Quigley spent time developing models to help manage the supply chain and suppliers through risk assessment. He explains: “We were identifying which suppliers were vulnerable, anticipating the better performers and finding some of the weaker ones, who we needed to invest in, and decide on the optimal levels of investment. We were trying to anticipate when we invest in a particular supplier, what the outcome would be: are we going to benefit from the intervention, and is it going to be worth it?”
Quigley is currently working on a project with Scottish and Southern Energy (SSE) and ScottishPower. He explains the process of how a model can take shape, and how it can ultimately deliver enhanced performance for a business: “On the SSE project we are looking at issues concerning hydro plant repowering and big investment decisions about the level of repowering in these big hydro plants.
“We’re working with a variety of different stakeholders, we’re eliciting views, organising these views to then inform a model. This model is a kind of collective view on what the issues are, what the relationships are between the different options, and what the consequences would be.
“What separates us from other quantitative modelling disciplines is that we’re very much focussed on the decision,” Quigley explains. “When there’s a decision that needs to be made, we’ll look at everything from the more qualitative aspects, we’ll have processes for bringing groups of people together, to structuring the problem, to understanding what the issues are. Then we will develop a model and use techniques for quantifying and making use of statistical methodologies, mathematics, probability, and structuring approaches.
“It looks at the whole picture, and that’s also what separates us from some of the other disciplines like mathematics. There is a lot of mathematics involved in this, we do a lot of statistics, we do a lot of mathematic modelling - but we also do the softer side as well, like the problem structuring.”
Quigley continues: “Once we have the model developed we identify the best way forward where we make use of optimisation techniques. Sometimes optimal means the most cost-effective way of doing something, or in the case of a hospital for example, optimal could mean being able to see the highest number of patients.
“It depends on what’s important to a decision maker, and that’s why it’s not just about data analysis but about involving the stakeholders for the organisations. A perfect example of the importance of this is when I worked on a project for the paediatric intensive care unit at the Children’s Hospital of Wisconsin in the United States, where stakeholder involvement was able to enhance the quantitative data analysis bringing issues such as staff stress levels and burn out into the model, ultimately resulting in a more broadly acceptable solution.”
Quigley has spent his entire career working in management science. Originally from Canada, he graduated with a degree in mathematics from the University of Waterloo, and had planned to become an actuary before coming to the UK to undertake a doctorate (PhD) in management science at the University of Strathclyde.
“The PhD was very interesting because it was working for a company called Lucas Aerospace, which designed engine controllers for aircraft engines.” Quigley says. “They wanted to know when they could stop developing, and when an engine was reliable enough to release it into the market.
“At the time there was no model that was satisfactory to give them that kind of decision support, so they sponsored a PhD to help them deliver this model. Over the course of three years I developed a model for them, and it is now part of the international standard for reliability models, so it was a very fruitful project.
“It was also around this time that Rolls Royce had introduced an idea called ‘Power by the Hour’, where they were transforming the aerospace industry so that the cost of unreliability for their aircraft engines was going to be borne by Rolls Royce and their supply chain. This was really exciting because it created an incentive for the aerospace industry to provide much more effective decision support on how to develop the reliability of aircraft engines and their supply chain.
“We got involved in a project that was funded by the Department for Trade & Industry developing these models to inform decisions like finding the best way of improving the reliability of an aircraft engine and its parts. That was a six-year project, and we created many different tools, techniques and processes for developing these models.”
Quigley’s career took him from assessing the reliability of engines to branching out into a wide range of sectors. “A core theme through my career has been work on decision making under uncertainty, and developing ways to help people make more effective decisions when they are faced with these uncertainties.
“I have worked with a lot of engineering companies and gone beyond aerospace, I’ve worked with the Ministry of Defence, Rolls Royce, ScottishPower, Scottish Water and a lot of the big utility companies – all having in common this decision making under uncertainty.”
Now he is also working on training the next generation of management scientists, providing specialist teaching for a number of programmes at the University of Strathclyde including operational research and business analysis and consulting.
Two new master’s degree (MSc) programmes have recently launched at the university, aimed at equipping graduates with the skills they need to enter the growing industries of fintech and data analytics.
The MSc programme in financial technology (fintech) aims to go beyond teaching finance theory and technological skills, and instead takes a blended learning approach that combines theory, intensive practice and industrial engagement.
“There is a lot of activity at the moment within fintech - the coming together of technology with applications in finance.” explains Quigley, “so to reflect that, this is a collaborative programme between ourselves, the department of finance, and the department of computer science.
“We are drawing on this range of skills to equip our students with a good understanding and a good basis in finance along with the necessary computing skills. From us, they can access expertise in problem structuring, data analytics, risk analysis and risk management.”
He says these skills we be valuable in terms of future career prospects for the graduates: “We need people with those skillsets who are not just familiar with analysing or working with computer systems, but are able to interpret this in a meaningful way to develop decision making strategies.
“As far as fintech is concerned, this is a new area. This is one of the first MSc fintech programmes in the world, and the students who will graduate from this have now got a good breadth of understanding across these three core disciplines. Fintech is a global phenomenon so I see lots of opportunities for the graduates.”
Alongside the new fintech programme, a data analytics MSc is also currently in its first year. The course focuses on the use of data analytics techniques within business contexts, making informed decisions, and extracting knowledge from data. The programme has been specifically designed to address the current and future challenges that organisations face across a variety of sectors.
Quigley says: “Data analytics is such an important topic at the moment, and one that transcends more than just one application area. I’m hard pressed to think of any industry that couldn’t make use of data analytics, whether that’s an engineering organisation that needs to understand the reliability of their systems for warranty reasons, in the health sector to look at patient data and reduce waiting times for services, or in the finance industry, identifying new services for banking in a crossover with fintech. I am currently supervising an industry-sponsored PhD project that is concerned with developing methods to assist companies in managing their on-line complaints through improved data analytics.”
Quigley has been passionate about data analytics and operational research since the beginning of his career, but explains that it is currently more relevant than ever: “We have seen an explosion in data analytics with access to data, the development of artificial intelligence techniques and data mining. Management science can clearly play a role here in structuring our problems, and making use of data analytics to inform decision making.
“We do a lot of collaboration within our department, so we work with a lot of different disciplines. We don’t just work with industry but we have a lot of collaborative projects with other departments in the university. MSc data analytics is a collaborative programme that we put together with management science, computer science, mathematics and statistics.”
One key component that is core to both the fintech and data analytics programme is a module called “becoming an effective practitioner” – it gets the students out of a classroom setting and into a situation where they can tackle real industry problems.
He says: “We have an integration module here where we bring in practitioners in fintech, and for data analytics we bring in practitioners from data analytics, and we give industrial problems to the class relevant to those areas, and we seek to meaningfully bring in these tools and techniques to solve industrial problems.
“Throughout the year we’ve got industrial cases running right from day one, and they typically work on a two-or three-week cycle where industry will come in and give them a problem and then the class will go off and work on that and come back with presentations.
“Quite often we find that you don’t just have one standalone solution, so the answer doesn’t just reside in one discipline, it’s not just in some artificial intelligence solution or not just understanding the financial situation. It’s actually often bringing these disciplines together, using a multi-disciplinary approach.”
As the university brings in organisations through the effective practitioner modules, it seeks to maintain its relationship with them so students can continue to work alongside them after graduation.
Quigley says: “We are providing industry-based projects for the data analytics students, and we are looking at developing similar projects for the fintech programme where the students would be put into organisations to do their final project. As far as opportunities afterwards are concerned, there are a lot.”
They are opportunities well worth pursuing, according to Quigley: “What has been really fun about this, and what really got me excited about doing management science, is that my whole career has been spent working with industry. I have had a very close relationship with organisations, being informed by problems that they have and then going back and developing methods to support those problems.
“The kind of problems they have been challenged with is always decisions under uncertainty. The types of decisions they have differ, the types of uncertainty they are faced with is different, the types of data problems that they are dealing with is quite different, so that always creates new challenges and the need to develop new techniques and new models.”
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