Exciting Times for Operational Research

These are exciting times for Operational Research. The rapid development of artificial intelligence (AI) and machine learning (ML) are fundamentally changing the way organisations work. Consequently, OR provision at universities is changing. A broadening out from traditional OR courses delivered in mathematics departments to more encompassing managerial courses offered in business schools has been under way for a while now. There is currently a clear move towards universities offering courses in data science, machine learning, artificial intelligence, etc. While this may be seen as a threat to OR, it seems to me that the skills of OR practitioners and academics, as well as its general and, yes, traditional, philosophy is needed now more than ever. It is, therefore, my great pleasure to join the OR Society’s Education Committee at this point in time.

The changing technology used by organisations to optimise their operations can easily give way to a sense that “AI will solve all problems.” Yet, as always, the quality of the output depends on the quality of the input and, in the case of machine learning algorithms, the link between model inputs and outputs is often not very clear (if it can be established at all). So, on the one hand, there is a trend that traditional OR content is de-emphasised in university curricula, while, on the other hand, the need for a deep understanding of models and algorithms is increasing.

These developments provide, in my view, a clear agenda for the OR Society and, in particular, its Education committee. There are currently several ongoing projects that aim at positioning the OR Society in this shifting educational landscape, led by OR Society superstar Dr Chiara Carparelli. A first project is to strengthen links with university course directors to ensure that OR content in the curriculum is recognised as such. A major part of this work is to inventorize the OR educational provision in the UK. This requires a careful look at the curricula of any course that could have OR content. Here, of course, we immediately run into a definitional issue: what exactly do we mean by OR content? During the most recent Education committee meeting, on 31 January 2025, this was therefore, unsurprisingly, a main item of discussion. And one that I suspect will be ongoing for a while!

The discussion about what constitutes OR content also has a bearing on the criteria that we use for eligibility of the Society’s PG and UG prizes. While we already have the well-established May Hicks award for the best student project for a client organisation, the Society has recently instituted a new award for the best student project conducted within a university. The committee also decided to review the OR Society undergraduate award with a view to increasing the Society’s visibility.

A second project is the development of an “OR Awareness Resource,” aimed at non-OR students. Such students are often not aware of the potential careers that are available to them if they move into postgraduate taught programmes in the area. In addition, non-OR students may not be aware that such programmes are open to them given their backgrounds in the first place. The need for such signposting has become more obvious in recent years in light of the AI/ML developments mentioned earlier. The Education committee has assisted putting together a brief for this resource and will continue to assist in its further development.

Third, over the last few years there have been several occasions where the OR Society has been asked to provide input into consultations about education-related issues or to attend education-related events. Recent examples include the consultation around the Academy for the Mathematical Sciences and last year’s Maths Summit. The Education committee will assist the Society’s professional services staff in fully engaging with the national policy landscape and, as a result, increase the Society’s visibility in the area.

Finally, the committee works closely with the “OR in Education” (ORiE) project. This great initiative provides OR-related resources for and liaises with school teachers to promote OR to school-aged children (and their parents), but also to engage with university students. Some of ORiE’s activities are school- or universityspecific, whereas others have a regional or national character. For example, ORiE members (and other volunteers) attend careers fairs at universities, ORiE will be present at the upcoming STEM Day at the National Memorial Arboretum on 25th February (in partnership with the Advanced Mathematics Support Programme), and last year, ORiE had a presence at the New Scientist Live event, where the Society contributed two speakers and connected with over 1,500 students and adults.

To conclude, I hope you will allow me to add some personal reflections on the future of OR in a world where AI and ML appear to crowd out traditional OR methods. There are, I think, two reasons why perhaps we may witness a return to the simpler models that we know so well. First, a major issue facing AI/ML applications is their high levels of energy usage. It seems to me that there is a case to be made for the construction of simple models that can be solved on one’s desktop or laptop to gain some initial insight, before running more elaborate and energy-intensive models using AI/ML. An added bonus of such an approach is that the simpler models also allow for an understanding of the causal relationships between inputs and outputs, which are more difficult to discern in more complex models. Hence, the use of simpler, traditional, OR methods can simultaneously serve two purposes. On the one hand, it gives a benchmark against which the outputs of more elaborate AI/ML-driven analyses can be sense-checked. On the other hand, simpler models give an insight in the structure of complex systems that can be reported to end users and help communicate the importance of OR in the management of complex operations.

These are, indeed, exciting times for OR.