Bob Scott, a board member of The Operational Research Society, shares his insights on why SME business leaders should consider OR as a vital tool for problem-solving. With an extensive career in OR at organisations like Cap Gemini and PwC, Bob has seen the value of OR in action.
In today’s data-driven world, where the buzz around AI and analytics dominates boardroom discussions, there is a forgotten hero quietly transforming business decision-making. SME business leaders may not have discovered it yet, but operational research (OR) has the potential to be a true game-changer.
What is Operational Research?
Operational research is a scientific approach to solving complex, real-world problems. Originating as a strategic tool during World War II military operations OR has evolved into a powerful methodology for tackling business challenges across industries. It offers a comprehensive approach to problem-solving that goes beyond data crunching and provides the evidence a business needs to select the best solution for solving a problem.
Businesses are using OR to unlock value in their data, model complex systems, and make better decisions with less risk. OR specialists work closely with businesses to understand their challenges and goals. They create mathematical models, algorithms, and customised tools to address specific problems. For example, an OR expert might work with a logistics company to optimise their delivery routes, considering factors like traffic, fuel expenses, and deadlines, or help a retailer analyse sales data to decide how much stock to hold, balancing customer demand with inventory costs.
How OR complements AI and Big Data
OR blends rigorous analytics with strategic thinking to structure problems, identify optimal solutions, and drive informed decision-making. While AI and big data are increasingly vital in today’s business world, they alone are not enough.
AI and big data can identify patterns and predict outcomes, but OR adds the human insight needed to interpret and act on that information. While an AI system might predict a surge in demand for a particular product OR can determine the best way to meet that demand – whether through optimising production schedules, adjusting inventory levels, or reconfiguring supply chains.
OR in Practice: The Pilkington Example
A classic example of how OR was used outside a military setting is by British glass manufacturer Pilkington UK, part of the NSG Group. The company faced the “cutting stock problem,” needing to cut large glass sheets into smaller sizes to meet specific customer demands while minimising waste.
By applying linear programming, Pilkington optimised cutting patterns, reduced costs, and improved production efficiency. This allowed the company to respond more flexibly to customer orders, offer competitive pricing, and improve delivery times. Pilkington’s use of linear programming set a precedent for solving real-world business problems with sophisticated analytical tools and positioned them as a leader in the glass industry.
OR in Industry Today
Today, OR is tackling challenges in various industries. At airports, for example, OR is used to optimise operations, from reducing security queue times during peak travel periods to streamlining baggage handling systems. Airlines use OR to design efficient flight schedules, minimising delays, and maximising aircraft usage.
In the NHS, OR is used to manage patient flow, optimise bed allocation, and reduce waiting times. By modelling patient pathways, OR helps healthcare providers allocate resources more effectively, improving patient outcomes and operational efficiency. In Wales, OR interventions have significantly improved cancer survival rates by streamlining diagnostics and implementing “rapid diagnostic hubs,” making Wales the first UK nation to introduce a single waiting time target for cancer patients.
Retailers use OR to analyse consumer behaviour, forecast demand, and manage supply chains for example, Tesco has used OR to manage expiring stock, reducing food waste, and increasing revenue across multiple product lines.
The Future: Simulation and Digital Twins
An exciting innovation in operational research today is the deployment of Digital twins and simulation technologies. A digital twin is a model or representation, often a simulation model, of a real business process, operation, or facility.
Digital twins enable companies to assess and optimise designs, such as trialling new paint colours for cars or refining aircraft components, long before physical production begins. This reduces development costs, minimises waste, and accelerates innovation. From automotive and aerospace to construction and manufacturing, digital twins provide a dynamic, interactive environment where real-world scenarios can be simulated and analysed, leading to better decision-making and improved product performance.
Digital twins are expected to become even more integrated into various sectors. In smart cities, they could optimise urban planning and infrastructure management and in healthcare, digital twins of patients could personalise treatment and improve medical outcomes.
Conclusion
Operational research provides SMEs with a powerful toolkit for problem-solving that complements AI and big data. It has already demonstrated its impact across industries – from optimising planning in the NHS to transforming retail operations at companies like Tesco. Looking to the future, the integration of OR with advanced technologies like digital twins and simulations has even greater potential. Businesses will be able to experiment with virtual replicas of their operations, optimising processes, and strategies before making real-world changes.
By embracing OR as a core component of their strategic toolkit, SMEs can not only enhance efficiency and resilience but also capitalise on future technological advancements.
