Connecting People Who Turn Data Into Action

Healthcare: Improving NHS Patient Flow

Challenge:
Hospitals across the NHS faced overcrowding and delays in patient admissions, creating pressure on staff and resources.

How OR + DS worked together:
Data scientists analysed millions of patient records to identify demand patterns and predict admissions. Operational researchers then built simulation and optimisation models to test new staffing and scheduling approaches.

Outcome:
This collaboration led to better use of resources, shorter waiting times, and more efficient patient flow. The NHS now uses similar OR–DS methods across several trusts to support data-driven decision-making in emergency care planning.

Transport: London Underground Demand Modelling

Challenge:
Transport for London (TfL) needed to manage increasing passenger demand while maintaining reliable service across a complex underground network.

How OR + DS worked together:
Data scientists processed millions of Oyster and contactless journeys to predict when and where congestion would occur. Operational researchers used optimisation and network modelling to redesign timetables, improve train frequency, and balance capacity with cost.

Outcome:
The integrated approach improved service efficiency, reduced delays, and saved operational costs. TfL continues to use OR–DS insights to adapt timetables in real time and plan for post-pandemic travel patterns.

Retail & Supply Chain: Amazon’s Global Inventory Optimisation

Challenge:
Amazon needed to deliver millions of products quickly while minimising storage and transport costs.

How OR + DS worked together:
Data Science produced demand forecasts using customer trends and seasonal data. Operational Research used optimisation models to decide where to place inventory across fulfilment centres for maximum efficiency.

Outcome:
By combining predictive analytics with mathematical optimisation, Amazon achieved faster deliveries, reduced waste, and improved cost efficiency, demonstrating how OR–DS collaboration drives agility in global logistics.

Sustainability: Balancing Renewable Energy Supply and Demand

Challenge:
Energy providers must balance unpredictable renewable energy generation with fluctuating consumer demand, a growing challenge in the shift to net-zero.

How OR + DS worked together:
Data Science forecasted wind and solar output using weather and consumption data. Operational Research applied stochastic optimisation to plan energy dispatch across multiple sources while maintaining grid stability.

Outcome:
This partnership reduced emissions, improved reliability, and supported smarter investment in green infrastructure, demonstrating how OR and DS can collectively power a more sustainable future.

Manufacturing: Optimising Production for Siemens

Challenge:
Siemens needed to reduce production downtime and increase efficiency across multiple manufacturing sites worldwide.

How OR + DS worked together:
Data scientists analysed real-time sensor and maintenance data from factory equipment to predict breakdowns and identify performance trends. Operational researchers used optimisation models to redesign production schedules, minimise bottlenecks, and plan maintenance windows dynamically.

Outcome:
The integration of predictive analytics with OR scheduling improved factory uptime by more than 10% and cut maintenance costs significantly, setting a benchmark for “smart manufacturing” globally.

Aviation: Streamlining Airline Scheduling

Challenge:
A major European airline struggled with flight delays caused by weather disruptions and complex crew scheduling.

How OR + DS worked together:
Data scientists used historical and live flight data to forecast disruption risks and passenger flows. OR experts developed simulation and optimisation tools to dynamically reassign aircraft and crew in real time, minimising cancellations.

Outcome:
The joint system reduced average delay time by 20% and improved resource utilisation across routes. The model has since become part of the airline’s AI-assisted operations centre.

Finance: Credit Risk Modelling in Banking

Challenge:
A leading retail bank needed to strengthen its credit risk management and decision-making while ensuring compliance with new regulations.

How OR + DS worked together:
Data Science teams built predictive models to assess customer creditworthiness and forecast loan default probabilities. OR specialists applied optimisation to balance portfolio risk and return while ensuring fair, transparent lending decisions.

Outcome:
The approach improved decision speed, reduced default rates, and enhanced regulatory transparency, helping the bank lend more responsibly while maintaining profitability.

Public Services: Emergency Response Planning

Challenge:
Emergency services needed to optimise the placement of ambulances and response teams across a large metropolitan area.

How OR + DS worked together:
Data scientists analysed historical incident data, traffic patterns, and population density to predict demand for emergency calls. OR experts built optimisation models to determine the best locations for stations and vehicle dispatch routes.

Outcome:
Response times dropped by up to 15%, saving lives and improving efficiency. The system is now used to support emergency coverage planning in several UK and European cities.

Education: Improving Student Outcomes with Predictive Modelling

Challenge:
Universities wanted to identify students at risk of dropping out and provide timely support.

How OR + DS worked together:
Data scientists built models using attendance, engagement, and assessment data to flag early warning signs. OR analysts used decision frameworks to prioritise interventions, ensuring limited staff and resources were used most effectively.

Outcome:
Dropout rates fell by up to 12% at participating institutions, while student engagement improved, an example of analytics directly improving human outcomes.