Harnessing mathematical optimisation and management science to solve the most complex strategic challenges in modern business.
The challenge: Navigating a world of complex problems
In an era of systemic shocks—from supply chain collapses to energy transitions—traditional business intuition is reaching its limit. Most organisations are data-rich but "insight-poor," struggling to convert vast datasets into definitive actions.
The solution: D3 Lab
The Data-Driven Decision-Making (D3) Lab at The University of Edinburgh Business School is a hub of advanced quantitative expertise. Led by Dr Aakil Caunhye and Dr Douglas Alem, we specialise in the intersection of Operations Research and Data Science.
We don't just provide "reports." We customise your insights and data analytics and build the mathematical engines that power resilient, high-performance organisations. Our approach moves beyond connecting data analytics with decisions: telling you not just what your data says, but exactly what you should do about it.
Our capability
We provide partners with the scientific edge needed to outpace competition. Our work focuses on three pillars:
Innovation through collaboration
The D3 Lab was born out of a necessity to bring the rigour of the University of Edinburgh’s research directly to practitioners. By sitting within the Management Science and Business Economics group, we combine deep theoretical knowledge of Operations Research and Data Science with an acute understanding of complex business problems.
How we work
Our process typically involves:
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Case study: Bridging the gap in Scotland’s circular economy
Understanding the circular economy challenge
As societies consume more, the volume of waste produced often outpaces our ability to process it sustainably. A circular economy aims to move away from the traditional "take-make-dispose" model toward one where materials are recycled and repurposed, retaining their value indefinitely. However, for this transition to succeed, a nation must ensure its physical infrastructure—recycling plants, anaerobic digesters, and composting sites—is capable of handling the specific types and volumes of waste its citizens and industries generate. Without this alignment, valuable materials often end up in landfills, representing a lost economic and environmental opportunity.
The problem: a growing capacity deficit
The specific challenge addressed in this project was a critical lack of insight into Scotland's future waste processing capabilities. While Scotland has clear ambitions for circularity, decision-makers lacked a comprehensive evidence base to compare projected waste "arisings" (the amount of waste generated) against the actual capacity of existing and planned infrastructure over the next decade. Preliminary modeling conducted during this study revealed a stark reality: Scotland faced an initial capacity gap of approximately 2.24 million tonnes in 2022. Without intervention, this gap is projected to widen to 4.05 million tonnes by 2035, meaning a significant portion of Scotland's waste may have no viable recycling or reprocessing destination within the country.
Innovation through collaboration
To tackle this, a collaborative research team—comprising experts from Ricardo and The University of Edinburgh (including Dr Aakil Caunhye and Dr Douglas Alem)—worked in conjunction with Zero Waste Scotland and the Scottish Government. The team innovated by developing a tailorable forecasting model that integrated publicly available data from the Scottish Environmental Protection Agency (SEPA) with diverse statistical methods.
Beyond standard forecasting, the team applied Pareto Analysis (the 80/20 rule) to identify the "vital few" materials—such as sludges, soils, and household waste—that contribute to 80% of Scotland's total waste volume. They further utilised predictive analytics to correlate waste generation with socioeconomic factors, such as population age bands and GDP, providing a more granular understanding of what drives waste production at the local authority level.
Project outcome and benefits
The project allowed policy-makers to:
The solution benefits the Scottish Government and Local Authorities by informing smarter infrastructure investments. Ultimately, it benefits the Scottish public and environment by diverting millions of tonnes of waste from landfills and lowering the nation's carbon footprint.
Support from the University and partners
This initiative was a prime example of academic and commercial expertise coming together. The University of Edinburgh Business School provided deep technical insights into the data and predictive modelling, while Edinburgh Innovations (EI) helped facilitate the partnership. Funding and strategic direction were provided by Zero Waste Scotland, ensuring the research remained aligned with national policy goals like the Recycling Improvement Fund. The project lasted four months.
Future opportunities
The next steps involve moving beyond broad waste categories to material-specific data collection. There is a significant opportunity for further collaboration to refine the model using real-time big data. Future initiatives will likely focus on developing a mathematical model to optimise capacity deployment, given waste generation predictions. Optimised actions will likely need to be aligned with legislative options, such as mandating landfill diversion targets for wood and mattresses, and expanding public engagement to improve waste segregation at the source.
Senior Lecturer in Business Analytics and Programme Director of the MSc Management at the University of Edinburgh Business School, Aakil Caunhye applies robust optimisation and stochastic programming to problems in power grid expansion, infrastructure resilience and disaster response planning. He holds a PhD in Systems and Engineering Management from Nanyang Technological University.
Through the D3 Lab, Caunhye advises partners in energy, government and public infrastructure on turning complex operational risk into actionable strategy, most recently supporting Zero Waste Scotland with capacity planning for Scotland's circular economy (see case study above).
Senior Lecturer in Business Analytics and Programme Director of the MSc Data and Decision Analytics (online) at the University of Edinburgh Business School, Douglas Alem applies optimisation under uncertainty to humanitarian logistics, disaster response planning and supply chain design. He holds a PhD in Computer Science and Computational Mathematics from the University of São Paulo.
Through the D3 Lab, Alem works with partners across logistics, humanitarian and supply chain sectors to design systems that remain resilient under pressure, including recent work with Zero Waste Scotland on Scotland's waste infrastructure planning (see case study above).
