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With global demand for energy ever increasing, environmental impact has become a major priority for the oil industry. A collaboration between researchers at the University of Glasgow and Shell Global Solutions has developed GWSDAT (GroundWater Spatiotemporal Data Analysis Tool). This easy-to-use interactive software tool allows users to process and analyse groundwater pollution monitoring data efficiently, enabling Shell to respond quickly to detect and evaluate the effect of a leak or spill. Shell estimates that the savings gained by use of the monitoring tool exceed $10m over the last three years. GWSDAT is currently being used by around 200 consultants across many countries (including the UK, US, Australia and South Africa) with potentially significant impacts on the environment worldwide.
Since 2008, statistical research at the University of Bristol has significantly influenced policies, practices and tools aimed at evaluating and promoting the quality of institutional and student learning in the education sector in the UK and internationally. These developments have also spread beyond the education sector and influence the inferential methods employed across government and other sectors. The underpinning research develops methodologies and a much-used suite of associated software packages that allows effective inference from complicated data structures, which are not well-modelled using traditional statistical techniques that assume homogeneity across observational units. The ability to analyse complicated data (such as pupil performance measures when measured alongside school, classroom, context and community factors) has resulted in a significant transformation of government and institutional policies and their practices in the UK, and recommendations in Organisation for Economic Co-operation and Development (OECD) policy documents. These techniques for transforming complex data into useful evidence are well-used across the UK civil service, with consequent policy shifts in areas such as higher education admissions and the REF2014 equality and diversity criteria.
In response to the deficiencies in bank risk management revealed following the 2008 financial crisis, one of the mandated requirements under the Basel III regulatory framework is for banks to backtest the internal models they use to price their assets and to calculate how much capital they require should a counterparty default. Qiwei Yao worked with the Quantitative Analyst — Exposure team at Barclays Bank, which is responsible for constructing the Barclays Counterpart Credit Risk (CCR) backtesting methodology. They made use of several statistical methods from Yao's research to construct the newly developed backtesting methodology which is now in operation at Barclays Bank. This puts the CCR assessment and management at Barclays in line with the Basel III regulatory capital framework.
From 1995 Professor Munjiza's research at QMUL has led to the development of a series of algorithms which can predict the movement and relationship between objects. These algorithms have been commercialised by a range of international engineering and software companies including Orica, the world's leading blasting systems provider (via their MBM software package), and the software modelling company, Dassault Systems (via their Abaqus software). Through these commercialisation routes Munjiza's work has generated significant economic impact which is global in nature. For example, his predictive algorithms have enabled safer, more productive blast mining for Orica's clients — in one mine alone, software based on Munjiza's modelling approach has meant a 10% increase in productivity, a 7% reduction in costs and an annual saving of $2.8 million. It has also been used in Dassault Systems' Abaqus modelling software, which is the world's leading generic simulation software used to solve a wide variety of industrial problems across the defence, automobile, construction, aerospace and chemicals sectors with associated economic impact.
Through a close collaboration with Ford Motor Company, simulation modelling software developed at the University of Southampton has streamlined the design of the car giant's engine production lines, increasing efficiency and delivering significant economic benefits in three key areas. Greater productivity across Ford Europe's assembly operations has generated a significant amount [exact figure removed] in direct cost savings since 2010. Automatic analysis of machine data has resulted in both a 20-fold reduction in development time, saving a large sum per year [exact figure removed], and fewer opportunities for human error that could disrupt the performance of production lines costing a large sum [exact amount removed] each to program.
Professor Hutton has applied her research on statistical models for survival analysis to cerebral palsy, a neurological disorder which afflicts around 1 in 500 of newborn children globally. The body of research has established medically-accepted norms for the life expectancy of people with cerebral palsy. Her research extends to the study of life expectancy for patients suffering from spinal cord injuries.
The impact of this work has been internationally substantial, influencing medical and legal professionals, and informing lay people with involvement in cerebral palsy. Her work is also widely cited by patient-networks and textbooks.
Hutton is regularly called by both defence and plaintiff lawyers, as an expert witness worldwide, assessing life expectancy for damages arising from negligence in obstetric or paediatric care, or from accidents. Her expertise is also used in brain and spinal cord injury cases, which also result in substantial awards. The award of appropriate damages in legal cases ensures that patients receive the best care for the rest of their lives. From Jan 2008 to July 2013 Hutton has provided expert evidence in 103 such cases around the world, which had impact on decisions about compensation totalling in the range £100M-450M.
A generalized additive model (GAM) explores the extent to which a single output variable of a complex system in a noisy environment can be described by a sum of smooth functions of several input variables.
Bath research has substantially improved the estimation and formulation of GAMs and hence
This improved statistical infrastructure has resulted in improved data analysis by practitioners in fields such as natural resource management, energy load prediction, environmental impact assessment, climate policy, epidemiology, finance and economics. In REF impact terms, such changes in practice by practitioners leads ultimately to direct economic and societal benefits, health benefits and policy changes. Below, these impacts are illustrated via two specific examples: (1) use of the methods by the energy company EDF for electricity load forecasting and (2) their use in environmental management. The statistical methods are implemented in R via the software package mgcv, largely written at Bath. As a `recommended' R package mgcv has also contributed to the global growth of R, which currently has an estimated 1.2M business users worldwide [A].
R is a free and open-source software programming language and software environment for expressing and implementing statistical algorithms and graphics. It has become the lingua franca for developing and implementing new statistical methodologies — not just in statistics, but in applications across the whole spectrum of industry, from marketing and pharmaceuticals to finance. It is used by companies for research, analysis and production. Its power in analysing and visualising data helps organisations from charities to government. About one half of the core statistical modelling and graphics engine included in R builds on research carried out in Oxford.
The WinBUGS software (and now OpenBUGS software), developed initially at Cambridge from 1989-1996 and then further at Imperial from 1996-2007, has made practical MCMC Bayesian methods readily available to applied statisticians and data analysts. The software has been instrumental in facilitating routine Bayesian analysis of a vast range of complex statistical problems covering a wide spectrum of application areas, and over 20 years after its inception, it remains the leading software tool for applied Bayesian analysis among both academic and non-academic communities internationally. WinBUGS had over 30,000 registered users as of 2009 (the software is now open-source and users are no longer required to register) and a Google search on the term `WinBUGS' returns over 205,000 hits (over 42,000 of which are since 2008) with applications as diverse as astrostatistics, solar radiation modelling, fish stock assessments, credit risk assessment, production of disease maps and atlases, drug development and healthcare provider profiling.