Summary of the impact

This impact case study is based on a Knowledge Transfer Partnership (KTP) between the School of Mathematics, Statistics and Actuarial Science, University of Kent and KROHNE Ltd, a world leading manufacturer of industrial measuring instruments. These precision instruments (typically flow meters and density meters) need to be calibrated accurately before being used and this is an expensive and time-consuming process.

The purpose of the KTP was to use Bayesian methodology developed by Kent statisticians to establish a novel calibration procedure that improves on the existing procedure by incorporating historical records from calibration of previous instruments of the same type. This reduces substantially the number of test runs needed to calibrate a new instrument and will increase capacity by up to 50%.

The impact of the KTP, which was graded as `Outstanding', has been to change the knowledge and capability of the Company, so that they can improve the performance of their manufacturing process by implementing this novel calibration method. This has been achieved by adapting the underpinning Kent research to the specific context of the calibration problem, by running many calibrations to demonstrate the effectiveness of the method in practice, and by supporting the implementation of the new calibration method within the Company's core software.

Moreover, the project has changed the Company's thinking on fundamental science, particularly industrial mathematics. The value of historical data, and the usefulness of Bayesian methods, is now widely appreciated and training for staff in Bayesian Statistics is being introduced. Thus the project has not only changed the protocols of the Company, it has also changed their practice.

Summary of the impact

Now-casting is the prediction of the present, the very near future, and the very recent past. It has been developed within a research programme led by Lucrezia Reichlin at LBS. It is relevant because key economic statistics, particularly quarterly measures such as GDP, are available only with a delay. Now-casting exploits information which is published early and at higher frequencies than the target variable and generates early estimates before the offb01cial fb01gures become available.

Now-casting has signifb01cant infb02uence and impact. The techniques reported in this case study are in widespread use by central banks and policy institutions. Furthermore, this research has achieved successful commercial impact via Now-Casting Economics Limited.

Summary of the impact

A two-dimensional flood inundation model called LISFLOOD-FP, which was created by a team led by Professor Paul Bates at the University of Bristol, has served as a blueprint for the flood risk management industry in the UK and many other countries. The documentation and published research for the original model, developed in 1999, and the subsequent improvements made in over a decade of research, have been integrated into clones of LISFLOOD-FP that have been produced by numerous risk management consultancies. This has not only saved commercial code developers' time but also improved the predictive capability of models used in a multimillion pound global industry that affects tens of millions of people annually. Between 2008 and 2013, clones of LISFLOOD-FP have been used to: i) develop national flood risk products for countries around the world; ii) facilitate the pricing of flood re-insurance contracts in a number of territories worldwide; and iii) undertake numerous individual flood inundation mapping studies in the UK and overseas. In the UK alone, risk assessments from LISFLOOD-FP clones are used in the Environment Agency's Flood Map (accessed on average 300,000 times a month by 50,000 unique browsers), in every property legal search, in every planning application assessment and in the pricing of the majority of flood re-insurance contracts. This has led to more informed and, hence, better flood risk management. A shareware version of the code has been available on the University of Bristol website since December 2010. As of September 2013, the shareware had received over 312 unique downloads from 54 different countries.

Summary of the impact

Loudness is the subjective magnitude of a sound as perceived by human listeners and it plays an important role in many human activities. It is determined jointly by the physical characteristics of a sound and by characteristics of the human auditory system. A model for predicting the loudness of sounds from their physical spectra was developed in the laboratory of Professor Brian Moore with support from an MRC programme grant.

The model formed the basis for an American National Standard and is currently being prepared for adoption as a standard by the International Organization for Standardisation (ISO). In addition, the model has been widely used in industry worldwide for prediction of the loudness of sounds, for example: noise from heating, ventilation and air-conditioning; inside and outside cars, and from aircraft; and from domestic appliances and machinery.

Summary of the impact

Garratt's research on methods for quantifying the uncertainty surrounding macroeconomic forecasts, uncertainty which arises from not knowing the true model of the economy and from having to use inaccurate data, has been applied by Central Banks and national statistical agencies in their forecasting exercises and their analysis of policy interventions. Notably, Norges Bank (the central bank of Norway) has developed a system called the System for Averaging Models, which they use when they make macroeconomic forecasts and when they predict the effects of possible monetary policy actions, which incorporates Garratt's results.

Garratt's research provides new methods to allow for uncertainty about the 'true' model by using combinations of different possible models, when making forecasts. His research provides new procedures to take `data uncertainty' into account, when forecasts have to be based on real-time data (that is, inaccurate data which is available to the policymaker when a forecast is produced but which is revised later on). Garratt's research quantifies the effect of this uncertainty on forecasts by constructing probability density functions. Central banks and statistical agencies have applied his findings when making forecasts and undertaking policy analysis. Garratt's research has been disseminated through refereed journal articles, conference presentations, consultancy work with policy makers, and presentations to policy makers, including an invited presentation to HM Treasury.

Summary of the impact

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

• driven the wide uptake, outside academia, of generalized additive models,
• increased the scope of applicability of these models.

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].

Summary of the impact

Onchocerciasis (river blindness) is a debilitating disease of major public health importance in the wet tropics. The African Programme for Onchocerciasis Control (APOC) seeks to control or eliminate the disease in 19 countries. Accurate mapping of Loiasis (eye-worm) was a requirement for implementation of APOC's mass-treatment prophylactic medication programme in order to mitigate against serious adverse reactions to the Onchocerciasis medication in areas also highly endemic for Loiasis. Model-based geostatistical methods developed at Lancaster were used to obtain the required maps and contributed to a change in practice of APOC in a major health programme in Africa. Our maps are used to plan the delivery of the mass-treatment programme to rural communities throughout the APOC countries, an estimated total population of 115 million.

Summary of the impact

Ocean circulation accounts for much of the energy that drives weather and climate systems; errors in the representation of the ocean circulation in computational models affect the validity of forecasts of the dynamics of the ocean and atmosphere on daily, seasonal and decadal time scales. Research undertaken by the University of Reading investigated systematic model errors that resulted from data assimilation schemes embedded in the key processes used to predict ocean circulation. The researchers developed a new bias correction technique for use in ocean data assimilation that alleviates these errors. This has led to significant improvements in the accuracy of the forecasts of ocean dynamics. The technique has been implemented by the Met Office and by the European Centre for Medium Range Weather Forecasting (ECMWF) in their forecasting systems, resulting in major improvements to the prediction of the weather and climate from oceanic and atmospheric models. The assimilation technique is also leading to better use of expensively acquired satellite and in-situ data and improving ocean and atmosphere forecasts used by shipping and civil aviation, energy providers, insurance companies, the agriculture and fishing communities, food suppliers and the general public. The impact of the correction procedure is also important for anticipating and mitigating hazardous weather conditions and the effects of long-term climate change.

Summary of the impact

Salford Business School researchers were commissioned by PA Sport, the sports division of the Press Association, the Football Association Premier League (FA), and FootballDataCo, which handles the rights to football data for the FA, to develop the quantitative analysis and models for an objective index of football player performance. The official player ratings system of the English Premiership, Championship and the Scottish Premiership and first of its kind:

• The EA Sports Player Performance Index (previously ACTIM) analyses player contributions to match outcomes;
• The Index informs squad selection, supports the refinement of team performance and Index statistics are presented worldwide, in print, online, and on television, and;
• The index is used in football gaming software and the English Premiership's official application for the Fantasy Football League, engaging fans across the world with quantitative analysis and generating statistics driven debate through connecting with their passion.

Summary of the impact

Research carried out from 2003 by Currie (Maxwell Institute) and his PhD students Djeundje, Kirkby and Richards (also Longevitas), and international collaborators Eilers and Durban, created new, flexible smoothing and forecasting methods. These methods are now widely used by insurance and pension providers to forecast mortality when determining pricing and reserving strategy for pensions. The methods were incorporated by the SME Longevitas in its forecasting package Projections Toolkit launched in 2009. This generated impact in the form of £400K turnover for Longevitas in licensing and consultancy fees, with further impact on the pricing and reserving strategies on Longevitas's customers. Since 2010 the methods have been adopted by the Office for National Statistics (ONS) to make the forecasts required to underpin public policy in pensions, social care and health and by The Continuous Mortality Investigation (CMI) to model and provide forecasts on mortality to the pensions and insurance industries. As a result, the research has changed practices in these advisory agencies and in the insurance industry.