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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].
The School of Mathematics at Cardiff University has developed important statistical and mathematical models for forecasting consumer buying behaviour. Enhancements to classical models, inspired by extensively studying their statistical properties, have allowed us to exploit their vast potential to benefit the sales and marketing strategies of manufacturing and retail organisations. The research has been endorsed and applied by Nielsen, the #1 global market research organisation that provides services to clients in 100 countries. Nielsen has utilised the models to augment profits and retain their globally leading corporate position. This has led to a US$30 million investment and been used to benefit major consumer goods manufacturers such as Pepsi, Kraft, Unilever, Nestlé and Procter & Gamble. Therefore the impact claimed is financial. Moreover, impact is also measurable in terms of public engagement since the work has been disseminated at a wide range of national and international corporate events and conferences. Beneficiaries include Tesco, Sainsbury's, GlaxoSmithKline and Mindshare WW.
Research at Newcastle University into stochastic rainfall models and their application has transformed the practice of impact assessment of climate change and risk assessment of environmental hazards across multiple sectors. The Newcastle methods underpin the "Weather Generator", a web-based tool which has been made available since 2009 by DEFRA as part of their official UK Climate Projections (UKCP09). The tool's incorporation into this official data source means that the models generated underpin multi-sectoral risk assessment throughout the UK and subsequently have led to the adoption of stochastic methods in general, particularly in the water and insurance industries to produce more robust risk assessments.
Pratt & Whitney (one of the world's largest makers of aircraft engines) has developed a process, "Design for Variation" (DFV), that uses Bayesian methods developed at Sheffield for analysing uncertainty in computer model predictions within the design, manufacture and service of aircraft engines. The DFV process significantly improves cost efficiency by increasing the time an engine stays operational on the wing of an aircraft, so reducing the time that the aircraft is unavailable due to engine maintenance. DFV also saves costs by identifying design and process features that have little impact on engine performance, but are expensive to maintain. Pratt & Whitney estimate the DFV process to generate savings, for a large fleet of military aircraft, of [text removed for publication].
The UK Food and Environment Research Agency (Fera) has used these methods in their risk analyses, for example in assessing risks of exposure to pesticides.
This case study describes impact resulting from research on assessing the performance of credit scoring models conducted by the Consumer Credit / Retail Banking Research Group of the Mathematics Department at Imperial College. The group's work has influenced both high-level industry strategies for developing scoring models, and also low-level performance measures for which such models are developed, refined and evaluated. We describe examples of companies or bodies that have benefitted from improved credit scoring models, including Prescient Models (a US credit scoring company), Experian and the US Office of the Comptroller of Currency. The group has established a very significant reputation for a wide range of commercially valuable work in this area — to the extent that the group received the major Credit Collections and Risk industry award for Contributions to the Credit Industry in 2012.
The Computational Optimization Group (COG) in the Department of Computing produced new models, algorithms, and approximations for supporting confident decision-making under uncertainty — when computational alternatives are scarce or unavailable. The impact of this research is exemplified by the following:
Telstra is an Australian telecommunications company. In the late 1990s, Telstra was faced by a new entrant, which would be competing against it with modern technology and a lower cost structure. Telstra needed to know how much share it would lose to undertake its resource planning. More importantly, Telstra also had to understand which customers it could retain and the actions it needed to take to retain them in terms of service design and delivery, pricing, and communications.
The underpinning research was conducted in conjunction with Telstra, and met their needs. This project generated published academic research output, and in parallel had a valuable impact on the client company. This impact was estimated, by Telstra, to exceed US$146 million.
In summary: this study reports research that was prompted by the direct need of a potential beneficiary, and which successfully achieved a signifb01cant fb01nancial impact for that beneficiary.
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.
The safe operation of ships is a high priority task in order to protect the ship, the personnel, the cargo and the wider environment. Research undertaken by Professor Alexander Korobkin in the School of Mathematics at UEA has led to a methodology for the rational and reliable assessment of the structural integrity and thus safety of ships and their cargos in severe sea conditions. Central to this impact is a set of mathematical models, the conditions of their use, and the links between them, which were designed to improve the quality of shipping and enhance the safety of ships. The models, together with the methodology of their use, are utilised by the ship certification industry bringing benefits through recognised quality assurance systems and certification.
Compiler research at Edinburgh over the last decade has had significant industrial and commercial impact. Early work on pointer conversion is now available in Intel's commercial compilers. Later ground-breaking work on machine-learning based compilation led to the release of MilePost GCC, an enhanced version of the world's widest-used open source compiler supported by IBM. More recent work on parallelism discovery and machine-learning mapping has led to a new ARM Centre of Excellence at Edinburgh.