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Our research team has developed new approaches to classifying demand series as `intermittent' and `lumpy', and devised new variants of the standard Croston's method for intermittent demand forecasting, which improve forecast accuracy and stock performance. These approaches have impacted the forecasting software of Syncron and Manugistics, through the team's consultancy advice and knowledge transfer. Subsequently, this impact has extended to Syncron International and JDA Software, which took over Manugistics. These companies' forecasting software packages have a combined client base turnover of over £200 billion per annum, and their clients benefit from substantial inventory savings from the new approaches adopted.
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].
Since its launch in 2009, the mobile phone package price comparison tool Billmonitor has identified £35 million worth of savings available to the 110,000 users whose bills have been analysed. It was the first price comparison tool to be accredited by Ofcom and it has been widely praised in the media. Exploiting techniques that they had developed for applications in finance and genetics, University of Oxford researchers Chris Holmes and Nicolai Meinshausen developed the statistical algorithms underpinning the package, which uses simulation-based inference and careful statistical modelling to analyse users' phone bill data. It searches over 2.4 million available packages to identify the best mobile phone deal for each user's particular pattern of usage. Widely quoted in the press, reports in 2011 and 2012 from the Billmonitor team estimated that approximately three quarters of mobile phone customers are on the wrong tariff, with an overspend of around 40%.
The research created programs for the Stata statistical software environment that are used by thousands of researchers in economics and other fields around the world in academia, the private sector, government and quasi-governmental organisations, with approximately 400,000 downloads in the REF 2014 period. The core programs enable researchers to rigorously analyse the causal impact of a policy in settings where an experiment is infeasible and for experiments where take-up of treatment is incomplete, i.e. for the settings in which the vast majority of empirical work is done. The programmes are used to analyse complex data to establish causal links across a broad range of policy areas.
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.
This research has profoundly influenced the practice of pharmacoepidemiology in 2008-13. The self-controlled case series (SCCS) method is particularly well-suited for working with computerised databases, which are increasingly used in epidemiology. The method has been recommended by international agencies (WHO, ECDC) and is now widely used by health practitioners within national public health agencies, including the CDC (USA), Public Health England (UK) and many other national and regional public health bodies. It has influenced practice within the private sector (notably the pharmaceutical and the healthcare industries). Use of the SCCS method has impacted on health by reducing costs, improving timeliness and improving the quality of evidence upon which policy decisions are based.
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.
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.
Clinical trials form a crucial step in translating fundamental medical research into improved healthcare. Many hundreds of trials are conducted every year, each involving hundreds, sometimes thousands, of patients. These trials are expensive, with costs as high as 20 or 30 thousand pounds per patient. Research in Bath on group sequential monitoring and the adaptive design of clinical trials has improved the conduct of clinical trials, leading to:
The impact of this research is economic (the business performance of pharmaceutical companies and businesses that support them), societal (by enhancing public health and by changing the policies adopted by regulators) and ethical (ensuring clinical trials remain safe, while bringing life-saving treatments into clinical use as rapidly as possible).
This impact case is based on economic impact through improved forecasting technology. It shows how research in pattern recognition by Professor Henry Wu at the School of Electrical Engineering and Computer Science led to significantly improved accuracy of daily national gas demand forecasting by National Grid plc. The underpinning research on predicting non-linear time series began around 2002 and the resulting new prediction methodology is applied on a daily basis by National Grid plc since December 2011. The main beneficiaries from the improved accuracy (by 0.5 to 1 million cubic meters per day) are UK gas shippers, who by conservative estimates save approximately £3.5M per year. Savings made by gas shippers benefit the whole economy since they reduce the energy bills of end users.