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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.
The Northern Doctors Urgent Care Group, a not-for-profit organisation that delivers out-of-hours urgent medical services for the NHS, achieved significant efficiency savings and improvements in-patient care as a result of adopting statistical assessment and forecasting processes, developed by Durham University. These improved processes also featured in the Group's successful competitive bids for two new contracts worth £9.2M per year. In addition, the Durham methodology was adapted to assess the results of a Government programme to encourage cycling in six UK towns, producing data on cycle use that helped to influence subsequent allocations of about £700M for sustainable transport projects.
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.
University of Bath research has contributed to a lean, `build-to-order' (BTO) production strategy for the European automotive industry. The study of `intelligent logistics' and supply chain configurations led to recommendations for building new production systems that are helping to address significant industry problems: global overcapacity, rising stock levels and low profitability. The research findings have been widely shared with vehicle manufacturers, suppliers, industry trade associations and government bodies, original equipment manufacturers (OEMs) and suppliers. The Bath research has had an impact on: the reduction of waste that is integral to the former `build to stock' production model; the development of an environmentally friendly manufacturing approach; improved profitability through the reduction of `inventory' (new cars losing value in large distribution parks); and on future innovation and growth challenges for the automotive industry. The research has influenced manufacturers and suppliers seeking to implement a more flexible automotive component supply chain across Europe.
Research by Professor Leonard Smith and the LSE Centre for the Analysis of Time Series (CATS) on forecasting in non-linear and often chaotic systems, with particular attention to weather, has led to advances in three areas: 1) national and international weather industry products and services that are built upon state-of-the-art research and knowledge, 2) dissemination of state-of-the-art practice in forecast production and verification to national, regional and local weather centres around the world, and 3) the introduction of, and new applications in, state-of-the-art forecasting methods in industries facing high uncertainty and risk, e.g. insurance and energy.
General insurers are required to have a capital reserve to cover outstanding liabilities, i.e. liabilities that have been incurred but not settled, or perhaps not even reported. Under the new Solvency II regulation, adopted by the EU Council in 2009, general insurers now face complex new capital requirements. These new regulations must be fully implemented by 2016. The development of new statistical methods led by Dr Bent Nielsen and his co-researchers, in collaboration with the general insurer RSA, extends traditional forecasting methods, and provides tools by which insurers are able to meet these new statutory requirements.
Impact: Multi-national developments in public policy and service provision related to earthquake risk reduction, derived from the work of the International Commission on Earthquake Forecasting for Civil Protection (ICEF), established following the 2009 L'Aquila earthquake.
Significance and reach: In 2012 the Italian Department of Civil Protection allocated €1billion for seismic protection, including a multi-year programme on operational earthquake forecasting. New programmes/policies have been enacted by government bodies in the USA (2012), Russia (2012) and Japan (May 2013).
Underpinned by: Research into earthquake dynamics and predictability, undertaken at the University of Edinburgh (1996 onwards), which led to the appointment of the sole UK representative to the ICEF.