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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.
An innovative method enabling firms to improve the accuracy of their demand forecasting has resulted from research analysing data from 70,000 company sales forecasts. It was concluded that, although judgmental adjustments to statistical forecasts were common, they often wasted management time, reduced accuracy and introduced bias. University of Bath led research, which determined how computer-based systems could support more effective forecasting adjustments, has informed the design of a new commercial product, ForecastQT™. This product is now being marketed globally. Early applications of the product suggest estimated savings of 2% of total revenue for one multinational company and $200m for another. The research has also influenced the development of software and services for clients at SAS, the world's largest privately owned software company.
The North East Economic Model (NEEM) was designed and developed at Durham University Business School (DUBS) from 2003. Customized to the regional economy, the aim of the research was for NEEM to model intra- and extra-regional economic relationships to provide quantitative estimates/projections of the impact of both long-term economic trends and shorter-term economic `shocks'. Its application has had significant impacts on policy practitioners in the region by: (1) facilitating more robust evidence-based policy analysis; (2) giving rise to knowledge transfer to policy-makers regarding the structure and workings of the regional economy; and (3) acting as a catalyst for an extended regional policy-modeling capacity. By influencing professional practice, it has had demonstrable impacts on regional economic policy, regional economic restructuring and local planning.
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.
Research on novel statistical methods for disease surveillance and influenza vaccine effectiveness has led to the development of a suite of automatic systems for detecting outbreaks of infectious diseases at Health Protection Scotland (HPS). This work has improved the public health response and helped to reduce costs in Scotland and also in the wider UK and EU by providing real-time early warning of disease outbreaks and timely estimates of the effectiveness of the influenza vaccine. This research, commissioned by the Scottish Government, through HPS, and also the UK National Institute for Health Research (NIHR) and the European Centres for Disease Control (ECDC), but used in a wider context by many others, formed the basis for the HPS response to the H1N1 Influenza Pandemic and monitoring of the effects of Influenza Vaccines.
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.