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About the management science group

Management Science at Saïd Business School is a vibrant research area, with faculty interests spanning a variety of applications of operational research and statistical methods. Researchers have consistently produced articles in top journals in the field, including Manufacturing & Service Operations Management, Management Science, and the Journal of the American Statistical Association. The Management Science faculty members have always had strong connections with the academic community, serving as journal associate editors and reviewers, and routinely presenting their research at academic workshops and conferences.

James Taylor is Professor of Decision Science at Saïd Business School. His research is in the area of time series forecasting. He has more than forty published and accepted research papers to his name (view full list of papers). His main research interests are probabilistic forecasting and exponential smoothing methods applied to a variety of contexts. The journals in which he has had papers published include Management ScienceJournal of the American Statistical AssociationJournal of Business and Economic Statistics, and Monthly Weather Review. James is a former Associate Editor of the International Journal of Forecasting and Management Science.

Ho-Yin Mak is Associate Professor in Management Science at Saïd Business School. Ho-Yin's research aims to tackle operations management and planning problems using tools of operations research and economics, with particular interest in the domain areas of (energy and environmental) sustainability, supply chain management, and health care operations. His research in these areas have been published in premier journals, such as Management Science and Manufacturing & Service Operations Management, and recognised with research awards, such as the INFORMS Energy, Natural Resources and Environment Young Researcher Prize.

Siddharth Arora is a Career Development Fellow in Management Science at Saïd Business School. His research is primarily aimed at developing statistical models for time series forecasting and has has applications in the following areas: Healthcare (modelling disease symptom severity using wearable technologies), Energy (predicting residential and SME electricity consumption recorded using smart meters), Macroeconomics (modelling GNP), and Climate (comparing forecasts from GCMs with statistical time series models).

Research projects

Academics are engaged in new research projects ranging from tax policies, social innovation in health care, to Global Cyber Security.