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Management Science research

Management Science examines operational research and applied statistics. The research area also provides courses on analytics within Saïd Business School.

Management Science includes Ho-Yin Mak, James Taylor and Siddharth Arora. In broad terms, their research lies in the areas of Operational Research and Applied Statistics. More specifically, their work focuses on aspects of supply chain optimisation, data mining, operations management and time series forecasting. They have applied their work to problems in energy, finance, transportation and revenue management.

James Taylor's research is in the area of time series forecasting. His research has tended to focus on the estimation of forecast uncertainty and exponential smoothing methods. His main areas of application are energy, call centres, inventory control, and financial market risk management. His research has appeared in a variety of journals, including Management Science, Journal of the American Statistical Association, and International Journal of Forecasting. James has an undergraduate maths degree from the University of Cambridge, a master’s degree in operational research from the University of Lancaster, and a doctoral degree in time series forecasting from London Business School.

Ho-Yin Mak’s research focuses on developing and applying operational research tools for supporting business decisions arising in a variety of domain areas, including supply chain management, sustainable energy and transportation, and health care. His research has been published in leading journals, including Management Science and Manufacturing & Service Operations Management (MSOM), and has been recognized with awards such as the INFORMS (Energy & Natural Resources) Young Researcher Prize. Ho-Yin grew up in Hong Kong and was educated in the US, having obtained his bachelor’s degree at Northwestern University, and master’s and doctoral degrees at the University of California at Berkeley. From 2009-2015, Ho-Yin served on the faculty at the Hong Kong University of Science & Technology.

Siddharth Arora's research interests include: time series analysis, probabilistic forecasting, biomedical signal processing, chaos synchronization, and model combination. His research 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).