Robert Fildes is Distinguished Professor of Management Science in the School of Management, Lancaster University and Director of the Lancaster Centre for Forecasting. He was co-founder in 1981 of the Journal of Forecasting and in 1985 of the International Journal of Forecasting. He has consulted and lectured widely on all aspects of the problem of improving forecasting in organisations. His major concern is that despite all the research, companies still stay with old-fashioned systems and methods. The solution, he thinks, is better designed forecasting systems and better trained forecasters.
Sven F Crone
Dr Sven F Crone is the director of the Lancaster Centre for Forecasting, and works as an Assistant Professor at Lancaster University Management School, UK. In addition to numerous publications in esteemed journals, Sven has over 15 years of expertise in corporate business forecasting.
He has widely consulted on corporate projects through the centre, in particular in supply chain forecasting for FMCG manufacturers, retail forecasting, as well as utilities and energy forecasting. His expertise ranges from improving software systems such as SAP APO-DP, to developing bespoke forecasting methods and model selection routines.
Sven has presented the Centre’s innovations and projects at 50+ international conferences, including keynotes at the SAS F2006 & F2008 forecasting and A2012 Analytics conferences, track speeches at APICS 2006 global conference, and annual appearances at IBF and ISF conferences. He frequently provides training courses for the centre, IBF and IEEE, educating over 400 demand planners on Forecasting Fundamentals,Statistical Forecasting with SAP APO-DP and Forecasting with Neural Networks all over the world.
John Boylan joined Lancaster University as Professor of Business Analytics in January 2015.
John has researched and taught in this area for over twenty years, having previously worked for seven years in industrial Operational Research groups. His research focuses on problems relating to the interface between demand forecasting and inventory management. His best known work is in intermittent demand forecasting, and he has advised software companies on the adoption of recent research developments into their packages. He also works on problems relating to seasonal demand forecasting and the benefits of information sharing in supply chains.
John has served as a director of the International Institute of Forecasters and is currently a member of the Executive Committee of the International Society for Inventory Research.
Dr Nikolaos Kourentzes is an Associate Professor in the department of Management Science, Lancaster University Management School. Nikos researches in several areas of business forecasting and his work has been presented in numerous international academic and practitioner conferences. He is regularly giving talks on improving and automating forecasting in organisations using established and state-of-the-art statistical methods. He has substantial experience in applied research projects with organisations in fast and slow moving consumer goods, advertising and media, retail and promotional modelling, new product forecasting and utilities energy forecasting. Nikos frequently provides training courses on business forecasting and demand planning, advanced statistical modelling, neural networks and SAP APO-DP. You can find more about his activities in his research blog or follow him on twitter.
Dr Nicos Pavlidis is an Assistant Professor at the Lancaster University Management School. His has been active in the fields of data mining, time series forecasting, machine learning, and big data analytics. He has participated in a number of projects addressing complex real-world problems and collaborated with both academic and industrial partners. He has offered seminars and courses on forecasting and his research has been disseminated through major international conferences and leading journals.
Ivan joined the Centre as PhD student. He has developed a new type of exponential smoothing which was called "Complex Exponential Smoothing". As a Research Associate Ivan developed an intermittent state-space model (in collaboration with John Boylan) and worked on ARIMA implementation for Demand Works company. HHe maintains an R package called "smooth" (available in CRAN), implementing and developing state-space models for time series analysis and forecasting purposes. He also works in C++ and Java.
Professor Paul Goodwin (University of Bath, UK)
Professor Konstantinos Nikolopoulos (Bangor University, Wales)
Dr Fotios Petropoulos (University of Cardiff, UK)
Dr Juan Ramon Trapero Arenas (University Castilla La Mancha, Spain)
Dr Stavros Asimakopoulos (National & Kapodistrian University of Athens, Greece)
Dr Jessica (Tun-I) Hu
Dr Devon Barrow (Coventry University, UK)
Yves Sagaert (Ghent University)
Fahad H al-Qahtani is a PhD student in the Department of Management Science, sponsored by Saudi Aramco oil company. His research interests include machine learning, data mining and time series forecasting. He is currently focusing his research on the utilisation of active learning techniques for selecting the most informative examples for training time series forecasting models. Fahad holds a Masters degree in Computer Science from the University of Southern California and has worked as a system analyst in the oil and energy sector in the Middle East.
Patrick Saoud is a PhD student in Management Science at Lancaster University Management School. His research focuses on the effect of promotions on inventory and other supply chain related costs, and whether sharing promotional information can help alleviate the “Bullwhip Effect”. He aims at assessing the importance of properly modelling demand in the presence of promotions, with respect to managerial objectives. His other interests are: promotional modelling, variable selection, supply chain forecasting, Neural Networks for time series forecasting and inventory management.
Oliver is a PhD student in the Department of Management Science, who joined following his MSc studies with the Department in 2013/14. His research explores the diffusion of online video content through social media platforms such as YouTube or Vimeo. Oliver aims to develop a model that can incorporate the viral nature of online media, which then allows the popularity prediction in terms of views of such content for a given time horizon on a cumulative basis. Other interests include machine learning, web analytics and the measurement of the ROI of corporate social media channels.
Daniel Waller is a PhD student with the STOR-i Centre for Doctoral Training. His research is co-sponsored by marketing analytics company Aimia, and focusses on forecasting demand for retail and examining the potential of large datasets in this context. The broad aim of the research is to develop forecasting models which incorporate causal information, such as promotions, to produce forecasts that are valuable for both marketing and supply chain purposes. Daniel has other interests in data mining, hierarchical and grouped forecasting, and variable selection.
Matt Weller is a PhD student in Management Science at Lancaster University Management School. His current research focuses on collaborative forecasting in the supply chain, firstly examining how firms are currently combining collaboration and forecasting in practice. The next phase of his research will be a modelling approach to examine which forecasting methods are most suited to alternative supply chain configurations and demand data properties. Prior to joining LCF, Matt worked in industry for 10 years as an IT consultant and has implemented planning solutions in several blue chip companies.
Christina Wright is a PhD student with the STOR-i Centre for Doctoral Training. Her PhD focuses on finding optimal routes for vehicles carrying hazardous materials. Thus her research combines the areas of both optimisation and forecasting. Values, such as risk, that will be used in the optimisation model must be forecast. This will require taking into account network structure of road layouts and the rarity of accidents.