Staff profile

Dr. Konstantinos (Kostas) Nikolopoulos is the Professor in Business Information Systems and Analytics at Durham University Business School. 

Dr. Nikolopoulos studied Electrical and Computer Engineering at the National Technical University of Athens (ΕΜΠ) in his native Greece (D.Eng. 2002, Dipl. Eng. 1997). He further completed the International Teachers Programme (ITP) at Kellogg School of Management at Northwestern University (2011). His research interests are Forecasting, Analytics, Information Systems, and Operations. 

Dr. Nikolopoulos was Professor of Business Analytics/Decision Sciences at Bangor University for a full decade, and completed three tenures as the College Director of Research (Associate Dean for Research & Impact) for the College of Business, Law, Education, and Social Sciences (2011-2018) in charge of the REF2014 submission for the Business and the Law school. Before that, he was Lecturer and Senior Lecturer in Decision Sciences at the University of Manchester, a Senior Research Associate at Lancaster University and the CTO of the Forecasting and Strategy Unit (www.fsu.gr) in the Electrical and Computer Engineering Department of the National Technical University of Athens (1996-2004). He has also held fixed-term teaching and academic appointments in the Indian School of Business, Korea University, Univerity of the Peloponnese, Hellenic International University, RWTH Aachen, Lille 2, and more recently in Kedge Business School.

Professor Nikolopoulos is an Associate Editor of Oxford IMA "Journal of Management Mathematics" and the "Supply Chain Forum, an International Journal" (Taylor & Francis); he is also the Section Editor-In-Chief for the "Forecasting in Economics and Management" section in the MDPI open access journal "Forecasting".

Professor Nikolopoulos is currently Co-Investigator in two major research grants for a) the GCRF; South Asia Self Harm research capability building initiative (SASHI) project funded by the Medical Research Council in UK (2017-2021), http://sashi.bangor.ac.uk/., and b) the H2020-FETPROACT; Radioactivity Monitoring in Ocean Ecosystems (RAMONES) funded by the EU (2021-2025). In the past he has succesfully bid as PI for more than £0.5M of research grants through the forecasting laboratory (forLAB) he founded and directed in Prifysgol Bangor University in Wales, UK.

Professor Nikolopoulos' work has been consistently appearing in the International Journal of Forecasting (29 outputs) but also in journals for broader audiences including the Journal of Operations Management, the European Journal of Operational Research, and the Journal of Computer Information Systems. His research outputs, citations, and respective research impact can be found at https://scholar.google.co.uk/citations?user=7u7ENCsAAAAJ&hl=en

• Analytics
• Forecasting
• Information Systems
• Operations

Chapter in book

• Mertzimekis, T., Lagaki, V., Madesis, I., Siltzovalis, G., Petra, E., Nomikou, P., …Maigne, L. (2022). RAMONES and Environmental Intelligence: Progress Update. In GoodIT '22: Proceedings of the 2022 ACM Conference on Information Technology for Social Good (244-249). ACM. https://doi.org/10.1145/3524458.3547255
• Nikolopoulos, K. (2022). The EU project RAMONES – continuous, long-term autonomous monitoring of underwater radioactivity. In P. Batista, D. Cabecinhas, L. Sebastião, A. Pascoal, T. Mertzimekis, K. Kebkal, …L. Maigne (Eds.), . Hydrographic Institute

Conference Paper

• Mertzimekis, T., Nomikou, P., Petra, E., Batista, P., Cabesinhas, D., Pascoal, A., …Maigne., L. (2021). Radioactivity Monitoring in Ocean Ecosystems (RAMONES). In GoodIT '21: Proceedings of the Conference on Information Technology for Social Good. https://doi.org/10.1145/3462203.3475906
• Monitoring in Ocean Ecosystems Event. . https://doi.org/10.6084/m9.figshare.13246406

Journal Article

• Litsioua, K., & Nikolopoulos, K. (in press). Social Collateral and consumer payment media during the economic crisis in Europe. Journal of Quantitative Finance and Economics,
• Aljuneidi, T., Punia, S., Jebali, A., & Nikolopoulos, K. (2024). Forecasting and Planning for a critical infrastructure sector during a pandemic: empirical evidence from a food supply chain. European Journal of Operational Research, https://doi.org/10.1016/j.ejor.2024.04.009
• Brookes, T., Nikolopoulos , K., Litsiou, K., & Alghassab, W. (2024). Forecasting and planning for special events in the pulp and paper supply chains. Supply Chain Forum: an International Journal, https://doi.org/10.1080/16258312.2024.2315029
• Grossmann, I., Rotella, A., Hutcherson, C. A., Sharpinskyi, K., Varnum, M. E., Achter, S., …Collaborative, T. F. (2023). Insights into accuracy of social scientists' forecasts of societal change. Nature Human Behaviour, 7(4), 484-501. https://doi.org/10.1038/s41562-022-01517-1
• Nikolopoulos, K., & Syntetos, A. (2023). Management, Mathematics, and Management-Mathematics: strengthening the link in a turbulent post-pandemic world. IMA Journal of Management Mathematics, https://doi.org/10.1093/imaman/dpad024
• Nikolopoulos, K., & Vasilakis, C. (2023). Forecasting the Effective Reproduction Number during a Pandemic: COVID-19 Rt forecasts, Governmental Decisions, and Economic Implications. IMA Journal of Management Mathematics, https://doi.org/10.1093/imaman/dpad023
• Makridakis, S., Spiliotis, E., Assimakopoulos, V., Semenoglou, A., Mulder, G., & Nikolopoulos, K. (2023). Statistical, Machine Learning and Deep Learning forecasting methods: Comparisons and ways forward. Journal of the Operational Research Society, 74(3), 840-859. https://doi.org/10.1080/01605682.2022.2118629
• Sanguri, K., Patra, S., Nikolopoulos, K., & Punia, S. (2023). Intermittent demand, inventory obsolescence, and temporal aggregation forecasts. International Journal of Production Research, https://doi.org/10.1080/00207543.2023.2199435
• Nikolopoulos, K., Tsinopoulos, C., & Vasilakis, C. (2023). Operational Research in the time of COVID-19: the ‘science for better’ or worse in the absence of hard data. Journal of the Operational Research Society, 74(2), 448-449. https://doi.org/10.1080/01605682.2021.1930208
• Alroomi, A., Karamatzanis, G., Nikolopoulos, K., Tilba, A., & Xiao, S. (2022). Fathoming empirical forecasting competitions’ winners. International Journal of Forecasting, 38(4), 1519-1525. https://doi.org/10.1016/j.ijforecast.2022.03.010
• Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Taieb, S. B., …Ziel, F. (2022). Forecasting: theory and practice. International Journal of Forecasting, 38(3), 705-871. https://doi.org/10.1016/j.ijforecast.2021.11.001
• Nikolopoulos, K. (2021). We need to talk about intermittent demand forecasting. European Journal of Operational Research, 291(2), 549-559. https://doi.org/10.1016/j.ejor.2019.12.046
• Katsagounos, I., Thomakos, D. D., Litsiou, K., & Nikolopoulos, K. (2021). Superforecasting reality check: Evidence from a small pool of experts and expedited identification. European Journal of Operational Research, 289(1), 107-117. https://doi.org/10.1016/j.ejor.2020.06.042
• Vangumalli, D., Nikolopoulos, K., & Litsiou, K. (2021). Aggregate selection, individual selection, and cluster selection: an empirical evaluation and implications for systems research. Cybernetics and Systems, 52(7), 553-578. https://doi.org/10.1080/01969722.2021.1902049
• Pochiraju, B., Seshadri, S., Thomakos, D. D., & Nikolopoulos, K. (2020). Non-Negativity of a Quadratic form with Applications to Panel Data Estimation, Forecasting and Optimization. Stats, 3(3), 185-202. https://doi.org/10.3390/stats3030015
• Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., & Vasilakis, C. (2020). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research, 290(1), 99-115. https://doi.org/10.1016/j.ejor.2020.08.001
• Petropoulos, F., Kourentzes, N., Nikolopoulos, K., & Siemsen, E. (2018). Judgmental selection of forecasting models. Journal of Operations Management, 60, 34-46. https://doi.org/10.1016/j.jom.2018.05.005
• Nikolopoulos, K., & Petropoulos, F. (2018). Forecasting for big data: Does suboptimality matter?. Computers and Operations Research, 98, https://doi.org/10.1016/j.cor.2017.05.007
• Syntetos, A. A., Babai, Z., Boylan, J. E., Kolassa, S., & Nikolopoulos, K. (2016). Supply Chain Forecasting: Theory, Practice, their Gap and the Future. European Journal of Operational Research, 252(1), 1-26. https://doi.org/10.1016/j.ejor.2015.11.010
• Nikolopoulos, K. I., Babai, M. Z., & Bozos, K. (2016). Forecasting supply chain sporadic demand with nearest neighbor approaches. International Journal of Production Economics, 177, https://doi.org/10.1016/j.ijpe.2016.04.013
• Nikolopoulos, K., Buxton, S., Khammash, M., & Stern, P. (2016). Forecasting branded and generic pharmaceuticals. International Journal of Forecasting, 32(2), https://doi.org/10.1016/j.ijforecast.2015.08.001
• Chakravarty, S., Thomakos, D., & Nikolopoulos, K. (2016). Growth, deregulation and rent seeking in post-war British economy. Applied Economics, 48(18), https://doi.org/10.1080/00036846.2015.1105928
• Nikolopoulos, K., Litsa, A., Petropoulos, F., Bougioukos, V., & Khammash, M. (2015). Relative performance of methods for forecasting special events. Journal of Business Research, 68(8), https://doi.org/10.1016/j.jbusres.2015.03.037
• Spithourakis, G. P., Petropoulos, F., Nikolopoulos, K., & Assimakopoulos, V. (2015). Amplifying the learning effects via a Forecasting and Foresight Support System. International Journal of Forecasting, 31(1), https://doi.org/10.1016/j.ijforecast.2014.05.002
• Petropoulos, F., Makridakis, S., Assimakopoulos, V., & Nikolopoulos, K. (2014). ‘Horses for Courses’ in demand forecasting. European Journal of Operational Research, 237(1), 152-163. https://doi.org/10.1016/j.ejor.2014.02.036
• Thomakos, D., & Nikolopoulos, K. (2014). Fathoming the theta method for a unit root process. IMA Journal of Management Mathematics, 25(1), https://doi.org/10.1093/imaman/dps030
• Spithourakis, G., Petropoulos, F., Nikolopoulos, K., & Assimakopoulos, V. (2014). A systemic view of the ADIDA framework. IMA Journal of Management Mathematics, 25(2), https://doi.org/10.1093/imaman/dps031
• Savio, N. D., & Nikolopoulos, K. (2013). A strategic forecasting framework for governmental decision-making and planning. International Journal of Forecasting, 29(2), https://doi.org/10.1016/j.ijforecast.2011.08.002
• Babai, M. Z., Ali, M. M., & Nikolopoulos, K. (2012). Impact of temporal aggregation on stock control performance of intermittent demand estimators: Empirical analysis. Omega, 40(6), https://doi.org/10.1016/j.omega.2011.09.004
• Nikolopoulos, K., Syntetos, A., Boylan, J., Petropoulos, F., & Assimakopoulos, V. (2011). An aggregate–disaggregate intermittent demand approach (ADIDA) to forecasting: an empirical proposition and analysis. Journal of the Operational Research Society, 62(3), https://doi.org/10.1057/jors.2010.32
• Bozos, K., & Nikolopoulos, K. (2011). Forecasting the value effect of seasoned equity offering announcements. European Journal of Operational Research, 214(2), https://doi.org/10.1016/j.ejor.2011.04.007
• Savio, N., & Nikolopoulos, K. (2010). Forecasting the Effectiveness of Policy Implementation Strategies. International Journal of Public Administration, 33(2), https://doi.org/10.1080/01900690903241765
• Nikolopoulos, K. (2010). Forecasting with quantitative methods: the impact of special events in time series. Applied Economics, 42(8), https://doi.org/10.1080/00036840701721042
• Fildes, R., Goodwin, P., Lawrence, M., & Nikolopoulos, K. (2009). Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting, 25(1), https://doi.org/10.1016/j.ijforecast.2008.11.010
• Goodwin, P., Fildes, R., Lawrence, M., & Nikolopoulos, K. (2007). The process of using a forecasting support system. International Journal of Forecasting, 23(3), https://doi.org/10.1016/j.ijforecast.2007.05.016
• Nikolopoulos, K., Goodwin, P., Patelis, A., & Assimakopoulos, V. (2007). Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches. European Journal of Operational Research, 180(1), https://doi.org/10.1016/j.ejor.2006.03.047
• Pagourtzi, E., Nikolopoulos, K., & Assimakopoulos, V. (2006). Architecture for a real estate analysis information system using GIS techniques integrated with fuzzy theory. Journal of Property Investment and Finance, 24(1), https://doi.org/10.1108/14635780610642971
• Nikolopoulos, K., Metaxiotis, K., Lekatis, N., & Assimakopoulos, V. (2003). Integrating industrial maintenance strategy into ERP. Industrial Management and Data Systems, 103(3), https://doi.org/10.1108/02635570310465661
• Patelis, A., Metaxiotis, K., Nikolopoulos, K., & Assimakopoulos, V. (2003). FORTV: decision support system for forecasting television viewership. Journal of Computer Information Systems, 43(4), 100-107
• Nikolopoulos, K., & Assimakopoulos, V. (2003). Theta intelligent forecasting information system. Industrial Management and Data Systems, 103(9), https://doi.org/10.1108/02635570310506133
• Petropoulos, C., Patelis, A., Metaxiotis, K., Nikolopoulos, K., & Assimakopoulos, V. (2003). Sftis: A Decision Support System for Tourism Demand Analysis and Forecasting. Journal of Computer Information Systems, 44(1), 21-32
• Assimakopoulos, V., & Nikolopoulos, K. (2000). The theta model: a decomposition approach to forecasting. International Journal of Forecasting, 16(4), https://doi.org/10.1016/s0169-2070%2800%2900066-2

Presentation

• NIKOLOPOULOS, K., & Mertzimekis, T. J. (2023, October). EXPANDING THE CONCEPT OF ENVIRONMENTAL INTELLIGENCE VIA INNOVATIVE TECHNOLOGIES FOR IN SITU RADIOACTIVITY MONITORING. Paper presented at 4th International Conference on Environmental Design (ICED2023), Athens, Greece
• Nikolopoulos, K., Hamo Younes, A., & Phan, M. (2024, July). Forecasting the success of International Joint Ventures. Poster presented at INFORMS Advances in Decision Analysis Conference (ADA 2024), Aalto University, Helsinki-Espoo, Finland
• Nikolopoulos, K. (2024, June). Forecasting M&A shareholder wealth effects to prevent value-destroying deals: Can it be done?