Forecasting at OR68
A Welcoming Space for Forecasting Research that Connects Methods with Real Decisions
- Call for Abstracts Closes: 1 May 2026
- Early Bird Closes: 7 May 2026
- Registrations Close: 6 September 2026
OR68 at a Glance
The OR Society Annual Conference (OR68), the flagship annual event of the Operational Research Society, will be held from 8–10 September 2026 at the Monica Partridge Building, University of Nottingham (NG7 2BF, United Kingdom). The conference brings together a global community of researchers, practitioners, and decision-makers to exchange ideas, present new work, and explore how analytical methods translate into real-world impact.
Under this year’s theme, From Data to Decisions, OR68 emphasises the growing importance of connecting advanced modelling, forecasting, and data-driven approaches with practical decision-making across sectors. The event provides a unique opportunity to engage with cutting-edge research while building meaningful academic and professional collaborations.
Forecasting Stream
The Forecasting Stream at the OR Society Annual Conference (OR68), taking place at the University of Nottingham from 8–10 September 2026, showcases a broad spectrum of forecasting methodologies, combining real-world applications with emerging techniques that support informed decision-making across sectors such as Supply Chains & Logistics, Healthcare & Public Health, Finance, Economics, Agriculture & Food, and Water, Energy & Environment.
The stream aims to inspire researchers and practitioners through thought-provoking talks and a diverse set of sessions that foreground practical relevance. Contributions are encouraged on Forecasting Principles, Machine Learning Approaches, Judgemental Forecasting, and Sector-Specific Applications. Submissions from the Public Sector, Private Organisations, and Academia are welcome, reinforcing the collaborative spirit of the event and encouraging cross-sector exchange of ideas, methods, and best practice.
By taking part in the OR68 Forecasting Stream, attendees will strengthen their understanding of the rapidly evolving forecasting landscape and its close connection to operational research. They will gain exposure to state-of-the-art techniques, innovative machine-learning applications, and applied case studies from multiple domains, while expanding their professional networks and identifying opportunities for future collaboration.
Why Apply?
Decision Relevance
Present your work to an audience interested not only in predictive accuracy, but also in how forecasting supports planning, operations, and policy.
Methodological Breadth
The stream welcomes classical forecasting, probabilistic methods, judgemental approaches, machine learning, and hybrid frameworks.
Practical Impact
Share forecasting research that matters in real settings such as healthcare, supply chains, finance, energy, and public services.
Strong Visibility
OR68 provides an excellent platform to connect with researchers and practitioners across the wider operational research and analytics community.
Who Should Apply?
We warmly encourage submissions from:
- Researchers
- Practitioners
- PhD Students
- Early-Career Researchers
- Interdisciplinary Teams
- Contributors from Academia, Industry, Government, and the Public Sector
Whether your work is methodological, applied, or decision-focused, the Forecasting Stream would be pleased to receive your submission.
Topics Include, but Are Not Limited to
- Time Series Forecasting Methods
- Machine Learning and AI in Forecasting
- Probabilistic and Distributional Forecasting
- Forecast Evaluation and Accuracy
- Judgemental Forecasting
- Forecasting in Supply Chains and Logistics
- Forecasting in Healthcare and Public Health
- Forecasting in Finance and Economics
- Forecasting in Agriculture and Food Systems
- Forecasting in Water, Energy, and Environment
How to Apply
Submitting your abstract is straightforward:
- Create an account on the abstract submission portal
- Enter your contact details
- Add your abstract title and select “FORECASTING” as your stream
- Add the author details
- Upload your abstract or synopsis
- Review and submit
Scan the QR Code or Use the Submission Link: Submit Your Abstract Here
Connect with the Forecasting Stream Team
Prof. Bahman Rostami-Tabar
Professor of Analytics and Decision Sciences, Director of the Data Lab for Social Good, and Theme Lead for Uncertainty & the Future at Cardiff University; Associate Editor, Journal of the Operational Research Society
Rostami-TabarB@cardiff.ac.uk
Udeshi Salgado
PhD Candidate, DL4SG Research Group, Cardiff Business School, Cardiff University salgadomu@cardiff.ac.uk
Rui Xu
PhD Candidate, DL4SG Research Group, Cardiff Business School, Cardiff University
XuR25@cardiff.ac.uk
Join Us in Nottingham
If your work contributes to forecasting theory, practice, or decision-making under uncertainty, we would be delighted to welcome your submission to the Forecasting Stream at OR68.
We look forward to seeing you in Nottingham in September 2026.