OpenForecast turns uncertain demand into confident decisions. We are a specialist demand forecasting and inventory consultancy and training provider, using research-grade statistical methods to help businesses cut stockouts, reduce excess stock, and free up cash — even in the hardest cases, like the intermittent demand of spare parts and industrial distribution.
The problem we solve
Most demand planning runs on tools that quietly cost money. Typical forecasting engines extrapolate the past and break the moment a promotion, price change, or supply disruption enters the picture. ERP forecasting modules produce numbers nobody can account for — you cannot tell why the forecast is what it is, or whether it should be trusted — and they underperform on exactly the items that matter most. The result shows up in two places at once: cash tied up in stock you don’t need, and lost sales on the lines you do.
We fix the whole chain, not just the forecast: from the data (including the demand your sales history doesn’t show, because you were out of stock), through the model, to the safety stock and replenishment decisions your team runs every week.
What we do
Most clients start with a diagnostic. It is fixed in scope, and priced from £5,000 depending on the size of the problem, and it tells you what better forecasting is actually worth to you before you commit to anything larger.
Forecasting & Inventory Diagnostic
A fixed-scope health check of your current setup: how accurate your forecasts really are, where the gaps are — censored demand, wrong error measures, mis-set safety stocks — and a prioritised action list you can act on with or without us.
Start here if you are not yet sure what the problem is worth fixing.
Consulting
We start with how your business actually runs — your planning process, your decisions, your constraints — and build forecasting and inventory models around it. Your demand patterns, your drivers (promotions, pricing, seasonality), your ERP, your cycle. No arbitrary rules imposed from outside, and no analysis that ignores how the numbers get used.
Engagements are scoped individually.
Practitioner training
Courses in demand forecasting, inventory management, statistics, and analytics — built on a decade of executive training we developed and delivered at Lancaster University. Your planning team learns not just which buttons to press, but why the methods work. Our next open course, Demand Forecasting Principles, runs online across four weeks in October 2026.
Open courses and bespoke training for teams.
Why OpenForecast
Research-grade methods, in the open
Our work runs on the open-source forecasting packages we build and maintain — smooth, greybox and others — grounded in a published methodology (ADAM), benchmarked on international forecasting competition data, and used by analysts worldwide. You can inspect exactly what our methods do before you ever hire us.
The right method, not the fashionable one
We use machine learning where it earns its place, and statistical models where they do. Neural networks and gradient boosting beat conventional approaches on some problems and lose badly on others, particularly with short histories or sparse demand. Knowing which case you are in is the expertise.
Explainable models
You can see the logic, explain the numbers to finance, and stand behind the plan. Where a less interpretable model is genuinely better, we say so and explain what you gain and give up.
Academic rigour, practitioner focus
OpenForecast is led by Ivan Svetunkov and Nikolaos Kourentzes — forecasting researchers with decades of published work between them and years of experience making forecasting work in practice, not in slides. We wrote the methods, built the software, and teach the courses. More about us →
The tutors are true experts in statistical forecasting models. Their insights on the simplest aspects of the field were valuable, even for a seasoned practitioner like myself.
Everything we know, published
Our methods are not proprietary secrets. Two books, a decade of articles, and full documentation are freely available — you can read exactly how we work before you ever hire us. Start with the resources, explore the packages, or read the blog.
Recent from the blog
ISF2026: PTS Taxonomy of Multiple Source of Error State Space Models for Demand Forecasting
This time, at ISF2026, I presented the paper that I have worked on together with Juan Ramon Trapero and Diego Pedregal. The idea of the paper is to introduce a taxonomy of the models in the Multiple Sources of Error (MSOE) framework. In the Single Source of Errors one, there is ETS, in the MSOE, … Read more
stick function for the EDA in time series
You have probably seen my post about the STI classification of Hans Levenbach (this one). Well, I’ve decided to implement it, and it has landed in the greybox package for R/Python. What’s greybox? It is a package for statistical modelling focusing on forecasting and time series analysis. I created it back in 2018 to split … Read more
smooth in python: Non-normal distributions in ETS/ARIMA
So, you know quite well that the normal distribution is one of the most popular distributions in statistics. The reasons are manifold, including convenience for the academic community and the fact that it is taught in every single statistics course in the world. But what if we don’t want to be normal? There are situations … Read more
If your forecasts drive real inventory decisions, we should talk.
Book a free 30-minute intro call — no preparation needed. We will ask about your situation and tell you honestly whether and how we can help.
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