Predictive Analytics & Demand Forecasting
We build demand forecasting models based on your own historical sales data, helping you make better-informed production and procurement decisions. Predictive analytics and machine learning help reduce inventory and waste while improving production planning. The model typically requires at least two to three years of well-structured sales data.
Request an assessmentWhen you need this
- The production plan runs on an experienced colleague’s judgement — and he retires next year.
- You regularly write off stock that ran out of shelf life before it sold.
- Or the reverse: a missed order, because stock was not there at the right moment.
- Nobody can quantify the effect of promotions and seasonality in advance, only explain it afterwards.
- You order raw materials with a safety buffer, because planning tighter feels too risky.
What you get
- SKU-level weekly forecasts, fitted on your own sales and production history
- A backtest report: how much more accurate the model is than your current process, and what that is worth in money
- Forecasts delivered inside your existing Power BI reports — not a separate system to open
- Promotions, seasonality and public holidays handled in the model
- Automatic retraining and accuracy monitoring, with alerts when the model starts drifting
- A short, honest guide to when you should NOT trust the model
How it works
Data check — 1 week
We check whether there is enough usable history. If there is not, you hear it in week one, instead of spending a project budget on a model that cannot work.
Baseline — 1 week
We set up the simplest possible method as a reference. Every later result is measured against it and against your current estimate — without that, nobody can tell whether the complicated model is worth anything.
Model and backtest — 2 to 3 weeks
We build the model and test it against the past, forecasting as if it did not know the future. You see how wrong it would have been before it goes anywhere near a decision.
Live and monitored — 1 to 2 weeks
We wire it into daily operations and set up retraining and accuracy monitoring. A forecast is not a one-off deliverable; it decays if nobody watches it.
Technologies
Scope & pricing
- Timeline
- 4 to 8 weeks
- Price
- Data check: €3,000 · Full rollout: €6,000 – 20,000
All prices are indicative and exclude VAT. A detailed quotation is provided after the assessment, once we understand your systems and requirements.
Frequently asked questions
How much history do we need?
As a rule of thumb, at least two years of weekly sales data, so seasonality appears twice. Less can work, but accuracy degrades noticeably. The data check measures exactly this.
What if the model is wrong?
It will be. The question is not whether it is wrong but whether it is less wrong than your current process. We show that with a backtest before go-live — and if it does not improve on what you have, we say so.
Do we need a data warehouse first?
Not necessarily, but you do need clean, consistent history. If that is missing, start with data integration — doing it in the other order is the most common reason forecasting projects fail.
Does this replace our planner?
No, and it is not meant to. The forecast gives a solid starting point that the planner can override when they know something the data does not. The aim is that they do not start from zero every Monday.
Related services
- Data Engineering & Data Warehousing
If month-end reporting requires manually reconciling multiple Excel files, the real problem is not the report—it is the data behind it. We integrate your ERP, manufacturing and warehouse systems into a single, trusted data platform so finance, production and sales all work from the same numbers.
- Power BI Reports & Dashboards
If management meetings focus on debating the numbers instead of making decisions, your reporting is not doing its job. We build Power BI dashboards that stay up to date automatically, so management, production and sales all work from the same trusted data.