Interactive demo
Spot at-risk stock before it runs out
Stock value, turnover, cover and alerts by warehouse, with an ABC analysis of your items.
- Power BI
- Excel / Power Query
Interactive demo
Forecast sales, with a margin of error
Seasonal sample data. Pick a horizon: the shaded area shows the likely range, which widens the further ahead you look.
Horizon
Forecast sales over 6 months€344k
Likely range€335k – €353k
vs same period last year▲ 5.5%
Average error measured on past data3.9%
- Actual sales
- Forecast
- Likely range (80%)
View the forecast as a table
Method shown: trend + seasonality, validated on the last 12 known months. On your data, several methods are compared before one is chosen.
Demo on sample data. The data is made up; the approach is the one I use on real projects: the same KPIs, the same consistency checks. Typical engagement: sales forecasting for a seasonal business.
The context
Orders, stock, hiring, cash flow: you have to decide today for the months ahead, often on gut feeling. A forecast with its margin of error makes those decisions safer.
How I built it
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History
I gather and check the history, ideally two years or more to capture seasonality.
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Model
I test several methods and keep the most reliable on past data. A simple, well-explained statistical method is often enough.
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Validation
I compare the forecast with what actually happened.
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Integration
The forecast updates itself in the dashboard.
Before and after
Before
- Decisions on gut feeling
- A single number, no margin
- A spreadsheet to recalculate
After
- A forecast for each horizon
- A likely range, shown and tracked
- Automatic update in the dashboard
What it changes
Better visibility on the months ahead, a known margin of error, and an explained model rather than a black box.
What about your business?
With less than two years of history, we work from trends, with a wider margin. From our first conversation, I’ll tell you what your history can support.
Need something similar? Write to me.
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