Numbers you can trace back
The starting point is a fictitious mid-sized chocolate manufacturer: €95 million net revenue, 380 employees, 12,000 tonnes of chocolate products per year, a milk- and filling-heavy range, sales to grocery retail, key accounts and a small direct channel. Every figure is derived from the documented assumptions.
Six foundations and three modules beyond the usual
Each module answers one operational question with visualisations, a transparent model calculation and the calculation path. The order follows the path of the goods: season, shelf, factory, raw material.
Three questions rarely answered with data although the data has long been there: the taste, the origin, the direct customer.
Not in the chart and not in the total: The Cocoa Coverage (€216,000). The amount is avoided dispersion of landed costs, not a cost advantage, and stands separately on the module page.
No theory - results in weeks
We work with the data you already have. No vendor lock-in, no cloud mandate, no hidden costs.
The nine modules use a fictitious mid-sized chocolate manufacturer (€95 million net revenue, 140 items, 4 lines, a milk- and filling-heavy range) to show how data science can be applied along the path of the goods - from the seasonal volume to the origin of the cocoa.
All euro amounts are model calculations. They are derived from clearly named assumptions (assumption base → lever → result) and illustrate the order of magnitude, not a commitment. The total is a gross effect before implementation costs; no module has independent evidence for its degree of effect, the lever ratios are assumptions.
In a real project, knowledge of your factory is the decisive factor: which channels deliver sell-out data? How are lines and allergen profiles cut? Where do the biggest losses arise - after the season, on the shelf or in purchasing? On this basis, models emerge that fit your reality.
This series shows which questions data science can answer in a chocolate factory. The concrete answers only emerge with your data.