Three maturity levels of analytics
Most manufacturers stand on level one or two. Level three is a lead that cannot be copied.
- Dashboards and reports
- Sell-in and sell-out in hindsight
- KPIs in real time
- Forecast of the next season
- Recommendations per item
- Learning from new weeks
- Ready-made forecast models
- Fast connection
- Forecast, generic
- Understanding of your seasonality
- Ownership of the model
- Lasting lead
- Learned on your data
- Forecast with industry context
- Weekly relearning
- Full ownership
- No vendor lock-in
- Open calculation path
What an own model gives that no dashboard gives
It is not about technology, but about knowledge that keeps working every week.
Large competitors use standard systems anyone can license. A model that learned on three years of your 140 items knows patterns no standard system knows: which chain orders three weeks before the peak and which five, which praline grows in spring and which stagnates. This knowledge only arises at your company.
A dashboard always shows the same thing, just with new numbers. A model learns with every week of new data. After one season it knows your patterns across all items, channels and promotions at once; a planner oversees a handful at once.
Code, weights, training path: everything stays in your infrastructure. No subscription, no data in someone else's cloud. If the collaboration ends, the model stays and keeps running. No vendor whose business lives on your dependency will offer you that.
"For this chain always add fifteen percent, they reorder" - that is in no system, only in the head of an experienced planner. A model codifies this knowledge because it is in the data even if it was never written down. It does not resign, does not forget and takes no holiday in Advent.
Cost of the model against cost of not knowing
The value of a model is not measured in forecast accuracy but in euros that are not written off, not lost and not pushed after by express.
€171,000 fewer write-offs, €85,500 recovered contribution margin from shelf gaps, €75,000 fewer extra shifts and express freight - the result of module 01 alone.
In the first year the model learns and delivers part of the potential. In the second it knows every season once. In the third it knows every item, every channel and every promotion, and finds patterns nobody knew of. A model does not wear out, it grows in value. How fast depends on your data situation; the figures of this series are annual values of the sample company, not a commitment.
From the data to the running model
Four phases, each ending with a tangible result. The duration depends on availability and quality of your data; dates emerge in the conversation, not on this page.
Export of three years of sell-in and sell-out. Assessment of data quality. First model in hindsight against your current method. Result: the forecast error of both methods and the potential in euros, calculated on your figures.
Weekly forecast twelve weeks ahead for the most seasonal category. Comparison with the production plan: which volumes could have been planned differently?
The modules your data situation supports are connected: shelf space, promotion, changeover sequence, remaining shelf life, cocoa coverage. Interfaces to ERP and warehouse system, training of planning.
Models in operation, weekly relearning, accuracy monitoring, hand-over of code and documentation to your IT. What the measurement refutes is replaced.
Three reasons for the timing
Three developments that turn a good project into an urgent one.
The value of a model lies not in the accuracy it achieves but in the decision it makes possible earlier.
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.
This series shows which questions data science can answer in a chocolate factory. The concrete answers only emerge with your data.