Demand planning:
Less stock, fewer rush orders
Over- and underproduction are two sides of the same forecast gap. Forecasting on your ERP and PPS history reduces tied-up stock and avoids the costly rush orders that patch the gap today.
01 The problem – Too much ties up capital, too little costs rush surcharges
Poor demand forecasts make both directions expensive
Overproduction ties up capital, blocks storage space and partly ends in scrapping. Underproduction triggers rush orders, special transports and extra shifts. Both stem from the same cause: a demand forecast based on gut feeling and last year.
Your history holds the patterns: seasonality, order lead time, call-off behaviour per customer, correlations between items. Forecasting turns this into a reliable demand curve per item and week.
Less tied-up capital, without endangering delivery capability.
02 The model – Demand per item and week
Gradient boosting and time series on order, call-off and seasonal patterns
WAPE Bedarfsprognose: 14.2% Ø Sicherheitsbestand: 1.040 Stk (vorher pauschal: 1.800 Stk)
The model delivers not just a point value but a confidence band. This lets safety stock be controlled per item: high for volatile parts, lean for stable ones. Flat safety stocks across all items – the most common source of excess stock – disappear.
03 Business impact – Cut tied-up capital and rush cost
The lever: lower stock × cost of capital, plus fewer rush and scrapping costs
No round number: every assumption comes from the sample plant and is stored centrally. With your real figures only the input changes, not the method.
| Item | Value |
|---|---|
| Tied-up inventory | €9,500,000 |
| Stock reduction 15% × cost of capital 8% | €114,000 |
| Rush cost / year → −30% | €114,000 |
| Scrapping / year → −30% | €66,000 |
| Result: savings / year | €294,000 |
Assumptions of a sample plant – in a real project your data replaces these values.
The better demand curve works threefold: it lowers tied-up stock (cost of capital), reduces expensive rush orders on shortfall and avoids scrapping on surplus. All three draw from the same model.
04 Next steps
From forecast to integrated planning
We use order, call-off and stock history from your ERP/PPS – without new data capture.
For one item group the forecast model is built. You see the hit rate validated against recent quarters.
Weekly demand proposal per item with a safety band – as input for your production and procurement planning.