Scrap forecast:
Know the defect before the batch runs
Which combination of material, machine parameters and conditions leads to scrap? Gradient boosting learns it from your inspection and process data, and warns before expensive material is processed.
This module is part of a six-part model calculation about a fictional mid-sized manufacturing company (48 machines, 3-shift operation, €85M revenue). All euro amounts are derived from the documented assumptions of the sample plant. They are not results of real clients and not a performance promise. What values are achievable on your real data is established only in a pilot project.
01 The problem: Scrap is detected too late today
Final inspection tells you something went wrong, not why
Scrap costs twice: the lost material and the rework. Today it usually surfaces only at final inspection, once material, energy and machine time are already invested.
Yet the causes sit in the data: batch properties, temperature, pressure, tool wear, feed rate. A model detects the critical combination a human cannot see in the parameter space.
Every avoided scrap part saves material AND the rework that would otherwise follow.
02 The model: Risk score per batch and parameter set
Classification across all process and material features known at production time
ROC-AUC Ausschuss-Klassifikation: 0.912 Top-Treiber: ['material_charge_B', 'werkzeug_temp', 'vorschub', 'werkzeug_standzeit_h'] Kritische Kombination -> Ausschussrate: 14.8%
Individual parameters in the green zone does not mean "all good": only the combination of material batch B, elevated tool temperature and high feed rate tips quality over. Gradient boosting (XGBoost) models exactly these interactions, and names the concrete driver via SHAP values.
03 Business impact: Lower material and rework cost
The lever: reduced scrap rate × material input, plus reduced rework
Every assumption comes from the sample plant and is stored centrally. With your real figures only the input changes, not the method.
| Item | Value |
|---|---|
| Material input / year | €24,000,000 |
| Material savings (Scrap rate: 4.5% → 3.4%) | €264,000 |
| Rework cost / year | €620,000 |
| Rework reduction (25%) | €155,000 |
| Result: savings / year | €419,000 |
Assumptions of a sample plant. In a real project your data replaces these values.
The model does not stop at the warning: the drivers become process windows that feed back into the controls. "Batch B + hot + fast" becomes a concrete parameter limit, permanently.
04 Next steps on your shop floor
From risk score to closed-loop control
We connect process parameters from the MES with the inspection results from quality assurance. The data already exists.
For the highest-scrap product family a pilot model is built. You see the drivers validated on real batches.
Risk score before production start: "This batch with these parameters has elevated scrap risk. Recommendation: adjust parameter X."