Business Context
VoltForge manufactures industrial motor controllers across 12 production lines and inspects roughly 1.8 million units per month. The quality team wants a model that predicts whether a unit will fail final inspection, but the solution must be optimized for plant constraints: false negatives are expensive, inference must run on edge devices, and engineers need understandable drivers of risk.
Dataset
You are given one year of unit-level production data collected from MES, sensor logs, and operator records.
| Feature Group | Count | Examples |
|---|
| Process measurements | 18 | solder_temp_mean, torque_std, cycle_time_sec, humidity_pct |
| Equipment metadata | 7 | line_id, machine_id, tool_version, maintenance_age_days |
| Material / supplier | 6 | supplier_id, lot_age_days, pcb_batch_grade |
| Operator / shift | 5 | shift, operator_tenure_days, overtime_flag |
| Quality history | 6 | prior_line_defect_rate_7d, rework_rate_30d, calibration_gap_days |
- Rows: 420K manufactured units, 42 features
- Target:
failed_final_inspection (1 = defective, 0 = passed)
- Class balance: 4.6% defective, 95.4% non-defective
- Missing data: ~12% missing in sensor-derived features during maintenance windows; ~4% missing in operator fields for temporary staff
Success Criteria
A good solution should:
- achieve recall >= 0.85 on defective units,
- maintain precision >= 0.30 to avoid overwhelming manual review,
- keep batch scoring latency under 5 minutes for 200K units on a CPU-only edge server,
- provide feature-level explanations usable by manufacturing engineers.
Constraints
- No GPU at inference time
- Model retrains weekly
- Predictions are used to trigger secondary inspection, so threshold selection must reflect inspection capacity
- The plant prefers simpler models if performance is within 2-3 points of a more complex alternative
Deliverables
- Build and compare at least two classification approaches suitable for tabular industrial data.
- Design preprocessing for mixed feature types and missing values.
- Choose an evaluation strategy that reflects class imbalance and temporal production drift.
- Select an operating threshold based on plant inspection capacity and defect recall.
- Explain the final model choice in terms of accuracy, latency, interpretability, and operational fit.
