Technical Implementation
Worker Productivity Forecasting
1,197 production records. Forecasts worker productivity from SMV, team size, incentives.
Approach
Stacking ensemble for worker productivity forecasting. 94.7% R虏 with 18% idle time reduction.
Objective
Build stacking ensemble for RMG productivity forecasting.
Challenge
Linear models <80% R虏. Equipment variability makes prediction hard.
Solution
Stacking: XGBoost + RF + NN meta-learner. 94.7% R虏, 18% idle time reduction.
Dataset
1,197 records
Features
14 engineered
Model
Stacking Ensemble
Functions
- Multi-source data ingestion.
- Feature engineering.
- Ensemble training & tuning.
- Real-time forecasting API.
Architecture
- Base: XGBoost, RF, GB.
- Meta: 3-layer NN.
- Stratified 5-fold CV.
- Weights optimized by meta-learner.
- research.tracks.items.garmentProductivity.details.modelDesign.item5
Preprocessing
- KNN imputation.
- IQR filtering.
- StandardScaler normalization.
- Encoding (one-hot, ordinal).
- Temporal features.
Training
- MSE loss with early stopping.
- Adam, LR=0.001.
- L2 regularization (位=0.01).
- RMSE, MAE, R虏 metrics.
Performance
| Model | RMSE | MAE | R虏 |
|---|---|---|---|
| XGBoost | 0.112 | 0.085 | 88.3% |
| RF | 0.118 | 0.091 | 86.7% |
| GB | 0.107 | 0.082 | 89.5% |
| NN | 0.101 | 0.078 | 91.2% |
| Stacking | 0.089 | 0.067 | 94.7% |
5-fold CV. Stacking outperforms individual models.