Business Context
ManufacturingTech, a leading manufacturing company, aims to enhance its production efficiency and reduce downtime. By leveraging historical and incoming process data, the company seeks to develop a predictive model that can provide insights into process performance, enabling proactive adjustments and improved operational efficiency.
Dataset
| Feature Group | Count | Examples |
|---|
| Historical Process | 50K | cycle_time, machine_id, operator_id |
| Incoming Metrics | 20K | temperature, pressure, humidity |
| Efficiency Metrics | 10K | output_quantity, defect_rate |
- Size: 50K historical records with 30 features, 20K incoming metrics with 10 features, and 10K efficiency records.
- Target: Continuous variable — process efficiency score (0 to 100).
- Class balance: Continuous values, no class imbalance.
- Missing data: 5% missing in incoming metrics, primarily in temperature readings.
Requirements
- Build a regression model to predict the process efficiency score based on historical and incoming data.
- Achieve a Root Mean Squared Error (RMSE) of less than 5.0.
- Provide a feature importance analysis to guide process adjustments.
- Address any missing data effectively during preprocessing.
- Explain your choice of model and evaluation strategy.
Constraints
- The model must provide predictions in real-time (within 1 minute) to be actionable for operators.
- Ensure interpretability of the model for operational staff, who may not have a technical background.
- The solution must be scalable to accommodate increasing data volume as the company expands.
