Context

Boeing has deployed a gradient-boosted classifier in Boeing AnalytX to predict whether incoming aircraft maintenance findings should be escalated for urgent engineering review. In offline validation, the model looked strong, but after deployment across the 737 fleet support workflow, production outcomes deteriorated: more critical findings were missed and engineers report lower trust in the scores.

Current Performance

The Problem

The VP of Engineering wants to know why a model that passed testing is underperforming in production and what should be done before expanding to additional Boeing programs. You need to determine whether this is primarily a threshold issue, calibration failure, data drift, label mismatch, or a broader validation gap.

Requirements

1. Interpret what the metric changes imply about production behavior.
2. Identify the most likely root causes and rank them by likelihood.
3. Propose a structured error analysis plan using the available evidence.
4. Recommend concrete remediation steps for both short-term stabilization and long-term validation.
5. Explain what additional monitoring should have been in place before rollout.

Constraints

• Missing a critical finding can delay corrective action on in-service aircraft.
• Excess false positives overload a limited engineering review team.
• Full retraining requires 10 days and formal validation sign-off before deployment.