Evaluate Model Health Regularly

Haley Vicini

1. Abstract

Regular model health checks help identify performance drift early and ensure forecasts remain aligned with current business realities.

2. Context

Use this best practice during scheduled reviews (for example, quarterly) or when significant business, market, or data changes occur.

3. Content

3.1 Why It Matters

Even strong models can become outdated as markets shift, new data sources appear, or behaviors change. Regular health checks prevent small issues from becoming major forecast failures.

Health monitoring helps teams:

• Detect drift before it impacts decisions
• Prioritize which models need attention
• Maintain confidence in production forecasts

3.2 How to Apply

1. Compare recent forecasts vs. actual outcomes.
2. Review key performance indicators, such as:
   1. Model Score
   2. Residual behavior
   3. Stability of coefficient signs
3. Check whether current drivers still reflect how the business operates.
4. Review recent business or market changes that may affect assumptions.
5. Decide whether to:
   1. Continue with routine updates
   2. Schedule a recalibration
   3. Retire the model

3.3 Example

An energy-demand model shows increasing error after regulatory changes. A health review identifies missing policy-related drivers, leading to a planned recalibration before the next planning cycle.

3.4 Common Pitfalls

• Reviewing models only when forecasts “feel wrong”
• Focusing exclusively on statistics without business input
• Letting outdated models remain in production
• Failing to record review outcomes and decisions

3.5 Expected Results

• Early identification of model degradation
• More predictable and proactive maintenance cycles
• Stronger alignment between forecasts and business reality
• Increased trust from stakeholders relying on model outputs