Reference Class Forecasting

The Hidden Problem

"Most forecasts fail because they look inward, not backward." We trust the experts. We trust the schedule logic. But in reality, most project forecasts are too optimistic — not because of bad modeling, but because of cognitive bias.

Why Forecasts Miss Reality

The planning fallacy leads teams to believe their case is different. The result? Baselines get reset, trust erodes, and claims rise.

• Durations are shaved based on hope, not history
• Risks are underestimated because "we've mitigated it"
• Confidence is based on plan detail, not track record

Enter Reference Class Forecasting (RCF)

RCF grounds your forecast in external evidence — similar scopes, past projects, and activity patterns. It's how Mavryck helps teams plan realistically, not optimistically.

How Mavryck Makes Forecasts Honest

Our approach uses historical data and pattern analysis to calibrate your forecasts against reality.

• Historical Pattern Matching: We compare your current durations to hundreds of similar scopes from completed projects
• Deviation Analysis: Mavryck shows where your assumptions break the pattern — and by how much
• RCF Adjustments: We suggest calibrated durations and costs based on median outcomes — not best-case logic
• Confidence Scoring: Every forecast gets an Optimal Bias Score, showing how likely it is to hold

A Quick Case: 18% Too Optimistic

One station project assumed a sequence could be built in 36 days. RCF flagged that similar builds took 43–52 days. The team adjusted early — avoiding a mid-project baseline reset and costly rework.

Why It Matters

It's not about being conservative — it's about being calibrated. With Mavryck, your forecast becomes more than a guess. It becomes defensible.

Want to Know If Your Forecast Will Hold?

Let us run a reference class scan. We'll show where history says your plan is weak — and how to fix it now.