Getting hung up on “perfect” failure rate data is one of the biggest blockers in system-level reliability modeling.

Engineers often feel they can’t begin building an FTA, FMECA, FMEDA, FMEA or system reliability model until every component has precise, verified, manufacturer-blessed failure rate numbers.

The reality? Your model doesn’t need perfect data to start producing meaningful insights. In this session, we’ll take a practical look at how good data vs. questionable data actually affects a system-level model, and why the impact is usually far smaller than most people expect.

Using real-world examples, we’ll show how:

• System architectures dominate risk far more than small differences in component failure rate estimates
• “Bad” data, when used carefully, is perfectly acceptable for early modeling
• Your first model iteration is never your last—data and system understanding evolve together
• Waiting for perfect data delays critical early decisions that improve design reliability and safety
• As your data matures, the system-level results often shift far less than anticipated

You will walk away with a more confident, practical approach to building system reliability models—without getting paralyzed by data perfectionism.

When? January 29th 2026 at 8:00 am MST · 4:00 pm CET

Join us, register now! No downloads needed—hosted on Teams, simple and accessible to everyone.