What Field Service Data Can Tell You Before You Reuse a Part

We're exploring a field performance insights capability — one that surfaces service data back to the people who design and qualify parts. Before we go too deep on data models and UI, I want to make sure we're solving for the right signal, not just the available one.

Here's the problem we're trying to solve:

Today, when a product design or quality engineer is deciding whether to reuse a part in a next-gen design, they're largely flying blind on field performance. Mean time between failures, common fault patterns, the maintenance burden the part creates for service teams — that data exists somewhere in ServiceMax, but it's not reaching the people who need it at the moment they need it.

The result? Reuse decisions get made without field feedback. Known problem parts get carried forward. And service teams keep absorbing the cost of design decisions that could have gone differently.

What I'm trying to understand from this community:

If you were making a part reuse decision for a next-gen product, what would you want to know about how that part has performed in the field?

To make it concrete:

• Has a part ever made it into a next-gen design that probably shouldn't have? What would have caught it?
• Failure rate, failure mode, mean time to first failure, repair turnaround — if you could only get one of these per part, which one actually moves your decision?
• At what point in your design process is it too late for field data to change anything — and are you getting it before that point today?
• Hot take: most part reuse decisions are made on cost and familiarity, not field performance. Agree or disagree?