Operations & KPIs
Operationalising Assurance
The KPIs that matter — yield, OEE, and capacity — and how to instrument them credibly
Most in-situ monitoring conversations stall because they get framed like a science project: sensors, images, feature resolution, algorithms. Interesting, up to a point. But production leaders don't buy algorithms. They buy predictable output.
If you want assurance to land in an operations environment, translate it into the three metrics that govern attention and funding: yield, Overall Equipment Effectiveness (OEE), and capacity. And do it credibly, without hand-waving.
KPI #1 — Yield
In metal AM, yield isn't simply whether the machine finished the job. It's whether the job finished without triggering a chain reaction of rework, repeat inspection, extra documentation, and escalations that drain the schedule.
Define yield in operational terms:
- •Build yield — % of builds that complete within agreed acceptance thresholds.
- •Part yield — % of parts that pass the agreed evidence criteria with no exception route.
- •Exception rate — % of builds or parts requiring quality review beyond the normal routine.
The trap is letting yield become a subjective debate. The fix is a predefined disposition language — proceed, hold, audit, escalate — tied to thresholds and criteria, then tracked relentlessly. AMiRIS is built around automated analytics and reporting to support exactly that kind of structured, repeatable output.
KPI #2 — OEE
Traditional OEE thinking gets distorted in AM, because "the machine is running" can still mean "we're accumulating risk." The real question isn't did we print — it's did we print with confidence.
A practical OEE view for AM:
- •Availability — downtime, changeover, and avoidable stops.
- •Performance — build time against planned time, including re-run penalties.
- •Quality — first-pass acceptance without exception loops.
Assurance earns its keep by reducing the quality-related OEE drag: the builds you re-run, the ones you finish and then quarantine, and the ones that trigger a slow-motion investigation downstream. There's another OEE killer Ops teams recognise immediately: waiting. Waiting for NDT or CT, waiting for a specialist to interpret an "interesting" plot, waiting for sign-off because the evidence isn't packaged in a form Quality can approve. Assurance should shrink that wait by making review repeatable.
KPI #3 — Capacity
If you want Finance to lean in, don't talk about monitoring. Talk about capacity unlocked. Capacity is typically lost in three predictable ways.
- •Late surprises that force repeat builds.
- •Over-inspection used as a substitute for confidence.
- •Bottlenecked downstream steps — NDT queues, CT availability, rework congestion.
Even a well-run post-build inspection strategy can become a tax: conventional NDT and validation commonly consumes 25–50% of part cost, and large-format builds are exactly where that pressure shows up first, which is why controlled deployment (often on-prem) matters for real-world adoption. The point isn't that inspection is bad. It's that inspection should be risk-based and informed by evidence, not a blanket coping mechanism.
How to Instrument These KPIs
A credible KPI story follows four rules, in this order.
Baseline First
Capture current yield, OEE, and capacity constraints before you try to improve anything.
Set Thresholds Up Front
Define what triggers Hold, Audit, or Escalate before the first exception happens, not during the argument about it.
Separate Signal from Noise
Track exceptions that actually change outcomes, not one-pixel curiosities that just generate discussion.
Keep Outputs Usable
Decision-ready reporting beats raw data dumps every time Ops or Finance has to act on it.
Build a KPI Case Ops and Finance Will Take Seriously
Start with a simple exercise: map your last ten quality events to yield loss, OEE drag, and capacity impact. Bring us your build lengths, inspection approach, rework patterns, and cost of delay, and we'll turn it into a practical ROI estimate.
Request an ROI Estimate