Aerospace Machining OEE Case Study: A US Tier-1 Supplier Lifts OEE 58% → 73% on 5-Axis Cells

A US-based aerospace & defense machining supplier — roughly $80M revenue, three AS9100-certified plants, high-mix low-volume precision parts in titanium and Inconel — lifted OEE from 58% to 73% (+15 points) on its 5-axis machining cells, cut unplanned downtime 22%, and reduced scrap on high-value parts by 18% within about six months of deploying TeepTrak. Here’s how a plant that thought its machines were “always busy” found the hidden capacity hiding in spindle wait time.

Representative case study — customer anonymized; figures reflect typical TeepTrak outcomes and industry benchmarks.

The challenge: expensive machines, invisible losses

Aerospace machining runs long cycles on extremely valuable material — a scrapped titanium part can cost thousands and a wrecked tool far more. Yet this supplier tracked OEE manually, and like most precision shops, leadership assumed the 5-axis centers were near fully utilized. The reality, once measured, was a starting OEE of just 58%: spindles sat idle waiting for setup, first-article inspection, tool changes and programs far more than anyone realized. In a low-volume, high-value environment, that idle time is the single biggest cost.

The TeepTrak solution: real-time OEE on the constraint

The team deployed PerfTrak for real-time OEE monitoring on the 5-axis cells, captured directly from the machine controllers with the TeepTrak Box edge sensor — no PLC integration required. MoniTrak tracked machine-level KPIs (spindle utilization, cycle time vs. ideal), and TeepTrak’s machine-learning anomaly detection flagged developing tool and spindle issues before they caused a scrapped part. Everything was standardized on ISO 22400-2 so the three plants were finally comparable.

The results: +15 OEE points in ~6 months

Within roughly six months: OEE rose from 58% to 73% (+15 points), unplanned downtime fell 22%, and scrap on critical parts dropped 18% as anomaly alerts caught issues before they ruined material. The recovered capacity let the supplier defer a planned 5-axis machine purchase — a capital avoidance that paid for the program many times over, with payback in about seven months.

“We thought our machines were always busy. The data showed how much spindle time we were losing to setup and waiting — once we could see it, we could fix it.”
— Director of Operations, aerospace machining supplier

Why spindle time is the aerospace metric that matters

In high-mix aerospace work, availability losses — setup, waiting, inspection, tool changes — dwarf the others. A machine that’s powered on but not cutting is the hidden factory. Making spindle utilization and the Six Big Losses visible in real time turns vague “we’re busy” into specific, ownable actions: which cell, which loss, starting when. Industry benchmarks put aerospace OEE roughly in the 60–75% range, so a shop in the high-50s is sitting on real upside.

The replicable pattern

This mirrors what TeepTrak has done across 450+ plants in 30 countries: replace unreliable manual OEE with direct machine measurement, surface losses in real time, and let anomaly detection protect expensive material. The same approach drove Hutchinson from 42% to 75% OEE across 40 sites. TeepTrak’s published ROI model shows typical payback of 3–12 months — and in high-value machining, a single prevented scrap event or wrecked spindle can cover months of platform cost.

What it would take in your shop

Most aerospace machining shops can instrument a 5-axis cell in days and have a machine-measured OEE baseline within two to four weeks — no controller integration project. From there, the biggest availability losses are usually obvious and fast to attack. Download the full case study with the deployment timeline and a replication checklist, or start your own baseline with a free proof of concept.

Frequently asked questions

What OEE can aerospace machining shops expect?

Industry benchmarks put aerospace OEE roughly in the 60–75% range. This anonymized Tier-1 supplier started at 58% and reached 73% (+15 points) in about six months — typical of TeepTrak deployments where availability losses (setup, waiting, inspection) dominate.

How does TeepTrak reduce scrap on expensive parts?

Machine-learning anomaly detection flags developing tool and spindle issues before they ruin material, while real-time OEE exposes the process drift behind defects. In this case scrap on critical parts fell 18%.

Does TeepTrak need PLC integration on CNC machines?

No. The TeepTrak Box edge sensor captures machine state directly without a PLC integration project, so a 5-axis cell can be instrumented in days and produce a machine-measured baseline in 2–4 weeks.