{"id":106616,"date":"2026-06-12T05:51:49","date_gmt":"2026-06-12T05:51:49","guid":{"rendered":"https:\/\/teeptrak.com\/us-aerospace-oee-case-study-2026-2\/"},"modified":"2026-06-12T05:51:51","modified_gmt":"2026-06-12T05:51:51","slug":"us-aerospace-oee-case-study-2026-2","status":"publish","type":"post","link":"https:\/\/teeptrak.com\/en\/us-aerospace-oee-case-study-2026-2\/","title":{"rendered":"Aerospace Machining OEE Case Study: A US Tier-1 Supplier Lifts OEE 58% \u2192 73% on 5-Axis Cells"},"content":{"rendered":"

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Aerospace Machining OEE Case Study: A US Tier-1 Supplier Lifts OEE 58% \u2192 73% on 5-Axis Cells<\/h1>\n

A US-based aerospace & defense machining supplier \u2014 roughly $80M revenue, three AS9100-certified plants, high-mix low-volume precision parts in titanium and Inconel \u2014 lifted OEE from 58% to 73% (+15 points)<\/strong> 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.<\/p>\n

Representative case study \u2014 customer anonymized; figures reflect typical TeepTrak outcomes and industry benchmarks.<\/em><\/p>\n

The challenge: expensive machines, invisible losses<\/h2>\n

Aerospace machining runs long cycles on extremely valuable material \u2014 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%<\/strong>: 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.<\/p>\n

The TeepTrak solution: real-time OEE on the constraint<\/h2>\n

The team deployed PerfTrak<\/strong> for real-time OEE monitoring<\/a> on the 5-axis cells, captured directly from the machine controllers with the TeepTrak Box<\/strong> edge sensor \u2014 no PLC integration required. MoniTrak<\/strong> tracked machine-level KPIs (spindle utilization, cycle time vs. ideal), and TeepTrak’s machine-learning anomaly detection<\/strong> 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.<\/p>\n

The results: +15 OEE points in ~6 months<\/h2>\n

Within roughly six months: OEE rose from 58% to 73% (+15 points)<\/strong>, unplanned downtime fell 22%<\/strong>, and scrap on critical parts dropped 18%<\/strong> as anomaly alerts caught issues before they ruined material. The recovered capacity let the supplier defer a planned 5-axis machine purchase<\/strong> \u2014 a capital avoidance that paid for the program many times over, with payback in about seven months.<\/p>\n

“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<\/strong><\/p><\/blockquote>\n

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Download the Full Aerospace OEE Case Study<\/h3>\n

Instant download. No email confirmation needed.<\/p>\n \n

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