AI Agent Operational Lift for Peco, An Astronics Company in Clackamas, Oregon

Why AI matters at this scale

PECO, an Astronics company, operates in the demanding aviation and aerospace sector from Clackamas, Oregon. With 201-500 employees and a legacy dating back to 1938, the company manufactures complex structural components and assemblies for aircraft. At this mid-market scale, PECO sits in a sweet spot for AI adoption: large enough to generate meaningful operational data, yet agile enough to implement changes faster than aerospace giants. The precision requirements of aerospace manufacturing—where tolerances are measured in thousandths of an inch—create natural high-value applications for machine learning. AI can directly impact the bottom line by reducing scrap rates, optimizing machine utilization, and accelerating engineering cycles, all while maintaining the rigorous quality standards that define the industry.

Three concrete AI opportunities with ROI framing

1. Computer vision for quality assurance. Deploying high-resolution cameras with deep learning models on the production floor can inspect parts for microscopic defects in seconds rather than hours. For a company producing thousands of complex machined parts monthly, reducing manual inspection time by 40% could save over $500,000 annually in labor and rework costs. The system pays for itself within the first year through scrap reduction alone.

2. Predictive maintenance on CNC assets. PECO's machine shop likely runs dozens of multi-axis CNC mills and lathes. Unplanned downtime on a single 5-axis machine can cost $1,000+ per hour in lost production. By feeding sensor data into a predictive model, the company can schedule maintenance during planned downtime, potentially increasing overall equipment effectiveness (OEE) by 8-12%. This translates to hundreds of thousands in recovered capacity without capital expenditure.

3. AI-assisted quoting and engineering. Aerospace RFQs are notoriously complex, with hundreds of specification pages. Natural language processing can extract key requirements and match them against historical jobs to generate accurate cost estimates in minutes instead of days. For a mid-market shop, winning just 2-3 additional contracts per year through faster, more accurate bids can add millions in revenue.

Deployment risks specific to this size band

Mid-market manufacturers face distinct AI adoption risks. The primary challenge is talent scarcity—PECO likely lacks dedicated data scientists, making reliance on external consultants or user-friendly platforms essential. Data quality is another hurdle; decades of tribal knowledge may not be digitized, requiring a concerted effort to instrument machines and centralize records before models can be trained. There's also the risk of pilot purgatory, where a successful proof-of-concept never receives the integration budget to scale across the factory floor. Finally, change management with a skilled, long-tenured workforce requires transparent communication that AI augments rather than replaces their expertise. Starting with a narrow, high-visibility win—like a single inspection station—builds the credibility needed to expand the program.