Greenfield Manufacturing: AI Quality Control System

The Client

Greenfield Manufacturing Co is a precision engineering firm based in Sheffield, producing components for the automotive and aerospace sectors. With 140 employees, they operate three production lines running 24 hours a day. Quality control was handled by a team of inspectors who manually checked components at various stages of production. CEO Rachel Tomlinson wanted to improve defect detection speed and reduce the cost of quality failures.

The Challenge

Manual inspection was slow, inconsistent, and expensive. Inspectors could check approximately 200 components per hour, and fatigue meant defect detection rates dropped during long shifts. Defects that slipped through to customers resulted in costly returns and reputational damage. The inspection team also represented a significant labour cost that did not scale with production volume.

The key challenges were:

• Manual inspection limited to 200 components per hour per inspector
• Inconsistent defect detection, especially during long shifts and night operations
• Defective components reaching customers, causing returns and complaints
• Inspection team costs scaling linearly with production volume
• No systematic data on defect patterns to inform process improvements

The Solution

The engagement began with an AI readiness audit that assessed Greenfield's production data, camera infrastructure, and team readiness. The audit found that their existing production line cameras were high-resolution enough to support AI visual inspection, and their quality team was enthusiastic about using technology to support their work rather than replace it.

ajairu designed and implemented an AI quality control system in three phases, with ongoing fractional CAIO support for governance:

1. Model training (weeks 1-4): Trained a computer vision model on Greenfield's historical defect data, using thousands of images of both defective and acceptable components. The model learned to identify surface defects, dimensional deviations, and assembly errors specific to Greenfield's product lines.
2. Integration and deployment (weeks 5-9): Connected the AI model to the existing production line cameras. The system inspects every component in real time, flagging potential defects for human review. Defective components are automatically diverted to a review station where an inspector confirms the AI's finding.
3. Governance and handover (weeks 10-12): Established an AI governance framework covering model retraining schedules, human-in-the-loop protocols, and performance monitoring. Trained the quality team on the system and handed over full operational control.

The Results

The AI system inspects over 1,000 components per hour, five times faster than a human inspector. Defect detection time dropped by 60% across all three production lines. The system catches defects that human inspectors were missing, particularly subtle surface defects and dimensional deviations near the tolerance boundary.

Annual savings of £240,000 come from three sources: reduced inspection labour costs (inspectors now focus on reviewing AI-flagged items rather than checking every component), fewer customer returns (defect detection accuracy improved from 92% to 99.2%), and reduced waste (early detection allows production adjustments before large batches are affected).

The system paid for itself within four months of deployment. The fractional CAIO engagement provides ongoing governance oversight, ensuring the model is retrained as product lines evolve and maintaining compliance with industry quality standards.

Defect issues that previously took up to five days to surface are now flagged and resolved within 24 hours, which is what makes the £240,000 annual saving and the four-month payback possible. Inspection staff time was cut by a third across all three lines, with inspectors reviewing AI-flagged exceptions rather than checking every component by hand.

Client Testimonial

Services Used

This engagement combined three of our core services:

• AI Readiness Audit to assess data, camera infrastructure, and team readiness
• AI Implementation to build, train, and deploy the computer vision quality control system
• Fractional CAIO for ongoing AI governance and model management oversight

Why It Worked

The system succeeded because it augmented the inspection team rather than replacing them. Inspectors still make the final call on flagged components, but the AI does the high-volume screening work that was prone to fatigue and inconsistency. The team embraced the system because it removed the tedious part of their job and let them focus on the judgment calls.

The governance framework was critical for a manufacturing environment. With clear protocols for model retraining, human-in-the-loop confirmation, and performance monitoring, the system meets industry quality standards while continuously improving. The fractional CAIO engagement ensures that as Greenfield adds new product lines or changes specifications, the AI system evolves with them.