For more than two decades, automation has steadily reshaped manufacturing. CNC machining centres have become faster and more autonomous, production lines more connected, and logistics increasingly data-driven. Yet in many factories, one critical function has remained stubbornly manual: quality inspection.
Inspection and qualification processes have long been treated as necessary checkpoints rather than productivity enablers. While machining and assembly have benefited from automation investment, quality departments have often been left managing growing workloads with legacy tools, spreadsheets and manual reporting.
As production volumes increase and components become more complex, that imbalance is becoming harder to ignore. “Manufacturers have done an excellent job automating how parts are made,” said Ben Anderson, Managing Director of AddQual. “But in many cases, the way those parts are measured, qualified and released hasn’t kept pace. That’s where bottlenecks start to appear.”
The Hidden Cost of Manual Inspection
In high-value sectors such as aerospace, defence and power generation, inspection is crucial. Every component must be verified, documented and traceable over long programme lifecycles. But the complexity of modern parts means inspection data volumes are growing rapidly, placing increasing strain on quality teams. The result is often slower throughput, delayed product launches and higher “cost of quality” — not because defects are increasing, but because the process of validating conformity is becoming less efficient.
“Quality engineers are data rich, knowledge poor” Anderson explained. “They’re short of time and clarity. Too much effort still goes into collecting, formatting and reporting data instead of analysing it and acting on it.”
Why Automation Is Finally ArrivingSeveral converging trends are now pushing automation into the quality lab. Labour shortages are making it harder to scale inspection teams. Customers and regulators are demanding faster, more transparent reporting. At the same time, advances in robotics, machine vision and software integration have made automated inspection workflows more accessible. AddQual, a Derby-based specialist in metrology and quality assurance, has been investing in this shift. The company recently deployed a collaborative robot — its JARViS (Joint Automated Recognition, Vision & Intelligence System) — designed to automate repetitive inspection tasks while integrating directly with structured data systems.
“Automation in quality means removing the repetitive, low-value tasks that prevent them from focusing on engineering judgment and continuous improvement.” he adds.
From Pass/Fail to Productivity
Unlike traditional automation projects focused purely on speed, quality automation must balance efficiency with trust. In regulated industries, confidence in the data is as important as cycle time. This is why AddQual emphasises structured metrology and data integrity alongside physical automation. At the heart of the approach is the company’s proprietary MiDAS (Metrology Intelligence & Data Automation System), which structures inspection data from multiple sources into consistent, auditable datasets.
“Automating inspection without fixing the data layer just moves the problem,” Anderson said. “MiDAS ensures that data is captured once, structured properly and available across the business. That’s when automation really delivers value.” The payoff is faster inspection cycles and clearer visibility for engineering, operations and management teams. Instead of inspection being the end of the process, quality data becomes a live input into decision-making.
A Competitive Necessity, Not a Luxury
As manufacturers face pressure to deliver more complex products at speed, quality automation is shifting from a “nice-to-have” to a competitive necessity. Those that fail to modernise inspection risk turning quality into a constraint on growth rather than a driver of performance.
“Inspection should enable manufacturing,” Anderson concluded. “The manufacturers who succeed will be the ones who treat quality as a data-driven, automated process — rather than a manual gate at the end of production.” With automation now firmly entering the quality lab, the message is clear: the next phase of manufacturing productivity will not come from making parts faster alone, but from qualifying them smarter.
