At the same time, the rise of Industry 4.0 is reinforcing the importance of connected measurement. In an increasingly digital factory environment, machines, sensors and systems are expected to communicate in real time, enabling faster and more informed decisionmaking. However, without reliable measurement data feeding that ecosystem, even the most advanced automation lacks the feedback required to function effectively.
“ People often talk about automation in terms of machines and robotics, but the reality is that automation without data is just motion,” Anderson explains.“ If you don’ t have structured, reliable measurement data feeding into your systems, you’ re still making decisions based on assumptions.”
It is within this context that AddQual has developed its MiDAS platform, designed to move inspection beyond verification and into the realm of operational intelligence. MiDAS acts as a digital decision aid, standardising how inspection and repair processes are planned, executed and continuously improved. Rather than simply recording measurement outcomes, the system captures a wide range of variables associated with part condition, process capability and inspection performance. This enables manufacturers to monitor capability in real time, identify risks, and model different scenarios— such as changes to tolerances or process parameters— to understand their impact on yield, throughput and turnaround time.
Crucially, this approach allows organisations to move towards what AddQual describes as a“ fail fast, pass fast” model of decision-making. By identifying nonconformance earlier in the inspection cycle and providing clear, data-driven guidance on repair or scrap decisions, manufacturers can reduce rework, protect margins and improve overall flow through the system. To achieve the Zero Defect Journey, the aim is to create a closed-loop process in which inspection, decision-making and learning are fully integrated.
“ The challenge for many manufacturers is decision capability, rather than measurement capability,” says Anderson.“ They have the data, but it’ s fragmented, inconsistent or not being used effectively. We are focused on turning that measurement activity into something that actually drives better outcomes.”
However, as major OEMs and tier-one suppliers come under increasing pressure to accelerate throughput, a familiar pattern is beginning to emerge. When bottlenecks appear in qualification and inspection, the instinctive response is often to invest in more hardware— typically additional CMM capacity— to push more parts through the same process. Ben Anderson believes that reaction, while understandable, risks solving the wrong problem.
“ When operational pressure falls on quality departments, the default is to buy more CMMs,” he says.“ But that’ s often just adding capacity to an inefficient system. You’ re speeding up the same decisions, not improving them.”
Anderson draws on a well-known analogy here to reframe the conversation.“ There’ s a famous quote attributed to Henry Ford:‘ If I had asked people what they wanted, they would have said faster horses.’ That’ s exactly the situation many manufacturers find themselves in today. The challenge isn’ t to make inspection faster in isolation— it’ s to rethink what inspection is actually there to do.”
This shift is being enabled by the growing adoption of digital measurement systems, which automatically capture inspection results in structured formats and make them available for analysis. Instead of isolated readings taken on the shop floor, manufacturers can now build continuous datasets that reveal patterns, trends and relationships that would otherwise remain invisible. The real constraint is not measurement capability, but decision capability. More machines may increase throughput locally, but without better use of data, they do little to address rework, delayed decisions or inconsistent outcomes across the wider system.
This is the gap that MiDAS has been designed to fill. Rather than focusing on increasing inspection volume, the platform transforms how inspection data is used— structuring it, contextualising it, and feeding it back into the decision-making process in real time. By capturing part condition, monitoring process capability and enabling scenario-based analysis, MiDAS allows manufacturers to make faster, evidencebased decisions on whether to pass, repair or scrap components.
The effect is a shift away from capacity-driven thinking towards intelligence-driven performance. Instead of asking how to measure more parts, manufacturers can begin to ask how to make better decisions, earlier in the process.
“ We challenge our customers with that example deliberately,” Anderson adds.“ Because the answer isn’ t always more equipment. Sometimes it’ s a different way
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