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As global aviation grapples with engine durability issues and extended shop visits, repair facilities are under unprecedented strain. A recent Reuters report detailed how GE Aerospace is investing up to $300 million in automation and Lean methods in Singapore to lift repair volumes by a third—without expanding its footprint—amid industry-wide backlogs and labour shortages. The message from Singapore is clear: the traditional repair model is under pressure.
Across the MRO sector, overloaded inspection lines, scarce specialist labour and unpredictable part conditions are stretching turnaround times into months. Airlines complain of grounded aircraft and rising costs. Engine OEMs are accelerating automation and digital programmes to protect margins and throughput. Yet beneath the headlines about robots and Lean transformation lies a more fundamental operational challenge: decision latency. In many repair shops, up to 70–90% of returned components ultimately fail tolerance or require major intervention. But that conclusion often comes late—after hours of inspection, routing and engineering input. Valuable inspection capacity is consumed confirming what could have been known earlier. Scrap and concession decisions are delayed. Floor space fills with parts waiting for clarity.
“It’s not just about automating tasks,” says Ben Anderson, Managing Director of AddQual. “The real bottleneck in repair is decision quality and timing. If you can’t fast-fail or fast-pass with confidence, you burn capacity and margin.” AddQual, the Derby-based metrology and quality specialist, has built its credibility supporting aerospace OEMs and MROs with independent NADCAP-accredited inspection, digital verification and re-engineering services. Its work spans turbine blades, NGVs and complex, high-value components where dimensional integrity is business-critical. Now the company is bringing that experience together in a structured digital decision environment: MiDAS – the Metrology Interface DAShboard. MiDAS is positioned not as another measurement system, but as a digital inspection and repair decision aid tool. It standardises how inspection and repair are planned, executed and learned from—turning measurement activity into operational intelligence.
At its core, MiDAS captures incoming part condition, monitors process capability through each inspection cycle, and models scenarios where tolerances, acceptance limits or process controls are adjusted. Crucially, it links those inputs to outputs such as yield, throughput and turnaround time.“ Early out-of-limit detection protects margin,” Anderson explains.
“If a part is beyond economic repair, you need to know immediately. If it’s viable, you need defensible evidence that supports a repair or concession decision. That’s how you move away from firefighting and towards preferred performance.” The system’s “Zero Defect Journey” framework embeds guided inspection workflows, consistent execution and traceable decision points—providing OEM-ready evidence while reducing variation between inspectors. In an environment where new staff must be onboarded quickly and experienced technicians are scarce, structured digital governance becomes a competitive advantage. As GE Aerospace and its peers invest in robotics and Lean methodologies to expand physical throughput, tools like MiDAS address the cognitive layer of the repair process: how data becomes decisions.In a sector where repair turnaround can make the difference between grounded aircraft and revenue service, the ability to fail fast, pass fast and learn systematically may prove as valuable as any robot on the shop floor.
