{"id":548083,"date":"2026-01-23T17:41:33","date_gmt":"2026-01-23T17:41:33","guid":{"rendered":"https:\/\/www.capgemini.com\/co-es\/?p=548083&preview=true&preview_id=548083"},"modified":"2026-01-23T17:43:04","modified_gmt":"2026-01-23T17:43:04","slug":"harnessing-predictive-analytics-to-transform-quality-in-civil-aeronautics","status":"publish","type":"post","link":"https:\/\/www.capgemini.com\/co-es\/insights\/expert-perspectives\/harnessing-predictive-analytics-to-transform-quality-in-civil-aeronautics\/","title":{"rendered":"Harnessing predictive analytics to transform quality in civil aeronautics"},"content":{"rendered":"\n

Harnessing predictive analytics to transform quality in civil aeronautics<\/h1><\/div><\/div><\/div><\/div>
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Naimeesh Chauhan<\/h5>
Feb 4, 2025<\/h5><\/div><\/div>
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In the high-stakes world of civil aeronautics manufacturing, quality transcends metrics\u2014it\u2019s about safety, reliability, and reputation. Throughout my journey in this field, I have encountered the complexities of quality management, particularly the inefficiencies and high costs stemming from issues like non-conformance and prolonged rework cycles. This raises a critical question: How can manufacturers revolutionize quality management, especially in the absence of a comprehensive IoT infrastructure?<\/p>\n\n\n\n

The transformative power of predictive analytics<\/h4>\n\n\n\n

Traditionally, ensuring quality has been reactive, focused on addressing defects only after they occur. However, as our industry evolves, it\u2019s increasingly clear that achieving a “zero defects” standard is essential. By embracing predictive analytics, we can move beyond reactive measures, enabling us to anticipate and prevent quality issues \u2018before they even surface\u2019. This proactive approach aligns with the industry’s drive toward zero defects, setting a new benchmark for excellence and reliability. Over the last two years, I have been focused on identifying how predictive models can fill gaps in our quality management processes. While specific use cases are still being developed, I recognize significant opportunities to leverage predictive analytics in various areas, including enhancing root cause analysis, improving process monitoring, and reducing human error. This shift is especially critical in civil aeronautics, where the integrity of every component is essential for safety and performance.<\/p>\n\n\n\n

Challenges that call for a proactive approach<\/h4>\n\n\n\n

In my experience, the following persistent challenges emphasize the need for predictive quality management:<\/p>\n\n\n\n