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Harnessing predictive analytics to transform quality in civil aeronautics

Naimeesh Chauhan
Feb 4, 2025

In the high-stakes world of civil aeronautics manufacturing, quality transcends metrics—it’s 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?

The transformative power of predictive analytics

Traditionally, ensuring quality has been reactive, focused on addressing defects only after they occur. However, as our industry evolves, it’s 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 ‘before they even surface’. 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.

Challenges that call for a proactive approach

In my experience, the following persistent challenges emphasize the need for predictive quality management:

  • Root cause analysis efficiency: Promptly identifying root causes is essential for maintaining production flow and minimizing costs. While Manufacturing Execution Systems (MES) collect critical data (e.g., work instructions, non-conformance reports), I have noticed that predictive models are rarely integrated with these systems. My ongoing exploration into predictive analytics has illuminated the potential for integrating these models with MES data, significantly reducing resolution times and enhancing operational efficiency.
  • Navigating complex manufacturing processes: The intricacies of civil aeronautics manufacturing mean that even minor deviations can lead to significant defects. I have learned that predictive analytics can facilitate continuous monitoring of process stability by assessing factors like material properties, tooling configurations, and process variables in real time. This proactive approach enables manufacturers to detect early warning signs, ensuring processes remain optimized and quality is upheld.
  • Minimizing human error: Human errors, often stemming from inconsistencies in work instructions or training gaps, introduce variability into the manufacturing process. Through my studies, I have seen how predictive quality initiatives can analyse trends in operator performance, identifying recurring issues and areas where targeted training is needed. Data-driven insights can help reduce errors and improve production consistency.
  • Dynamic PFMEA with real-time feedback: For Process Failure Mode and Effects Analysis (PFMEA) to be effective, it must be based on up-to-date data reflecting operational realities. By integrating feedback from MES with predictive insights, I believe PFMEA can dynamically evolve with current operational data, allowing for more proactive risk mitigation and relevant risk assessments.

Even in the absence of a comprehensive IoT setup, I have identified opportunities for manufacturers to leverage predictive analytics using historical data. Quality records and operator reports provide valuable insights that can inform predictive models and yield actionable strategies.

Foundations for effective predictive analytics

To effectively implement predictive analytics, civil aeronautics manufacturers should prioritize the following essentials:

  • Data infrastructure: Robust systems are needed to collect, store, and process data, creating a strong foundation for developing accurate predictive models.
  • Skilled personnel: Organizations require skilled data scientists and analysts who can interpret data and design predictive algorithms. Investing in training existing employees in data literacy enhances these capabilities.
  • Cultural shift: Fostering a data-driven culture encourages decisions based on insights rather than intuition, driving smarter, faster responses to quality challenges.
  • Integration with existing systems: Predictive models should seamlessly connect with current manufacturing and quality management systems, enabling real-time insights and actions.
  • Continuous improvement: Regularly updating predictive models based on new data and feedback ensures that analytics adapt to evolving conditions.
  • Cross-functional collaboration: Effective communication among operators, quality managers, and data analysts is key to embedding predictive insights within the broader quality strategy.

A balanced path for industry growth

In today’s high stakes manufacturing environment, adopting predictive analytics is not just a technological upgrade; it’s a strategic decision. Organizations should evaluate their current quality practices and consider how predictive analytics can enhance quality management. Building a data-driven culture and aligning training with strategic goals can lead to substantial improvements, particularly in precision-demanding sectors like civil aeronautics.

Redefining manufacturing through predictive quality

As I continue to explore the potential of predictive analytics, I see it as a transformative opportunity for manufacturers aiming to elevate their quality practices. By harnessing historical and process data, companies can proactively address quality issues, reduce costs, and comply with stringent civil aeronautics standards.

The journey towards predictive quality is not merely a technological shift; it’s a strategic imperative that can redefine the future of manufacturing. Meet with us at AeroIndia 2025 (Hall H, Booth, 1.7) to discuss the transformative power of predictive analytics for your organization and the industry. Click here to learn more about our presence and follow Capgemini A&D on LinkedIn for updates from my colleagues.

Learn more:

Digital Continuity in the Aerospace Industry

Digital Twins in Aerospace and Defense

Intelligent Supply Chain for the Aerospace and Defense Industry

TechnoVision 2024: Aerospace and Defense

Meet the author

Naimeesh Chauhan

Senior Director – Industry and Innovation
Naimeesh Chauhan brings over 25 years of leadership in the aerospace industry, specializing in manufacturing, digital transformation, and operations. As a trend leader for Intelligent Manufacturing at Capgemini, he leverages his expertise to drive tangible results across the Aerospace & Defense sector.