Could predictive analytics help solve the aerospace industry’s quality crisis?

Boeing is addressing the manufacturing quality crisis that forced it to slow production and face stricter scrutiny from federal regulators. As part of a broader plan to prevent manufacturing issues like the one that led to a jet-panel blowout in 2024, the company is taking proactive quality-improvement efforts, such as conducting more surprise inspections internally and with suppliers, establishing a new level of quality checks and outlining new performance goals.

Boeing is not alone. In the highly regulated and precision-driven aerospace manufacturing industry that requires exacting standards in every component and assembly process to ensure safety, quality and reliability, product recalls are at their highest levels in years. Manufacturers cannot solely rely on regulatory agencies to safeguard their brand reputation and reduce risks associated with quality failures. As all manufacturers know too well, a minor design flaw, tool defect or process system failure can derail production processes and jeopardize entire operations. Noncompliance issues aside, that reality has expensive and far-reaching implications that ultimately include a company’s reputation, as well as its bottom line.

Addressing this quality crisis involves more than enhanced inspections, tighter quality control, and other reactive measures. We must shift the conversation from a traditional, reactive approach to one that embraces proactive, data-driven strategies and enables actionable intelligence to predict and avert potential issues before they negatively impact production and quality. We need to shift to an approach that leverages proactive remediation to prevent quality issues and reduce production downtime. We need to drive a culture of quality where quality isn’t seen as an impediment but an enabler and part of standard processes.

Until recently, however, manufacturers had no choice but to address quality problems after they occurred. They simply didn’t have access to real-time data and analytics tools to support advanced predictive modeling. Today, predictive quality analytics (PQA) is changing all of that. It is enabling them to collect and analyze enterprise-wide quality data to predict future outcomes based on patterns and trends. It’s not about investing in entirely new systems and installing a million new sensors. It’s about gleaning valuable insights from existing data that often remains underutilized. Predictive analytics enables manufacturers to get the most out of the data they’re already collecting and gain new, actionable insights.

AI-driven analytics takes center stage in the quality toolkit

Predictive quality analytics leverages machine learning algorithms, trained on enterprise data to scout out anomalies in product and quality processes, before they can cause harm. These artificial intelligence (AI)-driven insights can recognize subtle patterns and anomalies in production data that may be undetectable by human inspectors, creating an automated feedback loop that adjusts manufacturing processes in real-time and improves over time. A 2023 Spherical Insights and Consulting study found that the global data analytics market is expected to skyrocket from $61.44 billion in 2023 to $581.34 billion in 2033.

Consider these three steps to leverage data-driven, predictive quality data.

●      Select and prepare the quality data. Successful predictive analytics starts with the right data. Data-driven manufacturing needs a foundation of high-quality, relevant and organized data to feed into predictive models. Identify key data sources, such as:

○      Machine and equipment sensors, including data on performance metrics like pressure, vibration, and energy usage.

○      Production logs. Data on production volume, cycle times and machine utilization.

○      Quality inspection reports. Historical data from inspections and audits, including defect rates and compliance with quality standards.

○      Maintenance logs. This data offers insight into the frequency of repairs, preventive maintenance schedules and downtime events, helping manufacturers predict future maintenance needs and prevent unplanned downtime.

○      Connected worker devices. Data collected from connected worker technology, such as RFID scanners, shop floor tablets, connected tools and wearables.

●      Set up predictive alerts. Predictive alerts are based on understanding issues that have occurred in the past and why, and then setting parameters or thresholds that, when achieved or exceeded, cause some type of alert (email, alarm, etc.) to one or more people. These alerts could be after a certain number of failures or quality issues, or after a value, such as temperature, has been exceeded, or a trendline has moved too far from average. There are a lot of potential quality measures that could trigger a predictive alert (they could even be combined for multiple parameters to be measured). Over time, as the system learns what causes a failure or quality issue, these values can be refined to issue alerts as early as possible, even to avoid quality issues.

●      Implement real-time monitoring. Data collection and alerts make predictive quality analytics possible, but you need real-time monitoring to make it actionable. This monitoring continuously feeds predictive models with the data required for accurate forecasting. Manufacturers can use real-time dashboards and analytics to keep an eye on operations and foster a more data-driven manufacturing environment. And when quality issues do arise, the system can aid in root cause analysis, focusing on the most likely causes for the problem.

With predictive alerts and real-time monitoring, factory floor workers can detect potential issues and implement corrective actions in real time, leading to a measurable reduction in downtime, product defects and scrap rates. Predictive quality analytics can further reduce production costs through process optimization and the elimination of inefficiencies. When monitoring workflows in real-time, quality professionals can easily identify bottlenecks and spot underutilized or underperforming equipment. Then, they can quickly make adjustments to improve resource allocation and optimize production. Over time, as the system learns what conditions could cause quality problems, it can predict when a failure might occur, leading to addressing issues before they even occur.

Aerospace manufacturing is facing significant challenges, including recent highly publicized equipment failures, expensive product recalls, ongoing supply chain issues, and skilled worker shortages. Embracing predictive quality measures across your entire production system goes beyond improving product quality, brand reputation, and the bottom line. It also enables your organization to accelerate time-to-market by reducing delays, increase operational efficiencies for better resource utilization and more streamlined workflows, and reduce waste and scrap by minimizing defective parts through early detection and adjustments.

In today’s complex aerospace manufacturing environment, you cannot rely on compliance alone to safeguard your brand reputation or your bottom line. The power of predictive quality is crucial in identifying and averting costly problems before they occur.

ETQ Reliance
https://www.etq.com

About the author: David Isaacson is vice president, Product Marketing, ETQ

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