Hello to all the Cache Experts out there and Happy Friday!

The company I work for uses Cache 2017.1.3 and we have been seeing intermittent errors in the Cache Error Trap when an SQL query runs.   The error message looks like the error is occurring within the cached query routine that Cache auto-generates.  Unfortunately, when I pulled up the routine in the SMP System Explorer, I only found an obj version, so was unable to look at any code.

Here is the error:    Note:  the .cls375 part will be different based on the different SQL queries that run, since that part of the cached query routine name is auto-generated.  Also, the generated method can be different, again, based on the SQL query that is run - since the methods in the cached query routines are also auto-generated.

<EXTERNAL INTERRUPT>%0AmBk1^%sqlcq.{namespace}.cls375.1

So my questions for the Cache Developer Experts out there are:

a) have any of you seen this type of issue before?   If yes, how did you resolve?

b) any idea how to get the *.INT (or *.MAC) code associated with the *.obj version of the auto-generated cached query routine so I can see the code which might also give me some clues as to what is causing the error?

c) Does anyone have any idea about what the <EXTERNAL INTERRUPT> error is?   Could that be happening due to a timeout?

Most of the SQL queries are very simple/basic SQL - an example is below.

Set sql="SELECT DISTINCT PrintBy FROM appPkg.appTable WHERE (Status <> '1' AND Status <> '4')"

Set resultSet=##class(%ResultSet).%New()

Set status=resultSet.Prepare(sql)

Set status=resultSet.Execute()

While(resultSet.Next()) {

    // loop and do stuff

}

Much Thanks in Advance for any thoughts, ideas, feedback, suggestions.

Best Regards and Go Team!!!

The global Aerospace Predictive Quality Analytics Market is experiencing a paradigm shift driven by the increasing demand for advanced data-driven solutions that ensure quality, efficiency, and safety in aerospace manufacturing and operations. With continuous advancements in artificial intelligence (AI), machine learning (ML), and big data analytics, predictive quality analytics is emerging as a critical component in aerospace quality assurance, fueling a significant transformation in the industry.

Predictive quality analytics empowers aerospace manufacturers and service providers to anticipate potential quality issues before they occur, optimize production processes, and enhance decision-making through real-time insights. The market is gaining momentum due to the rising complexity of aircraft systems and the necessity to adhere to stringent safety and regulatory standards. This has led to the widespread adoption of digital quality monitoring systems across OEMs, Tier-1 suppliers, and MRO (Maintenance, Repair, and Overhaul) operators.

According to Growth Market Reports, the global Aerospace Predictive Quality Analytics Market is projected to grow at a robust CAGR during the forecast period, supported by a surge in aircraft production, rising defense budgets, and the digitization of quality control processes across the aerospace sector.

Key Market Drivers:

• Demand for Real-Time Quality Monitoring: As aircraft systems become more complex, the need for continuous quality monitoring and early detection of potential faults is critical. Predictive analytics enables proactive maintenance, reducing downtime and operational costs.
• Digital Transformation in Aerospace Manufacturing: The integration of digital twins, IoT sensors, and AI-driven analytics enhances data collection and analysis, promoting smart manufacturing and streamlined quality assurance practices.
• Regulatory Compliance: Compliance with stringent quality and safety regulations imposed by aviation authorities is propelling the adoption of advanced analytics solutions that ensure process traceability and accountability.

Market Restraints:

• High Implementation Costs: The initial investment required for deploying predictive analytics infrastructure, including sensors, software platforms, and training, can be substantial for small and medium enterprises.
• Data Integration Challenges: Integrating data from diverse sources, such as legacy systems and different departments, remains a significant hurdle, especially for organizations with limited digital maturity.
• Cybersecurity Concerns: The digitalization of aerospace processes raises concerns regarding data security, especially when sensitive operational data is involved.

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Emerging Opportunities:

• Cloud-Based Analytics Platforms: The shift toward cloud infrastructure facilitates scalable, cost-effective analytics solutions with greater accessibility and real-time capabilities for aerospace stakeholders.
• AI-Driven Innovations: Advancements in AI and ML are refining prediction models, enabling even more accurate quality assessments and automated decision-making processes.
• Expansion in Developing Markets: Rapid industrialization and increasing aerospace investments in Asia Pacific, Latin America, and the Middle East present significant growth potential for predictive quality analytics providers.

Market Dynamics and Insights:

The Aerospace Predictive Quality Analytics Market is poised for substantial growth due to increased demand across both commercial and defense aviation. In the commercial sector, airline operators are leveraging predictive tools to monitor in-flight systems and minimize mechanical failures, thereby improving passenger safety and reducing maintenance-related delays. Meanwhile, the defense sector is integrating predictive analytics to enhance the reliability and lifecycle of mission-critical systems.

Geographically, North America leads the global market, supported by robust R&D investments, a highly digitalized aerospace ecosystem, and government initiatives focused on enhancing aviation safety. However, the Asia Pacific region is expected to witness the fastest growth, fueled by expanding aerospace manufacturing bases in countries like China, India, and South Korea.

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Growth Trends to Watch:

• Increased Use of Predictive Analytics in MRO: Maintenance, repair, and overhaul services are increasingly adopting predictive models to forecast equipment failures and streamline part replacements.
• Integration with AR/VR for Quality Training: Augmented and virtual reality tools are being combined with predictive analytics to simulate quality scenarios and train personnel effectively.
• Focus on Sustainability: Predictive analytics aids in optimizing resource usage, reducing waste, and enhancing energy efficiency—key priorities in sustainable aerospace manufacturing.

Segmentation Highlights:

The market is segmented by component, deployment mode, application, end-user, and region:

• By Component: Software, services, and platforms form the backbone of predictive quality analytics solutions, with software leading in market share.
• By Deployment: On-premise solutions are prevalent among legacy systems, while cloud-based solutions are gaining traction due to flexibility and scalability.
• By Application: Key applications include product design optimization, defect detection, performance analysis, and compliance tracking.
• By End-User: OEMs hold the largest market share, followed by Tier-1 suppliers and MRO operators.

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Statistical Overview:

• The Aerospace Predictive Quality Analytics Market is projected to surpass USD XX billion by 2033, growing from USD XX billion in 2024.
• AI and ML technologies account for more than 40% of the analytics platforms adopted across the aerospace sector.
• Over 60% of large aerospace manufacturers have already integrated predictive analytics into their quality management systems.

Challenges Ahead:

Despite its promising outlook, the market faces several challenges that could impede growth. Chief among them is the shortage of skilled professionals capable of developing and interpreting predictive models. Additionally, resistance to change within traditional aerospace enterprises can slow down the adoption rate of new technologies.

Standardization across platforms and processes also remains a concern, as inconsistent data formats can affect the reliability of predictions. Addressing these concerns requires a combined effort from regulatory bodies, technology providers, and aerospace firms.

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Conclusion:

The Aerospace Predictive Quality Analytics Market is redefining quality assurance in aviation, offering a smarter, safer, and more efficient approach to managing complexity and compliance. As digital transformation continues to gain pace, predictive analytics will be at the forefront of innovation, enabling aerospace stakeholders to make informed, data-driven decisions that ensure operational excellence and future-readiness.