Net-Inspect's Strategic Investments into The Future of Manufacturing Quality

In today's complex manufacturing environment, quality teams face mounting challenges—escalating compliance requirements, increasingly complex supply chains, and pressure to do more with less. For more than two decades, Net-Inspect has helped 8,000+ companies across 59 countries streamline quality processes and gain unprecedented supply chain visibility. Now, we're taking the next step.

We're investing in advanced Artificial Intelligence (AI) and Machine Learning (ML) capabilities to make the Net-Inspect platform even more powerful for quality and supply chain professionals. These strategic investments extend our core strengths in standardization, security, and scalability to deliver smarter insights and greater efficiencies throughout the quality lifecycle. Our artificial intelligence investments are also aimed at improving overall efficiency in manufacturing processes.

Introduction to Artificial Intelligence in Manufacturing

Artificial intelligence (AI) is revolutionizing the manufacturing industry by enhancing quality control processes, improving manufacturing efficiency, and reducing costs. AI-powered quality management systems can leverage methods such as computer vision and machine learning algorithms to automate inspection processes, identify patterns, and predict maintenance needs. These advanced technologies enable manufacturers to significantly improve production efficiency, reduce human error, and increase customer satisfaction. As the manufacturing industry continues to evolve, the adoption of AI technology is becoming increasingly important for businesses to remain competitive and achieve business growth. By leveraging AI, manufacturers can streamline their operations, enhance product quality, and respond more effectively to market demands.

Predictive Maintenance & Quality Analytics

Stop Defects Before They Happen

Manufacturing teams have traditionally relied on lagging indicators like finding issues after they occur through inspection. Years ago we launched a solution to turn this reactive approach on its head. Our Capability Charts assign a score to suppliers, part numbers, features, machines, operators and more based on the distribution of results for a given dataset. The closer the results are to target nominal, the lower the risk of defect, and thus the higher the capability score. We are continuing to invest in AI and ML-based analytics to help companies to identify and prevent risks before they occur.

By analyzing patterns across inspection data, measurement results, and event production variables, our machine learning models will be able to:

• Identify anomalies that might indicate emerging quality issues
• Forecast potential non-conformances before they materialize
• Link real-time PO information with quality data to predict delivery impacts
• Recommend preventive actions based on historical outcomes

Analyzing historical data is crucial for predicting future defect occurrences, enhancing the effectiveness of our machine learning algorithms in identifying flaws and improving overall product quality.

For example, in aerospace manufacturing, where a single defective component can have far-reaching consequences, these predictions enable timely intervention that can potentially save millions in rework, scrap, and potential in-service failures.

Intelligent Supply Chain Risk Management

Visibility Beyond Your Tier 1

Supply chain disruptions are increasingly common and costly. Whether from weather events, geopolitical issues, or quality escapes, these disruptions can halt production and damage customer relationships. For more than ten years, Net-Inspect has provided a built-in supplier map that supports multi-tier supply chain visibility. This map is kept up-to-date using through the suppliers' quality records generated at each level in the supply chain.

We are investigating options to combine our multi-tier supply chain visibility with additional data sources, including but not limited to:

• Supplier quality performance metrics and trends
• External risk factors (weather, logistics patterns, etc.)
• Anonymized cross-industry benchmarking data
• Production schedule impacts from quality issues

Combining these real-time insights will allow businesses to monitor ongoing data and trends to proactively address potential supply chain disruptions, enhancing efficiency and product quality.

For medical device manufacturers with strict traceability requirements, this means not just knowing which suppliers meet quality standards today but anticipating which ones might struggle tomorrow allowing for proactive mitigation rather than reactive firefighting.

Smart Data Insights & Dashboards

Finding Signals in the Noise

Quality data is abundant but often overwhelming. Many teams struggle to extract meaningful insights from mountains of inspection results, non-conformance reports, and supplier metrics. Net-Inspect has coined the phrase, TAPAS, which stands for Timely, Accurate, Personalized, Actionable, and Secure. These are all critical components for big data. One way to satisfy TAPAS is through smart dashboards.

Net-Inspect offers a built-in dashboard, and in the coming months will continue to incorporate AI and ML technologies to better personalize the dashboards for each user, proactively. We also introduced our first iteration of Natural Language Query and will further expand this offering so quality directors overseeing multiple facilities, can get instant answers to questions like "Which parts from Supplier X have trending quality issues across our plants?" without complex report building or data manipulation.

Intelligent Document Processing with Computer Vision

Reducing Data Entry, Increasing Accuracy

Today quality documentation is often submitted in the form of attachments on a FAIR or PDFs on an eSource records that Supplier Quality Engineers or Receiving Inspection personnel often review manually. This can include certificates of conformance, inspection reports and more. This manual review process creates bottlenecks and introduces potential opportunities for human error.

Computer vision systems can be used to automate data extraction and validation from quality documentation. We are evaluating options to use computer vision systems to:

• Automatically validate Form 2 special processes and process providers
• Extract critical data from certificates, inspection reports, and technical documentation
• Intelligently map extracted data to the correct fields in Net-Inspect
• Flag potential compliance issues or missing information

For defense contractors managing hundreds of suppliers across multiple tiers, this means dramatically reduced review time for First Article Inspection Reports while improving validation accuracy and compliance.

AI-Powered Support & Assistance with Machine Learning

Expertise When You Need It

Getting timely answers to questions about quality standards, system capabilities, or process requirements can slow down implementation and adoption.

AI-powered solutions can enhance the efficiency and accuracy of support and assistance, ensuring users receive precise and timely help.

Our AI assistant (already available in product) provides:

• 24/7 support for common questions about Net-Inspect functionality
• Guidance on industry standards like AS9102, PPAP, and more
• Contextual help based on what you're working on
• Seamless handoff to our support team when more detailed assistance is needed

For new users onboarding Net-Inspect, this means getting up to speed faster with fewer frustrations and support tickets, and real-time responses to questions. Although this solution has only been available for a few weeks, the feedback is very positive. Quality control personnel appreciate the ability to get immediate responses to questions, without having to search through pages of knowledgebase articles or wait for a customer support representative to get in touch with them.

Challenges in Implementing AI

Despite the many benefits of AI in manufacturing, there are several challenges that companies may face when implementing AI-powered systems. One of the main challenges is the initial investment required to develop and integrate AI technology into existing systems. Additionally, the quality of the data used to train machine learning models is critical, and poor data quality can lead to inaccurate results. Furthermore, the manufacturing environment can be complex and dynamic, making it difficult to develop AI systems that can adapt to changing conditions. To overcome these challenges, manufacturers must carefully plan and execute their AI implementation strategy, ensuring that they have the necessary resources, expertise, and data to support their AI initiatives. By addressing these challenges head-on, companies can unlock the full potential of AI and drive significant improvements in their manufacturing processes.

The Role of Data in AI

Data plays a critical role in the development and implementation of AI-powered systems in manufacturing. Large volumes of high-quality data are required to train machine learning models, and this data must be relevant, accurate, and up-to-date. The use of sensors and IoT devices can provide real-time data on production processes, allowing for continuous monitoring and analysis. This data can be used to identify patterns, predict maintenance needs, and optimize production processes. Furthermore, the use of predictive analytics and data-driven insights can help manufacturers make informed decisions and drive business growth. By leveraging data and analytics, manufacturers can unlock the full potential of AI and achieve significant improvements in quality control, efficiency, and customer satisfaction.

Net-Inspect's Commitment to Secure, Trusted AI

As a platform trusted by defense contractors and aerospace manufacturers, security is non-negotiable in everything we build. Our AI investments adhere to the same rigorous standards that have made Net-Inspect the go-to solution for regulated industries:

• All customer data remains protected with our FedRAMP-equivalent controls
• Machine learning models are trained while preserving data privacy boundaries
• Users maintain full control over AI-generated recommendations
• Every AI capability undergoes extensive security validation before development and release

Partner with Us to Shape the Future

As we roll out these capabilities, we're actively seeking customer partners who want to help shape these features. Your expertise and feedback are invaluable in ensuring our AI investments deliver real-world value for your specific challenges.

Interested in seeing these capabilities in action or discussing how they might transform your quality operations? Get in touch with our team or schedule a personalized demo here.

Net-Inspect continues to set the standard for quality and supply chain management in aerospace, defense, medical, and manufacturing industries. Our ongoing investments in AI reflect our commitment to innovation while maintaining the security, standardization, and scalability that have made us the trusted partner for 8,000+ companies worldwide.