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Industry Thoughts: The Reality of AI in Electronics Manufacturing

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Artificial Intelligence (AI) has been making headlines and capturing public interest and showing signs of becoming a real game-changer across multiple industries.

Generative AI tools like ChatGPT and DALL-E continue to get media attention, but what about the application and potential of AI in electronics manufacturing and test data management?

Is it really going to make an impact? In short – absolutely.

This year, we’ve enhanced WATS with new features powered with our AI technology: WATS Alvea. And we have more planned for the future.

Generative AI in Electronics Manufacturing

We’re very excited about AI here at WATS and the integration of AI into manufacturing processes. As a group of professionals whose working lives are built around managing data and get the most value of it, we’ve approached AI with a commitment to build practical value-driven solutions.

We’re sure you’ve already tried out ChatGPT or similar tools, so you’ll understand generative AI’s current capabilities. You’re also probably used to using AI without really thinking about it, such as using a chatbot on a website.

But with AI starting to spread throughout our personal and professional lives, it has yet to go mainstream in production environments.

Most of the new AI you’re seeing right now is built on Large Language Models – the same technology behind ChatGPT. However, these kinds of applications are often off-the-shelf modules giving varied benefits for the end user: designed to appease marketing goals rather than make a product genuinely better.

However, when it comes to hyper-technical applications – like managing test data –   a one-size-fits-all platform isn’t powerful enough.

Enhancing Test Data Management with AI

If you’re involved in testing within your business, you know just how much data you produce. You measure a huge range of variables, across a massive number of components, in different test stations, in other plants, and in various countries. As a result, test teams, or test-adjacent teams like R&D and Quality Assurance needs capabilities to get the value from these data.

Here at WATS, we thought about how to use AI to get even faster and more insights into this enormous amount of data. Fundamentally, we want to include features that add value to WATS, not just implementing AI for the sake of it. We very much believe in using the right technology for the right job.

That’s why we started out with WATS Alvea – our AI Technology designed to improve WATS and empower test engineers and colleagues in R&D and Quality with handling their Test Data and manufacturing insights.

Introducing WATS Alvea: Advanced Analytics with AI Technology

Accelerating Root Cause Analysis with AI

This year we’ve launched more tools such as Process Heatmap and Test Step Analysis with AI–powered suggestion. We wanted to empower test engineers to get more insights and do more analysis faster than ever. These tools and features are designed to accelerate root cause analysis and identifying yield trends faster, enabling companies to adapt a data-driven approach to improve manufacturing processes, R&D and product quality.

WATS Process Heatmap with AI

WATS Alvea surfaces hyper-relevant data for users who might otherwise not know where to look in the first place. For users who are more confident using test management systems, WATS Alvea just speeds up what they already do.

What’s Next: Combining rule-based AI with gen-AI?

While Generative AI can be very useful in both surfacing and presenting data. WATS Alvea gives you the heads-up on failure root causes, but what if you could quiz it further, asking for a step-by-step guide on what to investigate based on the measurements it’s presenting?

That thinking forms part of our plan for WATS in the future: combining the rule-based AI with the power of modern generative AI.

Predictive Analytics and Machine Learning

We’re closely watching to see what AI could evolve into, particularly in the field of predictive analytics. For example, something that is truly exciting would be for our users to have the ability to model the business impact of failures in components or proactive fixes in test tolerances.

Machine Learning holds the keys to much of that work and, with the right approach to training a model, this level of predictive analytics could be custom-made for individual businesses. One bonus that manufacturers have over other industries is how much data they already collect, which means when the models are ready to be trained, all the material they need will be there.

Dedicated to extract the value of Test Data

At WATS we have a whole team looking at ways to improve Test Data Management and getting the most value out of your Test Data. We’re all in on AI, just as long as it helps the people we’ve been supporting for decades, as well as newcomers to the WATS platform.

Test Step Analysis and Root Cause Analysis with AI Technology

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