Are Artificial Intelligence and Machine Learning catching eye words for PLM Projects?

Are Artificial Intelligence and Machine Learning catching eye words for PLM Projects?

As Artificial Intelligence (AI) and Machine Learning (ML) become more prevalent, many businesses are starting to ask how they can apply these technologies within their own organization. In this blog post, I will explore what AI and ML are, and discuss some of the ways that CAD and PLM software can benefit from implementing these technologies.

AI and ML that draw interest from the industries and developers. It is mindblowing to think about how AI/ML can change the way we work. Engineers are usually dreaming big and therefore think about how to bring a new level of smartness to the engineering world is always on the table.

DE Survey: AI and CAE Impact on Design

My attention was caught by Monica Schnitger’s article – Really? Are AI & CAE tied for impact on design over the next 5 years? Monica brought some interesting points from a recent DE magazine survey According to the survey, 47% of respondents indicated AI/ML as the biggest impact on design and development in the next 5 years. Here is an interesting passage:

AI is still essentially an unknown quantity in the world of design and engineering. We know that we can train machine learning algorithms with real-world data and simulation results to understand product behavior and save on test and simulation cycles, but that’s not at all common today (though that will change as vendors work to tie these technologies together). We need to understand the inputs we need and learn to interpret the outputs. We might need to gather and sanitize a lot of data points. All of this will get easier over time; we’re not there yet.

Applying AI/ML in the design and engineering realm in other ways is still largely unexplored. What do we want an ML algorithm to tell us, that’s relevant for design? We could incorporate more data into design decisions, like supplier on-time performance, that guides us to/away from specific suppliers. Or an algorithm could calculate all of the possible permutations of component cost, logistics, and eventual part disposal to develop a weighted sustainability score. There are so many opportunities for this technology, but I haven’t seen anyone concept emerge as a definite must-have

I completely agree – we need to gather and sanitize some data points. However, a combination of simulation and AI interest together is not accidental in my view.

Existing Applications and Industry Experience

So far, AI / ML had very few applications in CAD and PLM technology. The interest and hype were high, but I didn’t see a real output. I can notice a few interesting applications such as UX optimization and generative design projects and a few others. Check some of my earlier articles about ML.

Industry analysts and researchers predicted a big impact from AI/ML in the future. Check this article Will AI/ML change the value proposition of PLM software? The second part of the article speaks about data platforms and in my view, it is very important – I will talk about it later in my article.

In my Learning from Amazon AI, I brought a few data points you might find interesting and useful. The main one is about data usage over time.

In such a context, I like to bring Amazon as an example of a company that started to use data a long time ago and developed impressive results. Recent Forbes article – The Twenty-Year History Of AI At Amazon can give you an idea of how Amazon is using data.

Machine learning is used throughout the entire customer’s search journey on the site through features such as predictive typing, optimising page layouts, or recommendations and suggested products. In fact, without machine-learning powered search, hyperpersonalized offers are not feasible. Hyperpersonalization is the AI-enabled concept of treating each person as an individual and not generally bucketing them into certain groups. Using machine learning, companies can develop a unique profile of each individual, and have that profile learn and adapt over time for a wide variety of purposes including displaying relevant content, recommending relevant products, website layout optimization and more. Amazon was one of the very early adopters of this pattern applying personalization and making recommendations across Amazon for the last twenty years. AI-enabled systems are increasingly blurring the lines of search and recommendation systems. By knowing which customer is searching for what product, AI will now make it possible to bring up very relevant results and personalized recommendations.

Data Technology Impact

So, let’s get back and try to sort out what machine learning models, machine learning techniques, self-learning algorithms, and other AI/ML methods can bring to computer-aided design (CAD), product data management (PDM), supply chain management, product lifecycle management (PLM) and other related disciplines of engineering and manufacturing applications?

In my view, the answer to this question is “Data Impact”. The biggest impact I can see in current applications of modern CAD and PLM technologies is to learn how to leverage a massive amount of data that is accumulated by manufacturing companies and industry and start using this data in modern CAD and PLM applications to make them more intelligent. Although the data approach was very powerful in the last two decades and had big successes, I’d not underestimate the complexity of the engineering domain as well as the sensitivity of enterprise applications with regard to data usage.

I would like to bring 3 possible applications of how AI/ML can be used and what I think pros and cons in these applications for engineering and manufacturing products such as CAD / PLM / ERP.

Decision Support and Data Analytics

The usage of data in CAD/PLM applications for decision-making predictions and related fields is very limited. For the last decades, the industry was mostly focused on how to control the data rather than how to extract some smartness from the data. Most CAD and PLM applications were about data storage, revisions, workflows, etc. To change the trend and re-use this data from decision making can be an interesting approach and the demand for such results are high (based on the same survey mentioned in the beginning). There are many interesting applications in this space – sourcing, supply chain, maintenance optimization, production planning, and others.

UI/UX optimization

For the last few years, UI/UX was driving a lot of interest from developers. The time when ugly enterprise applications were able to survive is over and modern platforms are focusing on user experience and learning algorithms on how to improve it. Nevertheless, CAD/PLM space is loaded with a huge amount of complex legacy applications and it might take a very long time for UI/UX optimization to take an effect. Also, engineers are very conservative in time when it comes to changes in their habits, which will bring another level of complexity. Nevertheless, new applications must be given the focus on how to make AI/ML play in UX for CAD/PLM software.

Design Optimization

This is an interesting space. This is a place where generative design and its application such as additive technologies can make a big impact. Although, this space is very much disconnected from broad categories of PLM applications such as data management, processes optimization, and others.

People and Process Optimization

Last, but not least application and a very interesting question- can we use AI/ML to replace human intelligence in managing people and processes. The space is complex and it includes many sensitive human interactions and behaviors in the organization. It also touches on multiple aspects of privacy and can trigger conflicts in some applications when it will come to how these applications can be tuned to perform with different people. Nevertheless, process optimization and analysis related to project management can be very interesting. For example, how to make an assessment of project progress and time to completion of the design.

What is my conclusion?

Machine learning technology and artificial intelligence will have to find their ways in computer-aided design, product lifecycle management, and other applications in engineering and manufacturing. In my view, the biggest applications of the technology are related to data usage and data intelligence. Manufacturing companies are sitting on the goldmine of data and learning how to use it is a big opportunity. New SaaS applications and platforms have the potential to do so in a novel way because of the modern approach in data management technology and broad adoption by manufacturing companies of all sizes. Just my thoughts…

Best, Oleg

Disclaimer: I’m co-founder and CEO of OpenBOM developing a digital network-based platform that manages product data and connects manufacturers, construction companies, and their supply chain networksMy opinion can be unintentionally biased.


Share This Post