Understanding Innovation with Machine Learning and Artificial Intelligence

We’ve seen increased product innovation and diversification due to changes in demand. And right now, Original Equipment Manufacturers (OEMs) are prioritising agility and adaptability to keep on top of changing consumer focus and supply chain uncertainty.

As we step out into 2021, product and user data analyses are crucial to understanding how to deal with manufacturing and market changes such as these. So, how are artificial intelligence, machine learning, and automation being used to help further our understanding of these exponential changes?

“We already know that AI has truly arrived in the retail and brand e-commerce world; it’s now time to connect AI into the design, development and manufacturing world.”

– Teaching PLM to Become Intelligent

Mark Harrop
Founder and Managing Director | WhichPLM

The combination of big data and affordable, advanced computing has made machine learning practical and applicable to modern day manufacturing. And now, it’s driving positive change in product development – even throughout product lifecycles.

Are You Making the Most of Your Product Data?

Product Lifecycle Management (PLM) systems collect vast amounts of data on product performance. This data is used to varying effect through individual industries – but its usefulness should not be underestimated.

Manufacturers can now look to Artificial Intelligence to enable rapid searching and analysis of vast amounts current, and historical, product and user data to help designers identify new product and service opportunities. We’re unlocking the potential to determine which possible attribute combinations will result in better profitability. No small feat.

“Artificial intelligence is helping companies identify new product opportunities and iterate quickly to get them closer to perfection in a number of different ways.”

– How Artificial Intelligence Is Helping Companies Identify and Nail New Product Opportunities

Michelle Greenwald
Contributor | CMO Network

But these technologies are not just enabling better analysis from extrapolated statistics. They’re also making design development and manufacturing smarter. Machine learning is being used to create adaptive user interfaces that improve design productivity, or to optimize supply chains by quickly identifying suitable, alternative suppliers.

How Can You Make Product Innovation Intelligent?

All of this comes together under the label of Product Lifecycle Intelligence – a concept which applies these technologies to help PLM users extract meaningful insights from product data.

We’re familiar with manual analysis of product lifecycle data. And we understand how this is applied to product innovation and development. But the exponential pace of global innovation is something we are still getting used to. So, it’s clear to see the need for Product Lifecycle Intelligence.

“Concentrate on gaining time-to-market and speed advantages in the areas of Digital Prototyping, PLM, co-creation of new products with customers, Product Portfolio Management and Data Analytics and AI adoption.”

– 10 Ways AI is Improving New Product Development

Louis Columbus
Senior Contributor | Enterprise Tech

In Our Experience…

For the past few years, we’ve been investing significant research and discovery time in Machine Learning and Artificial Intelligence. We’ve created several internal proofs of the technology and we will continue to invest in the huge potential this domain has to offer.

One existing, practical application our experts have implemented is concerned with integration of state-of-the-art AI components for natural language interaction to provide enhanced smart searching capabilities. We’re now able to provide much more power to users by deploying these new technologies. You can see for yourself what I’m talking about in this presentation from our 3DEXPERIENCE Adoption & Usability experts.

Here, we outline our plans to leverage product data intelligence and provide businesses that crucial head start in their PLM processes. Most importantly, you can see here how we can empower all users in an integrated suite of unique add-on features packages to the Dassault Systèmes portfolio.

Digital transformation must begin to go beyond product and process assets.
We must strive to understand each integral part of the product lifecycle to the point of predictability.

Why sit on years of untapped product data? Most manufacturers already own the data needed to develop comprehensive performance averages and variances that will accelerate their product innovation. In many cases, this is the path to a richer customer experience – to developing brand relationships with customers that thrive throughout the product lifecycle.