COE 2019 Hot Topics: Data Analytics

Attend keynote presentations from industry leaders, see the latest product developments, learn solutions to your business challenges, connect with other experts during networking receptions and visit the TechniFair, featuring key industry vendors at the 2019 COExperience. Hot topics will include Additive Manufacturing, Data Analytics, Generative Design, Making the Lead to 3DEXPERIENCE, Manufacturing Operations Management (MOM) and Model-Based System Engineering. First, we will discuss the hot topic: Data Analytics.

COE spoke with Morgan Zimmermann, CEO of NETVIBES-EXALEAD at Dassault Systèmes, about where data analytics Photo Morgan Zimmermann.jpgstand in our industry, why it’s so important right now and what direction he predicts data analytics will go in next.

COE: How is data analytics being used in the industry as a whole?

While business intelligence is a well-known “practice” for sales and marketing, with a very proven track record of being a mandatory approach to drive performance, it has been very poorly implemented in engineering and manufacturing.

The reason for that is while BI and analytics technology have been built to support transactional systems, it has not been built to support the world of products. Indeed, a product is a highly complex, configurable graph (BOM driven) that requires a new class of technology.

At DS, we have built this new class of technology, and we see a massive increase in usage for performance of products, performance of processes and customer experience analytics.

COE: In your opinion, why is data analytics a rising topic COE members should be aware of?

With an increase in competition, price and demand for innovation, COE members need to empower their teams to drive more effective decisions, and free up engineering capacity for innovation. There is no way an organization can improve its efficiency without the tools and technology to measure it.

There is no way an engineer can understand the impact of their design decision and ensure they are designing in a “cost aware” manner without being informed of the financial, or weight impact of their design decisions every step of the way. Empowering COE members with such technology will result in performance improvement for their organizations.

COE: Can you discuss some recent advancements in the field of data analytics and Big Data?

The ability to support product configuration is a huge one. Also, the ability to deliver analytics in 3D context to be used in design-to-target methodologies, as well as the ability to mix analytics, predictive analytics and modeling, and simulation for what-if scenarios.

COE: How has the Internet of Things impacted big data? How do you think it will shape data analytics’ future?

We call IoT IoE, or “Internet of Experience.” We strongly believe that IoT feeds alone are providing limited upside. However, if you enrich IoT with the proper context, then you can both predict and model the future in an effective manner. This is how the real world enriches the virtual world in a unique manner. This is why we have built 3DEXPERiENCE TWIN. This model-driven Digital Twin allows users to not only to project real-time IoT data on a 3D model, but it also allows users to benefit from the full semantic richness of the virtual model to create data and learning models for accurate prediction. It then allows them to use modeling and simulation capabilities to check hypotheses or run what-if scenarios.

COE: What excites you about the current state of data analytics?

Engineering and manufacturing are at the beginning of the analytics journey, with many opportunities for massive performance improvement. As an example, we are now using Machine Learning technology to automatically identify duplicate parts, and cost inconsistencies for mechanical parts, which is driving recurring cost reduction for large OEMs between three and eight percent.

COE: Where do you see the future of data analytics heading?

I predict more automation and more collaboration driving transformation for every industry in the areas of engineering and manufacturing. Simultaneously, I see the path being paved toward greater abilities to support product in operations, connecting engineering and manufacturing data with “in-operation” data for greater customer experience and new business models.

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