Dassault Systemes: Artificial intelligence in industrial markets

Article Synopsis :

Industrial companies are expected to outspend their B2C counterparts on digital transformation solutions, according to this paper. But can it deliver the kind of disruption and transformation that is being experienced in retail, banking, insurance and healthcare?

Digitalisation is about more than just spending money on new kit

That will depend. Research from IDC suggests that most will be disappointed with their return on investment. Though discrete and process manufacturing industries will spend around US$333 billion on digital transformation solutions (almost a third of the global spend), 70% won’t achieve their goals.

Think big

The simple reason that they set their sights too low. Rather than seeking to digital transformation, they are looking to digitalisation, which focuses on improving efficiency.

Efficiency is a good thing, but digitalisation is not transformational and yields will be small because industries have spent decades refining processes to make them more efficient.

Digital transformation enables continuous and substantive process improvement, increased agility and, most importantly, breakthrough innovation.

What can be done?

Artificial intelligence (AI) and machine learning (ML) can have broad applications in industrial markets and for similar reasons. They can be used to anticipate needs, manage tasks and provide trusted recommendations across a number of fields:

Predictive maintenance and field operations where AI ad ML can combine to:

• predict plant failure, assist with maintenance;
• repair and operations planning;
• generate preventative and predictive maintenance recommendations;
• analyse quality issues;
• automate routine operations and maintenance tasks using automation software, robots, autonomous vehicles and drones;
• interpret and feedback operational data; and
• interpret and share performance and other quality-related data like customer feedback and warranty information, to manufacturing teams.

Design where AI and ML can:

• process millions of design options instantaneously;
• generate recommendations automatically for optimal solutions based on multiple criteria (cost, sustainability, time, regulatory requirements, etc.);
• use in cognitive search systems help designers explore existing design concepts via text and image searches
• help designers understand customer demand through analysis of sources like social media or internal customer feedback systems.

Reaching the parts other technology cannot reach

The technology will also demonstrate its worth within sales and marketing, by predicting trends, customising products and delivering highly targeted marketing.

Within testing, ML can develop highly accurate digital models of both physical objects and systems. This enables the development of realistic behavioural models that can be used to run performance simulations.

Finally, in manufacturing digital modelling and simulation are already being used to:

• plan production lines and systems;
• develop and integrate smart equipment, smart robots and production-line drones;
• recommend and execute proactive maintenance; and
• funnel important production data back to teams working on product design and specifications.

The use of such systems that share common digital models of products and assets and are deployed via a common collaborative platform will provide digital continuity that is required to generate continuous product and process innovation.

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