Tech Briefs Magazine - August 2024 - 14

Generative AI:
Reshaping the Industrial Value Chain
with the Industrial Copilot
C
ompanies worldwide are faced with a shortage of
skilled labor as experienced workers retire and not
enough new talent enters the workforce to compensate.
At the same time, design and manufacturing
continues to grow increasingly complex and competitive. To
address this, companies must find a way to capture knowledge
from experienced workers and make it both accessible and
enticing to new hires. Generative AI can help address this by
transforming the way humans and machines interact and collaborate
using solutions such as Siemens Industrial Copilot.
Industrial copilots are poised to revolutionize the way companies
design, plan, develop, engineer, and work by combining
the strengths of humans and AI to meet challenges across the
industrial value chain.
Generative AI allows users and information to interact in
ways that were never possible before and can serve as a layer
between complex systems, such as those found in design tools
or within state-of-the-art factories, and the people using them.
Generative AI excels at natural language processing or NLP,
granting it the ability to converse with humans in a way that is
natural and intuitive, bridging the gap between the way humans
and computers see the world.
A New Paradigm of Design
It is no secret that the tools used to design products, production
lines, and even entire factories are incredibly complex,
requiring extensive training and years of experience to master.
Industrial copilots can help flatten the learning curve by providing
conversational access to knowledgebase data as well as
linking into various functionalities within a tool itself to provide
pertinent suggestions based on training from expert users
and past designs. Beyond providing easy to access training and
assistance, an industrial copilot can also help maximize the impact
of a user's time through intelligent automation and process
improvements, allowing them to spend less time completing
mundane tasks and more focusing on the elements only a
human can do.
Industrial copilots can help streamline the design process by
assisting users in leveraging the full capabilities of engineering
tools for creating mechanical design concepts by tapping into a
vast database of expert knowledge and complex tool functionality.
This not only broadens possibilities for engineers and designers,
but also accelerates the design phase and ensures adherence
to all standards, criteria and design language.
Furthermore, it expedites the development of initial prototypes
and mock-ups, connecting with other AI elements found
in design tools to help generate new design variations for faster
iteration and experimentation.
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A copilot can also help to optimize simulation and planning
by improving resource allocation and ensuring the most efficient
use of materials, time and people. By generating different
planning scenarios, potential risks and optimal strategies can
be worked out efficiently not only before any changes are made
in the real world, but before any valuable expert time is spent
on the problem. For example, an industrial copilot could analyze
the throughput of a production line, test a potential improvement
such as adding a new machine to a particular step
in the process, then generate a report detailing the benefits,
changes and even specific details such as required throughput
across different conveyer belts all without the need to spend
any expert time and resources.
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Tech Briefs, August 2024
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Tech Briefs Magazine - August 2024

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