Digital Engineering and Manufacturing
Why autonomous operations?
75%
of manufacturing managers understand they need to reinvent operations to reach the full potential of data and AI in support of end-to-end process performance and sustainability
> 50%
of global manufacturers are finding it increasingly difficult to recruit manufacturing talent, from operators to production engineers and managers
> 50%
of the world's total energy is consumed by manufacturing-related activities, generating 20% of global emissions
What you can do
Understand what autonomous means for your manufacturing operations and how it translates into operational objectives. Create a plan that outlines self-funding steps and use cases to get there.
Achieving autonomous operations is similar to lean manufacturing: both require high-touch governance. The key difference is the use of data and AI to uncover and apply new performance levers, accelerating process innovation at scale.
40%
of manufacturers will deploy enterprise-wide AI-based tools to support decision-making processes and maximize the value of data by 2025
To evolve from experience-driven to data-driven operations, you need an integrated operating model that cuts through functional silos. Use a digital twin to integrate processes and improve performance and sustainability based on real-time insights.
25%
of industrial organizations will use real-time data capture and integration investments for sustainability initiatives to boost operational performance and visibility
Your architecture must evolve to take full advantage of the power and flexibility of an operational digital twin. No “rip and replace,” the twin uses data from existing IT/OT assets to support performance-enhancing digital use cases.
60%
of available PLM applications is expected to be built on top of composable technologies, enabling functional integration to other adjacent solutions to enable a digital thread
What’s trending in autonomous operations