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Siemens’ MindSphere Champions A “Digital Twin” Concept For It’s IoT Platform

Siemens IoT Platform & The “Digital Twin”

Siemens MindSphere is an IoT operating system that promises to help designers and engineers align themselves with your plant’s production and operations teams to create a “closed-loop” in which engineering and operations address inefficiencies proactively.

Siemens IoT platform competitors in the space: Amazon AWS, Microsoft Azure, IBM Watson, SAP, GE, and PTC ThingWorx all claim IoT solutions, but Siemens touts a full platform solution leveraging Product Life-cycle Management (PLM), Asset Life-cycle Management (ALM) and Manufacturing Execution Systems (MES).

Less than 4% of Siemens’s annual revenue is software sales, so the allure is that their solutions are by engineers for engineers and that their approach to IoT promises to be much more manufacturing-centric.

What Is A “Digital Twin”?

Siemens’ vision is that MindSphere will be the platform to build out a digital twin of your plants and fleet, AR facsimiles modeled on real data as close to a one-to-one ratio as possible. The real factory’s data helps model a virtual factory, which in turn provides product designers and engineers a proving ground for any proposed changes in production.

The closed-loop model creates bi-directional visibility with engineers and product designers looking at real production data, while production teams are able to validate planned production runs through simulations. Both teams benefit from real-world data and virtual simulations.

Siemens’ MindSphere Champions A “Digital Twin” Concept For It’s IoT Platform

The Digital Twin Concept Will Require Huge Investments For Powerful Results

The reality is that a fully-realized digital twin will be beyond the reach of most manufacturers in the near term. To build one, 3D and manufacturing systems models will need a level of detail few companies can afford– and that’s just the starting point.

A set of systems designed for data analysis and simulation will also need to be in place, not to mention fully integrated ALM and PLM products. Critical to the whole concept will be the analytics that feeds raw machine data to these models.

Where to Start? Build Towards The Strategy From The Ground Up

Eventually, the key to any digital twin strategy will be in the data that feeds the simulation and provides it with real-time variables. This data is immensely valuable itself even without a fully-realized digital twin.

As the MindSphere ecosystem expands with more SaaS connectivity, cloud analytics will be a critical part of creating a seamless closed loop from design to production. Products like Mingo, which gather and house machine analytics in the cloud can provide that data while providing ROI within a few months.

Our approach is to build a plug-in-play capability with systems like Siemens MindSphere making it easier for smaller manufacturers to implement a full IoT strategy (but more on that at a later date).

In the meantime, it makes more sense for mid-level manufacturers to invest in analytics from the ground up instead of top-down since immediate benefits can be realized just by gaining visibility at the plant floor by analyzing and eliminating machine downtime, reducing scrap, and gaining resource efficiencies.

Start Your IoT Strategy With 100% Machine Connectivity And Analytics

A more realistic scenario for most manufacturers will be to build towards 100% coverage of your machine analytics within each plant first, then seek to integrate critical operations into an IoT platform. Making this data available immediately while collecting it all in a historical record can be leveraged to build out a more predictive model later.

Want to learn more about Siemens MindSphere? Here’s a white paper.

Bryan Sapot
Bryan Sapot
Bryan Sapot is a lifelong entrepreneur, speaker, CEO, and founder of Mingo. With more than 24 years of experience in manufacturing technology, Bryan is known for his deep manufacturing industry insights. Throughout his career, he’s built products and started companies that leveraged technology to solve problems to make the lives of manufacturers easier. Follow Bryan on LinkedIn here.