Siemens MindSphere: Gathering and Interpreting IIoT Data

Siemens MindSphere provides manufacturers with a platform that helps them manage the vast amounts of data they generate.

The proliferation of internet-connected or “smart” devices is revolutionizing a wide range of industries, including manufacturing. Labeled “Industrial Internet of Things” or IIoT in the manufacturing context, the expanding ecosystem of interconnected devices (such as industrial monitors or sensors) is generating an increasingly large amount of data that is increasingly hard to manage. If today’s car has the computing power of 20 personal computers and processes up to 25 gigabytes of data an hour according to a McKinsey report, one can only imagine the amount of data being generated by a state of the art manufacturing facility with sophisticated machines.

 

In order to help manufacturers gather, process, and interpret all this data, global engineering conglomerate Siemens developed MindSphere, an open, cloud-based IIoT operating system that uses advanced analytics and AI to make sense of it all. The software can receive information from a wide range of industrial assets (such as production lines, vehicle fleets, etc) in real-time and then upload this data to the cloud, where it may be monitored and analyzed.

 

In order to collect the data, Siemens offers auxiliary products such as their MindConnect hardware and software, which follow standard industrial protocols. The Company generates revenue by selling these hardware components and offering the operating system “as-a-service,” which basically means charging a periodic subscription. The operating system is quite open, making it possible to obtain data from machines, plants, or entire fleets irrespective of the manufacturer. The open nature of the software also allows for an ecosystem of developers and makers to create ready-to-use apps that can fulfill more specific use cases. Users have the flexibility to connect to their preferred cloud infrastructure (such as AWS, Azure, Alibaba) and can even tap new sources of income as app providers in the MindSphere store.

 

There are seemingly endless industrial use cases for the MindSphere, with users leveraging big data generated by their interconnected devices in order to improve on areas such as profitability, efficiency, reporting, and safety. A use case example can be found in predictive maintenance – the Product Lifecycle Management (PLM) science of estimating when a piece of equipment will need servicing. The traditional, non-sophisticated way of doing this is to simply schedule routine or time-based preventive maintenance (i.e. clean and oil the machine once every week to minimize the number of breakdowns). However, the promise of predictive maintenance is to make servicing more efficient by monitoring the machine’s “vital signs” in real time, comparing it against volumes of historical performance data to determine when the next breakdown is likely to occur. Doing this accurately can generate substantial savings for the company by avoiding downtime, improving safety, and reducing maintenance expenditures.

 

Another success case comes from Rittal, one of the world’s leading system suppliers in the fields of enclosures, power distribution, climate control, IT infrastructure, and software and services. Through intelligently networked cooling solutions powered by MindSphere, the company was able to reduce its energy consumption and carbon footprint by up to 75%. The platform also allows them to constantly monitor their production environment and perform proactive maintenance.

 

With ever-increasing interconnectivity and data generation, IoT products and services (including IIoT) have a bright and exciting future ahead. Even though the manufacturing space is vast and it will take years to fully explore all of the IoT applications there, MindSphere may have the opportunity to expand its use cases even further in areas such as smart city management.  As technology advances, smart cities such as Hong Kong and Singapore will become more prevalent, and leaders will need proven platforms that can manage the massive amounts of data they generate.

 

While higher connectivity might provide many opportunities, it also presents considerable challenges, particularly in data protection and cyber security. According to an Ofcom report referenced by Atos, a french IT services firm, “companies and end users are often unaware of IoT implications on data protection, suggesting that more needs to be done to educate businesses about the security risk.” Atos goes on to mention that industrial applications of IoT are particularly vulnerable to an attack, given that devices can be left out in the field for years at a time without continuous maintenance. If Siemens wants to retain its reputation as a trusted industrial partner while ensuring the long-term success of MindSphere, it will have to keep the data of its clients safe by regularly investing in state of the art cybersecurity for its platform.

 

https://siemens.mindsphere.io/en/solutions/mindsphere-solutions.html

https://atos.net/en/blog/managing-iot-big-data-evolution-mindsphere

https://www.forbes.com/sites/forbestechcouncil/2018/05/08/how-well-does-your-car-know-you/?sh=42f077e65d26

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Student comments on Siemens MindSphere: Gathering and Interpreting IIoT Data

  1. Thanks for the article Rolando. I always find it interesting when traditional industries adopt new technologies. I think some of the biggest challenges other than data security that these companies face is a change in mindset within the organization and of course acquiring the right talent to get analytical and technical capabilities to handle this data and everything around it. I can also imagine that the data privacy problem is even broader within those organizations, where there are not even any processes or any operating model designed to handle any issues that might occur with data collection, privacy, or other. That being said, Siemens can find an opportunity in creating training programs or consulting services to support its clients in resolving those challenges. Alternatively, Siemens could offer the entire suite as a service where the company would get reports directly from Siemens and does not worry about any of the technical and analytical know-how, but then the issue of data ownership and privacy becomes very important

  2. This seems like a really interesting company. In my research, I’ve come across C3.ai (recently IPO’d), which also offers an end-to-end, systems-agnostic platform for ML and AI. While these companies are at the forefront of analytics, their cost is also incredibly high (annual contract value for C3.ai averages $12M). I wonder how quickly / if these companies will ever be able to serve smaller companies.

  3. The amount of data coming from IoT in the future just seems incredible compared to the data that exists currently. You touched on the seemingly endless opportunities and I am fascinated by The Anything as a Service (XaaS) idea, where industrial equipment manufactures, with products not typically connected, will see MindSphere as a way to increase the value of an already existing product. I am curious to know how many new apps and linked devices are being added on the platform and at what rate? How is Siemens incentivizing developers to build new capabilities?

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