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Internet of Things

5 Things I learned about Siemens

As Siemens MindSphere version 3.0 is now available on Amazon Web Services (AWS), IoT expert Dan Yarmoluk summarizes his findings about the company’s cloud-based IoT platform and solutions.

In the past year deploying my Industrial IoT Vibration solution, I have met with many sensor or hardware manufacturers, platform providers, vertical domain experts, system integrators, analytics providers and data scientists.  There are many pieces of the pie in the IoT technology stack, which often make it fuzzy where each piece provides value and which company to align with as you try to migrate to the digitization of industrial processes, workflows and assets.  Ultimately, you want to partner with an organization that balances both technical and domain expertise that provides value to the customer, of what I refer to as “Vertical A.I.”  The subject matter expertise with technical solutions and data science will also need to be merged with business models to deliver our value and allow them to be consumed.

With that context, I wanted to articulate 5 things I found very interesting about Siemens overall and the MindSphere solution below. You may think of German industrial powerhouse Siemens as being primarily a machine builder, but the company has a range of digital offerings that span throughout the entire value chain in manufacturing. From product development, production engineering, and production execution, the company offers a consistent data model across all levels of manufacturing, thanks to its product lifecycle management, digital twin software, and MindSphere IoT platform.

Dan Yarmoluk, IoT expert.

#1 Siemens is a large software company

“When you look at Siemens, your first thought probably isn’t software,” says Ralf-Michael Wagner, the company’s COO of data services and its cloud-based MindSphere IoT platform. And a decade ago, you'd have been right. While the company has long been a leader in fields such as industrial automation and power distribution, it didn’t previously have a reputation for being competitive in software. But by Wagner’s reckoning, the company is now among the biggest software companies in the world. “Beginning in the 2000s, we’ve invested more than $10 billion in industrial software acquisitions,” he explains. “These acquisitions make us the sixth-largest software company in the world.” Of note was the acquisition of Mentor Graphics in 2016, specifically to bolster its industrial software operations and help it keep pace with changes to manufacturing technology.  Beyond simply increasing the industrial company’s competitiveness in the software space, Siemens’ investments have changed its value proposition regarding the Industrial Internet of Things (IIoT) and have a much broader and holistic view regarding IoT.

#2 Siemens’ MindSphere is a holistic IoT vision that builds upon its PLM excellence

Siemens’ value is a much more holistic view when discussing IoT. It is Industrial IoT, but they think broader in the perspective of the digitization solutions and data-driven insights. It’s an extension of their Teamcenter Product Lifecycle Management (PLM) solution, which is an information management system that can integrate data, processes, business systems and, ultimately, people in an extended enterprise. PLM software allows you to manage this information throughout the entire lifecycle of a product efficiently and cost-effectively, from ideation, design and manufacture, through service and disposal. Thus, from Computer-Assisted Design (CAD) drawings, to embedded electrical and firmware design; Computer-Aided Manufacturing (CAM), it can form the basis of the “digital twin” (You can hear more from Executive leader of Mindsphere, Steve Bashada in a recent podcast, All Things Data.)

#3 Siemens’ Digital Twin is an Integrated Feedback Loop and really, three sets of twins

As the software acquisitions and PLM heritage provides the digital designed product, but it doesn’t stop there, a company can add in the digital production layer, or “production twin” in order to digitally render the best way in which we should assemble or produce the product. After the real product is performing in the field, the feedback loop of the “performance twin” is fed back into the system so one can see the real field performance versus the expected performance when you designed it.  You can now see what some refer to as the “digital thread” connecting design, production and performance and learning from it. This is true digitization and digital transformation, leveraging data and information every step along the product lifecycle and learn to optimize those steps for future generation of products and processes. (You can hear more from Jagannath Rao, a Siemens executive with decades of industrial automation expertise and education in artificial intelligence and computer science, on All Things Data.)

#4 Siemens has decades of experience with productionized and deployed A.I. and Data Science

While the company’s industrial automation systems paved the way for concepts such as IIoT and Industrie 4.0, and it has decades of experience deploying artificial intelligence technology like neural networks to improve the operation of machines, Siemens is now thinking bigger with weaving the digital thread throughout the entire industrial enterprise. 

Michael May, Ph.D., the company’s head of technology field business analytics and monitoring, told me at Hannover Messe that the corporation has been working on AI projects for decades. For instance, more than 20 years ago, Siemens implemented neural networks in more than 30 steel plants to monitor and improve quality, process, and efficiencies. The company also has substantial experience in using AI to calculate the remaining useful life of gas turbines. In addition, the company has used root cause analysis on the CERN Hadron Collider to prevent failures and maximize uptime.

For the past decade, Siemens’ AI efforts have been focused on improving control of industrial processes using deep learning and reinforcement learning. The company has won more than 50 patents focusing on optimizing complex industrial systems. This deep-learning research began with a mathematical proof that any dynamic system can be modeled with deep-recurring neural networks. An example of this technology is Siemens’ “self-optimizing” gas turbines that leverage reinforcement learning.

This branch of AI begins by learning from past behavior of a machine using hundreds of sensors with actions applied to control parameters. Deep-learning algorithms learn to simulate a device’s behavior by performing various actions and then monitor how the system responds to them. Over time, the system becomes more sophisticated at understanding and simulating that behavior. This capacity enables you to ask new questions like: What happens if I put the parameter in a state that has not been observed before? Industrial companies can then use data from operations to adapt and optimize a control policy and ultimately simulate entirely new behavior in a simulated environment. You can extend the classical control loop with a machine-learning loop using neural networks, making it dynamic and thus creating a new control policy.

As machine learning exploits available data to create new simulations previously unseen through reinforcement learning, it can provide a “digital reward” when the desired outcome occurs or hand out a “digital punishment” when something undesirable happens. While companies based in Silicon Valley hop on the AI and machine learning bandwagon promise to tailor their algorithms to work in industrial settings, Siemens’ AI work has produced such machines with baked-in intelligence for decades.

#5 Domain, domain, domain

Siemens has been producing industrial machines and automation for many decades. The market is yearning for deployed expertise, use cases to illuminate opportunities, technology and innovation balanced with real domain and subject matter expertise to create business value. One has to look beyond marketing propaganda and market predictions to discern where that value is and who to partner with. Upon scrutiny, you’ll find Siemens to be innovation powerhouse and a global team rooted in German engineering. They are leveraging the cloud partnership with Amazon Web Services (AWS) to offer open-source development, reliability and scalability while maintaining leadership in industrial manufacturing and automation landscape.

This article was written by Dan Yarmoluk, an expert on topics such as IoT, Data Science and AI. Dan is Director of Business Development, IIoT and Analytics at ATEK Access Technologies and he publishes frequently on data science, IoT and business models in a variety of publications and channels. Dan is an important influencer for Siemens and has engaged on different topics over the years. You can follow him on

Dan Yarmoluk
Picture credits: Dan Yarmoluk, Siemens AG