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What do all revolutions have in common? They typically result in massive transfers of wealth and the creation of a new elite. The disruptive change that industry analysts are forecasting will have profound consequences for manufacturing, the global economy, and even the environment. Viewed as a zero-sum-game, will today’s factory owners be the beneficiaries of Industry 4.0 or will the spoils go to the technology infrastructure providers?

Wall Street is encouraging historic R&D investments on the part of technology juggernauts such as GE and Siemens in their Smart Factory platforms. The billions of dollars spent today on the creation of entire technology ecosystems are based on an expected payoff. Research from PwC indicates that factories are committing 5% of revenue (US$904 B) on the Smart Factory.

The Smart Factory Vendor Model: Microsoft plus IBM

Microsoft has maintained decades worth of predictable cash flow by creating stickiness for its Desktop Operating System. The result is that the annuity income from white-color office workers has given Microsoft the financial stability to support its growth into other areas.
Why is this relevant to the Smart Factory? At the core of GE and Siemens strategy is to “own” the new industrial infrastructure. They are creating their platforms at a considerable cost and recruiting a multitude of complementary vendors and partners to build ecosystems that will provide a wide range of technology solutions.

Now for the catch. The investment in Predix, MindSphere or SAP Leonardo is not the equivalent of purchasing a new gas pump or wind turbine. Please forgive my hyperbole, but this is a Faustian pact. Selecting an IIoT platform vendor is a long-term commitment that will come with high switching cost. You may be reading this article on Apple or Android device, but sometime today you will log onto Microsoft Windows and use an Office product.
Whereas Microsoft’s revenue stream was primarily based on software, IBM built a global consulting organization. Perhaps one of the most overlooked cost aspects to implementing the Smart Factory is the labor-intensive requirement for implementation.

Let’s take the Digital Twin as an example. There has been significant hype generated about the Digital Twin. In theory, the Digital Twin is the Holy Grail of the Smart Factory: a real-time visual simulation of a machine factory asset. The problem is that the Digital Twin does not come as an off-the-shelf product. It is a complex and sophisticated technology.

To implement a Digital Twin, one needs access to the physical blueprints of a factory asset. From this blueprint, 3D technicians build a custom visual model. The factory’s facility and maintenance engineer will need to “teach” the vendor how the machine works. Furthermore, the Machine Learning methodology at the core of the Digital Twin is called Supervised Machine Learning. It is an iterative learning process that requires a significant human input.
The irony is that in an era of digitalization, a significant investment in (expensive) vendor supplied billable manual labor is required. Most production plants do not have big data scientists and algorithm engineers on staff. Welcome to the IBM Global Services business model.

The Software as a Service Model for the Smart Factory

Although the Machine Learning and Industrial Analytics solutions are not mature enough to be termed “legacy systems,” the vendor business models are closer to the California Gold Rush of the 1800’s than the economic models of this century.

Factory owners can make long-term investments in a single vendor or they can adopt a best-of-breed approach and deploy solutions that do not require infrastructure commitments. For instance, not all Machine Learning requires the development of a virtual clone. With Unsupervised Machine Learning, the learning algorithm does not need to access the blueprints of the factory asset – it detects degradation and machine failure by analyzing sensor data without regard to sensor type or asset class.

The example of Unsupervised Machine Learning is significant because the delivery and business model is based on Software as a Service. The big data engineers and scientists do not have to be onsite. The solution provider is responsible for the recruiting the human resources and providing back-end systems. There is no need for massive wealth transfer to infrastructure solution providers.

The fourth industrial revolution is underway. By performing careful due diligence on different approaches to Artificial Intelligence and ignoring the hype, there is an opportunity to avoid over-investment and over-reliance on a handful of powerful vendors.

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Avi Nowitz

Avi Nowitz

Avi Nowitz writes about the financial impact of Machine Learning on the industrial sector. Avi started his career on Wall Street as an equity analyst, providing institutional investor research on manufacturing companies. After completing his MBA in Finance from New York University, he joined the consulting division of Peppers and Rogers Group where his international clients included Bertelsmann, Ford Motor Company and Doğuş Holdings. For more than a decade, Avi led the Microsoft Consulting Practice at New Age Media and managed the execution of initiatives in EMEA and APAC.