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Internet of Things
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The industrial Internet of Things is not a product, but an approach toward greater digitalization and automation. So how can you profit from what is possible with the IIoT today? In my opinion there are 7 signs of highly effective strategies. I have teamed up with Andrew Crowley, systems engineer for our partner DMC Inc. to define and share these stragegies. Feel free to get involved here, share thoughts, and ask questions, or contact us on LinkedIn (Christoph Inauen).

The IIoT phenomenon is most timely

With Industry 4.0 imperatives driving a needed refresh of North America’s aging industrial infrastructure in the coming years, the Industrial Internet of Things (IIoT) phenomenon is most timely. What’s more, IIoT technologies can help industry address the wave of retiring industrial engineers and other technical talent that’s growing and still yet to crest.

Those IIoT technologies—secure connectivity, cloud platforms, artificial intelligence, and big-data analytics, chief among them—offer industrial enterprises the tools they need to achieve transformational digitalization and automation in their operations.

Of course, digitalization and automation are only a means to greater quality, efficiency, productivity, and profitability in those operations. And that’s not to mention how they can help keep industrial companies competitive as well as flexible and agile in response to ever-changing market conditions.

What self-organizing factories of the future look like

Ultimately, IIoT technologies with built-in intelligence will enable smart factories with advanced manufacturing models that are dynamically self-organizing. That’s to say, in factories of the future, customer orders will draw their specific instructions and recipes from intelligent software that provides the commands for their production execution directly to the machines.

In turn, the machines will be largely self-directed by those orders. And they’ll be supported by logistics chains that will automatically compile themselves in logical sequences, using preset rules, that bring feedstocks and assembly materials to the production lines and take finished goods to where they’ll be shipped.

Quality rates of 99.99885 percent

In fact, this smart factory model already exists. At the 108,000-square-foot Siemens PLC plant in Amberg, Germany, robots and modern machines controlled by intelligent SIMATIC PLCs produce more than 12 million SIMATIC products a year, with 120 variants each day—and with quality rates of 99.99885 percent.

Machines and computers handle 75 percent of the value chain, while technicians do the rest of the work. Only at the start of manufacturing, is anything touched by human hands, when an employee places the initial component (a bare circuit board) on a production line. The Amberg plant combines both real and virtual worlds: Products communicate with machines, and all production processes are optimally integrated and controlled via information technology.

7 strategies based on our combined experience

To be clear, IIoT is not a product, but an approach toward greater digitalization and automation and the benefits those can bring to industrial enterprises. Both Siemens, as a supplier or manufacturer of products enabling these capabilities, and DMC, as an industrial systems integrator, have experience in helping customers with developing and implementing their IIoT strategies. What follows are seven characteristics of the most effective strategies that we’ve seen, based on our combined experience:

They are strategies that consider the greater context.

Deploying IIoT technologies is part of a  bigger, multi-year journey toward the next stage in the modernization of production and both inbound and outbound logistics. Many call it Industry 4.0, a fourth industrial revolution characterized by cyber-physical systems, employing digital twins that are virtual proxies of physical production systems. Digital production twins can be used by plant operators to monitor and control their processes in real time. But for many if not most existing factories, the modernization journey is more an evolution, not a revolution. Fortunately, cloud-based, pay-as-you-go platforms, such as the Siemens MindSphere open IoT operating system, can help accelerate progress, while eliminating capital costs.

They are strategies that focus on business outcomes.

To be most effective, especially in terms of quantifiable returns on investments in them, IIoT technologies must be applied to drive specific business value, whether that’s addressing physical or data bottlenecks, opportunities to redesign workflows, or other issues facing the business. Can an IIoT technology application improve quality, speed, or cost? Consideration should also be made to potential deployment impacts elsewhere, up and down the production and logistics value chain. As industrial engineers well know, changes made in one part of a process can have unexpected consequences on other parts of that process.

They are strategies that define specific goals and roadmaps.

The best IIoT strategies are as specific in defining current and desired future states of plant processes. The latter are imaginative but realistic visions of quantum process changes, not step-changes in existing ones. That’s because IIoT technologies can enable whole new workflow possibilities. Highly effective IIoT strategies are clearly spelled out in written execution plans, with details about supporting tactics. These plans have timelines that properly sequence the steps required to achieve plan goals. While third-party IIoT experts can be a big help in facilitating the necessary discussions for breakthrough visions, the goals and roadmaps must come from those involved in day-to-day operations.

They are strategies that seek data quality, not quantity.

While data is the lifeblood of Industry 4.0 models, the most effective IIoT strategies are highly selective in the types of data drawn from machines to update digital twins. After all, not all data is of equal value. There’s informational data, real-time data, and mission-critical data. Sure, adding sensors to existing equipment and linking it to data collectors can capture floods of data. But higher-level systems and analytics need only the most relevant data. That’s why edge processing is growing. It uses intelligent devices on plant premises to pre-process machine data before forwarding specific, relevant data to higher-level systems, whether those are on-premise or in cloud-based ones, such as ones hosted on the Siemens MindSphere platform.

They are strategies that apply analytics to solve problems.

Advanced analytics, especially artificial intelligence and machine learning, offer a lot of promise to Industry 4.0 models, but at this time, these capabilities can be much more than what the initial steps of an effective IIoT strategy implementation requires. It’s advisable to find specific problems that can have visible and relatively quick beneficial results by solving, then figure out what data is needed to do that. Before data science, problems were diagnosed and resolved by addressing underlying issues of physics, engineering, and mechanics. Today, these are still invaluable starting points for troubleshooting, with data analytics enhancing deeper inquiries into them. But, often, to monitor the health of a motor or motor fleet, all that’s needed is data and a trend analysis on how much current amperage the motor is drawing, not a full vibrational analysis. On the other hand, turbomachinery monitoring could benefit from both, which is how Siemens Remote Diagnostic Services works. So, using advanced analytics is situational, but not necessarily required to get started on using IIoT technologies today.

They are strategies that employ industrial cybersecurity safeguards.

Connectivity is a core enabler of IIoT technologies and, with it, comes inevitable cybersecurity threats, like what enterprise IT networks and users have endured for two decades. Before, plants typically operated standalone machines and closed networks that kept threats out. But while it’s critical to protect OT operations with layered, defense-in-depth cyber safeguards, those protections must be designed for OT’s specialized requirements, such as high-speed, deterministic communications between machines. That’s why effective IIoT strategies will address industrial cybersecurity to ensure the integrity of OT networks, machines, edge devices, and their data, whether in motion or at rest.     

They are strategies that bring all stakeholders together.

Often industrial enterprises will assume that their IIoT strategies and their Industry 4.0 journey are the exclusive responsibilities of their industrial engineering team. Of course, the team’s expertise is core to the effort, but other stakeholders should be enlisted. Obviously, management needs to be, but also the shop floor’s operating personnel, who are intimately familiar with machinery and workflows. Collaboration with enterprise IT staff is critical, too, as OT and IT network integration is needed. Other stakeholder functions could or should include procurement, logistics, finance, and HR. Effective IIoT strategies will identify and document these stakeholders and their roles and responsibilities for the execution of those strategies.

https://www.youtube.com/watch?v=y_kN1SExehI

For industrial enterprises worldwide, the journey to Industry 4.0 is one driven by the need for ever more quality, efficiency, productivity, and profitability in their operations. Speed, flexibility, and agility in responding to customer requirements and market opportunities are also keys to staying ahead of the competition. Standing still is not option. Implementing a highly effective IIoT strategy can accelerate a company’s realization of an Industry 4.0 model and these advantages—with external IIoT expertise potentially a big help in developing such a strategy. 

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