As more Industrial Internet of Things (IIoT) data is being used, what do manufacturers have to do in order to segue to smart plants?

In manufacturing, a smart plant refers to a connected digital factory. However, when you look inside a typical plant today, you often see older infrastructure and assets. Common challenges that prevent manufacturers from achieving smart, fully connected plants can range from location – remote facilities sometimes without even basic Internet service or low connectivity – to issues of older assets that aren’t inherently IIoT-enabled. In the industrial world, these environments lead to stranded assets and up to 40 percent of a plant’s assets fall into this category.

Many manufacturers believe they must rip and replace their entire infrastructure to get connected assets and a smart plant. But, with sensors and edge device advancements, this isn’t the case. Manufacturers can drive reliability enterprise-wide without ripping and replacing, instead using network enabled edge gateways, wireless sensors, edge-based connection software and cloud computing. Connectivity at the edge makes it possible to run IIoT applications and helps seamlessly integrate and interoperate with legacy systems.

What are the first steps a manufacturer should take to do this?

It’s imperative to first take a step back and identify the business outcomes or operational excellence objectives. What’s at the top of the list? Preventing downtime? Environment, health and safety (EHS) initiatives? Profitability? More reliable operations help boost performance to reach these objectives, but job number one is to focus on priority improvement areas.

Next, do a quick inventory of what you already have (e.g., what assets are in place, what level of instrumentation and connectivity exist, etc.) and identify the gaps.

After manufacturers identify the missing pieces in their infrastructure, they should bring together their information technology (IT) and operational technology (OT) teams. It’s important for these teams to ask: What is our business objective and what new IIoT technology is needed for this to happen? The answers will include a closer examination of software applications because the key to success in today’s digital world is how the data is applied to solving problems and creating opportunities. And, the enabling technology making digital transformation a reality is software leveraging IIoT, cloud computing and advanced analytics.

Why are stranded assets still a problem for manufacturers?

One of the biggest reasons stranded assets are still a burden for manufacturers is cost. If a manufacturer takes the approach of upgrading its equipment to avoid stranded assets, it becomes a costly, capital in tense project and certainly requires shutdowns. A plant must take the equipment offline, engage a team to upgrade it and then get it back online—and if a plant does this with several machines, the costs add up quickly.

Alternatively, a manufacturer can more cost-effectively add sensors or a wireless network without ripping and replacing an entire infrastructure. This way, it can be up and running in hours, not weeks, avoid hefty installation costs and, in some cases, avoid downtime altogether.

How can manufacturers connect their stranded assets without replacing their entire infrastructure?

Today, technology leveraging the IIoT can be used to connect all of a manufacturer’s assets from any plant or facility, collect that data, and roll it up to their plant historian, enterprise data center, or the Cloud. What’s important here is an approach that avoids unnecessary heavy data lifting and shifting by making data science quickly and easily deployed and scalable. The return on investment (ROI) comes from the ability to interface to all commercial data systems without requiring unnecessary data lakes or IIoT platforms, although the solution must be able to integrate as needed. Each manufacturer’s use case must be carefully considered; not every solution requires a top-of-the-line cloud approach and companies should be considerate in using as much existing infrastructure as possible.

How do stranded assets in manufacturing differ from other verticals, such as stranded assets in oil and gas?

The reasons for stranded assets vary greatly, but when you look at stranded assets inside a plant, you are usually dealing with connectivity issues from a lack of network infrastructure, remote locations, old equipment not yet wired with sensors, a mash-up of incompatible protocols—or original equipment manufacturers (OEMs) equipped with custom controllers and programmable logic controllers (PLCs). Typically, in the oil and gas industry, the infrastructure already exists, so the biggest difference is stranded assets are usually caused by older automation. As such, companies have to remove and replace the technology or consider the addition of IIoT sensors.

Differences aside, the best ROI comes from asset-agnostic software that can work in any industrial environment.

How can companies utilize the IIoT to show ROI and optimize performance?

Where companies truly find value in the IIoT is after they connect their assets. ROI is achieved with the aggregating of data and performing advanced analytics around specific, real-world operational excellence use cases, like predictive and prescriptive maintenance. The IIoT, then, has the potential to offer a huge competitive advantage to companies that can then use those real-time operational insights to make faster and smarter business decisions, drive reliability and asset performance and reduce operating costs. However, being able to connect your assets won’t automatically translate to value if you can’t prioritize the areas that are most important to demonstrate ROI.

To project and report savings from prescriptive maintenance convincingly, think about the shared operational excellence goals everyone is trying to achieve. This is often in the form of cost savings, higher volume and quality outputs, and increased return on infrastructure or assets.

For example, a plant manager may have the business objective to save on costs from equipment downtime. With today’s machine learning capabilities, a manufacturer can move from preventive to predictive maintenance scheduling—the difference between fixing the plant during an optimally scheduled downtime or scrambling due to a surprise failure.

How can maintenance professionals best report these results back to senior leaders in a way that shows value?

A recent IDC report forecasts global IoT spending to grow by more than four percent and top $1 trillion by 2020—it’s growing exponentially. The National Association of Manufacturers (NAM) also reports that worldwide manufacturing is a $14 trillion business and that 10 percent is lost to breakdowns. For maintenance professionals, it makes sense to implement smart manufacturing initiatives now rather than later to remain competitive.

To best report savings from prescriptive maintenance to senior leaders in a way that shows value, think like them. Report back on metrics that can be directly linked to the company’s bottom line. This would be things like: How far in advance was the failure detected? How much downtime of equipment was prevented? What did that save in a dollar equivalent? These examples should help illustrate the shared operational excellence goals everyone is trying to achieve.

Keith Flynn

Keith Flynn, certified professional engineer, is the Sr. Director, Product Management, R&D – Architecture & Security, at AspenTech. With over 20 years of industry experience, his insight informs product development, ensuring that products integrate the latest technical capabilities and deliver the best results for customers. www.aspentech.com

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