INDUSTRY-SUPPLIED CONTENT 33 AW OCTOBER 2020
Digital Transformation in Manufacturing: Small Steps or One Giant Leap? How taking three practical steps can generate short-term wins and achieve long-term success with Industrial Internet of Things technologies. By Zach Gustafson,
vice president, business development— machine automation, Emerson Automation Solutions
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rom automotive production lines to oil and gas equipment, the shift from manual processes to digital technologies holds game-changing potential for industrial applications. But for many manufacturers, this digital transformation remains an abstract pipe dream. In fact, a recent survey by Emerson shows that more than 70% of companies don’t have a vision for data analytics with a clear roadmap to success. Additionally, many manufacturers find the Industrial Internet of Things (IIoT) and other enabling technologies too complex, costly, and time-consuming to implement. And with all the offerings vying for their attention on the market, it can be difficult to know which offering—if any—best fits their particular plant. In short, too many manufacturers are being kept from the digital Promised Land. But the reality is there are practical, easy-toimplement steps you can take to make digital transformation a reality—right now.
The role of the IIoT
A key aspect of digital transformation is IIoT, which allows you to gather process data from previously “unintelligent” components like valves, cylinders, and air preparation units. After collecting this data and transmitting it to gateways and other aggregation hardware and software systems, you can unlock new
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production insights that lead to less downtime, lower energy costs, faster cycle times, and higher overall productivity. But how can you be sure the machine data you’re gathering is actionable? And how can you apply this data to improve shop floor operations and higher-level decision making?
1. Figure out what you’re looking for
Manufacturing processes now yield more data than ever before. One of the most important steps on the road to digital transformation is to figure out your goal, and then pick out actionable data that can inform and drive your decisions. For example, if you’re looking to reduce your energy costs, it would be helpful to know how much energy you’re currently consuming, as well as any sources of potential waste. Once you know what you’re looking for, it’s important to partner with a reputable supplier of IIoT services and technologies. The best ones will partner with you to establish your baseline, and then map out the new insights provided by your new IIoT systems. This strategy can help you proactively address operational issues and avoid issues down the road. And once you’re equipped with more data and historical patterns, you can begin to benchmark various applications against each other—driving even
The Aventics AF2 Series flow sensor is an IIoT-enabled device that provides air consumption and leak detection analytics. Source: Emerson
more improvements and delivering greater returns across your operation.
2. Start small and scale: field-level intelligence
One reason you may be hesitant to adopt an IIoT program is the potential investment— both from an engineering and enterprise standpoint. Oftentimes, IIoT systems have the reputation of comprising complex data architectures to connect disparate hardware and software systems—requiring extensive engineering hours, materials, and cost to set up and deploy. Faced with the prospect of fronting millions of dollars, executives are right to wonder if they’re investing in the right option for their company. But the reality is that digital transformation doesn’t have to entail this all-or-nothing approach. Technological developments are already underway at the field level, distributing intelligence among formerly “unintelligent” devices, such as valve manifolds and air preparation units. This development opens the door to a simple, yet effective IIoT approach: start small and scale over time. For example, once you figure out your production goals, design your system and assess your return-on-investment (ROI) objectives, the next step is to start with a small pilot project on a few machines. From there, you can
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