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

From 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

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 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 monitor how these pilot lines improve. Once you see a tangible impact, you can expand these capabilities to additional machines in your facility.

One example of an intelligent field-level is the Aventics AF2 Series flow sensor, which can be retrofitted to existing pneumatic systems or included as part of new air preparation units or panels. This IIoT-enabled device provides air consumption and leak detection analytics, enabling customers to unlock remote monitoring capabilities, lower their energy costs and reduce their CO2 footprint.

3. Don’t lock yourself in: open toos and protocols

Digital transformation won’t happen overnight. With all the IIoT products and systems on the market, it’s important to select and deploy offerings that won’t restrict your future projects or plans to expand. One way to avoid locking yourself in is to implement open-source tools and protocols, enabling you to easily adjust or redirect information based on your evolving needs and ROI goals.

Flexible, open architectures can be cloudbased, on the premises or integrated into existing software systems. Two examples are Open Platform Communications United Architecture (OPC UA) and Message Queuing Telemetry Transport (MQTT), which are your best bet if you have sensor data coming in from sensors or want to include intelligent analytics. The AF2 Series flow sensor, for example, is compatible with OPC UA, enabling users to connect directly to the cloud or other IIoT gateway for advanced analytics.

In general, the best suppliers of IIoT technologies will tailor their systems based on your current infrastructure and then deliver machine insights via gateways, control systems or these open IIoT protocols. In addition, you have various open-source data aggregation, database, and visualization tools at your disposal, making it easier than ever to maintain an open-source environment. They include:

• Node-Red: a flow-based development tool for visual programming. By linking

input, function, and output blocks, you can record, process, and forward data without writing any code.

• InfluxDB: an open-source time series database, enabling you to quickly retrieve time series data in IIoT sensor data, operations monitoring, real-time analytics, and many other fields.

• Grafana: a multi-platform open-source analytics and interactive web application, allowing you to visualize, understand and receive alerts on your metrics. These open-source, easy-to-use tools can empower you to implement new IIoT offerings quickly—even if you don’t have a background in programming. And in addition to having a large community of developers working on and improving them, they come with many free, online resources like online tutorials and videos to train and support new users.

Partnering with a knowledgeable IIoT supplier

The road to digital transformation isn’t one giant leap. Rather, there are small, practical steps you can take right now to generate short-term wins and achieve long-term success. Bear in mind, a critical aspect of this success involves partnering with a knowledgeable IIoT technologies supplier that takes a consultative approach to all IIoT implementations. At Emerson, for example, we work with you to fully understand the requirements of your applications and then translate these requirements into actionable insights—putting you in the driver’s seat on the road to digital transformation.

An edge gateway, such as Emerson’s RXi2 LP, collects and transmits data to the cloud for improved analytics. Integration with an IIoT enablement platform like PACEdge enables data contextualization at the machine edge. Source: Emerson