The Industrial Internet of Things (IIoT) is not simply more sensing and final control elements available for monitoring and controlling—it is also about enabling new ways to collaborate to solve problems.
In a CIOReview article, New Application Business Models – The Real IIoT Difference, Emerson’s Peter Zornio describes some of the business models that these technologies enable.
The Internet of Things (IoT) in the manufacturing sector, often referred to as the Industrial IoT or IIoT, will represent a multi-trillion dollar opportunity by 2025 according to McKinsey & Company. This potential for energy savings, improved quality, increased throughput, and other manufacturing benefits is driving conversations—but too often this dialog is focused on the IIoT infrastructure and technology instead of the applications which will deliver value.
He notes the idea of sensors to decision makers pre-dates the Internet:
While the IIoT is new to many sectors of the economy, a form of it has been used since the 1960s in manufacturing. These early implementations didn’t use the Internet, which was decades away from being discovered, but instead relied on plant and enterprise wide intranets to deliver information from sensors to software and decision makers, where it drove operational improvements.
Peter highlights three IIoT implementation models: traditional, hybrid and outcome-based. The first is done with staff inside the facility with the existing communications networks. Hybrid adds service providers to help analyze and recommend actions based on the collected data.
Outcome-based models are:
…called an IIoT service. Sensor data is delivered to a third-party service provider via the Internet. The service provider then analyzes the data with their own software applications and experts. They not only analyze the data, but also send personnel to the manufacturing site to implement operational improvements by repairing or replacing malfunctioning components and equipment. Like UBER riders who don’t have to own a car or a driver’s license, the customer is purchasing an outcome directly.
Technologies such as data diodes secure the one-way passage of IIoT data outside the plant. Peter describes how these outcome-based services typically are charged on a monthly fee basis.
The sensor and related components to perform the monitoring can be purchased and installed by the plant, or be part of the service contract. This approach means no capital expenditure is required, which appeals to many manufacturers because it allows them to make bottom-line operational improvements without up-front investment. Additionally, they don’t need to train personnel on specialized data analysis techniques, while still having continuous access to remote experts extremely familiar with specific applications.
He shares an example of a major chemical producer who had critical valves monitored by Emerson valve experts. In one instance they identified a potential failure and sent to the plant to repair the valve before an unplanned shutdown occur which could result in lost production and revenues. Run to failure had been the prior practice before these IIoT based services were used.
Most manufacturers, especially large ones with many sites, are analyzing sensor data internally at centralized, remote corporate monitoring and diagnostic centers. Others are going straight to the outcome-based IIoT service model, especially if they lack in-house expertise. Savvy manufacturers will mix and match all three models, using the ones best suited to their particular level of domain expertise, capital, and opportunities for improvement.