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New Application Business Models - The Real IIoT Difference

Peter Zornio, CSO, Emerson Process Management
Peter Zornio, CSO, Emerson Process Management

Peter Zornio, CSO, Emerson Process Management

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.

As always, our customers, and all manufacturing facilities worldwide, aren’t interested in technology for its own sake, no matter how it’s labeled. They are instead interested in solutions which drive bottom-line results, with the actual technologies used to deliver these solutions of secondary importance.

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.

What is new is the IIoT service business model, and corresponding solutions. These IIoT solutions still start with sensors and deliver information to decision makers, but the infrastructure in between has changed drastically, offering three different and somewhat overlapping options for manufacturers.

  These new IIoT business models are a continuation of a long history of operational excellence applications in process facilities   

Emerson Automation Solutions and other companies offer three different IIoT implementation models: traditional, hybrid and outcome based. With the traditional model, in-plant intranets are used as the communications infrastructure to deliver sensor data to plant maintenance and engineering personnel. This raw data is then transformed into actionable information by various software applications licensed and operated by end users, augmented by their experienced domain experts.

Departing from this model is where the interesting new IIoT discussion begins.

For some applications, manufacturers can now choose a full outcome-based model, 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.

In between is the hybrid model, where the service provider analyzes sensor data and provides guidance to the plant on appropriate actions, with the plant taking final action. In all cases, the goal is to convert data to actionable information.

Many manufacturing plants are currently using two or even all three of these models, selecting which model to apply to each plant area based on a number of selection criteria including cost, time to implement, availability of qualified in-house personnel, comfort level of exporting the data, and other factors.

To illustrate how these models work in practice, let’s look at real-world examples, starting with the traditional model. All plants will have flow, pressure, temperature, level and other sensors in place and connected to some type of automation system for controlling and monitoring the plant. Additional sensors can be added to support other types of applications such as equipment reliability, energy management, environmental monitoring, etc. With the traditional model, intranet connections channel this sensor data to other parts of the organization for analysis by applications hosted in-house.

With this traditional model, sensors are added to large compressors at the plant, purely to monitor operating conditions. Data from these sensors is transmitted by the plant intranet and analyzed by in-house compressor experts, either onsite personnel looking at just local compressors, or in a centralized corporate engineering center. Centralized centers can monitor dozens of compressors located across multiple sites. Local plant service people are responsible for any corrective actions.

In the outcome-based method, this same compressor data goes over the Internet to a third-party service provider such as Emerson, where experts use models and software to perform analysis using the latest and most updated tools. When problems are detected, the service provider takes corrective actions at the plant, providing uninterrupted operation of the compressors.

Outcome-based services are typically purchased by plants by paying a monthly fee. 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.

In the hybrid model, service provider experts alert local plant personnel with a specific analysis, and the plant is then responsible for taking care of the problem.

The IIoT is being implemented now in plants worldwide, but the choice of where the data goes, either in-house or to third-party experts, is one that end users are facing for the first time. Data security and privacy are the most often cited concerns regarding the use of third-party IIoT services. Users want to be certain that only required data is accessible to the third-party provider, and want to prevent unwanted intrusions into their networks. Various security techniques such as data diodes can address these concerns.

The outcome-based method is used by a major chemical company in Freeport, Texas. Emerson used data from the plant to identify a potential failure on a critical valve that would have caused a plant shutdown for two to three days, resulting in millions of pounds of lost productivity. Emerson personnel were dispatched to the plant and repaired the valve.

According to the plant’s reliability manager, “We would not have caught this condition ourselves. We would have run this valve to failure and it would have shut the train down for at least two days. This would have also affected a downstream plant that relies on this plant for product.”

These new IIoT business models are a continuation of a long history of operational excellence applications in process facilities. They provide the promise of improved performance without the up-front expense and need for specialized in-house expertise.

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.

See Also:

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