IIoT and Industry 4.0 Revolutionizes the Industry
IIoT and Industry 4.0 continue to dominate the industry media with continued emphasis on how these technology initiatives will increase productivity and profitability. However, the narrative has since evolved. From a sensor to cloud model, to one that includes edge computing as an essential component for real-time analytics and as an intermediate data collection/storage point. As a firm believer in this evolution, it is always good when you have the chance to hear first-hand what is really going on inside industrial companies. The Internet of Manufacturing conference (IoM) in Chicago proved to be a good venue to engage with a combination of IIoT-related vendors and end users.
Let’s divide the end users into two distinct groups; those who are already moving down the IIoT road, and those who are in various stages of evaluation and implementation. Projects that are mostly discussed in the media are:
- Predictive maintenance
- Digital twinning
- Machine learning apps
- Enterprise-wide integration
Each of these leverage data from disparate parts of an organization to optimize operations.
One common thread here is that successful implementations hinge on the convergence of OT and IT. Without these two organizations working together successfully, it is going to be very hard to successfully implement IIoT projects. That’s because data is sourced in the plant (and for some applications elsewhere in the enterprise) and sent to repositories inside and outside of the OT environment. There, various forms of analytics are performed to achieve the desired objective.
Taking the Appropriate Steps
New applications and the transmission of data outside the plant generally raise common concerns for automation engineers. These are disruption of existing control processes and cyber-security. The ability to seamlessly add new applications using technologies such as virtualization and cyber-security layers is generally the realm of the IT professional. However, as edge computing makes its way deeper into the plant environment, and further from IT and even from OT, it’s increasingly important that computing platforms inherently support virtualization technologies. All in addition to being self-protecting and easy to maintain. The one thing we can say with certainty is that any form of analytics performs badly when data is lost!
While IIoT is often closely associated with machine learning and artificial intelligence technologies, companies are finding that a lot can be done with existing data. All without the need to immediately invest in those areas. Depending on the age of existing control systems, assets may need to be augmented with additional sensors. Careful consideration must be taken to ensure that the (additional) data destined for analytics can be extracted and stored locally.
One company at the Internet of Manufacturing Conference was struggling with this exact issue. Their modern PLC was out of processing power to extract and process data along with running the control loops. With some infrastructure modifications, an edge computing system is ideal for this company’s requirements.
In some situations, benefits can be gained from simply interconnecting processes of systems. This is done by replacing manual data collection and recording with electronic systems. The result?
It eliminates errors and provides almost instantaneous feedback with relatively simple graphical correlation tools. This is one area where reliable edge computing can play an important role.
Resolving Key Concerns
Most of the attendees at the Internet of Manufacturing Conference were in some combination of evaluation or planning stage. In some cases, they had a clear idea of what they wanted to achieve, in others they were trying to determine where to start. A few had implemented pilot projects and were grappling with full-scale deployments.
The 3 key concerns voiced by several people were:
- Latency of a plant floor
- Cloud implementation
- Cost associated with cloud-based implementations at scale
And while many people conceptually grasped the benefits that using the cloud could bring to an IIoT project, requirements that would make this type of implementation ineffective are:
- Real-time feedback for reactive process control
- Quality adjustments
- Imminent machine failure prevention
For those concerned about cost escalation, LTE mobile connection and cloud computing costs have fallen substantially and are still often usage-based. Additionally, they both fluctuate and escalate from month to month as an implementation scales, and more data is accumulated over time. Certainly, the cloud can be useful for pilot projects and perhaps the right answer for enterprise-wide deployments where data from the plant, sales, supply chain, logistics and other areas can be combined. For purer plant level applications, such as predictive maintenance, the cloud is not always the right answer.
Edge computing is a new concept that is only now making its way into the IIoT discussion. It provides significant additional options to place compute capabilities for local control, analytics and storage/pre-processing and data filtering for cloud-based IIoT. The Stratus ztC Edge led to several interesting discussions about IIoT-like applications, requirements for simplified installation, resiliency due to remote locations, remote management, automated fault diagnosis and serviceability by non-skilled personnel.
Industrial Companies Make a Move
Large Fortune 500 companies with deep pockets are investing in IIoT technologies. These projects are yielding results, although implementations are not without their challenges. An increasing number of industrial companies are looking at ways to leverage analytics, outside of the traditional applications that helped them on the production side. Some have a clear idea of what they want to achieve, some are trying to understand what various technologies could do for them. What does seem clear is that some form of IT/OT convergence and collaboration is necessary to achieve success.
While there is a lot of focus on cloud and the use of machine learning/AI, there is an increasing realization that edge computing has an important role to play. Edge computing can support a single production line, multiple lines, or an entire plant, each being in remote locations. When it comes to the edge, remote systems are almost always out of the reach of IT and OT, so simplicity, self-protection, remote management, easy maintenance and self-healing are critical considerations.
Want a closer look at edge computing? Check out the Edge Computing Trend Report to discover the evolution that is taking place in edge computing today and the solutions that can help organizations execute on the new opportunities.