How the IIoT Can Help Scale Your Operations

By Salma Ghafoor, Global Technical Lead – IOT, Rockwell Automation

I recently I found myself needing 1,000 jump rings for a project. I made a bunch of coils and then got out a pair of handheld snips and started cutting the jump rings off the coil, two or three rings at a time. It was quick and easy, and I was up and running in practically no time.

I was pretty happy with myself — for a short time anyway. After a while, my hand started to cramp, the cutters started sticking, and the whole operation slowed down to a crawl.

Soon, I’d had enough and figured I would invest in a ring-cutting machine. I researched and found one that best suited my requirements, waited for it to arrive, set it up, and, within a few minutes, I had my 1,000 jump rings. Plus, I found an unexpected advantage in that the cuts were much cleaner and flush, which increased the quality of my finished product.

Picking the Right Tool for Scalability

So, what does my ring cutting machine have to do with Industrial Internet of Things (IIoT)?

Well, the way I see it is that the IIoT can be the right tool for the job if you have a use case that you want to deploy across your entire enterprise. You can use existing tools and build something quickly; however, there’s a good chance that if you try to scale it up and out, you’ll hit a brick wall.

The IIoT encompasses many capabilities essential when building something that must scale, such as:

• Connectivity: Interact with any type of data source or system of record.
• Security: Secure and flexible authentication and authorization options to match your requirements.
• Data Model: Create common information models that correlate and contextualize data.
• Visualization: Offer intuitive and interactive dashboards for presenting the information.
• Orchestration: React to patterns in the data and create automated workflows.
• Accessibility: Control visibility and accessibility to the right people at the right time in the right format.

Let’s say, for example, you want a way to compare machine availability across lines, sites and even geographical regions. You can build a solution for one site. However, there’s no guarantee it can be easily rolled out to other sites, because of the variations that inevitably exist at plant-level. We all know that, just like fingerprints and snowflakes, no two sites are the same.

Plus, it’s highly likely that each machine has a level of flexibility and customization that will prevent a simple roll-out.

Building a Common Information Model

Instead, another approach would be to map out the high-level key performance indicators (KPIs), such as current machine status, total availability, average availability, number of faults, etc., then use these to build a common information model that would apply to all machines irrespective of machine or plant level variations.

In this way, you can build KPI calculations, dashboards and reports against the common information model and not an individual machine or set of machines.

By creating this abstraction layer, separating out the physical hardware from the solution, you can deploy across all sites quickly. With such a framework, you can build a solution that exactly suits your requirements, whether it be something small, one plant use case or something that you must roll out across your entire enterprise.

As it turns out, I need another 10,000 jump rings for my project, and I can tell you I am well pleased that I looked beyond my handheld cutters and invested in the right tool for the job.

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