Last weekend I found myself needing 1000 jump rings for a project. I made 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, it was easy, and I was up and running in practically no time. I was pretty happy with myself – well for a short time anyway. After a while, my hand started to cramp, the cutters started sticking, and the whole operation slowed right down to a crawl.
After a short while, I had 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 1000 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 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, you can build something quick; however, there is a good chance that if you try to scale it up and out, you will quickly hit a brick wall.
IIoT encompasses many capabilities that are essential when building something that must scale:
- 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 us 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 is no guarantee it can be easily rolled out to other sites. Why? Well, 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 is 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 KPIs (current machine status, total availability, average availability, number of faults, and so on) and 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 quickly deploy across all sites. 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!
