As with any case in which capital expenditures must be approved. The biggest hurdle for the client is ROI and trying to evaluate the payback of the improvement being considered for the infrastructure process or production line. In many cases ROI calculations involving the upgrading of machinery or infrastructure involves the purchase of specific hardware which in many cases is quite substantial. However, utilizing IIoT technologies often has a much lower Investment (I) portion of ROI.
IIoT can help with a multitude of functions of an existing process. For example, IIoT can monitor and visualize individual building blocks or sections of a process utilizing standard VFD, Servo, Stepper or Robotic motion including:
Torque ValuesLocation Downtime
OEE (Overall Equipment Effectiveness)
Remote Data Analytics
Remote Machine Diagnostics
The beauty of this data is that it can provided better clarity to each step within a process flow or a discrete manufacturing line. This data can be utilized to drive predictive analysis and implement machine learning. These algorithms can allow OEM machine builders as well as end users to predict maintenance downtimes and weak areas of the current machine that may need to be pinpointed.
Examples of Returns of your Investment with IIOT
IIoT can be achieved with the simple hardware addition of an edge controller/computer and/or a data collector. This can allow a machine to be monitored with predicative algorithms to prevent and predict downtimes before they happen. Imagine a plant without the need for a 3rd shift maintenance team to keep production going through the night.
If your machine OEE has slipped or if the OEE and production rate target required has moved, do not despair. There is a good chance you do not need a new production line or entire retrofit. Oftentimes there is a way to pinpoint the one bottleneck with the improvement of one small node/ (or step) in the overall machine structure. Or maybe it just requires the user to have the ability to run the machine in a “lights out” environment with the peace of mind, to know that the machine can predict logjams and bottlenecks before they happen.
Stay tuned Next Week for the next installment in our series: Common IIoT Architecture