IIoT (Industrial Internet of Things) in its fundamental form is not new to manufacturing; just replace the word “Internet” with “Intranet” and you get my point. We connect to industrial devices like PLCs and Control Systems, which are connected continuously to equipment, sensors, thermocouples, meters, etc. They control, monitor, and report information back to HMIs, internal web-sites, and KPI dashboards providing reports on many devices. What IS different is our potential ability to connect to and access this information from mobile devices through the cloud (internet hosted systems).
Here is the rub. There is a lot of hype around IIoT; there is no denial here. Its potential is enormous, but what it takes to provide the “things” (or data) is glossed over. It all looks and sounds reasonable and it is, however, the questions are what will it cost, what information do we need, what is meaningful, what is actionable, and what is usable? Ask this of several people within the same organization and you get differing answers. Defining and then designing these solutions takes infrastructure, and infrastructure takes investment and commitment of both time and attention. In reality there is no “easy button,” but you can get great benefit. Unlike the more personal IoT (Internet of Things), controlling industrial systems remotely brings safety, intellectual property, and cyber security into question, as evidenced by recent cloud based service interruptions. Hence the industry is hesitant and slower to move in that direction.
So, how do we start preparing for IIoT even if only for the extended “Intranet” version? Let’s use OEE (an key performance indicator for Overall Equipment Effectiveness), as an example of connectivity to these manufacturing devices. OEE provides a great ROI if implemented properly – as a continuous improvement tool. A PLC is the device typically used to connect, control, and monitor line elements like conveyors, motors, and other equipment. To apply OEE and get great visibility does not require a huge investment. At minimum you can get a run signal for a line based on a combination of conditions and some product counter sensors . Add to that some disciplined human intervention on the shop floor for assigning appropriate downtime reasons and you have the makings of a good and accurate system that you can slowly grow into with additional input/output connectivity as time and ROI permit. In a well-implemented system an example of some information that can be gleaned from such a system for improvement is:
- The top reasons for downtime by shift, line, and product
- The top reasons for scrap by shift, line, and product
- Standard OEE efficiencies like
- Changeover time by Line and product
- Sanitation time by Line (and maybe product)
- The frequency and duration of above etc.
To prepare for IIoT, we can start with the end in mind. In the case of OEE, what if I could extend my analytics to?
- The top reasons for downtime by plant location
- The top reasons for scrap by plant location or raw material vendor or supply chain
- Comparative standard OEE results by global region
Whether we utilize the Industrial Intranet of Things or the Industrial Internet of Things, we now expect 24/7 mobile access to reliable data delivered to match the decisions we need to make and the way we want to work. OEE is just one example and just the beginning.