OEE, i.e. Original Equipment Efficiency is the most common measure used to determine manufacturing losses. There are three key contributing variables to OEE:
- Availability loss (A%)
- Performance Loss (B%)
- Quality loss (C%)
An OEE is represented in % (Percentage) and is the product of A, B and C.
OEE of 100% is considered perfect (I don’t think, this exist anywhere). Most equipment operates with the range of 40% to 80%. There are some examples where the OEE is below 40% in some manufacturing plants. OEE should only be treated as an improvement metrics. It may get tempting to aggregate different types of equipment in a shopfloor environment to generate higher level OEE. But this approach can be misleading and should be avoided unless the processes and production are identical.
Most manufacturers apply TPM (Total Productive Maintenance) principles which comprise of Proactive and preventive maintenance to aim for fewer instances of breakdowns, equipment operating performance and machine induced defects. The TPM approach was developed in the 1960s and essentially has 5S as foundation and eight supporting pillars. A simple google search would offer several websites with good literature and examples to learn more about OEE, 5S and TMP.
Industrial IoT analytics has a lot to offer in Manufacturing. Commonly, there is a target OEE, and then processes are improved to achieve that. Now, there lies an opportunity, what should be the target OEE? How does one arrive at that value? What’s important for your business? Is it Quality or availability or performance? Or a mix of these criteria?
Industrial operational technology data can provide greater insights on the efficiency of the production environment; it can help identify patterns which are not obvious to human observations, e.g. is there any window of time when the part rejects are higher? Is there a specific mix which is affecting the quality? Is handling of specific material causing an impact on cycle time? Are right spare parts being used to prevent sudden breakdown? There are many more questions which IoT analytics can attempt to answer at much faster pace than human-centric RCA observations.
There is tremendous power in just knowing these patterns to make informed process improvements, uncover blind spots and maximize existing operating conditions with the help of OT data and analytics. With predictive analytics, there is another dimension of intelligence that opens several new opportunities to calibrate the operating conditions proactively and gain the competitive edge in the marketplace.