Revisiting Overall Equipment Effectiveness

A metric like this one can help you evaluate machine performance and make improvements when necessary.


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There is no doubt that manufacturers need to be aware of how their machines are performing. While there are many ways of doing this, I have found the Overall Equipment Effective (OEE) metric to be the most useful. Certainly OEE is not new, and there are many companies already using it to better understand the functionality of their machines. Yet some are still reluctant to employ the metric, believing it is too demanding to manage, requiring too much data collection and analysis. No doubt an accurate OEE metric does require accurate data, but this data is something we should be gathering anyway if we wish to get the most out of our key equipment. The good news is that monitoring equipment and software are available to make the data collection and analysis simpler today than ever before.
Fundamentally, OEE incorporates three key components of machine operation, namely machine availability, performance efficiency and rate of quality.
• Availability can be described as the percentage of time a machine is actually available for use when it is needed. The two biggest causes of machines not being available when needed are unplanned downtime (breakdowns) and job-to-job change-overs. Each of these factors significantly impacts our ability to keep a machine running. A breakdown is considered to be the total loss of a machine’s functionality or its ability to run safely. Worn out or damaged mechanical, electrical or electronic parts are common causes of breakdowns, although misapplication of tools, fixtures and auxiliary equipment can have similar effects. Change-over actions of varying degrees are necessary whenever one job ends and another has to start.
The availability percentage is calculated by dividing the time the machine actually runs by the time it is supposed to run. For example, on a one-shift operation, if the machine is scheduled to run for 8 hours but only runs for 6.5 hours, the availability is 81 percent, meaning that 19 percent of the shift is lost due to breakdowns or change-overs.
• Performance efficiency indicates how well the machine runs when it runs. If the machine stops frequently during a run to make adjustments, load material, change tools, reposition (or even relocate) a part, or remove chips and scrap material, performance efficiency is impacted. Some of the stoppages may be planned, such as a programmed cycle stop, but still result in lost time. While not considered a breakdown, unplanned stops, like jam-ups and tool breakages, lead to even more lost time.
The easiest way to calculate performance efficiency is to divide the number of parts made during a given period of time by the number of parts that should have been made during that time (excluding any time allocated to true breakdowns, which are included in availability losses). So if 10 pieces should be made per hour but only 9 pieces are actually made, the performance efficiency is 90 percent, meaning 10 percent of the time was lost due to the machine stoppages.
• Rate of quality is the final OEE component that represents the percentage of parts made right the first time. Parts that need to be scrapped obviously impact the rate of quality, but so does subsequent work required to bring parts into conformance after they are machined.
So, the rate of quality calculation is simply the number of good parts produced divided by the total number of parts produced. For example, if 100 parts are made, but five are scrapped and three need to be reworked or repaired, the rate of quality for that machine is 92 percent. Therefore, 8 percent of the time was spent making non-conforming parts.
Once the availability, performance efficiency and rate of quality percentages are determined, they are multiplied together to produce the final OEE metric. Using the previous examples, availability of 81 percent multiplied by performance efficiency of 90 percent multiplied by a rate of quality of 92 percent generates an OEE metric of 67 percent. In this case, a machine is producing needed parts of acceptable quality 67 percent of the time, while one-third of the time is lost for a variety of reasons. Perhaps more importantly, this OEE metric shows the need for improvement in this machine’s overall performance.
Those who believe that using the OEE metric is too demanding would probably be satisfied using a simple “uptime” metric, such as the availability component of OEE (81 percent in the example used). Yet such a focus can be misleading, because an “uptime” of 81 percent may be considered acceptable even if the machine is not running the way it should be nor providing the quality required.
OEE offers the benefit of an expanded view of machine performance, a view that is necessary if we are serious about finding ways to improve that performance.