Apr 15 2012
The aggregate metric for equipment most often mentioned in the Lean literature is Overall Equipment Effectiveness (OEE). I first encountered it 15 years ago, when a client’s intern who had been slated to help on a project I was coaching, was instead commandeered to spend the summer calculating the OEEs of machine tools. I argued that the project was a better opportunity than just taking measurements, both for the improvements at stake for the client and for the intern’s learning experience, but I lost. Looking closer at the OEE itself, I felt that it was difficult to calculate accurately, analyze, and act on. In other words, it does not meet the requirements listed in Part 1.
The OEE is usually defined as follows:
OEE = Availability × Performance × Quality
A perfect machine works whenever you need it, and is therefore available 100% of the time. It works at its nominal speed, and therefore its performance is 100%, and it never makes a defective product, so that its yield is 100%, and so is its OEE. The OEE of a real machine is intended reflects the combination of its failures to live up to these ideals.
The first problem is the meaning of availability. When we say of any device that it is available, we mean that we can use it right now. For a production machine that does one operation at a time, it would mean that it is both up and not in use. The main reason for it to be unavailable is that it is busy, which really shouldn’t count against it, should it? In telecommunications, availability for a telephone switch is synonymous with the probability that it is up. This is because it is supposed to be up all the time, and to have the capacity to handle all incoming calls. In principle, it could be unavailable because of saturation, but the availability formula does not even consider it. It is only based on uptime and downtime, or on time between failures and time to repair.
But a lathe doesn’t work like a telephone switch it at least two ways:
- It is rarely expected to work all the time: it may work two shifts a day, five days a week, and, whether it is down the rest of the time has no effect on performance.
- If you have one work piece on a spindle, you can’t load another one at the same time, and the spindle is unavailable.
In the OEE context, we are not talking about the machine being available in the sense being up and ready to take on a new task but instead of time available to a scheduler to assign it work in the course of a planning period, which may be a shift or a day, or whatever time interval is used for this factory.
If, in a 480-minute shift, a machine stops during a 30-minute break and has up to 60 minutes of unscheduled downtime and setups, then the planner can count of 480 -30-60 = 390 minutes in which to schedule work, which yields a ratio of: Availability = 390/480 = 87%.
This assumes that the machine’s ability to do work is proportional to the time it is up. My first moped as a teenager was a hand-me-down from a relative that had been garaged for 7 years. It started fine when cold, but the spark plug started malfunctioning once it was warm, about 15 minutes later. It would stay up for 75% of a 20-minute ride but that didn’t mean it completed 75% of the rides. It actually left me stranded about 100% of the time; it was unusable. Likewise, your link to a server may work 99% of the time while uploading a large file and break every time you try to save it. The formula makes it look as if it has 99% availability when in fact it is 0%.
There is also an issue with deducting setups from available time, because, unlike breakdowns, it is not just an issue of the technical performance of the machine but is directly affected by operating policies. The planner can influence the amount of time used for setups, reducing it by increasing the size of production runs or, if setup times vary with all pairs of from- and t0-products, by sequencing them so as to minimize the total setup time.
This is not to say that the formula is wrong but only that it commingles the effects of many causes and that its relevance is not universal. There may be better ways to quantify availability depending on the characteristics of a machine and the work it is assigned. Companies that calculate OEEs often do not bother with such subtleties and simply equate availability with uptime.
Performance is a generic term with many different meanings. As a factor in the OEE, it is the ratio of nominal to actual process time of the machine. If the machine actually takes two minutes to process a part when it is supposed to take only one, its performance is 50%. The times used are net of setups and don’t consider any quality issue, because quality is accounted for in the last factor. This factor is meant to account for microstoppages and reduced speeds, and it is a relevant and important equipment metric in its own right.
As discussed in Part 2, Quality is not a metric but a whole dimension of manufacturing performance with many relevant metrics. In the OEE, this factor is just the yield of the operation, meaning the ratio of good parts to total parts produced. It is not the First-Pass Yield, because reworked parts are still counted as good.
Conclusions on the OEE
While the OEE summarizes metrics that are individually of interest, not much use can be made of it without unbundling it into its different factors. Since the meaning and the calculation methods for its factors vary across companies, it cannot be used for benchmarking. Within a company and even within a plant, it is not always obvious that raising the OEE of every machine enhances the performance of the plant as a whole.
In principle, it should. Who doesn’t want all machines to be always available, perform to standard, and make good parts? The problem is that, in practice, increasing the OEE is often confused with increasing utilization, and that there are machines for which it is not a worthwhile goal. Such machines may be cheap auxiliaries to expensive ones, like a drill press following a large milling machine in a cell, or they may have been bought for their ability to take on a large variety of tasks on demand.
Unbundling the OEE into its component factors yields a more easily understandable set of equipment metrics that is less likely to mislead management. While these metrics can be collected on each piece of equipment, management must then be wary of aggregating them over machines that are intended to be used differently.