There is quite a difference between knowing in theory how to measure an Overall Equipment Effectiveness (OEE), and actually measuring it in practice. This post will give crucial tips and points on how to measure the OEE on a real shop floor.
As is frequently the case on the shop floor, reliable and good data is rare. This is especially true in the calculation of the OEE since obtaining the required data is difficult. As a result, I usually do not trust any OEE numbers on the shop floor unless I know the calculations behind it. Most OEE numbers calculated are, in my opinion, of such low quality that they are not worth the effort. Hence, getting the data for an OEE is not quite that easy.
In the previous post, we looked at the definition of OEE, including losses of availability, speed, and quality. In this post, we will investigate how to measure OEE. Following posts will detail the top three measures how the OEE is frequently fudged, and what the OEE is good for and what it’s not.
Time Basis for OEE
The first question you have to answer is the time basis of your OEE calculation. Do you want to look at only the planned shifts, or do you want to consider round-the-clock twenty-four hours per day seven days per week?
In most cases in industry, the basis is the shift model. If the operation is scheduled to work two shifts five days per week, then the time basis are those two shifts for five days. However, if your system is short of capacity and unable to meet customer demand, then it may be suitable to start with a 24/7 time basis. Yet, be prepared for opposition from your people. A 24/7 time basis will make an OEE go down, so on paper it looks worse even though the system has not changed, only the calculation. As your people may be judged by the OEE, they may have an interest in looking good with a nice OEE number (and maybe even you too).
In any case, regardless of what your time basis is, you need to figure out what you could have produced in that time.
Speed Basis for OEE
The second important number you need is the speed of the machine. What could the machine produce if everything else around it is perfect? The shortcut approach is to take any number you may have on record. However, in my experience, these numbers are usually incorrect or outdated. They may be simply the average of a good day of production. However, even a good day of production has losses, and taking the average would ignore these losses. They may be based on a theoretical calculation, maybe using MTM or REFA methods, which are often much slower than a good worker and include estimates and personal breaks.
To get a good estimate of the maximum speed of a process is to repeatedly measure the time between the completion of parts. You will find that this time is statistically distributed (don’t worry, we won’t go into the statistical details here). Most times clustered around the average, some took longer, a few took much longer maybe due to a breakdown, and some were faster or much faster than the average. The goal is to get the shortest time possible. However, if you merely take the smallest value in your set of measurements, then you probably got a measurement error. It is better to sort the measurements and take the time where 95% or 90% of all measurements are slower than that time (statistically speaking, the 5th or 10th percentile).
The OEE Itself
Now you have the time basis and the speed basis. Dividing the time basis by the speed basis gives you the theoretical number of parts that could have been produced. For example, if you decided to analyze two 8-hour shifts per day for 5 days, you have a total of 80 hours or 4800 minutes. If your process has a maximum speed of one part every 2.5 minutes, then you divide 4800 minutes by 2.5 minutes per part and get 1920 parts that you could have produced in that time.
These theoretically possible 1920 parts are now compared with the actual production during that time. This number is usually easy to get, as even the worst plants have a rough idea of what they have produced. After all, the operator’s salary may depend on it.
In the above example, let’s say that you have produced not 1920 but only 1132 parts, then your OEE would be 1132/1920 or 59%. That’s it.
Of course, you may also be interested in the losses that caused you not to produce 788 parts during that week. Now it gets a bit more challenging to get reliable data.
Digital Data Monitoring
If you’re lucky, you may have a digital data monitoring system on your machine that makes an automatic protocol of what the machine does when. However, in my experience, even if there is a digital data monitoring system, it usually does not have enough data to calculate the losses reliably. For example, quality problems can only be detected afterward and will not be logged in the system. The process may not know if a stop was due to a missing operator or a missing machine. In all likelihood, with a digital data system you still have gaps and need to find out at least some details in a different way.
Probably the gold standard in OEE measurements is manual observation over a longer period. In this observation, a separate worker is standing next to the machine to take a protocol of when the machine is doing what. He or she should write down any irregularity (e.g., when the machine stops, the operator is absent, material is missing, or products are scrapped). This will probably be the most reliable data you can get. However, this gold standard comes with a gold-plated price, as you need to pay for one operator round-the-clock to stand next to the machine to take a protocol.
If you nevertheless want to do this manual observation, here are a few practical tips:
- Prepare the data monitoring very carefully, possibly with some shorter test runs. It will do you no good to have three days’ worth of data that is missing crucial information.
- Include the unions and operators. Observing a machine alone should be no problem, but few machines are unattended. In effect, you are making a round-the-clock protocol of what the operator does and doesn’t do. Get the operators and unions involved so they understand what you are doing and why. Never do it against the will of the operators, as they have the ability to mess up your measurement beyond usability. It helps if the person monitoring is also an operator and colleague, not an outsider or supervisor.
- Be sure why you are taking the OEE. This effort in obtaining the details to the OEE losses is only useful as a first step if you plan a project to improve said losses as the second step. If the only reason for taking the OEE is because someone higher up wants a number, then there are much cheaper ways to get a number (Chinese plants, for example, are well known for simply guessing the right number). Of course, I would never suggest the possibility that this may have happened in Western industries too. 😉
Probably the most common way in industry to determine the details on the OEE losses is operator records. The machine operator takes a protocol of disturbances, which are then converted into digital records and analyzed. For practical reasons, the operators record only disturbances above a certain length (i.e., if there is a stop in excess of 5 or 15 minutes). The advantage is that this data can be obtained with little effort, only the analysis of the data requires some time. On the downside, all stops below the cut-off time are ignored and the quality of the observation may vary from operator to operator.
Making Sense of It All
During the calculation of the OEE, getting the available capacity and the produced parts are probably easiest. Quality losses are also often available or can be obtained by manual observation or operator records. Availability losses are a bit shakier, but manual observation or operator records can also usually give a good picture.
The trickiest part is the speed losses. Operator records and manual observations have difficulties picking these up. What we know for sure is that they have to be the remainder of the gap between the available capacity and the produced parts. In the example below, even if we would not know the speed losses, we could easily conclude that they are 10%, since these 10% are simply the remaining gap. While this tells us the size of the speed losses, it doesn’t help in detailing the causes of the speed losses. Unfortunately, these speed losses are rarely marginal and can easily make up 30% or 50% of the total losses. In this case, you have to dig deeper into the speed losses of the machine.