Most of my readers probably heard about the machine effectiveness measure of OEE, short for Overall Equipment Effectiveness. It is a composite measurement of the effectiveness of a piece of equipment, composed of three elements, i.e., availability, performance and quality. And I regularly see this indicator on the shop floor, posted near or on multiple machines. But when I try to engage a shop floor discussion on its actual use in improving operations, I am often disappointed. A blog post on four reasons why I dislike OEE for shop floor control and improvement.
1. OEE Hampers Learning and Improvement
As already mentioned, Overall Equipment Effectiveness or OEE is not one measure, it is a composite measure. It is based upon three underlying measures, i.e., availability, performance and quality. Availability measures the ratio of operating time and planned opening time. It focuses on planned and unplanned losses like changeovers (planned) and breakdowns (unplanned). Performance focuses on speed losses and short stops and is the ratio between actual and theoretical output during the operating time. Quality, finally, is the ratio between good output and the actual output signifying losses related to rejects for instance.
Now when we show a composite figure based upon three other figures, clearly, it is very hard to understand what the actual voice of the process (VOP) is. What is a poor OEE telling us? I don’t know. You always must dig deeper. So, it means you always must track the individual values related to availability, performance and quality anyway. If not, OEE doesn’t teach us anything. Due to its composite nature, a stable OEE also does not imply a stable process. One effect can cancel out another. So, it doesn’t help us learn and improve.
2. OEE Scares Employees Away
I am a big fan of shop floor control by the team members themselves. How well a process is running should be answerable by the team itself. Not by some office analyst that needs to be called down to the shop floor to explain. I already blogged about this in my “Report In Syndrome” post here (https://dumontis.com/2015/03/daily-huddle-report-in-syndrome/).
But as OEE is a composite measure, and even its components subject to many rules on what event and time to allocate to which category in the OEE definition, associates are often put off by the complexity and unclear allocations typically part of the OEE measurement system.
Associates subsequently get lost, book events in wrong categories and, more generally, don’t really understand the figure in the end. They disconnect from it. Most of them have lost interest and don’t bother to review it anymore and consequently also don’t pay attention anymore to how to book time in the first place. At that moment, OEE has become “a thing management has asked us to do”. And it has lost its purpose, if it ever had one.
3. OEE Is A Local Measure
As already mentioned, the fact that you can show a “good” OEE figure as such, does not necessarily mean anything to the overall objectives of the value stream. It could be, but it isn’t necessarily so. We should never forget OEE is a local measure; a measure of a single piece of equipment. And yes, I understand you can even aggregate OEE across machines, departments, sites, … How wonderful, but what are you learning from it?
A high OEE in the end only means that you got more output from the machine in the reporting period. But whether it was output that was actually needed and in the right mix? OEE doesn’t tell you anything about how the result actually contributed to better flow, improved reliability, shorter lead times and lower costs. It is a local measure, not directly connected to your company objectives. Don’t forget this.
4. “World-Class OEE” Is A Fallacy
Sometimes I stumble upon companies that set OEE targets of around 80-85 percent, stating that these are world-class OEE levels. To achieve the set targets, sites and teams regularly try and game the system. They favor running larger batches and overproduction, thereby reducing changeover and startup losses, positively impacting the quality and availability components of the OEE indicator. Or they speed up the machine and book possible rejects as product awaiting decision, another version of the product (e.g., the spare part or private label version) or even, by definition, as good product until final inspection (maybe days later) finds possible defects. OEE figures therefore should always be taken with a few grains of salt.
As often is the case, the moment you set an objective or a benchmark for a certain indicator such as the OEE, the indicator will fail to fulfill its initial purpose, i.e., serve as an instrument to understand the process and to enable its improvement.
This also impacts the use of OEE as a popular benchmark figure across machines and locations. OEE data should always be approached with the greatest suspicion. Furthermore, as I also argued in an earlier post here (https://dumontis.com/2016/07/muri-mura-kingman/) on the continued relevancy of Kingman’s formula, the level of utilization of a piece of equipment should be in line with the level of variation it experiences. If not, your lead time and inventories will be higher than what you have asked for. But hey, “great OEE you got there on your location… But what was it we were trying to achieve overall?”
Break-Down the OEE Measure
My advice therefore is to break up the composite OEE measure into its individual components. Shop floor measures, like those that relate to the operations on a machine, should be easy to collect, represent and interpret; by the team members on the shop floor. Focus therefore should be on the individual loss categories (the “waste”) that we try to eliminate. We all want less breakdowns and unplanned downtime, we all want to reduce stops and rejects. So, measure those losses, show them and act upon them!
And don’t get yourself lost in the traditional utilization and benchmark game. As Goldratt already mentioned, there is a clear distinction between “activitation” (traditional “utilization”) and real “utilization” (based upon the Latin word “utile”, meaning useful, beneficial and profitable).
Hi Rob.
Your comments and reasons to dislike OEE are common with most production organizations. (I contend that over 75% miss use OEE in many ways; aggregating machines, and shifts, weeks etc.). What if we turn the question around and ask ‘How can OEE be valuable?’
As you have indicated true OEE is really the throughput number relative to ‘ideal rate’. I contend that the only true benchmark is when you look at the same machine running the same process and product. Then you can draw good information on improvement when you construct and compare OEE performance curves by product run. (Auto data collection allows one to investigate the specific losses by category, but the business impact is mostly interested in the throughput number – assuming quality rate is about average). The OEE performance curves by product run should be used to set SMART goals for operation people to achieve because they are specific and relevant.
In the bigger picture, the granular information for OEE can be used to link to the Financial information by machine and by product, I call this Financial OEE which is ‘what Income From Operations (Profit) was made on this machine/product divided by what IFO could have been made if the throughput was at World Class?’ (At the very least, you could use the historical ‘Best Ever’ performance.) This analysis is done using Throughput accounting and focuses on the constraint machine. For simplicity, start by thinking of stand alone manufacturing machines making finished (sold) product.
Bottom line, I have developed a methodology that links OEE with financial information by product/machine which gives vision to the Business impact for better business decisions and competitive advantage. This ‘new light’ is also powerful information for operations planning (which machines should run which products) and for Sales and marketing strategies. For more info: http://www.oee-college.com
Hi Rob, in our improvement methodology we introduce metrics for the shop floor to really engage the operators there. As an important measure we use Mean Time Between Failure as a metric for our unplanned stops. This is one of the first metrics to focus upon as we want to restore the machines into base condition and eliminate unplanned stops, and then the Quality loss and Planned loss decrease will follow naturally as we progress through different phases of maturity.
I like Shingo – Utilize the men not the machines. OEE as an improvement benchmark really only applies to the quoting of new work. The investment to obtain the equipment on the shop floor is already spent. The loan payments
do not change in amount based on whether OEE is high or low.
A very concise summary of the typical disconnect between measurement and action in any industry. OEE, on paper, is a means to bring collars of all colors together to drive action. Unfortunately for some, OEE is seen as a pulse monitored and reported by a group well removed from the daily operations. All measurement is an interpretation of reality, and does not immediately impact change without a sound plan of attack. This plan must include the constant monitoring and ownership at the point of collection the factors that feed OEE–with clear contingencies for optimization.
interessante riflessione! grazie!. in realtà penso che OEE sia fondamentale come approccio iniziale per capire come sta funzionando un centro operativo o una risorsa. ogni strumento deve poi adattarsi alla propria organizzazione (e questa è la nostra fatica e il nostro lavoro) in modo che possa essere sempre SMART.
OEE è utile perchè fotografa una situzaione e indirizza un campo prioritario. altri strumenti poi individuano il segmento da migliorare. Comunque la riflessione è interessante!
The OEE sheets we’ve used in the past had a comments section as well as columns for down-time allocation. Agreed, times are not always written in the correct box. The gathering of the qualitative data written in comment area can be very powerful if put into a pareto. A broken drill or door sticking may be one of the biggest problems which can be quantified by time study. I see no reason to track OEE unless the work teams are willing to address opportunities when they are discovered.
It’s all in how you use it. I agree OEE is not the best tool in the toolbox but it is not why the employees fail to learn or improve. That would be the Leadership teams fault. OEE is a bit more tricky to calculate than things like TAKT time. But it is the responsibility of the Leadership team to constantly teach, mentor, and coach their employees. What I love about Lean is anything is possible if you create the proper mindset and influence the culture to change for the positive. Not all lean tools will work for everyone the same way. I’m sure many companies have benefited from using OEE. Also, the tools aren’t what teach the employees and help them improve. Again, that’s the Leadership teams job. The tools help you see and in theory yes will teach you about the process and may show you “where” to improve but none of that matters if your team is not fully engaged. And to achieve that, again, you need proper leadership. Spend time thinking about how it will work and avoid waisting time proving why something doesn’t work. You will find way more gains that way. I know this as I have done this with my teams. And it worked!
1 – It’s a composite measure… it is a positive characteristic from my point of view, if the OEE is low, then you can investigate about the causes. If the value satisfy your goal, then you are sure than you are producing without machine downtime, at the right speed, at the right quality.
2 – as the previous point, the fact that it is a composite measure is not a limit
3 – OEE is a local measure : yes …and what is the problem ?If you want a measure of the total value stream you don’t use OEE , it is useful for others scope.
4 – “World-Class OEE” Is A Fallacy : really ? and why ? if you have an equipment that works with an OEE of 80/85% ….it is a great result….it is not a fallacy, it is the reality. If you drug the system to reach great result the problem is not the measure, but the class of managers is the problem.
Venanzio,
I would just like to cite Shingo on the 85% fallacy:
“The machine-output ratio at Toyota is two to three times that of similar companies. Indeed, for the same level of production, Toyota has far more equipment than most other companies and this is one of its strengths. At Toyota, lower machine operating ratios are preferred to idle time for operators”.
Think about it. It has to do with a preference for flow instead of efficiency. Also read my post about Kingman if you haven’t done so.
Best regards, Rob
Thanks for quoting Shigeo Shingo. Best way to proove your points need some twicking: no people are not disappointed with machine Oee, they are proud of their own improvements. Lean and oee are an excellent match. By reducing waste, you reduce performance losses, improve oee and make proud operators.
I agree with the author that at times the race to get a perfect OEE score can lead to negligence of other productivity measures but still OEE provides a great insight into the Machine Downtime Tracking and the reasons leading to the frequent unplanned incidents. It also provides the necessary and synthesized information about which part of the production system is not working properly and causing the maximum loss and therefore although considering OEE may not turn out to be the only principle of for better production discarding it completely would also not be a good approach.
Righty said “Robert Hansen” most of the people miss use OEE in many ways. Collecting proper data for OEE calculation is very important. Only a well defined and arranged data can help in a right manner to increase the productivity. Though, I have used http://www.downtimecollectionsolutions.com for OEE calculation and tracking downtime for my Ice cream factory.