Metrics in Lean – Part 3 – Equipment

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:

  1. 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.
  2. 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.

12 comments on “Metrics in Lean – Part 3 – Equipment

  1. Comment in the Lean CEO discussion group on LinkedIn:

    Hi Michel:
    I think you have hit it out of the park with this post. In my experience metrics are often collected for the sake of collecting metrics without a detailed analysis of what you are collecting and for what reason you are collecting them. Often times they are collected because some other company is collecting them, a customer requires you to collect them, or to be in compliance with TS or ISO registrations. Thanks Michel for highlighting this.

  2. Comment in the Lean manufacturing & Kaizen discussion group on LinkedIn:

    Hi Michel
    I have seen OEE as a critical KPI for various companies, and the biggest flaw I have seen relates to building excessive inventories to maintain performance. There is another measurement to combat this which is the capacity factor which deducts periods waiting for orders from the available time, and then counters this portion of downtime keeping OEE high.
    I have found OEE as a handy measurement for a snap shot on daily performance, though I currently report on quality and downtime instead which works without the OEE figure

  3. Comment in the Lean CEO discussion group on LinkedIn:

    Hello Michel,

    I enjoyed your article.
    Well done.

    During past work with TPM I ran into some of the same Blocking Assumptions made by others you have presented in your article.

    I always spoke to Uptime, never Downtime for a simple reason.
    Always spoke to idled equipment as a good condition, because we were not making un-necessary inventory and the condition of Pushing not Pulling.

    Here is one for us to ponder.
    The term availability and your comment to the piece in the spindle; once the frame of reference changes from the machine to a component part of the machine i.e. spindle you are speaking in different terms for OEE. The spindle itself can have an OEE.
    You wake up the mind in a good way with your comments of the spindle.

    I noted you used terms like “it is up”.
    In my minds eye 2 conditions must be met. Operability and Availability
    Availability = The piece of equiment in question is not performing work at this time
    Operability = The piece of equipment is available to perform work and will functionally operate as designed.

    Thanks for the read. Great thinking you have Michel!

    Todd McCann

  4. Comment in the Lean CEO discussion group on LinkedIn:

    I agree. Most opportunity for improvement is masked because teams are being held to a measure that doesn’t expose things that can be improved. When the schedule is used to determine uptime, it’s easy to hide poor performing machines with shuffling the schedule. Keeping everything visible in e right OEE metrics shows where opportunity to meet demand still existed in spite of some downtime and where true machine performance vs other execution opportunities exist.

  5. Comment in the Lean manufacturing & Kaizen discussion group on LinkedIn:

    Hi Michel,
    As a long time TPM practitioner I have seen many companies collect OEE 24/7/365. It’s a waste of money and time. OEE is part of the PDCA cycle for equipment. Collect the 20 data points, what causes the problem, work on it ala A3 or whatever you use, check again and see if an improvement was made, do it again. Collecting data for the sake of collecting data is one of the wastes we don’t name when we go down the “list”.

  6. In general, frontline employees need to manage the metrics over which they have direct control. And, the more disaggregated, the better. So, managing the discrete elements of OEE are always preferable to rolling up three metrics to create an overall picture of “efficiency.” In any case, if the OEE result is below the target, to get to the root cause a team must inevitably decompose the elements to understand where performance can be improved.

    The company that I see touting OEE the most seems to be McKinsey, which scares me because it makes OEE feel like a metric invented by management consultants determined to oversimplify complex operational performance issues onto a dashboard or single PowerPoint slide for a high-level summary to the CEO.

    Except, when I looked into it, OEE wasn’t birthed by management consultants.

    It was developed by Seiichi Nakajima, one of the people credited with developing modern Total Productive Maintenance (TPM), and deploying this practice successfully across Nippon Denso, a key Toyota group supplier.

    Which is not to say that everything that emerges from Toyota is gospel, or shouldn’t be challenged, refined, and improved.

    Which is the spirit of Michel’s post. How can we take the intent behind OEE and use that to drive good behavior, create positive incentives on the shop floor, and promote a kaizen culture.

    Michel did a great job pointing out how OEE can fall short. The reality is that OEE is out there, and is attractive for its simplicity. How do we refine and improve OEE so it is utilized properly, since we’re not going to put the genie back in the bottle?

    As a tool and metric that emerged from the early days of TPS development, I’m biased to say that OEE still has some place in Lean management system. However, OEE may benefit from being modernized per Michel’s suggestions, to reflect the current state of general management in which OEE is used. I’m confident that Nakajima-san never intended for OEE to be used in the way that McKinsey and others recommend.

    The same can be said about all TPS tools and metrics, which can stand to be updated, refreshed, and modernized to reflect the world in which we live – rather than always looking backwards to a set of historical circumstances which prompted discrete innovations to solve specific operational problems. All too often, the Lean community spends too much time looking backwards in the mirror, at where we came from, and not enough time thinking about the current situation, much less where we are going.

    I say this as someone who worked at Toyota for several years, and watching their North American operations struggle to grasp nuances of TPS after 20+ years — because they, too, were copying.

    • Karthik:
      Yes, Nakajima discusses the OEE in his Introduction to TPM, and the OEE (sogokoritsu) appears in some but not all the Japanese literature on TPM. Why are you saying that Nakajima was not a consultant?

      My understanding of TPM is that it was a consulting product from the get-go. The story I heard that people in a room at consulting firm JMA decided to offer help in the area of maintenance and decided to call this service “TPM” before figuring out what it would consist of. In other words, TPM, like Hershey Hugs, was a marketing name set before the corresponding product was developed. There is a short chronology of TPM on the JIPM website.

      • I don’t know if Nakajima was a consultant or a Denso employee, but the work on TPM pre-dates by several years JIPM’s marketing efforts was doing to promote TPM. It may not have been formally codified and branded as TPM until the early 1960s but the practice of TPM was well underway at Toyota in the early 1950s.

        As Kris points out, OEE can be relevant if applied correctly to the bottleneck process in the value stream.

        Similarly, every Lean tools has potential to do damage if misapplied in isolation or lacks the proper Lean value system and management culture in which to operate.

  7. I did a lot of studying of what OEE is when my company started pursuing it’s use a few years ago. I started my research on the topic from the perspective that I absolutely despised OEE in all its forms. I couldn’t figure out where it came from and how anyone could be calling it “lean”. It seemed obvious to me that it was a direct conflict.

    What I discovered is that I believe the origins of OEE are much different than the current general application. The one valuable use of OEE comes when you apply its use in conjunction with Theory of Constraints. Measuring OEE at the bottleneck and holding the entire value stream responsible for the effectiveness of operations at the bottleneck could have the effect of focusing everyone on supplying the bottleneck with good parts continuously and ensuring that no parts are scrapped after the bottleneck. It would theoretically drive you to flow product to the bottleneck without interruption and then pace the entire value stream to that bottleneck to maximize throughput. I haven’t actually seen it work this way but I talked to a couple consultants that would swear by its use to drive this cultural change. It was pretty funny when I finally figured this out. I realized that I was talking to and reading articles from people who were vehemently defending the use of OEE but the OEE they were talking about had absolutely no similarity to OEE as it is generally defined (even the accepted definition on Wikipedia…).

    The two versions of OEE are calculated the exact same way but their application and use are completely different. The original users who will vehemently defend it, use it as a tool to focus an entire value stream on the bottleneck. Most current applications use it as a benchmarking tool where it should be used on every piece of equipment. When you use it at the bottleneck, OEE can focus an organization on total throughput to the customer as the effect of availability loss, performance loss, and quality loss is all the same – lost sales to customers.

    The other users (most) use it as an agregate benchmarking tool to identify problems. These individuals may defend results gotten from the use of OEE but they have no idea how they got those results.

    Since then, I treat people trying to use OEE the same way I treat people with pit-bulls. I assume that they are up to no good. I know that many pit-bulls have owners who love their dogs and they train them very well. Unfortunately, the other 95% of people with pit-bulls are training them to tear things apart. So when I come across someone with a pit-bull, I try to keep my kids away from them. It’s the same with OEE. I know that there are some people using it for good but 95% of people are using it in a way that will tear a company apart. So when I come across and OEE advocate, I try to keep my company away from them.

  8. hi Michel,this blog is very insightful..but more often than not I have seen various MNCs taking up Lean concepts for the sake of doing..they are not focusing on the essence of lean..likewise for personal KPIs OEE is monitored differently in MNCs..also whether the MAE is able to deliver more output or not its OEE keeps on increasing by playing with numbers..

Leave a Reply

Your email address will not be published. Required fields are marked *