Is OEE (overall equipment effectiveness) as relevant a metric in today’s digital age as it has been in the past?”
This question was posed to me recently, and I believe it is a legitimate one. I say this even though I’ve been an advocate of using OEE as a measuring stick ever since I helped implement the lean manufacturing program at Johnson & Johnson more than a decade ago.
There has been heated debate for many years over the value of OEE, and I’ve found myself at times in some contentious discussions. Concerns about the lack of quality data sometimes used, the so-called “fudging” of numbers, and how OEE scores can be misused or misrepresented have resulted in disagreement among manufacturing experts on the merits of this tool.
My answer remains: Yes, OEE is incredibly relevant today. In fact, I would say that its relevance now is as high or higher than it has ever been, because it remains a universal benchmark that applies across industries and countries.
We may add artificial intelligence, machine learning, virtual reality, even robotics, to our industrial tool set, but these will not reduce the need for calculating availability, performance and quality—the components of OEE.
Plus, we have a much deeper understanding today of what these underlying factors represent. OEE is embedded today within most major manufacturers’ maintenance improvement initiatives. At an automotive plant such as Toyota or Honda, you’ll likely see OEE on supervisors’ dashboards and on monitors throughout the plant floor. While it may no longer be the most talked about metric, OEE remains steeped in the consciousness of most manufacturers seeking continuous improvement.
On pages 28 and 30, I add more context to this unwavering support with my five recommendations for using OEE. But, first, let’s do a quick refresher.
Defining OEE: Same as it Ever Was
OEE has its origins in the early 1980s as an outgrowth of the lean manufacturing movement and as part of the total productive maintenance (TPM) methodology adopted at many automobile and other manufacturing companies.
It continues to be calculated by this equation:
Availability x Performance x Quality = OEE score
Here is a breakdown of the data used in this score:
- Availability = Run Time / Planned Production Time
Availability accounts for all events that stop planned production long enough to qualify and track as downtime.
- Performance = (Ideal Cycle Time × Total Count) / Run Time
Performance factors in anything that causes the manufacturing process to run at less than the maximum possible speed.
- Quality = Good Count / Total Count
Quality accounts for manufactured parts that do not meet quality internal or external standards, including parts that need rework.
The percentage of each top-level variable is multiplied to determine the overall score. As an example, an OEE score of 100 percent represents perfect production, which means manufacturing great products, as fast as possible, with no stop time (even for bathroom breaks).
A score of 40 to 60 percent is average and more typical, whereas 85 percent is considered exemplary and world class. Regarding overall scores, however, please see my caveats below.
5 Tips on How to Use OEE in Your Workplace
1. Pay attention to the underlying data, as much as you do the overall score.
I often come across cases where manufacturers score, say, 70 percent on a given day and then 70 percent again on the following day. They are pleased at the consistency.
But is this true consistency? Often, it is not.
For example, the first day may have a high mark for quality but some unplanned downtime (in other words, lower availability). The next day may see the opposite—a dip in quality of goods produced, but high availability.
Which is better? Hard to say. Is this consistency?
No. These are completely different days, in my book.
My point here is that underlying factors determine whether consistency is truly achieved.
2. Use OEE to help standardize assets globally.
OEE has incredible value in helping companies shine the spotlight on how similar assets are performing at plants around the world. For example, a toothpaste company may have 100 or more like assets producing tubes of toothpaste at different plants.
Use OEE to evaluate their availability, performance and quality within each facility. Maybe you find your assets in some locations produce 100 tubes of toothpaste an hour, and at others only
50 tubes an hour.
Surfacing this data is a key first step in improving overall productivity. Then you can drill down into the conditions, practices and the environment involved at the different plants to find answers to why some assets are producing greater volumes than others.
3. Use OEE to help evaluate effectiveness of various brands & equipment types.
Similarly, OEE enables you to test and measure different brands and equipment types used for manufacturing similar products. You can determine which work best and are worthy of greater future investment.
At Johnson & Johnson, for example, we had plants worldwide using different brands of equipment to make sutures. Many of us felt we already knew which brands performed better than others. But applying OEE as the standard method for collecting data provided us with concrete evidence, and this remains a smart way for using OEE today.
4. Avoid using OEE as a measure of overall plant effectiveness.
I continue to see organizations measuring the OEE of their plants as a whole, and I feel strongly that this is not the best use of the metric. Why? Because it often hides problem areas and even encourages bad practices. For example, you may be ecstatic that your flagship plant scores 85 percent for several days of a given week. That typically means your underlying components are averaging around 95 percent, and you cannot be unhappy about that.
However, if your plants have 1,000 or more assets, you may have a small number performing extremely poorly, warranting your attention. If you are focused on the overall plant OEE, you may end up neglecting the health of some of your assets.
I remember hearing about a manufacturer that had one plant scoring at 90 percent and another at 70 percent. Those at the higher-scoring plant felt they had the formula for making their OEE score improve, so they began scheduling production to increase their score, rather than to run their production more efficiently.
Meanwhile, the plant at 70 percent was determined to be doing a more effective job of meeting customer demand.
5. Focus on continuous improvement.
Implementing an OEE program with standardized data is not the easiest thing for any company, but the results are extremely beneficial. It works best as part of a larger continuous improvement program such as total productive maintenance (TPM). The biggest value of OEE is not the score itself, but the evaluation and the action it triggers. A substandard score for a given time frame may be a disappointment—but if it leads to successful efforts to reduce downtime or improve quality, that is a worthwhile outcome. That is why tracking over time and working on continuous improvement is most important.
OEE as a statistic is alive and well, even if it no longer is the industry buzzword phrase that it once was. The fact that articles continue to be written and read about it speaks to its continued relevance in the digital age.