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OEE – the Key KPI for Production Performance

OEE

OEE – the Key KPI for Production Performance

The metrics hierarchy for Overall Equipment Effectiveness (OEE), is a basic KPI for performance measurment at the work center level.

OEE – Overall Equipment Effectiveness

The Goal is 100%

Seiichi Nakajima developed a metrics hierarchy for Overall Equipment Effectiveness (OEE), which he described as a central module of the TPM Methodology in his 1982 book, TPM tenkai. The book bubbled out of his many years of experience, studying for instance in the 1960s the degree of effectiveness of a manufacturing operation, and how well it utilized resources. This is based on Harrington Emerson hierarchy studies of overall labor efficiency. If for some reason, a production line’s cycle time is reduced, the OEE level will increase. That is, less resources are being used to produce more product. Fine tuning the OEE number of an operations processes is a rare skill.
So basically, the Overall Equipment Effectiveness is a method of Best Practices to achieve higher OEE scores, reflecting improvements in performance for each area of production. The goal is an OEE score of 100%, meaning that no known changes to production’s current plan cycle could produce better results; the system is completely optimized. It means good parts are being produced, in as timely a way as possible, and with no stoppages.

The Forerunner

Before there was OEE, there existed Total Productive Maintenance (TPM), introduced by Japan’s Institute of Plant Maintenance (JIPM) in 1971. This eventually became enhanced and renamed Total Productive Manufacturing (TPM). OEE was developed with TPM by the Japanese process methodology guru, Seiichi Nakajima. The TPM method was being accepted in the US and other Western countries by the 1990s. There were even English translations of two of Nakajima’s books that were widely read in the West.

OEE Measurement

The Key Performance Indicator (KPI) is commonly derived directly from the OEE measurement. OEE can be explained with a brief listing of the 6 metrics that make up a system OEE measurement number. This is a hierarchy of measurements. The 4 underlying measurements along with the 2 top level measurements form the measurement hierarchy. This means the KPI is a real-time OEE measurement.

The Two Top-Level Metrics

The OEE and Total Effective Equipment Performance (TEEP) are interrelated measurements to indicate overall facilities utilization level, and the materials and labor time needed for production to operate effectively. These 2 metrics are the biggest indicators to find the difference between ideal performance and what was actually achieved, so far. The OEE value quantifies manufacturing unit performance, versus its capacity by design. TEEP compares OEE with total available production hours for hours, days, months, quarters, and years.

The Four Underlying Metrics

To find out where the TEEP and OEE gaps exist, use the following underlying measurements:
• Loading: The TEEP Metric that tracks the total calendar time percentage for which an operation is actually scheduled
• Availability: The Up Time, or actual percentage of time, when the operation is able to operate, versus the time that was scheduled
• Performance: The percentage of speed when the Work Center actual ran versus the designed speed.
• Quality: The First Pass Yield (FPY), is the total percentage of good units produced versus the percentage of total units started.

Calculating TEEP

To calculate TEEP effectively, the values in question must be kept at the part number or work area production level. Trying to apply TEEP numbers at a higher level, in aggregate will not yield accurate numbers. The TEEP metric is important to measure percentage of time a process operation is scheduled for, compared to actual calendar time availability. First a Loading Metric is calculated to measure scheduling effectiveness, regardless of the high or low level of operation production. This is the formula:

Loading = Scheduled Time ÷ Calendar Time

For example, if a work center schedules for operations to be performed 6 days a week, 16 hours a day, Loading would be:

Loading = ((6 days x 16 hrs) ÷ (7 days x 24 hrs))
Loading = 57.1% of total availability

The calculations are not complete until OEE is calculated and takes the TEEP value into account. OEE separates production unit performance into 3 categories: Quality, Performance, and Availability. Each category relates to specific process aspects that are possible to improve. One of the versatile attributes of OEE is that it works on the work center level, but can also be rolled up in aggregate to higher levels of operations management.

Calculating OEE

Although it is seemingly impossible for any production process to attain a true 100% OEE number, it is easily possible to gain close to that efficiency. After many iterations of production cycles, it is common for major manufacturers to attain over 80% OEE. Here is the OEE formula:

OEE = (Quality) x (Availability) x (Performance)
OEE = (79.2% Quality) x (89.3% Availability) x (90.8% Performance)
OEE = 64.2%

There is another way to calculate OEE using simple timing reports from production logs, but it has not been proven to be as effective as the Quality x Availability x Performance formula.

Availability Metric

The OEE measure of Availability is the percentage of time that was actually scheduled for production to operate. Availability is a measure of Up time, excluding Quality and Performance effects and any scheduled down time occurrences. The opportunity cost of unused Availability is considered an Availability loss. It is a very important factor in production effectiveness feedback.

Consider this example of production line scheduled for 8 hour shifts, punctuated by a half-hour worker break, so 450 total possible minutes per day. Calculate Availability as follows:

Operation Time = (450 daily minutes possible) – (60 minutes downtime)
Operation Time = 390 minutes
Availability = Operation Time ÷ Scheduled Time
Availability = 390 mins. ÷ 450 mins.
Availability = 86.6%
Productivity Performance Metric

The actually measured process rate is the Performance Metric of OEE. Think of it as the percentage of recorded speed versus the scheduled speed. This is a singular measurement of raw speed, regardless of Availability and Quality effects. Wasted performance are Speed Losses in OEE parlance. To Calculate Performance, use the following formula:

Performance = ((Parts Produced) x (Ideal Time)) ÷ Operation Time

Consider the example of a 450 minute shift, or 7.5 hours after a half-hour worker break:

Operation Time = (450 daily minutes possible) – (60 minutes downtime)
Standard Rate = 50 items per hour (1.2 minutes per each unit)

Production actual produced: 294 units of some level of quality
Production Time = (294 units) x (1.2 mins per unit)
Production Time = 352.8 minutes

Performance = (Production Time) ÷ (Operation Time)
Performance = 352.8 ÷ 390
Performance = 90.4%

Quality Metric

OEE’s Quality metric is a measure of Good Units that were produced versus the number of Total Units Started. In this measure, any losses of product due to design or manufacturing defects are named Quality Losses in OEE nomenclature. To calculate Quality, use the following formula:

Quality = ((Produced Units) – (Quality Losses)) ÷ (Produced Units)
Produced Units = 239
Quality Losses = 14
Quality = (239 produced – 14 losses) ÷ 239 produced
Quality = 94.1%

These are all you need to calculate accurate OEE percentages. Remember this simple formula, it is all you will need:

OEE = (Quality) x (Availability) x (Performance)

So, for the example calculations of these 3 metrics, above, we finally have an example OEE percentage of: 73.7%. Almost 1/4 of the possible number. Improvements must be made to this particular set of examples, in the iterative way of calculating OEE.

Six Losses

The 3 main OEE metrics, Quality, Performance, and Availability, do not tell the whole story, however. The Six Loss factor helps determine which areas need attention first, and it is the dividing of Quality, Performance, and Availability into the ‘Six Big Losses’ to OEE.

The Six Losses are some variety of these sub-factors:
• Quality
• Performance
• Availability
• Planned Downtime
• Minor Stops
• Production Rejects
• Breakdowns
• Speed Loss
• Start Up Rejects

If losses are not shown, then no specific plans to recover those losses will be worked to benefit the OEE. Calculate the Six Loss level yourself, or use an online Six Loss calculator, of which there are plenty.

Playing By the Rules

The only problem with OEE is that it is not bullet proof, and it can be easily manipulated through fraud. It probably is not a management measuring tool that will ever set anything in stone. But it has moved the science of feedback controls in manufacturing to improve production effectiveness into the 21st century. So, what OEE provides is more like fuzzy logic is to a computer. OEE does not form a hard and fast rule, even though there is math involved, it more produces a rule of thumb, a heuristic guide to improving an operation’s efficiency and achieve maximum continuous production.

Of course, the reason it is not fixed with a rigid mathematical rule, is that other factors creep in. It is true that the OEE acts as a Geometric Mean in mathematics. It is after all, a relatively simple calculation. There are times when it may not be helpful. The weather, or a change in climate, or a banking crisis can throw a real monkey wrench into trying to use OEE to directly guide changes in a business. Sometimes it may be a good idea to take the OEE with a grain of salt, and listen to your top shop foreman, instead. OEE is then, not well suited for times of upheaval or where random changes in supply and overhead costs suddenly crop up, such as when war is declared, a major drought hits, or an economic downfall befalls us.

Mission Critical

Of course, productivity views change, depending on how mission critical, how high the tolerances required, how life-and-death the operation is. A company producing toy widgets is going to have a much laxer view of Quality than the company that produces medical defibrillator implants. In the latter case, total emphasis is given to Study then Change to achieve the very highest quality. OEE works best with standard, so called, Lean production environments. It is more for the middle of the bell curve producers, but not a perfect solution for outlier operations, such as the example given of a mission critical medical device.

Conclusion

The constant jumps in costs, funding, and extenuating social and governmental circumstances, that cannot be calculated in a formula, cause OEE to work less well in those operations. It is important to do some level of analysis of an operation’s fragile points and the frequency of major changes it experiences before deciding to use OEE. Yet, for most standard products being manufactured in the world today, OEE makes sense. It is a very tried-and-true management methodology that allows for some variable implementations while still staying true to the mathematical purity of feedback measurement. The OEE management cycle corresponds directly to production’s cycle, and produces ever improving production results.