Concise OEE made e-a-s-y: overall equipment effectiveness in the hybrid version.

As I stated in the article "Just How Good is Your Process?" (T&P September 2004), there is no better statistical tool to use when evaluating the efficiency of a production process than Overall Equipment Effectiveness, or OEE. This calculation is becoming faster and easier to use with the hybrid version--Concise OEE, or COEE.

Let's begin with a short review of basic OEE. The calculation is used to break down the production process into three categories:

1) Availability (actual uptime compared with scheduled runtime)

2) Performance Efficiency, or PE (number of total parts produced compared with optimum capability)

3) Rate of Quality Products, or ROQP (the number of conforming parts compared with the number of total parts produced)

OEE = Availability x PE x ROQP, with a level of 85 percent considered world-class.

For example, consider these documented numbers for the production shift: scheduled runtime was 432 minutes (7.2 hours); our actual uptime was 387 minutes. With an optimum cycle time of 15 seconds/part, 387 uptime minutes could produce 1,548 parts. We kicked out 1,357. Of the 1,357 parts, 1,331 were conforming (26 were scrap or needed rework).

Our OEE calculation looks like this:

OEE = (387/432) x (1,357/1,548) x (1,331/1,357) = 0.896 x 0.877 x 0.981 = 0.771 (77.1 percent)

Now here's where it gets real simple. In our example production shift, we can calculate the optimum number of conforming parts that could be produced:

(432 minutes) x (15 seconds/part) = 1,728 parts (in a perfect world)

This is a pure "bottom line" number that I'd bet everyone involved in production planning or scheduling would know. Concise OEE uses the data from the production run and compares the data with the optimum (perfect world) parts amount using one simple equation:

COEE = actual conforming parts produces/optimum number of possible parts

In our example, this would be:

COEE = 1,331/1,728

= 0.7703 (or 77 percent)

Just because we didn't use the data to calculate Availability, Performance Efficiency, and ROQP does not mean we won't use it at sometime. The advantage of COEE is that is allows the data to be collected, but no time is wasted calculating when it's not necessary. Let me explain this process. We could set a goal of 85 percent COEE and a lower limit as 75-percent OEE. As long as our OEE stays between these limits, there isn't a need to break the calculation down into Availability, Performance Efficiency, and ROQP. If we notice an unacceptable COEE level, the data to investigate the root cause of the downward trend are at our fingertips. For example, tomorrow our COEE calculation looks like this:

COEE = actual conforming parts/optimum number of possible parts

COEE = 1,158/1,728 = 0.6701 (or 67 percent)

We could use the data from the run to determine where the problem lies:

Availability or poor machine uptime

Performance Efficiency or short stoppages of the process

ROQP or unacceptable levels of non-conforming parts

The breakdown from this production run:

Availability = actual uptime of 303 minutes/scheduled runtime of 432 minutes

Availability = 0.701 (or 70 percent)

Performance Efficiency = 1,168 total parts/1,212 possible parts (303 minutes)

Performance Efficiency = 0.964 (or 96.4 percent)

ROQP = 1,158 conforming parts/1,168 total parts

ROQP = 0.991 (or 99.1 percent)

The root cause of our poor Concise OEE was the 129 minutes of machine downtime documented in our Availability calculation. Now we can investigate and countermeasure the real bottleneck.

This simple version of SPC is poetry in motion. Collect the data and have it on hand, but don't overanalyze and only calculate what and when you need to.

Richard Clark is a metrologist who has developed Measurement Systems Analysis programs for facilities to meet QS-9000 and TS-16949 requirements. To reserve a copy of his forthcoming book Exposing the Myths of Industrial Precision Measurement or to offer feedback, e-mail [e-mail]