It’s not what you know but what you understand that matters most. ~ Redge
Discerning perceived knowledge from understanding is a challenge for many leaders. For example, it is possible for anyone to memorize facts and figures and to correctly answer related questions simply by recalling this same information from memory. Similarly, many of us “perform” simple multiplication from recall – without even thinking about the calculations involved.
Why this matters
Having knowledge of metrics is not necessarily the same as understanding what the metric is measuring or what it means. Consider that the formula for Overall Equipment Effectiveness, or OEE, is the product of three factors: Availability, Performance, and Quality. After basic training, anyone can recite the formula and calculate OEE correctly. This basic knowledge does not necessarily equate to any real level of understanding what is actually being measured.
OEE measures how effectively an asset’s time was used to produce a quality part. Confusion as to what is really being measured typically occurs when the Quality factor is calculated. For a single run, numerous texts teach that we can calculate the quality factor as:
Quality Factor = (Good Parts Produced / Total Parts Produced) x 100.
While the calculation will yield the correct result for a single instance, the formula isn’t quite complete as presented and doesn’t work when attempting to calculate OEE for multiple parts running through the same machine. The Quality formula should actually be stated as:
Quality Factor = (Good Parts Produced x Cycle Time / Total Parts Produced x Cycle Time)
Quality Factor = Pure Time to Produce Good Parts / Pure Time to Produce ALL Parts.
When expressed this way, we can state how much time was spent producing good parts, total parts, and defective parts! The time lost to produce defective or scrap parts is given by the formula:
Lost Quality Time = Time to Produce ALL parts – Time to Produce Good Parts.
OEE is not complicated when we understand what it is we’re measuring. By way of example, assume a production shift consists of 435 minutes of scheduled production time where breaks and lunches have already been accounted for. For the sake of simplicity, we will assume the process is running at rate (performance = 100%). A part having a cycle time of 2 minutes was scheduled to run for the entire shift where 160 good parts from a total of 180 parts were produced.
From this basic data and assuming the process was running at rate – (Performance = 100%) – we can derive the following:
Availability = Up Time / Total Time = ((180 x 2) / 435) x 100 = (360 / 435) x 100 = 82.76%
Performance = 100% (assuming run at rate) = 100%
Quality =Time to Produce Good Parts / Time to Produce ALL Parts
Quality = ((160 x 2) / (180 x 2)) x 100 = (320 / 360) x 100 = 88.89%
OEE = A x P x Q = 82.76% x 100% x 88.89% = 73.56%
Cross Check: 435 x OEE = 435 x 73.56% = 320
Before calculating the percent values for each factor, we can see that time is common to all factors. We can readily determine that we lost 40 minutes due to the production of defective parts (360 -320) and that we also lost 75 minutes due to unplanned downtime events.
To calculate OEE for a given machine, shift, department, or plant we can easily sum the total “time” based values for each factor and calculating the percentages accordingly. These calculations are clearly conveyed in prior posts and in our free downloads (see our free downloads page or on the widget on the sidebar).
What you know is taught, what you understand is learned. ~ Redge
When we truly understand what is being measured, the data that forms the basis for our calculations becomes more meaningful too. We can even challenge the data before the calculations are made. The greatest frustration occurs when the results are not what we expected and the reasons are either in the very data that generated them or worse, when someone doesn’t understand the calculation they’re actually performing.
Many years ago I recall reading a sign that stated, “The proof of wisdom is in the results“. While their is truth in this statement, the implication is that we understand the results too!
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Until Next Time – STAY leanFollow @Versalytics