The document discusses the challenges organizations face with data clarity, particularly in the calculation of Overall Equipment Effectiveness (OEE), a crucial manufacturing metric. Misinterpretations often arise from nonstandard approaches to defining OEE's three components: availability, performance, and quality, leading to unreliable performance measures. To enhance clarity and improve performance, it's essential for organizations to standardize metric calculations across all departments and ensure consistent understanding of what the metrics indicate.

1. Clarity First: Know How Data Is Calculated
Section from the Performance chapter in Karen Martin’s latest book, Clarity First
Even when data is clear, clean, complete, and correctly interpreted, organizations may create
ambiguity by using nonstandard approaches to calculating various measures.
Overall equipment effectiveness (OEE) is a classic example. This common manufacturing metric
is frequently misinterpreted due to differences in how it’s calculated—specifically the
definitions of the factors that go into the product.
OEE reflects the health of a production line. It’s as critical to a manufacturer as heart rate,
blood pressure, or body temperature are to a doctor assessing a patient. It provides insight into
operating margins, capital utilization, and process efficiency and effectiveness. OEE is the
product of three variables—availability, performance, and quality—and is expressed as a
percentage.
Sounds simple, right? Nope. We find that many manufacturing leaders can’t list the three
variables. Even when leaders can name them, they are not always clear how each variable
should be calculated, and the same method of calculation is not always used by everyone in the
same organization. Availability is typically based on scheduled production time, and yet some
teams use a 24- hour clock. Some organizations calculate the quality variable based on end- of-
line quality, which can obscure in-line rework that’s occurring or scrap that’s accumulating,
both of which are costly problems. Quality could be 99 percent or 75 percent, depending on
who’s measuring and how they’re measuring. Such confusion can render a critical measure
useless as a performance improvement tool. If you don’t know what goes into the final number,
how can you understand what OEE is telling you? And if you don’t understand what OEE is
telling you, there’s no way to know how well you’re doing or what you need to do to improve
performance.
OEE is only one example of a common problem we see concerning measurement and
performance clarity. Be very clear on what the organization is measuring, what questions the
metrics answer, how the numbers are obtained, what the numbers mean, and what
conclusions leaders can draw based on what they learn. If multiple departments or levels in
your organization do their own calculations, standardize the method across the organization so
that everyone is using the same formulas.
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