While always important, equipment performance and reliability have become even more so as businesses deal with the onslaught of post-pandemic challenges. Is your equipment doing what it’s supposed to do, first time, every time? What causes poor performance? Where should you focus?

Measuring and improving equipment performance has become an extremely hot topic in today’s plants. So, what do you know about overall equipment effectiveness? Drew Troyer has been exploring this important lagging indicator in some of his recent articles, including discussing OEE’s individual elements/metrics in depth (see three article links below). The fact is there’s more to OEE than meets the eye.

As Drew has noted, the basic measure associated with Total Productive Maintenance (TPM) since the 1980s has been overall equipment effectiveness. It incorporates three basic elements/metrics of equipment performance and reliability: Availability (or uptime), performance efficiency, and quality output.

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Click The Following Links For The Referenced OEE Articles By Drew Troyer

“Lagging Indicators For Asset Management: OEE (Overall Equipment Effectiveness”)

“Lagging Indicators For Asset Management: OEE’s First Element (Availability)”

“Lagging Indicators For Asset Management: OEE’s First Element (Availability, Part 2)

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OEE is not a measure of just how well a maintenance department works. Equipment design and installation, as well as how equipment is operated and maintained, among other variables, all affect OEE. This metric measures both “efficiency” (doing things right) and “effectiveness” (doing the right things) with equipment.

Here’s an example OEE calculation for a critical piece of equipment running 70% of the time (in a 24-hr day), operating at 72% of design capacity (flow, cycles, units per hour), and producing quality output 99% of the time. Factoring those three percentages together results in an OEE rating of 49.9%, which reflects how well the equipment is loaded and doing what it is supposed to do. (Running at 55% OEE meets the plant’s production requirements.)

With OEE data, we can determine the “cost of unreliability” or poor equipment performance. For example, a 5% decline in OEE may have led to 500,000 units not being produced in a year. At a sales price of $12 per unit, the cost of unreliability is $6 million of lost sales (revenues). This makes a strong business case for improving reliability of critical equipment.

The OEE rating for critical equipment provides a relative comparison or “report card” on equipment performance and how well reliability-improvement efforts are going. However, the real use of OEE comes by using its individual elements/metrics and the actual losses to determine root causes and corrective actions.

What caused the 5% decline in OEE in the example above? What changed? Oddly enough, OEE was not intended to be a calculated percentage. This is where its individual elements/metrics become more important than the percent of OEE itself.

By tracking and trending those factors, one can quickly spot whether a machine experienced more downtime (be it planned or unplanned) or was run at a slower pace, or had minor stops, or produced more defects. Improper or inefficient operation can cause lower availability (setups, tool- or part-changing). Root-cause analysis begins by focusing on the type and extent of loss, not the OEE rating.

Here are several additional (and often overlooked) ways to think about OEE as a percentage rating in various settings:

Individual machines: Performance of a machine is compared only to itself over time (historical trending).

Integrated manufacturing cells: Despite individual machine performance, the entire cell must function
as a single unit. OEE for the cell is a good relative-performance comparison.

Flexible manufacturing cells: The parts produced will have different cycle times (efficiency).
OEE percentages can be misleading.

Bottom line: As reliability professionals, we must always be vigilant in our use of OEE.TRR