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3 Points to Consider for OEE Planning

Posted by Robert Mallich

Aug 26, 2014 9:00:00 AM

By now, most manufacturing companies are familiar with Overall Equipment Effectiveness (OEE) concepts and apply them to their own operations to understand the performance of their operating assets (lines and machines).

To review, OEE is a metric that expresses the operational effectiveness of an asset over a time period.  It is expressed as a percentage (from 0% to 100%) and is the result of this simple formula:

Availability x Efficiency x Quality

This metric is popular because it is an easily understood concept; it provides quick insight about the performance of an asset; it is relatively easy to gather the underlying data; and it can serve as a starting point for a “deeper dive” investigation into an asset’s performance problem.

Given the apparent simplicity of the concept, you’d think that all manufacturers would be able to implement Overall Equipement Effectiveness programs easily and uniformly.  In reality, this is not the case because of differences in how manufacturers interpret the meaning of the three effectiveness factors and how they measure them.

The fact that companies implement Overall Equipment Effectiveness programs differently is not necessarily a problem; it’s simply something a manufacturer should be aware of when it plans and executes an implementation, and especially, as it attempts to benchmark its own operations vs. its industry peers.

Let’s take a look at a few considerations when planning what to measure in an implementation.

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Availability
Availability is generally thought of as the amount of time that an asset actually operates to produce goods divided by total time.  But what measure of total time should be used?  If your company is interested in measuring the total availability of an asset, it makes sense to compare the asset’s operating time to total calendar time.  By the way, determining Availability in this way is used in calculating the Total Effective Equipment Productivity (TEEP) metric, not overall effectiveness.
 
But let’s say your company is a food processor that runs around the clock, except for a required 12 hours of downtime on assets per week to perform sanitation and cleaning activities.  The asset will never process food during the 12 hour period, thus, production should never be scheduled during that time.  In this case, your company may want to omit the 12 hours from total time when determining Availability.  By doing this, Availability becomes the result of the amount of time that an asset actually operates divided by the total time available (scheduled) for production.  Determining Availability in this way will produce a true OEE metric. 
 
Example: Machine runs 150hrs/week with a production schedule of 156hrs/week (7x24-12)
150/156=96.15%
 
Efficiency
The Efficiency value is derived by dividing the actual number of items produced over a time period by the asset’s maximum throughput rate during that period.  If the same maximum throughput rate can apply to all products, the calculation is practically a no-brainer. 
 
However, it is common for an asset to run several types of products, each of which could have a different maximum throughput rate.  These rates are usually established by operations and quality personnel and are based on historical performance.  The Efficiency value should, therefore, be calculated as the actual throughputs divided by the maximum throughputs per product type, for all types run during the time period.
 
Example: (T1+T2+T3)/(TMAX1+TMAX2+TMAX3)
  
Quality
The Quality factor can be problematic to implement.  Everybody wants plant-floor data (including Overall Equipment Effectiveness results) to be available in real-time.  The problem with Quality is that, frequently, it can’t be determined quickly.  This is because it sometimes takes hours or even days to run the quality tests and obtain the results on which Quality is based.  The timeliness in obtaining test results can depend on the nature of the products being made, the types of tests to execute, and the sophistication of the test equipment and test system automation.  Another problem in providing timely Quality results is that retesting is occasionally required.  This takes more time, and retesting results may differ from initial test results, producing corresponding changes to the Quality value and therefore to the effectiveness metric.  Yet, everyone wants to see data in real-time.
 
If having meaningful OEE results before testing is completed is important enough to your company, you may want to consider implementing tentative (pre-testing) and final (post-testing) effectiveness values.  The purpose of a tentative effectiveness value is to provide adequate information to personnel in the short term until final test results become available on which to calculate a final Quality value.
 
To implement a tentative equipment effectiveness value, you’ll have to determine a proxy value to use for Quality.  There are several simple options for this, including a default value to apply to every type of good produced on an asset (e.g., 87% for all goods), or a default value specific to each type of good produced (e.g., 70% for green bottles, 82% for white bottles, 75% for red bottles).  A more sophisticated approach would be to incorporate historical quality results for all or several types of goods instead of using default values.
Of course, your company’s long-term asset performance analysis and strategic decision-making would naturally be based on final OEE metrics.

These are just a few of the implementation issues to consider when planning an Overall Equipment Effectiveness program.  Be aware of how to best measure the factors for your company.  Anticipate how they affect your comparisons to industry benchmarks and how they’ll be used for strategic decision making going forward.

 


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Topics: Tips and Tricks, 3 things you should know about...., Consulting, OEE

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