- Subscribe Today
- All Topics
- Training & Events
- Buyer's Guide
In today’s competitive global environment, we are constantly being asked to do more with less. Now more than ever, companies are asking their employees to become more productive, more efficient and more “lean.”
Strategic planning, value stream mapping, reliability engineering, loss elimination – these phrases have become popular from the boardroom to the shop floor. But where does their value really lie? How do we make decisions that uncover value, eliminate loss and allow for proper strategic planning for the future?
The answer lies not in the decisions we make but in the data we use to make those decisions. Good, strong data is key to making good, strong decisions. We are all familiar with the axiom of “garbage in, garbage out.” So what kind of data should we use? What metrics give us the best snapshot of our current levels of performance?
Asset utilization (AU) and overall equipment effectiveness (OEE) are key performance-based metrics that, when calculated and communicated properly, allow for effective facility management and provide sound backing for potentially difficult business decisions to be made. However, much confusion lies in their definition and understanding when and where to use them.
AU is defined as: availability x rate x quality
OEE is defined as: uptime x rate x quality
As you can see, AU and OEE are similar – both calculations for rate and quality are the same, and are defined as follows:
where average rate is the speed or efficiency of the system under analysis over a given time period.
The best demonstrated or design rate is determined by the nameplate capacity of the system or the best demonstrated rate recorded. I prefer to use the best demonstrated rate, as empirical data is a better model of the system in question. A 100-percent rate calculation would signal that the system is operating consistently at its maximum demonstrated speed.
where first pass units is defined as the good units produced over a certain time period that meet customer specifications.
These “quality” units are compared to the total units produced, including those that do not meet customer specifications. A 100-percent quality calculation would signify no scrap or rework in the process.
Based on the above formulas, you can easily see that the difference between OEE and AU lies in how you compare availability and uptime. So what is the difference? Availability and uptime are defined as follows:
where operating time is defined as the amount of time that the system is actually operating.
Calendar time is based on the 24/7/365 schedule as we know it. Scheduled time is defined as the amount of time the system under analysis was planned or scheduled to operate. Therefore, if I operate my system for six hours on one eight-hour shift during a given day, my uptime is 6/8 or 75 percent, while my availability is 6/24 or 25 percent. That is quite a difference.
Typically, OEE is used to understand how well systems or assets perform based on current business demands and production schedules. AU allows for a better understanding of how well that system or asset is currently utilized and allows insight for future business planning by calculating what type of production could be achieved.
In some industries (petroleum, specialty chemical, etc.), scheduled time is equal to calendar time. In this case, availability equals uptime, so OEE and AU are equal. This scenario is similar to how a square is a rectangle, but a rectangle isn’t a square.
It is important to understand the terminology. My background in Six Sigma differentiated between availability and uptime for AU and OEE, respectively. Lean terminology defines OEE as the product of availability x rate x quality, with availability defined in the same way uptime is defined (above). So which is correct? The truth is it doesn’t matter. What is important is to clearly define what you and your organization are trying to capture, agree on common terminology to allow for clear and concise discussion, and determine the best way to capture the needed data.
As always, the devil is in the details. When setting up data-capture systems to determine rate and quality and uptime/availability calculations, take care to ensure consistent measurement and data validity.
In summary, AU and OEE can be defined as:
About the Author
A Life Cycle Engineering reliability subject matter expert (SME) and a skilled Six Sigma Black Belt, Josh Rothenberg utilizes the tools learned in the tire manufacturing, specialty chemical and semi-conductor industries to help facilitate change. With experience in CMMS, fixed/rotating equipment, logistics, planning/scheduling and a talent for fostering interpersonal relationships, Josh brings a unique perspective to reliability-centered maintenance that fosters the growth and development of cross-functional teams. You can reach Josh at jrothenberg@LCE.com.