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Overall Equipment Efficiency (OEE): Standardized Work
After you start collecting OEE data for your processes, you may notice significant variance between departments, shifts, and even employees performing the work. Of the many aspects that you will be inclined to investigate, standardized work should be one of them.
Making sure that all employees are executing a process or sequence of processes correctly and exactly the same way every time is the topic of standardized work. The OEE data may also direct you to review how the processes are being executed by some of the top performers to determine if they are truly demonstrating best practices or simply cutting corners.
Lean practices are founded on learning by observing. We cannot stress the importance of observing an operation to see first hand what opportunities for improvement (waste elimination) are available. OEE data is a compass that directs you where to look; however, the destination for improvements is the process, the very source from where the data originated.
Establishing Standard Cycle Times
One of the first questions we usually ask is, “How were the standard cycle times determined?” Was the standard based on best practices, quoted rates, time studies, name plate ratings, or published machine cycle times?
We recommend conducting an actual time study using a stop watch and calculating part to part (button to button) cycle times accordingly. We have used the stop watch capability of the BlackBerry many times. Results for lap times and total elapsed time are easily recorded and can be e-mailed as soon as the study is complete.
The sample size of course will depend on the actual rate of the machine and should be statistically relevant. One or two cycles is not sufficient for an effective time study.
For operator “controlled” processes, we recommend involving the employees who normally perform the work when conducting the time study. It doesn’t make sense to have the “office experts” run the equipment for a short burst to set a rate that cannot be sustained or is just simply unreasonable.
Many processes, those dependent on human effort or automation, are usually controlled by PLC’s that are also capable of providing the machine cycle time. At a minimum, we recommend validating these cycle times to at least satisfy yourself that these are part to part or “button to button” cycle times.
For automated operations, PLC’s can typically be relied upon to provide a reasonable cycle time. Without going to far into process design and development, you will need to understand the elements that control the process sequences. Some processes are driven by time controls (an event occurs after a period predetermined period of time) versus those that may be event-driven (an event occurs based on satisfying a dependent “sensor on-off” condition or similar “event signal” mechanism.
The real key to understanding the process being studied is to develop a flow chart clearly defining each of the process steps. It is of equal importance to observe the differences that may be occurring between employees performing the work. Either the instructions lack clarity or habits (good or bad) have been developed over time. Although templates exist to aid in the development of standardized work, don’t wait to find the right tool.
Using Video – Record it Live
We highly recommend using a video recorder to capture the process in action. With the technology available today, video is readily available and a very cost-effective method of documenting your processes. Video presents several advantages:
Captures activities in real-time.
Provides instant replay.
Establish process or sequence event timing in real-time.
Eliminates need for “stop watches” to capture multiple event timing.
Can be used as a training aid for new employees to demonstrate “standardized work practices”.
Can be used to develop “best practices”.
Reduces or minimizes potential for time measurement error.
We have successfully used video to not only develop standardized work for production processes, but also for documenting and recording best practices for tool changes, set up, and checking or inspection procedures.
Standardized work eliminates any questions regarding the proper or correct way of performing the work required. Standardized work procedures allow additional development work to be completed “offline” without further disruption to the production process.
Of course, Standardized Operating / Work procedures are required to establish effective and meaningful value stream maps but even more importantly, they become an effective tool to understand the opportunity for variances in your OEE data, certainly where manual or “human” controlled operations are concerned.
It has been argued that OEE data in and of itself is not statistically relevant and we are inclined to agree with this statement. The simple reason is that the processes being measured are subject to significant internal and external variances or influences. Examples may include reduced volumes, product mix changes, tool change frequency, employee turnover, and economic conditions.
As mentioned in many of our posts, it is important to understand “WHAT and WHY” we are measuring. Understanding the results is more important than the result itself. A company looking to increase inventory turns may resort to smaller production runs and more frequent tool changes. This will reduce Availability and, in turn, will result in a lower OEE. The objective may then be to find a way to further reduce tool change times to “improve” the Availability.
The use of OEE data can vary in scope, ranging from part specific performance to plant wide operations. As the scope of measurement changes, so do the influences that impact the net result. So once again, we urge you to use caution when comparing data between personnel, shifts, departments, and production facilities. Typically, first or day shift operations have greater access to resources that are not available on the “off’ shifts.
Perhaps the greatest “external” influence on current manufacturing operations is the rapid collapse of the automotive industry in the midst of our current economic “melt down”. The changes in operating strategy to respond to this new crisis are bound to have an effect on OEE among other business metrics.
The ultimate purpose of Lean practices is to reduce or eliminate waste and doing so requires a rigorous “document and review” process . The ability to show evidence of current versus proposed practices will reduce or eliminate the roadblocks that may impede your continuous improvement objectives.
While the post is brief today, hopefully the message is helpful.
We have presented the methods of calculating OEE for a process and also demonstrated how weighted OEE is calculated for multiple processes. Our next challenge is to determine how this data can be used to make sure we are targeting the right processes for improvement.
Over the next few posts, we will show you how to calculate weighted OEE factors for each process. This weighting will include calculations for each of the factors as well as the overall OEE. The results of the individual weighted factors may well serve to point us in the right direction.
Calculating the weighted OEE and it’s factors is not just a simple calculation of averages as you can see from our previously calculated data. It is easy to fall into this trap and it is also for this very reason that we have put forth the effort to show you how it should be done.
We highly recommend reviewing the posts presented over the past few days to refresh yourself with the ongoing development of our key Lean metric: OEE.
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Calculating Weighted OEE
We will continue to use the examples presented in our previous posts to develop our OEE metric. We will start with the overall OEE percentage to help you understand the weighting concept applied here.
The basic formula to determine the weighted OEE for each individual process follows:
Weighted OEE = Process OEE * (Net Available Time / Total Net Available Time)
The OEE data taken from our previous examples is summarized in the table below:
Machine A: OEE = 80.22%, Net Available Time = 455 minutes
Machine B: OEE = 70.05%, Net Available Time = 455 minutes
Machine C: OEE = 55.90%, Net Available Time = 455 minutes
The total Net Available Time for all machines = 455 * 3 = 1365 minutes. Now we can calculate our “weighted OEE” for each machine as shown:
Adding the individual weighted OEE together for each machine, we find the total is 68.72%. Note that this matches the total OEE calculation from our previous post.
Warning: Don’t fall into the trap of assuming that the same result could have been achieved by simply averaging the three OEE numbers. The results in the calculation appear to be a simple average, however, this is misleading because you will also note that the Net Available Time and Total Net Available Time ratio is the same for each machine. This is not always the case. Many times, a machine may run for only half a shift or a few hours at a time. This may significantly change the weighted OEE for a given machine and the result is not a simple arithmetic average.
Our next step will be to calculate the individual weighted factors for Availability, Performance, and Quality for each machine. These calculations will readily demonstrate that it’s not a simple averaging process.
Weighted Availability Factor:
The basic formula to determine the weighted Availability Factor for each individual process follows:
Weighted Availability = Availability % * (Net Available Time / Total Net Available Time)
You will note that the weighting factor for availability is the same as the weighting factor for the overall OEE weight. The Availability data taken from our previous examples is summarized in the table below:
Machine A: Availability = 92.97%, Net Available Time = 455 minutes
Machine B: Availability = 96.04%, Net Available Time = 455 minutes
Machine C: Availability = 95.16%, Net Available Time = 455 minutes
The total Net Available Time for all machines = 455 * 3 = 1365 minutes. Now we can calculate our “weighted availability” for each machine as shown:
Adding the individual weighted Availability factors together for each machine, we find the total is 94.72%. Note that this matches the total weighted Availability calculation from our previous post.
Warning: because all process have the same Net Available Time you may be thinking that this seems like a lot of work to simply get an average of the numbers. More on this later when we take a look at Performance and Quality.
Weighted Performance Factor:
The basic formula to determine the weighted Performance Factor for each individual process follows:
Weighted Performance = Performance % * (Net Operating Time / Total Net Operating Time)
You will note that the weighting factor for performance is different. This is because performance is a measure of how well the operating time was used to make parts. The Performance data taken from our previous examples is summarized in the table below:
Machine A: performance = 88.26%, Net Operating Time = 423 minutes
Machine B: Performance = 77.23%, Net Operating Time = 437 minutes
Machine C: Performance = 61.70%, Net Operating Time = 433 minutes
The total Net Operating Time for all machines = 1293 minutes. Now we can calculate our “weighted performance” for each machine as shown:
Adding the individual weighted Performance factors together for each machine, we find the total is 75.63%. Note that this matches the total weighted Performance calculation from our previous post.
Finally: You will note that the Weighted Performance is NOT the same as the Arithmetic Average! The arithmetic average in this case is 75.73%. Although it doesn’t appear to be a significant difference, you wil see that it can be.
Weighted Quality Factor:
The basic formula to determine the weighted Quality Factor for each individual process follows:
Weighted Quality = Quality % * (Ideal Operating Time / Total Ideal Operating Time)
You will note that the weighting factor for quality is different. This is because quality is a measure of how well the ideal operating time was used to make good (saleable) parts. The Quality data taken from our previous examples is summarized in the table below:
Adding the individual weighted Quality factors together for each machine, we find the total is 95.92% as expected. Note that this matches the total weighted Quality calculation from our previous post.
Finally: You will note that the Weighted Quality is NOT the same as the Arithmetic Average!
Remember to get your free downloads. We have created a number of Excel spreadsheets that are immediately available for download from our FREE Downloads page or from the Free Downloads widget on the side bar. These spreadsheets can be modified as required for your application.