We added a new page to our site to address some of the more frequently asked questions (FAQ’s) we receive regarding OEE. We trust you will find this information to be of interest as you move forward on your lean journey. We always appreciate your feedback, so feel free to leave us a comment or send an e-mail directly to LeanExecution@gmail.com or Vergence.Consulting@gmail.com
We have had an incredibly busy summer as more companies are pursuing lean manufacturing practices to improve their performance. OEE has certainly been one of the core topics of discussion. We have found that more companies are placing a significant emphasis on Actual versus Planned performance. It would seem that we are finally starting to realize that we can introduce a system of accountability that leads to improvements rather than reprimands.
Keep Your Data CLEAN
One of the debates we recently encountered was quantity versus time driven performance data when looking at OEE data. The argument was made that employees can relate more readily to quantities than time. We would challenge this as a matter of training and the terminology used by operations personnel when discussing performance. We recommend using and maintaining a time based calculation for all OEE calculations. Employees are more than aware of the value of their time and will make every effort to make sure that they get paid for their time served.
Why are we so sure of this? Most direct labour personnel are paid an hourly rate. Make one error on their pay or forget to pay their overtime and they will be standing in line at your office wondering why they didn’t get paid for the TIME they worked. They will tell you – to the penny – what their pay should have been. If you are paying a piece rate per part, you can be sure that the employees have already established how many parts per hour they need to produce to achieve their target hourly earnings.
As another point of interest and to maintain consistency throughout the company, be reminded that finance departments establish hourly Labour and Overhead rates to the job functions and machines respectively. Quite frankly, the quantity of parts produced versus plan doesn’t really translate into money earned or lost. However, one hour of lost labour and everyone can do the math – to the penny.
When your discussing performance – remember, time is the key. We have worked in some shops where a machine is scheduled to run 25,000 parts per day while another runs a low volume product or sits idle 2 of the 5 days of the the week. When it comes right down to the crunch for operations – how many hours did you earn and how many hours did you actually work.
Even after all this discussion we decided it may be an interesting exercise to demonstrate the differences between a model based on time versus one based (seemingly) only on Quantitative data. We’ll create the spreadsheet and make it available to you when its done!
Remember to take advantage of our free spreadsheet templates. Simply click on the free files in the sidebar or visit our free downloads page.
Welcome! If you are a first time visitor interested in getting started with Overall Equipment Effectiveness (OEE), click here to access our very first post “OEE – Overall Equipment Effectiveness“.
We have presented many articles featuring OEE (Overall Equipment Effectiveness), Lean Thinking, and related topics. Our latest posts appear immediately following this welcome message. You can also use the sidebar widgets to select from our top posts or posts by category.
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What gets managed MUST be measured – Including VARIANCE.
It is easy to get excited about the many opportunities that a well implemented LEAN Strategy can bring to your organization. Even more exciting are the results.
Achieving improvement objectives implies that some form of measurement process exists – the proof. A clear link should be established to the metric you choose and the activity being managed to support the ongoing improvement initiatives.
Measure with Meaning
Why are you “collecting” OEE data? While OEE can and should be used to measure the effectiveness of your manufacturing operations, OEE on its own does not present a complete solution. It is true that OEE presents a single metric that serves as an indicator of performance, however, it does not provide any insight with respect to VARIANCES that are or may be present in the system.
We have encountered numerous operations where OEE data can be very misleading. OEE data can be calculated using various measurement categories: by machine, part number, shift, employee, supervisor, department, day, month, and so on.
VARIANCE: the leading cause of waste!
Quality professionals are more than familiar with variance. Statistically capable processes are every quality managers dream. Unfortunately, very little attention or focus is applied to variances experienced on the production side of the business.
Some may be reading this and wonder where this is going. The answer is simple, rates of production are subject to variance. Quite simply, if you review the individual OEE results of any machine for each run over an extended period of time, you will notice that the number is not a constant. The performance, availability, and quality factors are all different from one run to the next. One run may experience more downtime than another, a sluggish machine may result in reduced in performance, or material problems may be giving rise to increased quality failures (scrap).
So, while the OEE trend may show improvement over time, it is clear that variances are present in the process. Quality professionals readily understand the link between process variation and product quality. Similarly, variation in process rates and equipment reliability factors affect the OEE for a given machine.
We recommend performing a statistical analysis of the raw data for each factor that comprises OEE (Availability, Performance, and Quality) for individual processes. Analysis of OEE itself requires an understanding of the underlying factors. It is impractical to consider the application of ANOVA to OEE itself as the goal is to continually improve.
How much easier would it be if you could schedule a machine to run parts and know that you will get them when you needed them? You can’t skip the process deep dive. You need to understand how each process affects the overall top-level OEE index that is performance so you can develop and implement specific improvement actions.
The best demonstration we have seen that illustrates how process variation impacts your operation is presented through a “process simulation” developed from Eli Goldratt’s book, The Goal. We will share this simulation in a separate post. Experiencing the effect of process variation is much more meaningful and memorable than a spreadsheet full of numbers.
Conflict Management and OEE
In some environments we have encountered, the interpretation of LEAN strategy at the shop floor level is to set minimum OEE performance objectives with punitive consequences. This type of strategy is certainly in conflict with any Lean initiative. The lean objective is to learn as much as possible from the process and to identify opportunities for continual improvement.
Management by intimidation is becoming more of a rarity, however, we have found that they also give rise to the OEE genius. If performance is measured daily, the OEE genius will make sure a high performing job is part of the mix to improve the “overall” result. This is akin to taking an easy course of study to “pull up” your overall average.
It is clear from this example, that you will miss opportunities to improve your operation if the culture is tainted by conflicting performance objectives. The objective is to reveal sources of variation to eliminate waste and variation in your process, not find better ways to hide it.
Variance in daily output rates are normal. How much are you willing to accept? Do you know what normal is? Understanding process variance and OEE as complementary metrics will surely help to identify more opportunities for improvement.
FREE Downloads
We are currently offering our Excel OEE Spreadsheet Templates and example files at no charge. You can download our files from the ORANGE BOX on the sidebar titled “FREE DOWNLOADS” or click on the FREE Downloads Page. These files can be used as is and can be easily modified to suit many different manufacturing processes. There are no hidden files, formulas, or macros and no obligations for the services provided here.
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.
Free Excel 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.
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:
The total Ideal Operating Time (to make all parts) for all machines = 978 minutes. Now we can calculate our “weighted quality” for each machine as shown:
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.
