We agree that collecting and tracking OEE data is a task best suited for a database, however, all the bells and whistles of an OEE system don’t serve much purpose if the calculations are wrong. Before you make a significant investment in your OEE data collection, tracking, and monitoring system, make sure the system you plan to purchase is calculating the OEE results correctly.
The ultimate system is one that supports automated data collection technology to minimize data entry costs, reduces the risk of entry errors, and provides reporting or monitoring of OEE in real time. These solutions may be purchased “off the shelf” or customized to your specific process application.
If a database is the best approach, you may ask why we use Excel spreadsheets to present our examples or why we supply templates to allow you to track and monitor OEE. We have four primary reasons:
Almost everyone is familiar with spreadsheets and most people have access to them on their computer.
We determined that a customized database solution being used was not calculating the weighted OEE factors correctly and the overall OEE index was also wrong. We found it necessary to develop a spreadsheet that made it easy to validate the database calculations.
Database enhancements were easier to develop and demonstrate using a spreadsheet. We encountered a production process that was equipped with automated data collection capability and provided an overwhelming amount of performance data in real time. It was easier to perform database queries and use the power of PIVOT tables to develop the desired solutions.
Spreadsheet templates allow you to start collecting and analyzing data immediately. It also allows the users to get a “feel” for the data. Although the graphs and drill downs offered by databases are based on predetermined rules, humans are still required to make sense of the data.
In summary, validate the software and its capabilities prior to purchase. We have observed installations where the OEE data is used to monitor current production performance and the reports generated by the system are used to support the results – good or bad.
We have also evaluated a number of other free OEE spreadsheet offerings on the web and observed that some of these also fail to correctly calculate OEE where multiple machines or part numbers are concerned. Take a look at our free spreadsheets offerings (see the sidebar). Our tutorial provides an in depth explanation of how to calculate OEE for single and multiple machines or parts.
The purpose of measuring OEE is to ensure sustained performance with the objective to continually improve over time. Don’t fall into the trap of setting up a system that, once installed, will only be used to generate reports to justify the current results.
Take the time to train your team and demonstrate how the results will be used to improve their processes. Involve all of your employees from the very beginning, including the system selection process, so they understand the intent and can provide feedback for what may be meaningful to them while, in turn, they can support the company’s goals and objectives.
We encourage you to visit our previous posts showing how to calculate OEE for multiple parts and machines.
In its simplest form, availability measures the uptime of a machine or process against the planned production time. As one of the factors of Overall Equipment Efficiency (OEE), Availability is expressed as a percentage. The uptime is calculated by taking the difference between the planned production time and total duration of the downtime events that occurred during the planned production period.
We specifically address the “Availability” factor in this post for the simple reason that the definition of availability is likely to be one of the most debated and hotly contested topics of your OEE implementation strategy. The reason for this, in many cases, is the lack of clarity in some of the most basic terminology. The purpose of this discussion is to present some topics for consideration that will allow you to arrive at a clear definition that can perhaps be formed into a standard policy statement.
We will also demonstrate that it is possible to calculate the downtime by simply knowing the cycle time or process rate, the quantity of parts produced, and the planned production time. We recommend using this technique to validate or reconcile the actual documented downtime. We would argue that the first and foremost purpose of any machine monitoring or downtime event measurement system is to determine the “WHY and WHAT” of the downtime events and secondly to record the “When and How Long”.
You will learn that monitoring your processes to determine causes and duration of downtime events is key to developing effective action plans to improve availability. The objective of any machine automation, sensor strategy, or data collection and analysis is to determine methods and actions that will improve the availability of the equipment through permanent corrective actions, implementing more effective trouble shooting strategies (sensor technologies), improved core process controls, or more effective preventive maintenance.
Define the purpose of OEE
While it looks like we’re taking a step back from the topic of discussion, bear with us for just a paragraph or two. A clear statement of purpose is the best place to start before executing your OEE implementation strategy:
To identify opportunities to improve the effectiveness of the company’s assets.
You will quickly realize that, when attempting to define the measurement criteria for the OEE factors, in particular Availability, your team may present rationale to exclude certain elements from the measurement process. These rationalizations are typically predicated on existing policy or perceived constraints that simply cannot be changed. People or teams do not want to be penalized for items that are “out of their control” or bound by current policy. Continuous improvement is impeded by attempts to rationalize poor performance.
We understand that some of these “exclusions” present a greater challenge, however, we do not agree with the premise that they cannot be improved. Again, it is a matter of “purpose”. Limiting the scope of measurement will limit the scope of improvement. Now it’s time to explore what could be the foundation for a sound definition of availability.
It may seem reasonable to assume that, at a minimum, the only planned down time events that should be excluded from the availability factor are planned preventive maintenance activities, mandatory break periods, and scheduled “down” time due to lack of work. We would argue and agree that the only justification for an idle machine is “Lack of Work”.
What would be the reason to settle for anything less? If Preventive Maintenance is critical to sustaining the performance of your process, doesn’t it make sense to consider it in the measurement process? The rationale that typically follows is that Preventive Maintenance must be done and it’s really out of our control – it is a planned event. We would argue that the time to complete Preventive Maintenance can be improved.
Is it possible that the Mean Time Before Failure or Required Maintenance can be extended? Is it possible to improve materials, components, or lubricants that could extend the process up time? Is it possible to improve the time it actually takes to perform the required maintenance? If so, what is the measure that will be used to show that additional capacity is available for production.
If set up times for die changes or tool changes can be improved from hours to minutes, could the same effort and devotion to improve Preventive Maintenance techniques yield similar results? We think so.
One example is the use of synthetic oils and lubricants that have been proven to significantly extend the life of tools and components and also reduces the number changes required over the service life of the machine. Quick change features that can assist with easy and ready access to service points on tooling and machines can also be implemented to reduce preventive maintenance times.
The other exclusion that is often argued is break times. Labour laws require you to provide break times for your employees. However, since automated processes are not subject to “Labour Laws”, the “mandatory break times” do not apply. We would argue that methods should be pursued to reduce the need for human intervention and look for ways to keep the machine running. Is it possible to automate some of the current processes or rotate people to keep the machine running?
Aside from this more obvious example, consider other organizational policies that may impact how your organization runs:
Shift start-up meetings
Employee Communication Meetings
End of Shift clean up periods
Quality first off approval process
Shift first off versus Run first off
Weld Tip changes – PM or Process Driven
What is the purpose of the shift start-up meeting? What is the purpose of the monthly employee communication meeting? Could this information be conveyed in a different form? What length of time is really required to convey the message to be shared? Is the duration of the meeting actually measured or do you resort to the standard time allotted?
Clean up periods at the end of the shift are also a common practice in many plants. What is being cleaned up? Why? Is it possible to maintain an orderly workplace during the shift – clean up as it happens in real-time? Again, do you record the actual clean up time or do you just enter the default clean up time allotted?
How much time is lost to verify the integrity of the product before allowing production to commence? What process parameters or factors would jeopardize the quality of the product being produced? No one wants to make scrap or substandard components, however, the challenge remains to determine what factors influence the level of quality. If it is possible to determine what factors are critical to success in advance, then perhaps the quality verification process becomes a concurrent event.
There are other factors that can impact availability including, but certainly not limited to, personnel (illness, inclement weather), material availability, other linked processes (feeder / customer), material changes, tool changes, quality concerns, and unexpected process, equipment, or machine faults.
It is possible to use manual or automated systems to collect various machine or process codes to record or document the duration and type of downtime event. We recommend and support the use of automated data collection systems, however, they should be implemented in moderation. One of the primary impediments to success is overwhelming volumes of data that no one has the time to analyze.
The Goal = 100% Up Time = ZERO Down Time = Zero Lost Time = Zero Defects = 100% Availability
The goal is to use the data and tools available to either permanently resolve the problem by implementing an effective corrective action or to assist the trouble shooting process by identifying the failure mode and to minimize the duration of the downtime event.
We have witnessed data collection strategies where an incredible number of sensors were installed to “catch” problems as they occur. The reality was the sensors themselves became the greater cause of downtime due to wear or premature failure due to improper sensor selection for the application. Be careful and choose wisely.
When used correctly, automation can be a very effective tool to capture downtime events and maintain the integrity of the overall measurement process. With the right tools, trouble shooting your process will minimize the duration of the down time event. Monitoring the frequency of these events will also allow you to focus your attention on real opportunities and circumvent nuisance faults.
The objective of collecting the “downtime event” history is to determine what opportunities are available to improve uptime.
Duration versus Frequency
The frequency of a downtime event is often overlooked as most of the attention is devoted to high duration downtime events. Some sources suggest that short duration downtime events (perhaps as little as 30 seconds) are not worth measuring. These undocumented losses are reflected, or more accurately hidden, by a corresponding reduction in the performance factor.
Be careful when setting what appears to be simple policy to document downtime. A 20 second downtime event that occurs 4 times per hour could quickly turn into 10 minutes a shift, 30 minutes a day, 2.5 hours a week, 125 hours a year. Rather than recording every event in detail, we recommend implementing a simple “tick” sheet to gain an appreciation for the frequency of failures. Any repetitive events can be studies and reviewed for corrective action.
Verify the Downtime
One of the advantages of OEE is that it is possible to reconcile the total time – OEE should never be greater than 100%. Of course this statement requires that the standard cycle time is correct and the total quantity of parts produced is accurate. So, although all of the downtime events may not be recorded, it is very easy to determine how much downtime occurred. This will help to determine how effectively downtime data is being recorded.
A perfect example to demonstrate this comes from the metal stamping industry. Progressive dies are used to produce steel parts from coil steel. The presses typically run at a fixed “predetermined” optimum run rate. Depending on the type of part and press, progressive dies are capable running at speeds from as low as 10 strokes per minute up to speeds over 300 strokes per minute.
For ease of calculation, assume we have a press that was scheduled to run a part over an 8 hour shift having two 10 minute breaks. The standard shift hours are 6:45 am – 3:15 pm and 3:30 pm – 12:00 am. The company provides a 30 minute unpaid meal break after 4 hours of work. The optimum press speed to run the part is 20 strokes per minute (spm). If a total of 6200 parts were made – how much downtime was incurred at the press?
To determine the press time required (also known as earned time), we simply divide the quantity of parts produced by the press rate as follows:
Machine Uptime: 6200 / 20 = 310 minutes
Our planned production time was 8 hours or 480 minutes. Assuming that company policy excludes break times, the net available time to run the press is 480 – (2 x 10) = 460 minutes.
Availability = Earned Time / Net Available Time = 310 / 460 = 67.39%
We can see from the above example that it easy to determine what the downtime should have been and, in turn, we could calculate the availability factor. This calculation is based on the assumption that the machine is running at the stated rate.
The Availability TWIST (1):
Knowing that press and die protection technologies exist to allow presses to run in full automatic mode, the two break periods from our example above do not apply to the equipment, unless company policy states that all machines or processes must cease operations during break periods.
Assuming that this is not the case, the press is available for the entire shift of 480 minutes. Therefore, the availability calculations from above would be:
Availability = Earned Time / Net Available Time = 310 / 480 = 64.58%
The Availability TWIST (2):
Just to expand on this concept just a little further. We also indicated that the company provided an unpaid lunch period of 30 minutes. Since meal breaks don’t apply to presses, the reality is that the press was also available to run during this period of time. The recalculated downtime and availability are:
Availability = Earned Time / Net Available Time = 310 / 510 = 60.78%
The Availability TWIST (3):
Finally, one last twist (we could go on). We deliberately indicated that there was a 15 minute break between shifts. Again, is there a reason for this? Does the machine have to stop? Why?
Availability – NEXT Steps
As you begin to look at your operations and policies, start by asking WHY do we do this or that? The example provided above indicates that a significant delta can exist in availability (close to 7%) although the number of parts produced has not changed. The differing results are related to policy, operating standard, or both.
If the performance (cycle time or production rate) and total quantity of parts produced data have integrity, the availability factor can be reconciled to determine the integrity of the downtime “data collection” system. From this example it should also be clear that the task of the data collection system is to capture the downtime history as accurately as possible to determine the opportunities to improve availability NOT just to determine how much downtime occurred.
This example also demonstrates why effective problem solving skills are critical to the success of your lean implementation strategy and is also one of the reasons why programs such as six sigma and lean have become integrated as parallel components of many lean execution strategies.
The Goal: 100% uptime / Zero downtime / Zero lost time /100% availability
Regardless of the measurement baseline used, be consistent. Exclusions are not the issue, it is a matter of understanding what is involved in the measurement process. For example, maintenance activities performed during break periods may be a good management practice to improve labour efficiencies, however, the fact that the work was performed during a break period should not exclude it from the “downtime” event history. We would argue that all activities requiring “equipment time” or “process time” should be recorded.
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.