Performing a time study is relatively easy compared to only few years ago. The technologies available today allow studies to be conducted quite readily.
Time Studies and OEE (Overall Equipment Effectiveness)
The Performance factor for OEE is based on the Ideal Cycle Time of the process. For fixed rate processes, the Name-Plate rate may suffice but should still be confirmed. For other processes such a labour intensive operations, a time study is the only way to determine the true or ideal cycle time.
When measuring the cycle time, we typically use “button to button” timing to mark a complete cycle. It can be argued that an operator may lose time to retrieve or pack parts or move containers. Including these events in the gross cycle time will hide these opportunities. It is better to exclude any events that are not considered to be part of the actual production cycle.
When calculating the Performance factor for Overall Equipment Effectiveness (OEE), the efficiency shortfalls will be noted by the less than 100% performance. The reasons for this less than optimal level of peformance are attributed to the activities the operator is required to perform other than actually operating the machine or producing parts.
All operator activities and actions should be documented using a standardized operating procedure or standardized work methodology. This will allow all activities to be captured as opposed to absorbed into the job function.
The BlackBerry Clock – Stopwatch
One of the tools we have used on the “fly” is the BlackBerry Clock’s Stopwatch function. The stopwatch feature is very simple to use and provides lap time recording as well.
When performing time studies using a traditional stopwatch, being able to keep track of individual cycle times can be difficult. With the stopwatch function, the history for each “lap” time is retained. To determine the individual lap time or cycle time, we recommend dividing the total lapsed time by the number of completed cycles (or laps).
The individual lap times are subject to a certain degree of uncertainty or error as there will always be a lead or lag time associated with the pushing of the button on the BlackBerry to signal the completion of a cycle. Although this margin of error may be relatively small, even with this level of technology, the human element is still a factor for consideration.
Once the time study is complete you can immediately send the results by forwarding them as an E-mail, PIN, or SMS.
The BlackBerry Camera – Video Camera
Another useful tool is the video camera. Using video to record operations and processes allows for a detailed “step by step” analysis at any time. This is particularly useful when establishing Standard Operating Procedures or Standardized Work.
Uploading videos and pictures to your computer is as easy as connecting the device to an available USB port. In a matter of minutes, the data is ready to be used.
Video can also be used to analyze work methods, sequences, and also serves as a valuable problem solving tool.
Until Next Time – STAY Lean!
We are not affiliated with Research In Motion (RIM). The intent of this post is to simply demonstrate how the technology can be used in the context described and presented.
We have learned that an industry standard or definition for Overall Equipment Effectiveness (OEE) has been adopted by the Semi Conductor Industry and also confirms our approach to calculating and using OEE and other related metrics.
The SEMI standards of interest are as follows:
SEMI E10: Definition and Measurement of Equipment Reliability, Availability, and Maintainability.
SEMI E35: Guide to Calculate Cost of Ownership Metrics.
SEMI E58: Reliability, Availability, and Maintainability Data Collection.
SEMI E79: Definition and Measurement of Equipment Productivity – OEE Metrics.
SEMI E116: Equipment Performance Tracking.
SEMI E124: Definition and Calculation of Overall Factory Efficiency and other Factory-Level Productivity Metrics.
It is important to continually learn and improve our understanding regarding the development and application of metrics used in industry. It is often said that you can’t believe everything you read (especially – on the internet). As such, we recommend researching these standards to determine their applicability for your business as well.
Best practices and methods used within and outside of your specific industry may bring a fresh perspective into the definition and policies that are already be in place in your organization. Just as processes are subject to continual improvement, so are the systems that control them. Although many companies use benchmarking data to establish their own performance metrics, we strongly encourage benchmarking of best practices or methods – this is where the real learning begins.
World Class OEE is typically defined as 85% or better. Additionally, to achieve this level of “World Class Peformance” the factors for Availability, Performance, and Quality must be at least 90%, 95%, and 99.5% respectively. While this data may present your team with a challenge, it does little to inspire real action.
Understanding the policies and methods used to measure performance coupled with an awareness of current best practices to achieve the desired levels of performance will certainly provide a foundation for innovation and improvement. It is significant to note that today’s most efficient and successful companies have all achieved levels of performance above and beyond their competition by understanding and benchmarking their competitors best practices. With this data, the same companies went on to develop innovative best practices to outperform them.
A Practical Example
Availablity is typically presented as the greatest opportunity for improvement. This is even suggested by the “World Class” levels stated above. Further investigation usually points us to setup / adjustment or change over as one of the primary improvement opportunities. Many articles and books have been written on Single Minute Exchange of Dies and other Quick Tool Change strategy, so it is not our intent to present them here. The point here is that industry has identified this specific topic as a significant opportunity and in turn has provided significant documentation and varied approaches to improve setup time.
In the case of improving die changes a variety of techniques are used including:
Quick Locator Pins
Standard Pass Heights
Standard Shut Heights
Quarter Turn Clamps
Dual Coil De-Reelers
Change Over Teams versus Individual Effort
Standardized Changeover Procedures
As change over time becomes less of a factor for determining what parts to run and for how long, we can strive reduced inventories and improved preventive maintenance activities.
The manufacturing community has been devastated by the recent economic downturn. We are challenged to bring out the best of what we have while continuing to strive for process excellence in all facets of our business.
Online Training is more rampant now than ever. If you want to learn about OEE and how to calculate it correctly then we have all the information you need right here. Simply click on the categories of interest to you and research your specific topic or Click Here to get started. This is the first article that got us started in November of 2008. All of our online content is presently available at no charge.
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We trust that the content presented here is of interest to you as well. We have provided many articles of interest related to OEE and Lean. Simply review the categories and posts available or visit our pages for more information. Our articles present detailed discussions and best practices applicable to the featured topic.
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.
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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!
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