Tag: TEEP

OEE: Planned Downtime and Availability

Injection Molding Press
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As a core metric, Overall Equipment Effectiveness or OEE has been adopted by many companies to improve operations and optimize the capacity of existing equipment.  Having completed several on site assessments over the past few months we have learned that almost all organizations are measuring performance and quality in real-time, however, the availability component of OEE is still a mystery and often misunderstood – specifically with regard to Set Up or Tool Changes.

We encourage you to review the detailed discussion of down time in our original posts “Calculating OEE – The Real OEE Formula With Examples” and “OEE, Down time, and TEEP” where we also present methods to calculate both OEE and TEEP.  The formula for Overall Equipment Effectiveness is simply stated as the product of three (3) elements:  Availability, Performance, and Quality.  Of these elements, availability presents the greatest opportunity for improvement.  This is certainly true for processes such as metal stamping, tube forming, and injection molding, to name a few, where tool changes are required to switch from one product or process to another.

Switch Time

Set up or change over time is defined as the amount of time required to change over the process from the last part produced to the first good part off the next process.  We have learned that confusion exists as to whether this is actually planned down time as it is an event that is known to occur and is absolutely required if we are going to make more than one product in a given machine.

Planned down time is not included in the Availability calculation.  As such, if change over time is considered as a planned event, the perceived availability would inherently improve as it would be excluded from the calculation.  Of course, the higher availability is just an illusion as the lost time was still incurred and the machine was not available to run production.

If we could change a process at the flip of a switch, set up time would be a non-issue and we could spend our time focusing on other improvement initiatives.  While some processes do require extensive change over time, there is always room for improvements.  This is best exemplified by the metal stamping industry where die changes literally went from Hours to Minutes.

To remain competitive and to increase the available capacity, many companies quickly adopted SMED (Single Minute Exchange of Dies) initiatives after recognizing that significant production capacity is being lost due to extensive change over times.  Overtime through extended shifts and capital for new equipment is also reduced as capacity utilization improves.

Significantly reduced inventories can also be realized as product change overs become less of a concern and also provide greater flexibility to accommodate changes in customer demand in real-time.  Significantly increased Inventory Turns will also be realized in conjunction with net available cash from operations.

Redefining Down Time

The return on investment for Quick Tool Change technologies is relatively short and the benefits are real and tangible as demonstrated through the metrics mentioned above.  Rather than attempt to categorize down time as either planned or unplanned, consider whether the activity being performed is impeding the normal production process or can be considered as an activity required for continuing production.

We prefer to classify down time as either direct or indirect.  Any down time such as Set Up, Material Changes, Equipment Breakdowns, Tooling Adjustments, or other activity that impedes production is considered DIRECT down time.  Indirect down time applies to events such as Preventive Maintenance, Company Meetings, or Scheduled IDLE Time.  These events are indeed PLANNED events where the machine or process is NOT scheduled to run.

Redefine the Objective

Set up or change over time is often the subject of much heated debate and tends to create more discussion than is necessary.  The reason for this is simple.  Corporate objectives are driven by metrics that measure performance to achieve a specific goal.

Unfortunately, in the latter case, the objectives are translated into personal performance concerns for those involved in the improvement process.  Rather than making real improvements, the tendency is to rationalize the current performance levels and to look for ways to revise the definition that creates the perception of poor performance. Since availability does not include planned down time, many attempts are made to exclude certain down time events, such as set up time, to create a better OEE result than was actually achieved.

Attempts to rationalize poor performance inhibits our ability to identify opportunities for improvement.  From a similar perspective, we should also be prudent with. and cognizant of, the time allotted for “planned” events.

It is for this reason that some companies have resorted to measuring TEEP based on a 24 hour day.  In many respects, TEEP eliminates all uncertainty with regard to availability since you are measured on the ability to produce a quality part at rate.  As such, our mission is simple – “To Safely Produce a Quality Part At Rate, Delivered On Time and In Full”.  Any activity that detracts from achieving or exceeding this mission is waste.

Remember to get your OEE spreadsheets at no charge from our Free Downloads Page or Free Downloads Box in the sidebar.  They can be easily and readily customized for your specific process or application.

Please feel free to send your comments, suggestions, or questions to Support@VergenceAnalytics.com

Until Next Time – STAY lean!

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Going DEEP with OEE

Does anyone actually look at their daily equipment availability? Instead of using TEEP that is typically based on calendarized availability, looking at the Daily Equipment Effectiveness Performance of your operation may provide some interesting insights.

Working overtime due to material or equipment availability occurs many times.  Unfortunately, we find that sometimes these very same machines are idle during the week.

A detailed explanation for calculating DEEP can be found in one of our earlier posts, “OEE, Downtime, and TEEP.”  Understanding machine utilization patterns may provide greater insight into the actual versus planned operating pattern of your process.

Just something to invoke some thoughts for your operation and to perhaps identify another opportunity to improve performance.

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.

Please forward your questions, comments, or suggestions to LeanExecution@gmail.com.  To request our services for a specific project, please send your inquiries to Vergence.Consulting@gmail.com.

We welcome your feedback and thank you for visiting.

Until Next Time – STAY Lean!

Availability and OEE

What is Availability?

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.

Availability Considerations

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:

  1. Shift start-up meetings
  2. Employee Communication Meetings
  3. End of Shift clean up periods
  4. Quality first off approval process
  5. Shift first off versus Run first off
  6. 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.

Measuring Downtime.

 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.

Calculated downtime = Available – Earned = 460 – 310 =150 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:

Calculated downtime = Available – Earned = 480 – 310 =170 minutes

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:

Calculated downtime = Available – Earned = (480 + 30) – 310 =200 minutes

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.

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.

Please forward your questions, comments, or suggestions to LeanExecution@gmail.com.  To request our services for a specific project, please send your inquiries to Vergence.Consulting@gmail.com.

We welcome your feedback and thank you for visiting.

Until Next Time – STAY Lean!

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Practical OEE – How To Calculate and Use Weighted OEE

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:

  1. Machine A:  OEE = 80.22%, Net Available Time = 455 minutes
  2. Machine B:  OEE = 70.05%, Net Available Time = 455 minutes
  3. 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:

  1. Machine A:  Weighted OEE = 80.22% * (455 / 1365) = 26.74%
  2. Machine B:  Weighted OEE = 70.05% * (455 / 1365) = 23.35%
  3. Machine C:  Weighted OEE = 55.90% * (455 / 1365) = 18.63%

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:

  1. Machine A:  Availability = 92.97%, Net Available Time = 455 minutes
  2. Machine B:  Availability = 96.04%, Net Available Time = 455 minutes
  3. 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:

  1. Machine A:  Weighted Availability = 92.97% * (455 / 1365) = 30.99%
  2. Machine B:  Weighted Availability = 96.04% * (455 / 1365) = 32.01%
  3. Machine C:  Weighted Availability = 95.16% * (455 / 1365) = 31.72%

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:

  1. Machine A:  performance = 88.26%, Net Operating Time = 423 minutes
  2. Machine B:  Performance = 77.23%, Net Operating Time = 437 minutes
  3. 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:

  1. Machine A:  Weighted Performance = 88.26% * (423 / 1293) = 28.87%
  2. Machine B:  Weighted Performance = 77.23% * (437 / 1293) = 26.10%
  3. Machine C:  Weighted Performance = 61.70% * (433 / 1293) = 20.66%

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:

  1. Machine A:  Quality = 97.77%, Ideal Operating Time = 373.33 minutes
  2. Machine B:  Quality = 94.44%, Ideal Operating Time = 337.50 minutes
  3. Machine C:  Quality = 95.20%, Ideal Operating Time = 267.17 minutes

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:

  1. Machine A:  Weighted Quality = 97.77% * (373.33 / 978) = 37.32%
  2. Machine B:  Weighted Quality = 94.44% * (337.50 / 978) = 32.59%
  3. Machine C:  Weighted Quality = 95.20% * (267.17 / 978) = 26.01%

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.

Until Next Time – STAY Lean!

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OEE, Downtime, and TEEP

We have received several inquiries regarding equipment down time – periods of time when the machine is not scheduled to run.  We consider this to be scheduled down time or idle time and does not affect Overall Equipment Effectiveness (OEE), since no production was planned during this period.

OEE measures overall equipment effectiveness during planned production or SCHEDULED up time.  Do not confuse idle time with tooling or material change over as these activities should be part of the scheduled machine time – periods where the machine is not scheduled to run.  After hours or weekends are examples of idle time.

TEEP or Total Equipment Effectiveness Performance is another variable, similar to OEE, and measures the Total Equipment Effectiveness Performance based on calendar time – the total time the equipment is “present”.  If process “A” is in your plant for 24 hours a day, 7 days a week, then the total time required to make good parts is divided by the time the asset, process, or equipment is “present” and is therefore “technically available” for the time frame being considered.  Typically this is based on calendar time – 24 hours per day and 7 days per week.

Another way to view TEEP is to consider it as a measure of how effectively the total capacity of a process or machine is being utilized to make GOOD parts.  In short, TEEP could be defined as a measure of Equipment Capacity Utilization Effectiveness.

TEEP Calculation Example:

In the metal stamping business, raw coil steel is processed through a die that runs in a stamping press to manufacture the parts.  The ideal cycle time for may be 30 strokes (or parts) per minute.  While the press may be scheduled to run for 16 hours, it is technically “present” or available 24 hours.  If, in a given day, a total of 18,000 GOOD parts were produced over 16 hours of scheduled production time, the OEE is easily calculated.

We will first calculate the IDEAL hours required to produce 18,000 parts at 30 spm.  The IDEAL rate per hour is 1,800 parts (30 spm * 60 minutes  / hour).  Therefore the IDEAL time to produce 18,000 good parts is 10 hours (18,000 parts / 1,800 per hour).

If this is a two shift operation, the net available time is 16 hours (scheduled) and the OEE for the day is calculated as 10 / 16 = 62.5 %.

Since the press is always present, 24 hours per day – 7 days per week, the Daily Equipment Effectiveness Performance (DEEP) in this case is 10 / 24 = 41.7 %.  While this example only represents a single 24 hour day, the basis for calculation is the same.  If the time frame is one week, one month, one quarter, the Total Equipment Effectiveness Performance for that time frame is calculated using the following formula:

TEEP = Total IDEAL Time to Produce Good Parts / Total Gross Time Available

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.

Feel free to leave any comments or send your questions to LeanExecution@gmail.com

Until next time – STAY Lean!

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