Tag: Down Time

OEE in the Automotive Industry

The automotive industry appears to be rebounding at a faster rate than most (if not all) experts may have anticipated.  Many OEM’s and their suppliers are attempting to boost production to replenish inventories and support renewed demand for their products.  Reduced inventories throughout the supply chain are creating demand that is difficult to replenish at the rate required.  Short runs to bootstrap the “pipeline” are taking their toll on OEE rates but also provide the opportunity to identify new improvement initiatives.

General Motors and Toyota have both announced that increased demand for their product is anticipated for the next few months.  The increases are exciting for all involved, however, the ramp up to recovery may be more painful to achieve for some.  How is your company performing?  Those with fixed “cells” or processes may not be experiencing the same degree of frustration as those having flexible processes running multiple part numbers.

Overall Equipment Effectiveness (OEE) typically suffers during these times due to the frequent changeovers and short volume runs.  If there was a time when you can’t change over or setup and run fast enough, this may be it.  Hang on and enjoy the ride.

Until Next Time – STAY lean!

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OEE for Batch Processes

Coke being pushed into a quenching car, Hanna ...
Image via Wikipedia

We recently received an e-mail regarding OEE calculations for batch processes and more specifically the effect on down stream equipment that is directly dependent (perhaps integrated) on the batch process.  While the inquiry was specifically related to the printing industry, batch processing is found throughout manufacturing. Our more recent experiences pertain to heat treating operations where parts are loaded into a stationary fixed-load oven as opposed to a continuous belt process.

Batch processing will inherently cause directly integrated downstream equipment (such as cooling, quenching, or coating processes) to be idle. In many cases it doesn’t make sense to measure the OEE of each co-dependent piece of equipment that are part of the same line or process. Unless there is a strong case otherwise, it may be better to de-integrate or de-couple subsequent downstream processes.

Batch processing presents a myriad of challenges for line balancing, batch sizes, and capacity management in general.  We presented two articles in April 2009 that addressed the topic of  where OEE should be measured.  Click here for Part I or Click  here for Part II.

Scheduling Concerns – Theory of Constraints

Ideally, we want to measure OEE at the bottleneck operation.  When we apply the Theory of Constraints to our production process, we can assure that the flow of material is optimized through the whole system.  The key of course is to make sure that we have correctly identified the bottleneck operation.  In many cases this is the batch process.

While we are often challenged to balance our production operations, the real goal is to create a schedule that can be driven by demand.  Rather than build excess inventories of parts that aren’t required, we want to be able to synchronize our operations to produce on demand and as required to keep the bottleneck operation running.  Build only what is necessary:  the right part, the right quantity, at the right time.

Through my own experience, I have realized the greatest successes using the Theory of Constraints to establish our material flows and production scheduling strategy for batch processes.  Although an in-depth discussion is beyond the scope of this article, I highly recommend reading the following books that convey the concepts and application through a well written and uniquely entertaining style:

  1. In his book “The Goal“, Dr. Eliyahu A. Goldratt presents a unique story of a troubled plant and the steps they took to turn the operation around.
  2. Another book titled “Velocity“, from the AGI-Goldratt Institute and Jeff Cox also demonstrates how the Theory of Constraints and Lean Six Sigma can work together to bring operations to all new level of performance, efficiency, and effectiveness.

I am fond of the “fable” based story line presented by these books as it is allows you to create an image of the operation in your own mind while maintaining an objective view.  The analogies and references used in these books also serve as excellent instruction aids that can be used when teaching your own teams how the Theory of Constraints work.  We can quickly realize that the companies presented in either of the above books are not much different from our own.  As such, we are quickly pulled into the story to see what happens and how the journey unfolds as the story unfolds.

Please leave your comments regarding this or other topics.  We appreciate your feedback.  Also, remember to get your free OEE spreadsheets.  See our free downloads page or click on the file you want from the “Orange” box file on the sidebar.

Until Next Time – STAY lean!

Vergence AnalyticsVergence Analytics

OEE: Frequently Asked Questions

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.

We trust you’re enjoying your summer.

Until Next Time – STAY Lean!

Vergence Business Associates

How to Improve OEE – Any Questions?

Ask any Quality or Engineering manager and they will tell you that measurement systems are valuable tools to identify problems and opportunities.  The measurement system itself is not the answer – it is the data source, the EVIDENCE that drives the questions.  It is a part of the discovery and validation process to confirm the opportunity or problem and the effectiveness of the solutions to resolve it.

A well integrated OEE system should provide the data to answer the questions on everyone’s mind, “What do we need to do to improve?” or “Why aren’t we improving?”  The simple answer is, “We need to fix it.”  Of course the real question may not be, “What do we need to fix?” but, “Why did it break?”

Yes, we will likely have to replace the part(s) that failed to get the line back up and running, but what really caused the failure to occur?  What was the real root cause?  This introductory post to problem solving and root cause analysis will kick start some of the techniques used to solve problems effectively.

The Problem Statement:

The key to effective problem solving starts with identifying the problem to be solved.  This is typically a brief statement describing the problem.  For external concerns, the problem is usually stated in customer terms.

This post presents some simple examples of problems to be solved.  You will quickly discover that defining the problem may not be as simple as it looks.  We will discuss this in more depth in our future posts.

Root Cause Analysis

Identifying the real root cause(s) for the failure is the secret to successful problem solving.  The method you use to arrive at the root cause should allow you to confirm and validate your solution before taking action.  Here is an important point to remember:

Do not confuse symptoms with root causes.  

For example, you are driving down the road and suddenly find yourself struggling to maintain control of your vehicle.  Your expert driving skills allow you to pull over and stop on the side of the road.  You get out of the car and walk around to discover that you have a flat tire.  The flat tire is a symptom – not the root cause.

As luck would have it, a police officer who just happened to be following you in an unmarked car, notices your sudden erratic driving behavior and charges you with recklessness and careless driving.  Since none of the tires on the police car are flat, the officer presumes the condition of your vehicle is the direct result of your poor driving skills and bad habits after many years on the road.  Another point to remember:

Do not jump to conclusions

You, like many people, would argue that your many years of driving provided you with the experience necessary to avert danger.  The officer quickly recognizes that your many years of experience have caused you to lose perspective of the potential hazards of driving.  The officer advises that your driving record shows no record of any tickets or accidents and clearly suggests that you have had very few “experiences” with the law and minimal exposure to poor road conditions.

The officer proceeds to charge you, the operator, because you simply weren’t paying attention to the conditions and potential hazards of the road.  You are given a ticket to serve as a reminder to pay more attention to the road and to be mindful of your driving habits in the future.  Then to add insult to injury, the officer advises you to fix your tire and drive carefully. 

Unforgiving of the circumstances and since quota’s have to be met, the charges stand and you find yourself on your way to court.  As you sit in your vehicle, stunned that you just got a ticket for getting a flat tire, you are conflicted and fuming because the officer blamed you, your poor driving skills, and your bad habits for driving recklessly down the road!  The following tip will help you remember:

Operator Error is not a Root Cause

Many times, management is too quick to attribute the root cause to operator error.

5 WHY Analysis

One of the best methods for identifying the real root cause is the 5-Why approach.  The concept of asking the question “WHY?” five times is quite simple.  In practice though, you will find it may not be that easy.  Why?  Because the wrong answer will lead you through a continuing series of wrong answers that ultimately lead to the wrong conclusion.

There is always more than one answer – Which one is correct?

Referring back to our example of the flat tire, you now need an argument to absolve yourself of any blame for the incident on the highway.  In court, the judge asks, “How you plead to the charges before you?”  You answer, “Not Guilty your honor.”

  1. Why?  While I was driving down the road, I got a flat tire.
  2. Why?  Because all the air ran out of my tire.
  3. Why?  Because there was a hole in it.
  4. Why?  Because the tire didn’t have anti-puncture technology.
  5. Why?  Because the manufacturer didn’t design it properly.

Were it not for my expert driving skills, this situation could have been much worse.  As it was, using my superior driving skills, I successfully managed to maneuver my vehicle, without incident, to the side of the road, averting what could have been a disastrous crash.  Therefore, I request to be completely absolved of any and all wrongful doing and I am filing a class action suit against the tire manufacturer to cover court costs, lost wages, and damages as well as my emotional stress.

Clearly not satisfied, the judge requests you to take a 10 minute break to rethink your case.  On your return to the courtroom, you are prepared to present the following argument:

  1. Why?  While I was driving down the road, I got a flat tire.
  2. Why?  Because all the air ran out of my tire.
  3. Why?  Because there was a hole in it.
  4. Why?  Because there was a nail on the road.
  5. Why?  Because the government refuses to keep the highways clean.

Were it not for my expert driving skills, this situation could have been much worse.  As it was, using my superior driving skills, I successfully managed to maneuver my vehicle, without incident, to the side of the road, averting what could have been a disastrous crash.  Therefore, I am filing a class action suit against the government to cover for court costs, lost wages, and damages as well as my emotional stress.  To resolve this matter quickly, I request that all charges be dropped and I in turn will drop my counter-claim.

The purpose of the above example was to demonstrate how the answer to the question – WHY? – can lead to completely different conclusions.  On one hand we’re ready to sue the tire manufacturer and on the other, we’re ready to take on the government.  If there was indeed a nail on the road, how did it get there?

Don’t Assign Blame

Solving problems and getting to the root cause is not about assigning blame to someone or something.  You can’t blame the government or the tire company for the fact that there was a nail on the road.  It is to easy to assign blame and it happens everywhere, everyday.  Perhaps the nail manufacturer should be sued as well for failing to provide adequate protections should the nail become lost or misplaced.

The question that wasn’t asked is, “Why was the nail on the road?”  The answer may be that it likely fell out of a board or from a truck or trailer that may have been carrying construction materials.  Again, being careful with the answer, we don’t want to come to the conclusion that nails should be banned completely.

On the other hand, it may be worthwhile to advise that all companies and contractors must make a reasonable effort and take appropriate precautions and measures to ensure that all loads are secure and free from loose raw materials.  Any nails must be placed in a sealed container and secured to the vehicle for the purpose of transport.  A maximum fine of $2,000.00 may be imposed and made payable to the “Operator Error Trust Fund.”

Leading the Witness:  The solution BIAS

STOP! – if you think you already know the answer – Stop!  We know that the right question doesn’t always lead to the right answer as we attempted to show in our example.  Another major pitfall is thinking we already have the answer and we just need to frame the questions and answers to support that conclusion.  This isn’t problem solving, this is creative story telling.  Don’t lead your team into following what “appears” to be a logical conclusion – be prepared to prove it.

Don’t Assume Anything – Follow the EVIDENCE

At a minimum, follow the evidence.  What is the data telling you?  It’s time to start thinking like a crime scene investigator (CSI) or good lawyer.  Asking questions and continuing to probe for answers is the secret to uncovering the less obvious and, more than likely, real solution.

Many OEE equipment / software integrators provide the ability to record and track downtime events in real time.  This data is extremely valuable for trouble shooting and problem solving; however, they are not necessarily root causes.  The integrators provide the capability to readily identify what part of the process failed or what is broken.  While this may be the cause of the line down condition, it is not the root cause of the problem.

Do not confuse the Point of Failure (Source) with the Root Cause

Don’t fall into this trap:

  • Supervisor:  “The OEE system report showed that we lost two hours on the paint line last night.”
  • Maintenance:  “Yeah, I saw the report too.  This OEE system tracks everything!”
  • Supervisor:  “Why did the line go down?”
  • Maintenance:  “The A-Tank feed pump overheated.  The OEE system told us exactly which pump failed.  It saved us a ton of time.”
  • Supervisor:  “What did you do?”
  • Maintenance:  “Oh, we replaced it.  The line is running fine now.”
  • Supervisor:  “OK, that’s good.  Thanks.”

End of conversation.

So, WHY did the pump overheat?  Some questions just never get asked, but I’m sure the OEE will be just fine on the next shift.  We recognize that most effective TPM managers are sharper than this.  Our point is that not everyone is looking at the data from the same perspective.

We’ll discuss “How to Improve OEE” in more detail in our next post:  “How to use the 5 Why Approach.”

Until Next Time – STAY lean!

OEE Measurement Error

How many times have you, or someone you know, challenged the measurement process or method used to collect the data because the numbers just “don’t make sense” or “can’t be right”?

It is imperative to have integrity in the data collection process to minimize the effect of phantom improvements through measurement method changes.  Switching from a manual recording system to a completely automated system is a simple example of a data collection method change that will most certainly generate “different” results.

Every measurement system is subject to error including those used to measure and monitor OEE.  We briefly discussed the concept of variance with respect to actual process throughput and, as you may expect from this post, variance also applies to the measurement system.

Process and measurement stability are intertwined.  A reliable data collection / measurement system is required to establish an effective baseline from which to base your OEE improvement efforts.  We have observed very unstable processes with extreme throughput rates from one shift to the next.  We learned that the variance in many cases is not always the process but in the measurement system itself.

We decided to comment briefly on this phenomenon of measurement error for several reasons:

  1. The reporting systems will naturally improve as more attention is given to the data they generate.
  2. Manual data collection and reporting systems are prone to errors in both recording and data input.
  3. Automated data collection systems substantially reduce the risk of errors and improve data accuracy.
  4. Changes in OEE trends may be attributed to data collection technology not real process changes.

Consider the following:

  1. A person records the time of the down time and reset / start up events by reading a clock on the wall.
  2. A person records the time of the down time event using a wrist watch and then records the reset /start up time using the clock on the wall.
  3. A person uses a stop watch to track the duration of a down time event.
  4. Down time and up time event data are collected and retrieved from a fully automated system that instantly records events in real time.

Clearly, each of the above data collection methods will present varying degrees of “error” that will influence the accuracy of the resulting OEE.  The potential measurement error should be a consideration when attempting to quantify improvement efforts.

Measurement and Error Resolution

The technology used will certainly drive the degree of error you may expect to see.  A clock on the wall may yield an error of +/- 1 minute per event versus an automated system that may yield an error of +/- 0.01 seconds.

The resolution of the measurement system becomes even more relevant when we consider the duration of the “event”.  Consider the effect of measurement resolution and potential error for a down time event having a duration of 5 minutes versus 60 minutes.

CAUTION!

A classic fallacy is “inferred accuracy” as demonstrated by the stop watch measurement method.  Times may be recorded to 1/100th of a second suggesting a high degree of precision in the measurement.  Meanwhile, it may take the operator 10 seconds to locate the stop watch, 15 seconds to reset a machine fault, and 20 seconds to document the event on a “report” and another 10 seconds to return the stop watch to its proper location. 

What are we missing?  How significant is the event and was it worth even recording?  What if one operator records the “duration” after the machine is reset while another operator records the “duration” after documenting and returning the watch to its proper location?

The above example demands that we also consider the event type:  “high frequency-short duration” versus “low frequency-long duration” events.  Both must be considered when attempting to understand the results.

The EVENT is the Opportunity

As mentioned in previous posts, we need to understand what we are measuring and why.  The “event” and methods to avoid recurrence must be the focus of the improvement effort.  The cumulative duration of an event will help to focus efforts and prioritize the opportunities for improvement.

Additional metrics to help “understand” various process events include Mean Response Time, Mean Time Between Failures (MTBF), and Mean Time To Repair (MTTR).  Even 911 calls are monitored from the time the call is received.  The response time is as critical, if not more so, than the actual event, especially when the condition is life-threatening or otherwise self-destructive (fire, meltdown).

An interesting metric is the ratio between Response Time and Mean Time To Repair.  The response time is measured from the time the event occurs to the time “help” arrives.  Our experience suggests that significant improvements can be made simply by reducing the response time.

We recommend training and providing employees with the skills needed to be able to respond to “events” in real time.  Waiting 15 minutes for a technician to arrive to reset a machine fault that required only 10 seconds to resolve is clearly an opportunity.

Many facilities actually hire “semi-skilled” labour or “skilled technicians” to operate machines.  They are typically flexible, adaptable, present a strong aptitude for continual improvement, and readily trained to resolve process events in real time.

Conclusion

Measurement systems of any kind are prone to error.  While it is important to understand the significance of measurement error, it should not be the “primary” focus.  We recommend PREVENTION and ELIMINATION of events that impede the ability to produce a quality product at rate.

Regrettably, some companies are more interested in collecting “accurate” data than making real improvements (measuring for measurements sake). 

WHAT are you measuring and WHY?  Do you measure what you can’t control?  We will leave you with these few points to ponder.

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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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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