Tag: How to Calculate OEE

Improving OEE: A Hands On Approach

We have explored Overall Equipment Efficiency (OEE) from several perspectives and how it can be used as an effective performance metric.  The purpose of measuring and monitoring OEE, at a minimum, should be three fold:

  1. To ensure the current performance levels are sustained,
  2. To identify new opportunities for improvement,
  3. To assess the effectiveness of current improvement initiatives.

The Culture of Continuous Improvement and Innovation

A continuous improvement “mindset” must be part of the organizational culture to achieve maximum results.  Too many companies charge the engineering department or some other “arm” of the organization to generate the ideas that can be implemented to improve availability, performance, and / or quality.  We strongly urge you to include everyone in the improvement process, especially the very people who perform the tasks on a daily basis.  Why?  The simple answer is, “They are the eyes and ears of the process”.

Despite some of the old school thinking that may persist in industry, most people take pride in their work and want to do a good job.  OEE is as much a performance metric for the individuals on the shop floor as it is for the management and leadership of the company.  Even the most educated doctor will ask the patient what the symptoms are as part of the assessment process.

While it may be difficult to assess what level of improvement can be achieved, it has been suggested that world class OEE is 85%.  We suggest that you establish a reasonable baseline and determine relative improvements accordingly.  The baseline you use should be comprised of two key components:

  1. Historical data for OEE and each factor (Availability, Performance, and Quality)
  2. A detailed Standard Operating Procedure for each process under consideration

Getting Started – Collect and Communicate Data

Almost every continuous improvement (CI) activity or project is accompanied by a list of actions that must be implemented.  Where does this list come from?

There are at least two very basic approaches to getting the improvement process underway:

  1. Collect and analyze data from the current process
  2. Set up a FLIP Chart at the line or machine

Step 1 should be fairly straightforward.  The premise here is that OEE data is already being collected and analyzed on a regular basis.  Step 2 may not be as familiar to you.

FLIP Charts

This is probably one of the most fundamental and basic data collection tools available on the market.  This approach may seem overly simplistic but the objective is to keep it simple and effective.

Advantages:

  1. Data collection in “real time”
  2. Anyone can add to the List
  3. Anyone can update the List
  4. Readily Available to ALL
  5. Writing Skills ONLY
  6. Instant Feedback
  7. Highly Visible

What do we record on the FLIP chart?  We have experienced the best success with the following simple format.  At the top of the FLIP chart write down Today’s Date and Shift, then setup the following headings:

Time   Problem/Concern   Assigned To   Task Completed   By (Initials)

Any time an event occurs or an opportunity arises for improvement, simply enter the appropriate data under the headings shown.  The flip chart can also be used to track progress – INSTANTLY.  Whenever a task is completed, the person responsible for the “fix” simply enters the time / date and their initials.

FLIP Chart – Built in Accountability

Using the flip chart as a living “action item list” introduces accountability from all levels to the process on the shop floor.  As tasks or actions are completed, everyone will see that the concerns are being addressed causing the improvement cycle to continue and reinforcing the value of everyone’s input to the process.

Our experience has shown the FLIP chart to be one of the most engaging improvement processes on a continuing basis.  Improvement history is readily available on the shop floor.  No complex searches, computer programs, or advanced skill set is required to see what is going on and what is being done about it.  As much as we don’t like to put problems on display, you may be surprised how impressed your customers are with this type of interactive CI process.

The FLIP chart is a very primitive but effective tool for collecting data and communicating results.

Improving OEE

Since OEE is comprised of three elements, it stands to reason that at least three major improvement initiatives exist:  Availability, Performance, and Quality.  How do we go about improving these elements?

Availability: Start with a downtime assessment:

  1. Categorize Events (Planned vs. Unplanned)
  2. Frequency / Occurrence Rate
  3. Duration
  4. Type:  Planned, Preventable, Predictable, Unplanned, Unknown

From our previous discussions on Availability, the known “Planned” events may include such change events as materials, tooling, and personnel (shift changes and / or breaks).  Improving availability requires the elimination of UNPLANNED events and reducing the duration of PLANNED events.  Successful improvements can only be developed and achieved if there is integrity in the baseline information and data.

Implementing SMED (single minute exchange of dies) is one strategy to reduce the duration of die changes.  A detailed die change process is used to determine the activities that can be performed while the machine is still running (External Events) and those that can only be performed while the machine is down (Internal Events).  Further assessments are conducted to determine what improvements are possible to reduce the duration of the internal events.  Such improvements may include hydraulic clamping, quarter turn screws, standardized shut heights, standardized locating pins, standardized pass heights to name a few.

Scheduling sequences may also be an important factor in the change over process.  If a common material (type or color) is used for two different parts, it may be more effective to run them back to back through the same machine.  Tooling may be shared among different part numbers and would require less change over time if they were considered as a product family for scheduling purposes.

Policy changes and capital investments are easily justified when you are able to demonstrate the improvements using a “plan vs actual” strategy that is complimented by data and a standard operating procedure.

Performance: Improving performance is not to be confused with reducing the process time (making it faster).  They are two different activities entirely.  If the original cycle time or process rate was calculated correctly, then 100% performance should be achievable right?  Once again, the answer to this question depends on company policy and the method that was used to establish the standard.

Our purpose is not to introduce more confusion, but rather, to make sure that whatever policy is in place is clearly defined and understood.  Remember, the only real industry standard for OEE is the formula used to calculate the result:  A x P x Q.  A standard definition or criteria for determining the individual factors does not exist.

The cycle time for an automated process can easily be determined by measuring the output without disruption over a known period of time.  Is this consistent with company policy?  Is the standard cycle time based on the stated nameplate capacity (rate) or is it based on the actual achieved (optimum) cycle time?

A “button to button” cycle time may be established for a manual operation in a similar manner.  Although it may be perceived as a flaw, the button to button analysis may not necessarily consider container changes or restocking of components that may be required from time to time.  If these “other” tasks are not factored into the cycle time, then it would be impossible to achieve 100% performance unless someone other than the operator was made responsible for those activities.

Start with a Performance Assessment

  1. Confirm company policy and methods for calculating the cycle time.
  2. Confirm the Cycle Time or Production Rate (Time Study)
  3. Compare the Actual versus Standard Operating Procedure
  4. Review the process performance history and data records.
  5. Equipment Condition Assessment – Preventive Maintenance
  6. Process Type:  Automation, Semi-Automation, Manual (Human Effort)
  7. Confirm Reporting Integrity

Only after you have reviewed the data and discussed the opportunities with the team will you be able to develop a performance improvement plan.

Using the “button to button” manual process described above, we already indicated that a person other than the operator could be responsible for restocking components and changing containers to allow the operator to run the machine without interruption.  There may be other activities as well that could be performed someone other than the operator.  A detailed Standard Operating Procedure complete with clearly defined steps (step tasks) and timing for each is the best tool available to improve performance.

Is it possible to change the method or sequence of events that the operator is following to reduce the time taken to perform a step task.  Is the operation “handed”, in other words, does it favor right versus left handed people?  Is the material arranged in such a way as to optimize (minimize) the operator’s movements during the cycle?  Are all operator’s performing the step tasks per the standard operating procedure?  Is the machine itself performing at the optimized cycle or is it running at a slower speed due to electrical, mechanical, or fluid faults?

Some of the activities identified may result in speed increases that will lead to performance improvements relative to the current standard.  Again, company policy should dictate when and how standards are to be updated.  If the standard is updated everytime the cycle time is reduced, how will you recognize the improvement?  We would recommend resetting the standards annually in conjunction with the new fiscal year.  The new performance levels should also be reflected in the business plan.

Quality: This is perhaps one of the easiest to factors to define and may be one of the more difficult factors to improve.  Again this will depend on the definition or criteria used to calculate the Quality factor.  The typical definition adopted by most manufacturers states that any parts failing to meet First Time Through quality criteria include those designated as scrap, test, rework, sort, and / or hold.  In other words, First Time Through quality applies only to those parts that are considered acceptable at the point and time of production.

When do you start counting?  Should set up parts be included in the Quality definition?  We would argue against including set up parts in the quality calculation, however, that doesn’t mean they shouldn’t be accounted for because the material loss is a real cost to the company.  We would define set up time as starting from the last good part produced to the first good part produced for the next job in.

The objective of any Quality improvement strategy is obviously zero defects.  The task is getting it done.

Quality: Start with a Quality Assessment:

  1. Review Process Failure Modes Effects and Analysis (PFMEA)
  2. Review Current Quality Control Plans (Inspection Requirements)
  3. Review and Analyze Quality Performance Data
  4. Review scrap and rework analysis
  5. Identify Top Opportunities (Pareto Analysis)
  6. Initiate Problem Solving Activities (DMAIC, PDCA, PDSA, IDEA Loops)
  7. Execute problem solving strategy
  8. Update Lessons Learned and Best Practices

The ultimate goal for any quality program is to achieve a level of zero defects.  A second, closely related goal is to eliminate, reduce, and control variation in our processes.  Variation and defects are directly correlated and are typically quantified by statistical modeling tools such as the normal distribution or bell curve.  Many tools are available to study and analyze the various attributes of a process to effectively determine the root cause for a given defect.

Some of the many problem solving methods and tools include 8-Discipline Analysis, 5 Why, Fault Tree Analysis, Cause and Effect Diagrams, Pareto Analysis, Design of Experiments (DOE), Analysis of Variance (ANOVA) tools among others.

Next Steps

We have identified the various methods to generate improvement activities. The key to success is developing the action plans and executing them in a timely manner.  This is the critical part of the improvement process.

A word of caution:  Don’t confuse activity with action.  Too many times, the data collection and study processes consume all the resources and more time is spent on data presentation than real analysis.  The goal is to improve the process, solve the problems, and eliminate the defects.

No Input Change = No Output Change

Lessons Learned and Best Practices

It is possible that the wrong process was selected for the product being manufactured.  This may range from the actual tooling to the very equipment that is used to run it.  It is also possible that the capability of the machine was overstated or over-rated prior to purchase.

Maintaining a lessons learned database is one way to make sure that we don’t make the same mistake twice.  It can also serve as a future reference when developing standards for future products or processes.

Perhaps a product or process requires a technology that simply doesn’t exist.  Could this be the stepping stone for a future research and development project?  How do we take things to the next level – the break through?

Until next time – STAY lean!

Twitter:  @Versalytics

Please feel free to forward your questions or comments to us by e-mail at LeanExecution@gmail.com

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OEE For Dedicated – Single Part – Processes

OEE For Dedicated – Single Part – Processes

Definition: 

Dedicated – Single Part – Process:  A process that produces a single product or slight variations on a theme and does not require significant tooling or equipment changeover events.

A single part process is the easiest application for a OEE pilot project.  The single part process also makes it easier to demonstrate some of the more advanced Lean Thinking tools that can be applied to improve your operation or process.  In our “Variation, Waste, and OEE” post, we introduced the potential impacts of variance to your organization.  We also restated our mission to control, reduce, and eliminate variation in our processes as the primary objective of LEAN.

We need to spend more time understanding what our true production capabilities are.  The single part process makes the process of understanding these principles much easier.  The lessons learned can then be applied to more complex or multipart processes.  In multipart or complex operations, production part sequencing may have a significant impact on hourly rates and overall shift throughput.  How would you know unless you actually had a model that provided the insight?

Process Velocity:  Measuring Throughput

Let’s start this discussion by asking a few simple questions that will help you to get your mind in gear.  Do you measure variation in production output?  Do you measure shift rates?  Do you use the “average” rate per hour to set up your production schedules?  How do you know when normal production rates have been achieved?  Does a high production rate on one shift really signify a process improvement or was it simply a statistically expected event?

Once again an example will best serve our discussion.  Assume the following data represents one week of production over three shifts:

Machine A:  Production Process Performance Report

Cycle Time (Seconds):   57      
Shift Standard (440 minutes) 440      
             
Day Shift Planned Quantity
Production Time Total Test Scrap Accept
Mon 1 440 420 1 2 417
Mon 2 440 390 1 1 388
Mon 3 440 320 1 3 316
Tue 1 440 361 1 1 359
Tue 2 440 392 1 5 386
Tue 3 440 365 1 2 362
Wed 1 440 402 1 7 394
Wed 2 440 317 1 6 310
Wed 3 440 430 1 1 428
Thu 1 440 453 1 5 447
Thu 2 440 419 1 3 415
Thu 3 440 366 1 1 364
Fri 1 440 400 1 2 397
Fri 2 440 411 1 4 406
Fri 3 440 379 1 2 376
Totals 15 6600 5825 15 45 5765

The following table is an extension of the above table and shows the unplanned downtime as well actual, standard, and ideal operating times.

Day Shift Unplanned Operating Time
Down Time Actual Standard Ideal
Mon 1 25 415.0 399.0 396.2
Mon 2 55 385.0 370.5 368.6
Mon 3 122 318.0 304.0 300.2
Tue 1 84 356.0 343.0 341.1
Tue 2 65 375.0 372.4 366.7
Tue 3 82 358.0 346.8 343.9
Wed 1 45 395.0 381.9 374.3
Wed 2 130 310.0 301.2 294.5
Wed 3 30 410.0 408.5 406.6
Thu 1 5 435.0 430.4 424.7
Thu 2 40 400.0 398.1 394.3
Thu 3 90 350.0 347.7 345.8
Fri 1 45 395.0 380.0 377.2
Fri 2 45 395.0 390.5 385.7
Fri 3 60 380.0 360.1 357.2
Totals 15 923 5677 5533.8 5476.8

The table below shows the OEE calculations for each day and shift worked.  Note that this table is also an extension of the above data.

Day Shift Overall Equipment Effectiveness (OEE)
Availability Performance Quality OEE
Mon 1 94.3% 96.1% 99.3% 90.0%
Mon 2 87.5% 96.2% 99.5% 83.8%
Mon 3 72.3% 95.6% 98.8% 68.2%
Tue 1 80.9% 96.3% 99.4% 77.5%
Tue 2 85.2% 99.3% 98.5% 83.3%
Tue 3 81.4% 96.9% 99.2% 78.2%
Wed 1 89.8% 96.7% 98.0% 85.1%
Wed 2 70.5% 97.1% 97.8% 66.9%
Wed 3 93.2% 99.6% 99.5% 92.4%
Thu 1 98.9% 98.9% 98.7% 96.5%
Thu 2 90.9% 99.5% 99.0% 89.6%
Thu 3 79.5% 99.3% 99.5% 78.6%
Fri 1 89.8% 96.2% 99.3% 85.7%
Fri 2 89.8% 98.8% 98.8% 87.7%
Fri 3 86.4% 94.8% 99.2% 81.2%
Totals 15 86.0% 97.5% 99.0% 83.0%

The results from the table above suggest that the process is running just short of world-class OEE (83% versus 90% for dedicated processes.  Note that 85% is considered world-class for multipart variable processes).  As you can see from the daily and shift results, a lot of variation is occurring over the course of the week.  This is the opportunity that we need to pursue further.  A quick scan of the data suggests that Wednesday 2nd shift and Monday 3rd shift are the main contributors to the reduced OEE.  We will investigate the data a little further to really understand what opportunities exist.

A dedicated, continuous process should yield a higher OEE since the process is not subject to continual setup and change over.  Although some model changes or variations to the existing product may exist, they are typically less disruptive.  A OEE of 90% may be an achievable target and is typical for most dedicated operations.

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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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.

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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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