Category: FREE Downloads

Using TRIZ for Problem Solving – Resources

In our first post on this topic, “Using Triz for Problem Solving – Introduction“, we provided a very basic introduction to TRIZ.  In the spirit of TRIZ, it is not our intent to rewrite or redefine the TRIZ process when excellent information is already available.  Our intent is to identify the few of the many excellent and exceptional resources that we have found.

What is TRIZ?

To learn more about TRIZ and it’s applications we suggest visiting the following web sites that present a tremendous amount of information on the development and application of TRIZ.

TRIZ Principles

40 Inventive Principles with Examples:

Examples for each of the 40 Inventive Principles can be found at the following link:  http://www.triz-journal.com/archives/1997/07/b/index.html

TRIZ Resources

The Contradiction matrix:

As you will have learned from reading the “What is TRIZ?” page from the link above, one of the tools of TRIZ is the Contradiction Matrix that consists of 40 elements.  The TRIZ Contradiction Matrix is available as an Excel Spreadsheet through the following link:

http://www.triz-journal.com/archives/1997/07/matrix.xls

The TRIZ Journal ARTICLES:

The Triz Journal presents many informative articles.  One very intriguing article, “TRIZ / Systematic Innovation Enhances Hoshin Kanri“, by Darrell Mann and Ellen Domb, demonstrates the principles of TRIZ in a unique application.

An excellent article, “Create a High Performance Culture with Hoshin Kanri”, by Frank Deno can be found at the following link http://www.realinnovation.com/content/c080623a.asp

WEB Sites:

TRIZ Books:

A number of books are available on the topic of TRIZ.  Click here to preview the selections currently available.

TRIZ Challenges:

TRIZ is not without its challenges.  Although TRIZ has evolved over many years, it still remains relatively unknown and few companies seem to be ready to adopt this problem solving method.

An excellent article, “Enhancing TRIZ with Dr. Deming’s Philosophy“, by Ellen Domb and Bill Bellows, presents some interesting insights to this challenge.

We typically tend to avoid “labels” for the method we are using to solve a specific problem.  Unlike a surgeon “requesting specific tools (scalpel)” while performing an operation, our strategy tends to be a blended “hybrid” approach to problem solving; TRIZ happens to be one of the more effective methods that we have learned to use over the past few years.

The acceptance of TRIZ may be attributed to the current struggles many companies experience simply attempting to complete an 8D or 5-Why.  Of course, that would only be true of companies who are void of the Lean principles and methods – right?  TRIZ also has a perceived complexity that does not lend itself to ready adaptation as a company-wide problem solving tool.

We would recommend reviewing the many available books on the topic of TRIZ.  Integrating a tool such as TRIZ will require someone to become the leading expert.  Click here to see the various book selections that are available.

Unfortunately, for many companies, the discipline or the structure is simply not there to support effective problem solving efforts.  Perhaps if more time was spent solving the real problems, they would have more time to solve problems not yet realized.

Remember to get your TRIZ – Click Here!

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!

OEE For Manufacturing

We are often asked what companies (or types of companies) are using OEE as part of their daily operations.  While our focus has been primarily in the automotive industry, we are highly encouraged by the level of integration deployed in the Semiconductor Industry.  We have found an excellent article that describes how OEE among other metrics is being used to sustain and improve performance in the semiconductor industry.

Somehow it is not surprising to learn the semiconductor industry has established a high level of OEE integration in their operations.  Perhaps this is the reason why electronics continue to improve at such a rapid pace in both technology and price.

To get a better understanding of how the semiconductor industry has integrated OEE and other related metrics into their operational strategy, click here.

The article clearly presents a concise hierarchy of metrics (including OEE) typically used in operations and includes their interactions and dependencies.  The semiconductor industry serves as a great benchmark for OEE integration and how it is used as powerful tool to improve operations.

While we have reviewed some articles that describe OEE as an over rated metric, we believe that the proof of wisdom is in the result.  The semiconductor industry is exemplary in this regard.  It is clear that electronics industry “gets it”.

As we have mentioned in many of our previous posts, OEE should not be an isolated metric.  While it can be assessed and reviewed independently, it is important to understand the effect on the system and organization as a whole.

We appreciate your feedback.  Please feel free to leave us a comment or send us an e-mail with your suggestions to leanexecution@gmail.com

Until Next Time – STAY lean!

Welcome to LeanExecution!

Welcome! If you are a first time visitor interested in getting started with Overall Equipment Effectiveness (OEE), click here to access our very first post “OEE – Overall Equipment Effectiveness“.

We have presented many articles featuring OEE (Overall Equipment Effectiveness), Lean Thinking, and related topics.  Our latest posts appear immediately following this welcome message.  You can also use the sidebar widgets to select from our top posts or posts by category.

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All downloads mentioned in our articles and feature posts are available from the FREE Downloads page and from the orange “FREE Downloads” box on the sidebar.  You are free to use and modify these files as required for your application.  We trust that our free templates will serve their intended purpose and be of value to your operation.

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Until Next Time – STAY lean!

Vergence Analytics

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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Variance, Waste, and OEE

What gets managed MUST be measured – Including VARIANCE.

It is easy to get excited about the many opportunities that a well implemented LEAN Strategy can bring to your organization.  Even more exciting are the results.

Achieving improvement objectives implies that some form of measurement process exists – the proof.  A clear link should be established to the metric you choose and the activity being managed to support the ongoing improvement initiatives.

Measure with Meaning

Why are you “collecting” OEE data?  While OEE can and should be used to measure the effectiveness of your manufacturing operations, OEE on its own does not present a complete solution.  It is true that OEE presents a single metric that serves as an indicator of performance, however, it does not provide any insight with respect to VARIANCES that are or may be present in the system.

We have encountered numerous operations where OEE data can be very misleading.  OEE data can be calculated using various measurement categories:  by machine, part number, shift, employee, supervisor, department, day, month, and so on.

VARIANCE:  the leading cause of waste!

Quality professionals are more than familiar with variance.  Statistically capable processes are every quality managers dream.  Unfortunately, very little attention or focus is applied to variances experienced on the production side of the business.

Some may be reading this and wonder where this is going.  The answer is simple, rates of production are subject to variance.  Quite simply, if you review the individual OEE results of any machine for each run over an extended period of time, you will notice that the number is not a constant.  The performance, availability, and quality factors are all different from one run to the next.  One run may experience more downtime than another, a sluggish machine may result in reduced in performance, or material problems may be giving rise to increased quality failures (scrap).

So, while the OEE trend may show improvement over time, it is clear that variances are present in the process.  Quality professionals readily understand the link between process variation and product quality.  Similarly, variation in process rates and equipment reliability factors affect the OEE for a given machine.

We recommend performing a statistical analysis of the raw data for each factor that comprises OEE (Availability, Performance, and Quality) for individual processes.  Analysis of OEE itself requires an understanding of the underlying factors.  It is impractical to consider the application of ANOVA to OEE itself as the goal is to continually improve.

How much easier would it be if you could schedule a machine to run parts and know that you will get them when you needed them?  You can’t skip the process deep dive.  You need to understand how each process affects the overall top-level OEE index that is performance so you can develop and implement specific improvement actions.

The best demonstration we have seen that illustrates how process variation impacts your operation is presented through a “process simulation” developed from Eli Goldratt’s book, The Goal.  We will share this simulation in a separate post.  Experiencing the effect of process variation is much more meaningful and memorable than a spreadsheet full of numbers.

Conflict Management and OEE

In some environments we have encountered, the interpretation of LEAN strategy at the shop floor level is to set minimum OEE performance objectives with punitive consequences.  This type of strategy is certainly in conflict with any Lean initiative.  The lean objective is to learn as much as possible from the process and to identify opportunities for continual improvement.

Management by intimidation is becoming more of a rarity, however, we have found that they also give rise to the OEE genius.  If performance is measured daily, the OEE genius will make sure a high performing job is part of the mix to improve the “overall” result.  This is akin to taking an easy course of study to “pull up” your overall average.

It is clear from this example, that you will miss opportunities to improve your operation if the culture is tainted by conflicting performance objectives.  The objective is to reveal sources of variation to eliminate waste and variation in your process, not find better ways to hide it.

Variance in daily output rates are normal.  How much are you willing to accept?  Do you know what normal is?  Understanding process variance and OEE as complementary metrics will surely help to identify more opportunities for improvement.

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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Problem Solving with OEE – Measuring Success

OEE in Perspective

As mentioned in our previous posts, OEE is a terrific metric for measuring and monitoring ongoing performance in your operation.  However, like many metrics, it can become the focus rather than the gage of performance it is intended to be.

The objective of measuring OEE is to identify opportunities where improvements can be made or to determine whether the changes to your process provided the results you were seeking to achieve.  Lean organizations predict performance expectations and document the reasons to support the anticipated results .  The measurement system used to monitor performance serves as a gauge to determine whether the reasons for the actual outcomes were valid.  A “miss” to target indicates that something is wrong with the reasoning – whether the result is positive or negative.

Lean organizations are learning continually and recognize the need to understand why and how processes work.  Predicting results with supported documentation verifies the level of understanding of the process itself.  Failing to predict the result is an indicator that the process is not yet fully understood.

Problem Solving with OEE

Improvement strategies that are driven by OEE should cause the focus to shift to specific elements or areas in your operation such as reduction in tool change-over or setup time, improved material handling strategies, or quality improvement initiatives.  Focusing on the basic tenets of Lean will ultimately lead to improvements in OEE.  See the process in operation (first-hand), identify opportunities for improvement, immediately resolve,  implement and document corrective actions, then share the knowledge with the team and the company.

Understanding and Managing Variance:

OEE data is subject to variation like any other process in your operation.  What are the sources of variation?  If there is a constant effort to improve performance, then you would expect to see positive performance trends.  However, monitoring OEE and attempting to maintain positive performance trends can be a real challenge if the variances are left unchecked.

Availability

What if change-over times or setup times have been dramatically reduced?  Rather than setting a job to run once a week, it has now been decided to run it daily (five times per week).  What if the total downtime was the same to make the same number of parts over the same period of time?  Did we make an improvement?

The availability factor may very well be the same.  We would suggest that, yes, a signficant improvement was made.  While the OEE may remain the same, the inventory turns may increase substantially and certainly the inventory on hand could be converted into sales much more readily.  So, the improvement will ultimately be measured by a different metric.

Performance

Cycle time reductions are typically used to demonstrate improvements in the reported OEE.  In some cases, methods have been changed to improve the throughput of the process, in other cases the process was never optimized from the start.  In other instances, parts are run on a different and faster machine resulting in higher rates of production.  The latter case does not necessarily mean the OEE has improved since the base line used to measure it has changed.

Quality

Another example pertains to manual operations ultimately controlled through human effort.  The standard cycle time for calculating OEE is based on one operator running the machine.  In an effort to improve productivity, a second operator is added.  The performance factor of the operation may improve, however, the conditions have changed.  The perceived OEE improvement may not be an improvement at all.  Another metric such as Labour Variance or Efficiency may actually show a decline.

Another perceived improvement pertains to Quality.  Hopefully there aren’t to many examples like this one – changing the acceptance criteria to allow more parts to pass as acceptable, fit for function, or saleable product (although it is possible that the original standards were too high).

Standards

Changing standards is not the same as changing the process.  Consider another more obvious example pertaining to availability.  Assume the change over time for a process is 3o minutes and the total planned production time is 1 hour (including change over time).  For simplicity of the calculation no other downtime is assumed.  The availability in this case is 50% ((60 – 30) / 60).

To “improve” the availability we could have run for another hour and the resulting availability would be 75% (120 – 30) / 120.  The availability will show an improvement but the change-over process itself has not changed.  This is clearly an example of time management, perhaps even inventory control, not process change.

This last example also demonstrates why comparing shifts may be compromised when using OEE as a stand-alone metric.  What if one shift completed the setup in 20 minutes and could only run for 30 minutes before the shift was over (Availability = 60%).  The next shift comes in and runs for 8 hours without incident or down time (Availability = 100%).  Which shift really did a better job all other factors being equal?

Caution

When working with OEE, be careful how the results are used and certainly consider how the results could be compromised if the culture has not adopted the real meaning of Lean Thinking.  The metric is there to help you improve your operation – not figure out ways to beat the system!

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 for Multiple Parts – Single Machine (Multipart Processes)

How to Calculate OEE for Single Machine and Multiple Parts.

Flexible manufacturing provides the advantage of producing many different parts on the same piece of equipment.  The same is true for processes such as stamping presses, molding machines, or machining operations.

The first question most often asked is, “How do we calculate OEE for a piece of equipment that is capable of manufacturing multiple parts?”  The overall OEE for a stamping press, molding machine, machining process, or other “multipart” process is easily calculated using the same formulas presented in our previous posts “How to Calculate OEE” and “Practical OEE“.

We presented three machines running at various rates and producing unique products.  We demonstrated how to calculate the OEE for each part individually and for all parts collectively.  The machines A, B, and C could very easily be parts A, B, and C running on one machine.  The application of the OEE formulas presented for these three machines is the same for multiple parts running on the same machine.

We have prepared two Excel spreadsheets that demonstrate how to calculate OEE for a single machine that produces multiple parts.  We have also created a separate Excel spreadsheet that will show you how to calculate OEE for Multiple Departments and Multiple Machines running Multiple Parts.

Calculating OEE for any period of time, department, or group of equipment is a simple task.  With the understanding that OEE measures how effectively Net Available Time is used to produce good parts at the ideal rate, the formula for any OEE calculation follows:

OEE (Any Category) = Total SUM of IDEAL Time / Total SUM of NET Available Time

Once this basic premise for OEE calculations is clearly understood, any combination of OEE summaries can be prepared including OEE summaries by Shift, Operator, Manager, Division, Process, and Process Type.

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.

Multipart OEE – Confronting the Challenges

Most manufacturing environments are challenged with the task of minimizing inventories requiring more frequent change-overs or setups.  By far, the greatest challenge of multipart equipment is managing the change-over process and is usually reflected in the OEE Availability factor.

We recommend including setup or change-over time as part of the unplanned downtime calculation.  Then, by definition, one method to improve Availability is to reduce change-over or setup time.  Reductions in change-over time will also be reflected by improved Availability.  The Availability factor is now a useful metric for tracking improvements.

According to our definition, change-over time or setup time is measured from the end of the current production run (“the last good part made”) to the start of the next production run (“first good part produced”).  We have worked with some manufacturers that decided to do change-overs on the off shift so that they could avoid the down time penalty.  They clearly didn’t get the point – deferring the time when the change-over is performed doesn’t change the time required to perform it.

Several programs such as SMED (single minute exchange of dies) are available and, when coupled with best practices for quick die change (QDC) or quick tool change techniques, can greatly reduce the time lost during your tool change events.

We will consider posting best practices for SMED or QDC and would welcome any reader comments in this area.

We always welcome your feedback and comments.  Feel free to send us your questions or comments to leanexecution@gmail.com

Until Next Time – STAY Lean!

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How to Reduce Costs with OEE: Cost Control

OEE is a great metric to help identify where you may be incurring losses in your processes or operation.  As one of the goals of implementing a Lean strategy is to reduce costs, it only seems natural that we should be able to determine what processes to focus on that are driving the greatest losses.

From the example developed in our previous posts we determined that the OEE and related factors for our three processes were as follows:

Machine Availability Performance Quality OEE
A 92.97% 88.26% 97.77%  80.22%
B 96.04% 77.23% 94.44% 70.05%
C 95.16% 61.70% 95.20% 55.90%

Based on the OEE results, one would be inclined to take a look at Machine C as it has the lowest OEE.  Is this really the greatest opportunity?  The only way to answer the question is to understand what factors are driving costs and ultimately affecting profitability.

The performance factor for machine C is definitely pulling down the OEE for this process.  What would you think if the machine is 100% automated (no labour) and the cycle time, although it may be less than standard, is still meeting the takt time to meet customer demand?  Is there really a cost?  Of course there is, but the impact to your business may be minimal in terms of cost when compared to the other machines.

It is clear that we need to develop a model to understand what losses and ultimately costs are associated with each of the factors.  In turn, we will be able to better understand the overall OEE.

What costs do we consider?  We recommend keeping the model simple.  There are typically three cost components associated with any given process or product:  Material, Labour, and Overhead.  Burden is another term used for Overhead and we will use these terms interchangeably.

Our goal over the next few posts will be to develop a simple cost model for each process and, in turn, determine which one may be the process of choice for improvement.  For now, we will provide a general discussion of some of the potential cost considerations.

Improving quality typically yields the greatest return on investment because all of the cost elements stated above are impacted by the Quality factor.  Raw material, Labour, and Burden are all expended to produce a part scrap part.

The costs associated with Quality losses are further challenged when considering the number of parts that would have to be produced in order to recover these lost costs.  If you are lucky enough to enjoy a 10% profit margin (clear), then, at a minimum, 10 parts would have to be produced for every part scrapped.  Of course, more parts would have to be produced to recover other infrastructure costs incurred including documentation, record keeping, and scrapping of the actual parts.

Performance losses typically affect labour and overhead.  Labour losses are easy enough to understand.  If a machine is operator dependent, then we will have to pay a person to stand at the machine to run it.  If it is running slowly, more costs are incurred to cover the additional labour time.

In many cases, direct losses related to overhead are sometimes difficult to assess unless a truly activity based costing system is in place.  The reason for the complexity arises because some of the costs are “fixed”.  Because the equipment exists, expenses such as depreciation or property taxes are incurred whether or not the equipment or, for that matter, the plant is running.  The performance of the machine or any of the other factors for that matter won’t change this fact.

Availability then becomes somewhat more obscure when it comes to calculating hard costs.  If the labour can be redeployed to another process when a machine goes down, perhaps some of the labour losses can be avoided.  If not, then waiting for a machine to be repaired or material to be delivered is a real loss that should be addressed.

Intangible costs are also difficult to quantify but we should be aware of their existence.  The costs associated or related to poor OEE may include overtime, expedited freight, and infrastructure costs related to extra handling of material or management of non-conforming material (containment, extra inspection, rework, and scrap).  Although this is a relatively short list, it addresses the most obvious potential losses.  With a little more thought, the list could easily grow longer.

Other key metrics in your facility such as customer delivery or quality performance indicators may also point to problems that can be traced directly to poor OEE performance.  Although difficult to measure, a company’s competitive position is compromised when efficiencies are low and eventually the costs of poor performance make their way into the “burden” costs required to manage the operation.

While OEE is an effective metric for operations, on its own, it does not provide a direct indicator of real financial losses.  As Lean Practitioners we are challenged to provide an analysis that not only improves the metrics of the business but also translate into real financial improvements on the balance sheet and ultimately – the bottom line.  We would suggest that OEE is a time driven metric (asset time management strategy) versus our proposed COEE which is Finance or “Value” driven (cost management strategy).   We are presently developing a model that will allow your OEE data to be sensitized with cost data as demonstrated by the table below.

We have coined the term COEE or Cost of Overall Equipment Effectiveness.  Consider the following OEE results converted to Cost based drivers using standard costs as our baseline.  The sample data and spreadsheet used to calculate this data will be available as a download soon.  The overall spreadsheet is quite large and based on a fully detailed three shift operation.

Cost driven OEE model - Summary
Cost driven OEE model - Summary

Our OEE cost model clearly presents the real costs or “losses” incurred per part.  Our Weighted OEE Cost Model will change the way you view OEE data, enabling you to set priorities and identify real, quantifiable, opportunities for improvement.  The above snapshot represents the goal of our COEE project – a clean, clear, summary of the losses incurred correlated directly to your OEE index.  Another advantage is that the Availability, Performance, and Quality factors are recalculated based on cost and presents a realistic breakdown of losses for each of these factors from a financial perspective.  Our spreadsheet presents an advanced OEE example that will bring real value to your OEE implementation strategy.

NOTE:  The fully developed spreadsheet is available from our FREE Downloads page or from the FREE Downloads box on the sidebar.

A well implemented OEE strategy should become evident on the balance sheet through improved material utilization, reduced labour variance (straight and overtime reductions), reduced scrap costs, reduced rework costs, and other burden account reductions.

Take quick, effective, and efficient action to solve the problems having the greatest financial impact to your business.  Last but not least, don’t confuse activity with action.  Decisions are not actions and talking about a problem or even writing about it could be construed as activity.  Real actions produce real, measurable, results.

Change requires Change.  Profit is to business as oxygen is to humans – you need it to survive. 

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.  There are no hidden files, formulas, or macros and no obligations for the services provided here.

If you have any questions or comments, feel free to send an email to LeanExecution@gmail.com

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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