Tag: OEE Formula

OEE – Reporting Live Part 1

How do you report Overall Equipment Effectiveness?

The next greatest challenge after learning how to calculate Overall Equipment Effectiveness (OEE) is reporting it.  This is often a topic of great debate and likely a reason why so many avoid discussing it at all.

Note that we have prepared several Excel spreadsheets to help you get started and they are available free of charge from our downloads page.

The question is, “What do we report?”  Some will argue that you can’t compare OEE between plants, departments, shifts, or processes. While we tend to agree with them in some respects, there is relevance to understanding the differences in the results.  In a comparative context, we would also add that we never intended to use OEE as competing metric, rather …

Our objective is to continually improve OEE over time.

Our objective is to provide a report that calculates OEE for multiple parts and processes such that a “summary OEE” can be determined from any combination of factors included in our production report.

Our report can be further extended to include other factors derived from the reporting system itself.

How to Report OEE

While technologies exist that offer instantaneous OEE reporting on the shop floor, they do little to help you in the boardroom.  Over the next few posts, we will create a relatively simple reporting structure using Excel as our development platform.

Before we get started with our spreadsheet, lets first understand what data we need to collect.  We can then decide what elements to add to our spreadsheet accordingly.

Data Collection

We need a method for collecting the minimum amount of data that will satisfy our requirement to establish a robust OEE reporting system.  For now we will consider collecting the following data using a very simple production shift report::

  • Date
  • Shift
  • Employee (Name / Number)
  • Start Time
  • Finish Time
  • Part Number
  • Work Order (Job Number)
  • Sequence (Step Number)
  • Work Center (Machine)
  • Quantity Good
  • Quantity Scrap

This basic report can easily be enhanced by adding columns for setup, material changes, breaks, or other events to better understand what transpired over the course of a given shift.  We recommend keeping it short and simple.  Only add more rigorous reporting requirements as needed and if the results demand it.  A simple format encourages people to complete the forms more readily.

Reporting OEE

In our next post, we will introduce a spreadsheet where we can input our data and generate our OEE report.  Our spreadsheet will allow you to calculate OEE for any combination of the above data entries.

Until Next Time – STAY lean!

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OEE and Human Effort

A girl riveting machine operator at the Dougla...
Image by The Library of Congress via Flickr

I was recently asked to consider a modification to the OEE formula to calculate labour versus equipment effectiveness.  This request stemmed from the observation that some processes, like assembly or packing operations, may be completely dependent on human effort.  In other words, the people performing the work ARE the machine.

I have observed situations where an extra person was stationed at a process to assist with loading and packing of parts so the primary operator could focus on assembly alone.  In contrast, I have also observed processes running with fewer operators than required by the standard due to absenteeism.

In other situations, personnel have been assigned to perform additional rework or sorting operations to keep the primary process running.  It is also common for someone to be assigned to a machine temporarily while another machine is down for repairs.  In these instances, the ideal number of operators required to run the process may not always be available.

Although the OEE Performance factor may reflect the changes in throughput, the OEE formula does not offer the ability to discern the effect of labour.  It may be easy to recognize where people have been added to an operation because performance exceeds 100%.  But what happens when fewer people have been assigned to an operation or when processes have been altered to accommodate additional tasks that are not reflected in the standard?

Based on our discussion above, it seems reasonable to consider a formula that is based on Labour Effort.  Of the OEE factors that help us to identify where variances to standard exist, the number of direct labour employees should be one of them. At a minimum, a new cycle time should be established based on the number of people present.

OEE versus Financial Measurement

Standard Cost Systems are driven by a defined method or process and rate for producing a given product. Variances in labour, material, and / or process will also become variances to the standard cost and reflected as such in the financial statements. For this reason, OEE data must reflect the “real” state of the process.

If labour is added (over standard) to an operation to increase throughput, the process has changed. Unless the standard is revised, OEE results will be reportedly higher while the costs associated with production may only reflect a minimal variance because they are based on the standard cost. We have now lost our ability to correlate OEE data with some of our key financial performance indicators.

Until Next Time – STAY lean!

Vergence Analytics

Seasons Greetings

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Image via Wikipedia

On behalf of the Lean Execution Team here at Vergence Analytics, I wish everyone a safe and enjoyable holiday! I wish you all the best of success in the new year.

I would also like to thank our many subscribers for your kind comments, suggestions, and many questions.

Merry Christmas and Happy New Year!

RedgeVergence Analytics

Differentiation Strategies and OEE (Part II): The Heart of the Matter

An article published in Industry Week magazine comprises part of our pursuit of differentiation strategies and OEE.  This will serve as the topical element of our post for today.

Enjoy the article, OEE:  The heart of the matter, and we’ll provide our thoughts and insights as well.  If the above links do not work, you can copy and paste the following link into your browser:

http://www.industryweek.com/articles/oee_the_heart_of_the_matter_18211.aspx

Until Next Time – STAY lean!

Vergence Analytics

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!

Benchmarking OEE

Benchmarking Systems:

We have learned that an industry standard or definition for Overall Equipment Effectiveness (OEE) has been adopted by the Semi Conductor Industry and also confirms our approach to calculating and using OEE and other related metrics.

The SEMI standards of interest are as follows:

  • SEMI E10:  Definition and Measurement of Equipment Reliability, Availability, and Maintainability.
  • SEMI E35:  Guide to Calculate Cost of Ownership Metrics.
  • SEMI E58:  Reliability, Availability, and Maintainability Data Collection.
  • SEMI E79:  Definition and Measurement of Equipment Productivity – OEE Metrics.
  • SEMI E116:  Equipment Performance Tracking.
  • SEMI E124:  Definition and Calculation of Overall Factory Efficiency and other Factory-Level Productivity Metrics.

It is important to continually learn and improve our understanding regarding the development and application of metrics used in industry.  It is often said that you can’t believe everything you read (especially – on the internet).  As such, we recommend researching these standards to determine their applicability for your business as well.

Benchmarking Processes:

Best practices and methods used within and outside of your specific industry may bring a fresh perspective into the definition and policies that are already be in place in your organization.  Just as processes are subject to continual improvement, so are the systems that control them.  Although many companies use benchmarking data to establish their own performance metrics, we strongly encourage benchmarking of best practices or methods – this is where the real learning begins.

World Class OEE is typically defined as 85% or better.  Additionally, to achieve this level of “World Class Peformance” the factors for Availability, Performance, and Quality must be at least 90%, 95%, and 99.5% respectively.  While this data may present your team with a challenge, it does little to inspire real action.

Understanding the policies and methods used to measure performance coupled with an awareness of current best practices to achieve the desired levels of  performance will certainly provide a foundation for innovation and improvement.  It is significant to note that today’s most efficient and successful companies have all achieved levels of performance above and beyond their competition by understanding and benchmarking their competitors best practices.  With this data, the same companies went on to develop innovative best practices to outperform them.

A Practical Example

Availablity is typically presented as the greatest opportunity for improvement.  This is even suggested by the “World Class” levels stated above.  Further investigation usually points us to setup / adjustment or change over as one of the primary improvement opportunities.  Many articles and books have been written on Single Minute Exchange of Dies and other Quick Tool Change strategy, so it is not our intent to present them here.  The point here is that industry has identified this specific topic as a significant opportunity and in turn has provided significant documentation and varied approaches to improve setup time.

In the case of improving die changes a variety of techniques are used including:

  • Quick Locator Pins
  • Pre-Staged Tools
  • Rolling Bolsters
  • Sub-Plates
  • Programmable Controllers
  • Standard Pass Heights
  • Standard Shut Heights
  • Quarter Turn Clamps
  • Hydraulic Clamps
  • Magnetic Bolsters
  • Pre-Staged Material
  • Dual Coil De-Reelers
  • Scheduling Sequences
  • Change Over Teams versus Individual Effort
  • Standardized Changeover Procedures

As change over time becomes less of a factor for determining what parts to run and for how long, we can strive reduced inventories and improved preventive maintenance activities.

Today’s Challenge

The manufacturing community has been devastated by the recent economic downturn.  We are challenged to bring out the best of what we have while continuing to strive for process excellence in all facets of our business.

Remember to get your free Excel Templates by visiting our FREE Downloads page.  We appreciate your feedback.  Please leave a comment an email to leanexecution@gmail.com or vergence.consultin@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“.

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Until Next Time – STAY lean!

Vergence Analytics

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

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!

"Click"

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.

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Until Next Time – STAY Lean!

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