Tag: Using OEE

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!

OEE – A Vantage Point Metric

The automotive industry has recently been plagued by doom and gloom as the economy continues to erode any chances for a rebound.  Chrysler has already met with the reality of an uncertain future and has subsequently filed for bankruptcy.  Unfortunately, as Chrysler plants shut down and cease operations for the next 30 – 60 days, suppliers will also feel the impact as well.  This will affect other OEM’s such as GM, Honda, and Toyota, as the remaining suppliers will not have the sales to keep their doors open to support these demands.

These times demand extremely effective use of resources to minimize waste and reduce losses in all facets of the business.  For this reason, it is a good time to revisit the metrics you use to manage your organization and to make sure that they are providing the right information, at the right time to manage your business effectively.  This post was also presented on our companion blog.

We use the term Vantage Point Metrics to identify those metrics that are core to the day to  day management of your business.  The objective is to distinguish these metrics from the “noise” that is easily created by other less significant metrics.  (While it is important to measure everything that is important to your business, not all measurements are equally important at all times or at the same time.)

Determining Vantage Point Metrics is simply a matter of identifying those metrics that are critical to the profitability of your operation.  A few simple examples include Safety, Overall Equipment Effectiveness (OEE), Labour Efficiencies, Profit, Absenteeism, Cost of Non-Quality, Delivery Performance, Performance to Schedule, and Inventory Turns.

Climate Control

Keep the number of vantage point metrics to a minimum.  Note that we are NOT suggesting that you stop measuring.  There is a difference.  Too many metrics can create noise and loss of focus.  What we are suggesting is that metrics that are critial to the day to day operation must be reported more frequently than others.  What is important to who.  Can the metric be measured in real time?  Can the people reviewing the data make changes in real time?  If the data being collected cannot be used to make decisions to correct a current trend then why is it being measured?

Although we can’t control the weather, we certainly spend a signficant amount of time, money, and resources to get up to the minute data and short term forecasts.  Only the few key metrics are reported with an explanation of the various environmental influences that may affect the forecast in the short term.

In a similar way, Vantage Point Metrics are those few that have a direct relationship or impact on the current business climate.  Changes in the weather may cause us to alter both personal and business plans.  Agriculture, NASA, and Aerospace companies rely on the weather as a dynamic component of their business.  Vantage Point metrics should assimilate the same sensitivity to climate change in your business.

Measuring for measurement’s sake is waste.  Measure critical to success factors that can be used to alter or change the course of business as required.  Real time data collection in conjuction with short response-control loops is the key to using these metrics successfully.

Consider managing your personal cash flow in real time versus performing a month end summary.  Anyone who has purchased fuel for their vehicle this past year is keenly aware of the fluctuations in pricing and the cost of filling the tank.  As the variation in price increases, we in turn become more sensitive to our purchasing habits.

What is the Vantage Point Metric that would allow you to manage your fuel purchases?  Is it the price per gallon or litre at the gas station?  Could it also be worthwhile to monitor the price of a barrel of oil?  Our logic suggests that a direct correlation between price at the pump and the cost of barrel of oil should exist although our experience strongly suggests otherwise.  Repair costs, refinery costs, weather, and the value of the dollar, all seem to have an impact on the price at the pump.

Fortunately, gas pumps display the price in real time as the tank is being filled.  Knowing our current cash position allows us to fill our tanks to such an amount that we don’t exceed our ability to pay for it.  This is measuring and managing in real time.  The Vantage Point metric in this case is CASH.  It is possible to manage cash flow in real time to determine what and how much can be purchased.  If cash is the Vantage Point metric, it becomes clear that many business transactions can be controlled and understood from this one simple metric.

Consider what Vantage Point Metrics can do for your business.  Focus on the few metrics that provide the greatest level of control and management of your business.  Keep it simple and keep it real.  Keep measuring and be prepared to drill down when you have to.

Overall Equipment Effectiveness or OEE provides a single metric that combines Availability, Performance, and Quality into one unique index.  It is very possible to drill down into the details of the metric as required.

Inventory turns is another Vantage Point metric that tells a story much greater than just how well material is moving through your organization.


Turning ordinary metrics into Vantage Point metrics requires training all employees, associates, or business partners.  They need to clearly understand what the metric is measuring and the various aspects or elements of the business climate that can affect positive change.

When the weather is reported on the news, the forecast is typically accompanied by an explanation of the reasons for the current weather conditions and future forecast.  They don’t just post a weather map on the screen and leave it for the viewer to determine the emerging weather patterns.  Similarly, Vantage Point Metrics require supporting details and explanations to educate the team on the meaning of the data and how to respond to the current conditions and what the effects will be if we don’t.

Metrics can be extremely valuable if selected and used wisely.  Easy data is not necessarily meaningful data.  Collecting and analyzing the data is the beginning not the end of the process.  Measuring absenteeism for example is not a difficult task.  Understanding why certain behavior patterns exist and affecting change to produce positive results is the journey to be pursued.

The Six Sigma DMAIC model (Define, Measure, Analyze, Improve, and Control) suggests that we already know that a problem exists.  This model does not support Vantage Point Metrics in that a defined outcome or goal has been established.  The data is analyzed to determine and define the root cause for the current trend.  Improvements are then formulated into an action plan, implemented, and monitored accordingly.  The metric continues to serve as continual feedback as actions are taken.

You can either wait for the problem to define itself or implement Vantage Point Metrics that will prevent the problem from occurring all together.

Most people have had the opportunity to drive a car or truck and know that we need to obey the rules of the road.  A speeding ticket is a problem that is readily defined.  It is a problem not only because it could cost you some money but repeated occurrences could also result in the loss of your licence to drive.  To avoid this dilemma from happening you can monitor your Vantage Point Metric (the speedometer) and respond to the current conditions – all in real time – and avoid the ticket.

When or if we do receive a speeding ticket, it is the result of a conscious decision to speed, not mere fate or bad luck.  As such, the issue, like many, becomes that of not adhering to policy or standard operating procedure.

Focus on the vital few Vantage Point Metrics to achieve stability in your organization.  As problems are resolved and opportunities to improve are pursued, new metrics will begin to surface that will bring even more positive results to the bottom line.

Until Next Time – STAY lean!

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OEE Integration – Where do We Measure OEE? – Part I

OEE Integration Part IX – Where do we measure OEE?

Our recent posts have included numerous examples to calculate OEE correctly. We also discussed integration of OEE as an effective metric for managing your processes and ultimately how to analyze and use the data to improve your profitability.  We spent little time discussing where this measurement should occur.  OEE can be measured for both manual and automated lines as well as any stand alone operation.

The OEE factors (Performance, Availability, and Quality) are process output results.  The expectation, of course, is to manage the inputs to the process to assure the optimal result is achieved.  Availability, Performance, and Quality can be measured in real-time during production. However, the results should be subject to a due diligence review when production is complete.

At a minimum, it makes sense to measure OEE at the end (output) of the line or process but this is not always ideal.  The complexity of OEE measurement occurs when single or multiple sub-cells are constrained by an upstream or downstream operation or bottleneck operation.  The flow, rate, or pace of a process is always  restricted or limited by a  sequence / process constraint or bottleneck operation.  Just as a chain is only as strong as its weakest link, so too is the line speed limited by the bottleneck operation.

We contend that the “Control-Response” loop for any process must enable immediate and effective corrective action based on the measured data and observations.  Measuring OEE in real-time at the bottleneck process makes it an ideal “Trigger Point” metric or “Control-Response” metric for managing the overall process even in “isolation” at the bottleneck operation.  Any variations at the bottleneck correlate directly to upstream and downstream process performance.

A disruption to production flow may occur due to a stock-out condition or when a customer or supplier operation is down.  While these situations affect or impact the OEE Availability factor, external factors are beyond the scope of the immediate process.

Real-time OEE requires that these events and others, such as product disposition, are reported in real-time as well.  External events are more difficult to capture in real-time and by automated systems in particular.  Operator interfaces must accommodate reporting of these events as they occur.

Reporting PITFALL – After-the-Fact events

If a quality defect is discovered several days after reporting production and all parts are placed on hold for sorting or rework, the QUALITY Factor for that run should be changed to ZERO.  In turn, the net OEE for that run will also be ZERO.  If the system is not changed, the integrity of the data is lost.  This also exemplifies that real-time data can be deceiving if proper controls are not in place.

“Where do we measure?” is followed by “When do we measure?” The short list of examples provided here are likely events that are far and few between.  If this is a daily occurrence, consider adopting the banking policy of, “adjustments to your account will be reflected on the following business day”.  Your process / system is in need of a rapid fix.

OEE is one of the few vital signs or key performance metrics for your manufacturing operation.  As such, measure where you will reap the greatest benefit and focus your attention on the process or operation accordingly.  OEE is as much a diagnostic tool as it is a monitoring tool.

Until Next Time – STAY lean!

Vergence Analytics

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!


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!