Tag: Metrics

Are you Winning? A Hockey Lesson for Lean Metrics.

Toronto Maple Leafs
Image via Wikipedia

The world of sports is rife with statistics and hockey is no exception, especially here in Canada.  Over the past few weeks, local Toronto hockey fans anxiously watched or listened for the results of every Toronto Maple Leafs hockey game – all the while hoping for a win and a shot at making into the playoffs.

As has been the case for the past many years, the Leaf’s contention for a playoff spot was equally dependent on their own performance and that of their competitors.  The Leaf’s finally started to win games as did their competitors.  In the end they didn’t make it.

It is interesting to note that, despite their lack luster record, the Maple Leafs are one of the top franchises in the National Hockey League (NHL).  Thanks to the Toronto Star, I learned that we have 45 reasons to hope.

What is the lesson here?

All the statistics or metrics in the world won’t change the final outcome for the Toronto Maple Leafs and neither will any of the excuses for their poor performance.

These players are paid professionals, hired for the specific purpose of contributing to the overall performance of their team to win hockey games.  In the end, no one cares about player performance data, injuries, shots on net, penalties, goals against, or any other metric.

To me it really comes down to one question:

Are you Winning?

The answer to this question is either Yes or No.  There is no room for excuses or “it depends”.  You either know or you don’t.  In hockey, it’s easy.  The metric that matters is the final score at the end of the game.

We are all paid to peform – excuses don’t count.  Determine which metric defines winning performance and be ready when someone asks:

Are you Winning?

As any rainmaker knows, customers expect a quality, low cost product or solution, delivered on time, and in full.  To do anything less is inexcusable.

Until Next Time – STAY lean!

Vergence Analytics
Twitter:  @Versalytics

Seasons Greetings

Holly, attributed to the Drummonds, MacInneses...
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

Killer Metrics

Dead plant in pots
Image via Wikipedia

Managing performance on any scale requires some form of measurement.  These measurements are often summarized into a single result that is commonly referred to as a metric.  Many businesses use tools such as dashboards or scorecards to present a summary or combination of multiple metrics into a single report.

While these reports and charts can be impressive and are capable of presenting an overwhelming amount of data, we must keep in mind what we are measuring and why.  Too many businesses are focused on outcome metrics without realizing that the true opportunity for performance improvement can be found at the process level itself.

The ability to measure and manage performance at the process level against a target condition is the strategy that we use to strive for successful outcomes.  To put it simply, some metrics are too far removed from the process to be effective and as such cannot be translated into actionable terms to make a positive difference.

Overall Equipment Effectiveness or OEE is an excellent example of an outcome metric that expresses how effectively equipment is used over time as percentage.  To demonstrate the difference between outcome and process level metrics, let’s take a deeper look at OEE.  To be clear, OEE is an outcome metric.  At the plant level, OEE represents an aggregate result of how effectively all of the equipment in the plant was used to produce quality parts at rate over the effective operating time.  Breaking OEE down into the individual components of Availability, Performance, and Quality may help to improve our understanding of where improvements can be made, but still does not serve to provide a specific direction or focus.

At the process level, Overall Equipment Effectiveness is a more practical metric and can serve to improve the operation of a specific work cell where a specific part number is being manufactured.  Clearly, it is more meaningful to equate Availability, Performance, and Quality to specific process level measurements.  We can monitor and improve very specific process conditions in real time that have a direct impact on the resulting Overall Equipment Effectiveness.  A process operating below the standard rate or producing non-conforming products or can immediately be rectified to reverse a potentially negative result.

This is not to say that process level metrics supersede outcome metrics.  Rather, we need to understand the role that each of these metrics play in our quest to achieve excellence.  Outcome metrics complement process level metrics and serve to confirm that “We are making a difference.”  Indeed, it is welcome news to learn that process level improvements have translated into plant level improvements.  In fact, as is the case with OEE, the process level and outcome metrics can be synonymous with a well executed implementation strategy.

I recommend using Overall Equipment Effectiveness throughout the organization as both a process level and an outcome level metric.  The raw OEE data at the process level serves as a direct input to the higher level “outcome” metrics (shift, department, plant, company wide).  As such, the results can be directly correlated to specific products and / or processes if necessary to create specific actionable steps.

So, you may be asking, “What are Killer Metrics?”  Hint:  To Measure ALL is to Manage NONE.  Choose your metrics wisely.

Until Next Time – STAY lean!

Vergence Analytics

Cost Weighted OEE and other free OEE Spreadsheet Templates

OEE Spreadsheet Templates – One Click Closer:

As the days of summer are upon us, we thought it would be good idea to make it easier for you to access our free downloads so you can spend more time doing the things you want to do.  We have updated our site and we are pleased to offer you four ways to download our OEE spreadsheet templates:

  1. We added a new page titled “Downloads
  2. We also added a new Link List to the sidebar titled “Download Files”
  3. We made the FREE DOWNLOADS orange Box file a little larger and easier to read.
  4. We will include direct access links in the content of our posts.

Your OEE templates are literally a click away – saving you time and effort.

Cost Weighted OEE – Advanced OEE Template

We have received numerous requests for our “Cost Weighted OEE” template.  Many people are starting to realize that the OEE factors for availability, performance, and quality are not directly correlated.  Of course, we have also discussed our concerns in this regard on several occasions and will state again that OEE is not a stand alone metric.  As a vantage point metric, it can provide a valuable perspective on operations in real time, however, it is only one part of the overall equation.

Rex Gallaher wrote an excellent article titled “OEE Oxymoron; Are all factors truly equal?” that was published by ReliablePlant.com on February 18, 2009.  This article also conveys the premise that the OEE factors are not equal.  Understanding the financial impact of each of the OEE factors will assure that efforts and energy are focused on activities that will provide the greatest return on investment for your company.

To celebrate our site updates, we thought we would give you at least one more reason to see how our download venues work.  A copy of the Cost Weighted OEE Template is now available through all three of our download venues or you can Click HERE to get immediate access to the file.

For a detailed discussion of OEE and how it can (and should not) be used to identify opportunities to eliminate waste and reduce costs, click on one of the links below:

  1. OEE and Cost Control – Published in December, 2008
  2. 6 Things OEE is NOT! – Published in April, 2009
  3. Make or Break with OEE – Published in May, 2009

In light of the current economy, many companies have been forced to look inward to find “new” money.  OEE is one of the few lean metrics available that can help your organization to focus on the greatest opportunities with measurable returns.  We trust the templates and spreadsheet solutions that we offer here will help you in your quest.

For more information, click on the Categories section of the sidebar to search for other articles on our Blog that may be of interest to you.  They can provide significant insight into the many aspects of operations and OEE and may serve as part of your ongoing training efforts.

We appreciate your feedback.  Please feel free to leave a comment or send an e-mail with your suggestions for a future topic, comments, questions, or concerns to leanexecution@gmail.com or versalytics@gmail.com

Until next time – STAY lean!


Some of our readers have expressed an interest in the application of Statistical Analysis Tools for OEE.  We have also reviewed various texts and articles that have expressed opposing views on the application of Statistical Tools with OEE data.

Our simple answer is this:

  •  At best, statistical tools should only be used on unique or individual processes.   Comparisons may be made (with caution) between “like” processes, however, even these types of comparisons require a thorough, in-depth, understanding of product mix, customer demand, and potentially unique process considerations.
  • At worst, it may be worthwhile to use statistical analysis tools for the individual OEE factors:  Availability, Performance, and Quality.

Negative trends may have Positive returns

While certain top level improvements can be implemented across the company, such as quick die change or other tool change strategies, their very integration may (and should) result in changes to the existing or current operating strategy.  A quick tool change strategy may provide for substantially reduced set up times and, as a result, operations may schedule shorter and more frequent runs.  In turn, the net change to OEE may be negligible or even lower than before the new change strategy was implemented.

A drop or decline in OEE does not necessarily translate to negative financial performance.  From a cash flow perspective, the savings may be realized through reduced raw material purchases, reduced inventories, and subsequently lower carrying costs that may more than offset any potential decrease in OEE.  As we have discussed in previous posts, OEE should not be regarded as a stand alone metric.  It is important to understand the financial impact of each of the OEE factors to your bottom line.  We’re in business to make money and Cash is King.

Scope of Analysis – Keep it Simple

As the scope of the OEE analysis increases from shift, to daily, to weekly, to monthly summaries, the variables that affect the end result are compounded accordingly.  Extending statistical techniques to OEE data across multiple departments or even company wide introduces even more sources of variation that make statistical modeling unrealistic.

While the application of statistics may sound appealing and “neat”, it is even more important to be able to understand the underlying factors that affect or influence the final result in order to implement effective countermeasures or action plans to make improvements or simply to eliminate the source of concern.

Regardless of the final OEE index reported, someone will ask the question, “So, what happened to our OEE?”  Whether an increase or decrease, both will require an answer to explain the variance.  If there was a significant increase, someone will want to know why and where.  This improvement, of course, will have to be replicated on other like machines or processes.  If there was a significant decrease, someone will want to know why and where so the cause of poor or reduced performance can be identified and corrected.

Ironically, no matter what the result, you will have to be prepared to supply individual process OEE data.  So why not just review the primitive data and respond to the results in real time?  While we recommend this practice, there may be certain trends relative to the individual factors that can be statistically evaluated in the broader sense (Quality – PPM, Labour – Efficiencies).

Statistics on a larger scale

We would suggest and recommend using statistical techniques on the individual Availability, Performance, and Quality factors of OEE.  For example, many companies track labour efficiencies relative to performance while others measure defects per million pieces relative to Quality.

While most people readily associate statistics with quality processes, many operations managers are applying statistical analysis techniques to a variety of metrics such as run time performance, performance to schedule, downtime, and setup times.  Maintenance managers are also analysing equipment availability for Mean Time to Repair, Meantime Time Between Failures, and equipment life cycle performance criteria.

As one example, we have conducted and encouraged our clients to consider statistical analysis of production data.  Tracking the standard deviation of daily production over time can reveal some very interesting trends.  These results will also correlate with the OEE factors.  Where the standard deviation is low, the increase in production is reflected in other metrics as well including financial performance.

A final note

Lastly, OEE is a tool that should be used to drive improvements.  As such, the goal or target is forever changing whether in small or large increments.  Another SPC solution for OEE that may be a little easier to understand and execute is as follows:

  1. System – Define and establish an effective system for collecting, analyzing, and reporting OEE data – preferably in real time at the source.
  2. Process – Understand and establish where and how OEE data will actually be collected in your processes and how it will be used to make improvements.
  3. Control– Establish effective methods to control both systems and processes to assure OEE is and remains a truly integrated metric for your operations.

We strongly recommend and support “at the source” thinking strategy.  Quite simply, we prefer points of control that are as close to their source as possible whether it be data, measurement, or product related.  Quality “at the source” (at the machine in real time) is much easier to manage than final inspection on the dock (hours or even days later).  Similarly, OEE managed in real time, at the process or machine, will serve the people and the company with greater control.

We welcome your feedback.  Please leave a comment or send us an e-mail (leanexecution@gmail.com) with your questions, suggestions, or comments.

Until Next Time – STAY lean!

We respect your privacy – we will not distribute, sell, or otherwise provide your contact or other personal information to any outside or third party vendors.

Measuring Effectiveness – Getting started with OEE

Getting Started with Overall Equipment Effectiveness (OEE) – Your definitive guide to Overall Equipment Effectiveness

The “internet” is a powerful tool that can provide information in a matter of seconds.  Use Google to search for Overall Equipment Effectiveness or OEE and you will quickly receive an overwhelming number of “hits” leading you to the latest promotion selling you on the latest data collection technology or a ream of books that will provide wisdom and guidance to implement OEE.  We’re here because … you’re at your desk, not the book store.  Nor are you waiting for some sales professional to call you after submitting your information on line.

Although we have nothing against these venues, we are here to help you get started now.  While the information presented here is based on our experience with lean manufacturing and OEE integration, we do recommend some key reading that can serve as valuable references for anyone looking to implement OEE.  If you are looking for on line references, click here to access the most up to date books available on the market today or visit our “References – Books” page for a quick review of our top selections.

We provide numerous examples to demonstrate the concepts presented here so you can see for yourself how to calculate OEE and immediately put it to good use as a key performance metric to help you manage your business.  If you are looking for on line OEE training, all the information you need is here.

OEE Spreadsheet Templates

We are currently offering our OEE Spreadsheet Templates and example files at no charge.  You can download our files from the ORANGE BOX on the sidebar titled “FREE DOWNLOADS”, click on the Free OEE Templates page, or simply click on the name of the file you need from the Download Files link list on the sidebar.  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.  So let’s get started …

What is OEE?

OEE measures how effectively time is utilized to manufacture a product, or products, using a piece of equipment.  It can also be expanded to measure how effectively time is utilized in your entire operation.

OEE is comprised of three (3) basic factors expressed as a percentage:  Availability, Performance, and Quality.  We will discuss how these factors are measured a little later in this post.

  • OEE = Availability X Performance X Quality

Availability:  measures the uptime of the machine during the course of the production run.

Performance:  measures the cycle time of the machine or process against the ideal or standard cycle time.

Quality:  measures the time required to make good parts against the time required to make all parts.

Note that most texts indicate that Quality is the yield of good parts made (Good Parts / Total Parts).  While this may be true for an individual part or production process, you will learn that this can be misleading when attempting to determine the true weighted OEE for an entire department or operation.  More on this later.


Comparing OEE between departments, divisions, or even companies can be very misleading unless a standard definition for OEE and its related factors has been determined.  We’ll discuss this in more detail later.  For now, be advised that, unless everyone is following the same definition, the results cannot be used for comparison purposes.


Availability measures the actual equipment up time over the planned production time.  In other words, the amount of time that the machine was available to make parts when the machine was scheduled to make them.  The easiest way to measure uptime is to measure the down time and subtract that from the time the equipment is available.  We need to determine the available time and understand what actually qualifies as down time.

A typical shift is usually 8 hours or 480 minutes.  Each shift is provided with two 10 minute paid breaks and a 30 minute unpaid meal break.

  • Net Available Time:  480 minutes – Break Times (20 minutes) = 460 minutes.
Shift Time Break Time Calculations Net Available
Hours Minutes Break Frequency Meals Total Time
8 480 10 2 0 20 460

Down time events include machine change over or setup, material changes, adjustments, break downs, or other events that take away from the production of parts.

Availability = (Net Available Time – Event Down Times) / Net Available Time

Example, a full shift of production was scheduled to run on machine A.  Production was stopped for 20 minutes due to a machine failure, 10 minutes for a bin change, 30 minutes for a quality concern requiring a tool repair.

Assuming the net available shift time is 460 from our example above, the availability is calculated as follows:

  • Availability = (460 – (20 + 10 + 30)) / 460 = 400 / 460 = 86.96%
Availability Factor Net Operating
Down Time Event Time (Up) Time
Machine Failure   20 440
Bin / Container Change   10 430
Quality Concern     30 400
Total     60 400
Availability = 400 / 460 86.96%


Performance measures the actual cycle time against the ideal or standard cycle time established for the process.  From our example above, the net operating time for the process is 400 minutes.  The next step is to determine how effectively this net operating time used to produce parts.  In OEE terminology this is known as the process Performance.

Continuing with the above example, let’s assume that a total of 1200 parts were produced.  The actual cycle time is 1200 parts /400 minutes = 3 parts / minute.  If the ideal cycle time established by engineering or the equipment manufacturer is 4 parts / minute, the performance for the process is calculated as follows:

  • Performance = Actual Rate / Ideal Rate = 3 / 4 = 75%
  • Performance = Ideal Cycle Time / Actual Cycle Time = 15 seconds / 20 seconds.
Performance Factor      
Ideal Cycle Time (seconds) 15
Total Quantity Produced 1200
Ideal Operating Time (Total Quantity x Cycle Time) 300 minutes
Actual Operating Time (Net Operating Time) 400 minutes
Performance = 300 minutes / 400 minutes 75.00%

So far so good!  Now all we need to do is calculate the Quality Factor.


Almost everyone in the industry assumes that Quality is a measure of yield (good parts / total parts made).  This simple definition of the Quality factor is misleading if we recall that OEE is a measure of overall equipment effectiveness and this in turn is based on how well that time was used to make good parts.  More on this later.

Moving on with our original example, 6 parts were scrapped during the course of the production run due to various quality defects.  This means that only 1194 of the 1200 parts produced were acceptable.  The simple yield calculation is as follows:

  • Quality = Good / Total = 1194 / 1200 = 99.50%

Since OEE measures how effectively an asset is used, we should be basing our calculation on how much TIME was lost to produce the 6 parts.  From a capacity planning and utilization perspective, the lost time is the real concern.

The ideal production rate is 4 parts per minute.  Therefore, the lost time can be calculated as follows:

  • Lost Time = Lost Parts / Ideal Rate = 6 / 4 = 1.5 minutes OR,
  • Lost Time = Lost Parts * Ideal Cycle Time = 6 * 15 = 90 seconds (1.5 minutes)

To put this into proper perspective, we should calculate the time required to make GOOD or Acceptable parts only.  From our example, this is the time required to make 1194 parts.  The calculations are as follows:

  • Time to Produce Good Parts = 1194 / 4 parts per minute = 298.5 minutes
  • Time to Produce Good Parts = 1194 * 15 seconds = 17,910 seconds = 298.5 minutes

The quality factor can now be calculated correctly as follows:

  • Quality % = Time to Produce GOOD Parts / Time to Produce Total Parts = 298.5 / 300 = 99.50%
Quality Factor      
Ideal Cycle Time (seconds) 15
Quantity Scrap / Defective 6
Total ACCEPTABLE Quantity 1194
NET Ideal Operating Time (Quantity Accepted * Cycle Time) 298.5 minutes
Ideal Operating Time (Total Quantity x Cycle Time) 300 minutes
Quality = 298.5 minutes / 300 minutes 99.50%

Again, OEE is a measure of how effectively a machines time was used to make good parts.  The time element will become clearer when we pursue department, and company wide OEE calculations.

OEE:  Overall Equipment Efficiency

We finally get to put it all together.  Now that we have calculated the Availability, Performance, and Quality factors we can calculate the Overall Equipment Efficiency (OEE) using the basic formula:

  • OEE = Availability * Performance * Quality = 86.96% * 75.00% * 99.50% = 64.89%
OEE Calculations        
Availability 86.96%
Performance 75.00%
Quality 99.50%
OEE = 86.96% x 75.00% x 99.50% 64.89%

OEE Quick Check:

If you recall earlier, we said that the definition of OEE is how effectively the machine time was used to produce a quality part.  Now for the proof and yet another simple quick way to verify or calculate your OEE.

From our example above, Net Available Time = 460 minutes and the Net Ideal Operating Time =  298.5 minutes.  We could have calculated our total OEE as follows:

  • OEE = Net Ideal Operating Time / Net Available Time = 298.5 / 460 = 64.89%
OEE Quick Check      
NET Ideal Operating Time 298.5 minutes
Net Available Time 460 minutes
OEE = 298.5 minutes / 460 minutes 64.89%

Other considerations:  Weighted OEE

Although we discuss weighted OEE in depth in another post, some people just can’t wait to get started and don’t return to find out how to do the weighted OEE calculations.  For starters, it is not a simple arithmetic average of the results.  The following example provides a solid basis for calculating the weighted quality factor for multiple processes.

Process A has a cycle time of 1 minute and process B has a cycle time of 2 minute.  Let’s assume that processes A and B made a total of 50 and 100 parts respectively.  Let’s also assume also that 10 parts were scrapped at each process.  The quality factor for each process should be calculated as follows:  Time to make good parts / Time to make total parts.

  • Process A = (40 * 1) / (50 * 1) = 40 / 50 = 80%
  • Process B = (90 * 2) / (100 *2) = 180 / 200 = 90%

First, does it make sense to use a simple average between the two processes knowing that one process ran 4 times longer than the other?  We would say no.  The simple arithmetic average in this case is (80% + 90%) / 2 = 85%.

Second, does it make sense to base the quality factor on the total yield?  Again, we would say no.  A total of 130 good parts were produced from a total of 150.  In this case the simple yield would be 130 / 150 = 86.7%.

Third, does it make sense to base the quality factor on the time required to make good parts versus all parts?  We would say yes.  Most people conclude that the cycle times “cancel” and are irrelevant but they do not consider what happens when two or more processes are viewed together.

The Quality factor looks quite different when the Processing Time is considered.  The total time to make GOOD parts for processes A and B is 40 and 180 minutes respectively for a grand total of 220 minutes.  The total time to make ALL parts processes A and B is 50 and 200 minutes respectively for a grand total of 250 minutes.  The Quality factor is calculated as 220 / 250 = 88.0 %

The point of the example presented here is that we have three (3) uniquely different numbers that theoretically represent the same data.  While the differences may seem small, it becomes even more relevant when determining the overall OEE for a department or plant where machines are used to manufacture products having substantially different cycle times and quality yields.  The effect could be contrasted further when comparing automated and manual production operations, or high speed equipment (stamping presses) and low speed assembly operations.

The secret to calculating line item and weighted OEE is simple:  The calculations applied to each line item process also apply to the sum of the whole.  You will see this at work in our post specifically written to address weighted OEE calculations.

Now that you know the “quick” OEE calculation, you can easily calculate the OEE for any process simply by knowing the cycle time, the good and total part quantities, and the total Net Available Time.

This introduction provides the basics to calculating OEE correctly.  In the next post we will teach you how to calculate the weighted OEE so you can determine the overall performance of your plant, department, or company.  As easy as it is to do, most people just don’t seem to “get it”.  Unfortunately, too many heated discussions in the boardroom have erupted as a result.

If you would like a copy of our free fully functional spreadsheet with detailed explanations – no strings attached – please visit our Downloads page or select the files from the Orange Download Widget in the sidebar.

Until Next Time – STAY lean!


Vergence Analytics

Twitter:  @Versalytics
Originally Published:  19-Nov-2008 @ 00:49