Category: OEE: Overall Equipment Efficiency

Understand and implement Overall Equipment Effectiveness (OEE) in your operation. We explain the details to help you implement a meaningful measurement process across your entire operation.

OEE and Human Effort

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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


Killer Metrics

Dead plant in pots
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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

OEE: The Means to an End – Differentiation Where It Matters Most

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Does your organization focus on results or the means to achieve them?  Do you know when you’re having a good day?  Are your processes improving?

The reality is that too many opportunities are missed by simply focusing on results alone.  As we have discussed in many of our posts on problem solving and continuous improvement, the actions you take now will determine the results you achieve today and in the future. Focus on the means of making the product and the results are sure to follow.

Does it not make sense to measure the progress of actions and events in real-time that will affect the end result? Would it not make more sense to monitor our processes similar to the way we use Statistical Process Control techniques to measure current quality levels?  Is it possible to establish certain “conditions” that are indicative of success or failure at prescribed intervals as opposed to waiting for the run to finish?

By way of analogy, consider a team competing in a championship race.  While the objective is to win the race, we can be certain that each lap is timed to the fraction of a second and each pit stop is scrutinized for opportunities to reduce time off the track.  We can also be sure that fine tuning of the process and other small corrections are being made as the race progresses.  If performed correctly and faster than the competition, the actions taken will ultimately lead to victory.

Similarly, does it not make sense to monitor OEE in realtime? If it is not possible or feasible to monitor OEE itself , is it possible to measure the components – Availability, Performance, and Quality – in real-time?  I would suggest that we can.

Performance metrics may include production and quality targets based on lapsed production time. If the targets are hit at the prescribed intervals, then the desired OEE should also be realized.  If certain targets are missed, an escalation process can be initiated to involve the appropriate levels of support to immediately and effectively resolve the concerns.

A higher reporting frequency or shorter time interval provides the opportunity to make smaller (minor) corrections in real-time and to capture relevant information for events that negatively affect OEE.

Improving OEE in real-time requires a skilled team that is capable of trouble shooting and solving problems in real-time. So, resolving concerns and making effective corrective actions in real-time is as important to improving OEE than the data collection process itself.

A lot of time, energy, and resources are expended to collect and analyze data. Unfortunately, when the result is finalized, the opportunity to change it is lost to history.  The absence of event-driven data collection and after the fact analysis leads to greater speculation regarding the events that “may have” occurred versus those events that actually did.

Clearly, an end of run pathology is more meaningful when the data supporting the run represents the events as they are recorded in real-time when they actually occurred.  This data affords a greater opportunity to dissect the events themselves and delve into a deeper analysis that may yield opportunities for long-term improvements.

Set yourself apart from the competition.  Focus on the process while it is running and make improvements in real-time.  The results will speak for themselves.

Your feedback matters

If you have any comments, questions, or topics you would like us to address, please feel free to leave your comment in the space below or email us at or  We look forward to hearing from you and thank you for visiting.

Until Next Time – STAY lean

Versalytics Analytics

Viral Differentiation – Another Great Site

We are still working on our Differentiation Strategies and OEE series.  We decided to share just a glimpse of what we have found to be a uniquely evolved infrastructure within the Excel Development community.

For the many Excel – VBA experts that continue to use our Excel Pages as a resource and reference for top notch sites, we have added yet another great site to our list.  Rob Van Gelder’s site serves as an excellent source for some practical VBA solutions that may enhance the development of your next project.

We learned of this site through a comment posted by Rob on Daily Dose of Excel (another one of our preferred sites worth visiting on regular basis). DailyDoseOfExcel recently featured a portion of our post “Lean Office with Excel and VBA” as a discussion topic that also yielded some interesting discussion.  Click here to view Learn VBA to Be Lean.

How does this apply to differentiation?  Although each of the recommended sites present some common areas of interest, they also have identified a niche purpose within the Excel community.  Even the casual visitor to these sites will note that the efforts within this community are both collaborative and complementary.

While our focus is Agile-Lean Manufacturing with a core focus on Overall Equipment Effectiveness (OEE), even we have managed to contribute to the Excel community in our own unique way.

This open system creates an open working environment that is unparalleled in the manufacturing community.  Complementary business models with a common purpose, a mutual respect for the talents of others, and a sense of humility that keeps everyone wanting and willing to help.

As lean practitioners, we continue to find answers where we least expected them.

Until Next Time – STAY lean!

Vergence 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:

Until Next Time – STAY lean!

Vergence Analytics

Lean, OEE, and How to beat the “Law of Diminishing Returns”

Are your lean initiatives falling prey to the Law of Diminishing Returns?  Waning returns may soon be followed by apathy as the “new” initiative gets old.  For those who have not studied economics or are not familiar with the term, it is defined by Wikepedia as follows:

The law states “that we will get less and less extra output when we add additional doses of an input while holding other inputs fixed. In other words, the marginal product of each unit of input will decline as the amount of that input increases holding all other inputs constant.

In simple terms, continued application of time and effort to improve a process will eventually yield reduced or smaller returns.  The low hanging fruit that once was easy to see and resolve has all but disappeared.  Some companies would claim that they have finally “arrived”.  We contend that these same companies have simply hit their first plateau.

Methods and Objectives

Is it inevitable that a process has been refined to the point where additional investment can no longer be justified financially?  The short answer is “Yes and No”.  As the Olympics are well under way, we are quick to observe the fractions of seconds that may be shaved from current world records.  If you’re going for Olympic Gold, you will need every advancement or enhancement that technology has to offer to gain the competitive edge.  These advances in technology are refinements for existing processes that are governed by strict rules.  Clearly, there are much faster ways to get from point A to point B.  However, the objective of the Olympics is to demonstrate how these feats can be accomplished through the physical skills and abilities of the athletes.

In business our objectives are defined differently.  We want to provide (and our customers expect) the highest quality products at the lowest cost delivered in the shortest amount of time.  How we do that is up to us.  Lean initiatives and tools such as overall equipment effectiveness (OEE) can help us to refine current processes but are they enough to stimulate the development of new products and processes?  Or, are they limited to simply help us to recognize when optimum levels have been achieved?

Radical change versus refinement

Objectives are used to determine and align the methods that are used to achieve a successful outcome.  This is certainly the case in the automotive industry as environmental concerns and availability of non-renewable resources, specifically oil and gas, continue to gain global attention and focus.  The objectives of our “transportation” systems are being redefined almost dynamically as new technologies are beginning to emerge.  At some point, the automotive industry leaders must have realized that continuing to refine existing technologies simply will not satisfy future expectations.  With this realization it is now inevitable that a radical powertrain technology change is required.  Hybrid vehicles continue to evolve and electric cars are not too far behind.

How to Beat the Law of Diminishing Returns

Overcoming the law of diminishing returns requires another look at the vision, goals, and objectives of the company and to develop a new, different, or fresh perspective on what it is you are trying to achieve.  The lean initiatives introduced by Toyota, Walmart, Southwest and many others were driven by the need to find a competitive edge.  They recognized that they couldn’t simply be a “me too” company to gain the recognition and successes they now enjoy.

The question you may want to ask yourself and your team is, “If we started from scratch today, is this the result we would be looking for?”  The answer should be a unanimous and resounding “NO”.  Get out your whiteboard, pens, paper, and start writing down what you would be doing differently.  In other words, it’s time to re-energize the team and refocus your goals and objectives.  Vision and mission statements are not tombstones for the living.  5S these documents and take the time to re-invigorate your team.

Turning a company around may require some new radical changes and we need to be mindful of the new upstarts with the latest and greatest technology.  They may have an edge that we have may just haven’t taken the time to consider.  We are not suggesting that you need to replace all the equipment in your plant in order to compete.  Proven technologies have their place in industry and the competitive pricing isn’t always about speed.  The question you may need to consider is, “Can our technology be used to produce different products that have been traditionally manufactured using other methods?”

While many companies pursue a growth strategy based on their current product offerings and derivatives, we would strongly suggest that manufacturers consider a growth strategy based on their process technology offerings.  What else can we make with process or machine XYZ?  We anticipate that manufacturing sectors will soon start to blend as manufacturers pursue products beyond the scope of their current industry applications.

Be the Leader

Leading companies create and define the environment where their products and services will thrive.  Apple’s “iProducts” have redefined how electronics are used in everyday life.  As these tools are developed and evolve, so too can the systems and processes used throughout manufacturing.  The collective human mind is forever considering the possibilities of the next generation of products or services.

There was a time when manned space flight and walking on the moon were considered unlikely probabilities.  Today we find ourselves discovering and considering galaxies beyond our own and we don’t give it a second thought.  How far can we go and how do we get there?  The answer to that question is …

Until Next Time – STAY lean!

22 Seconds to Burn – Excel VBA Teaches Lean Execution

Cover of "Excel 2003 Power Programming wi...
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VBA for Excel has once again provided the opportunity to demonstrate some basic lean tenets.  The methods used to produce the required product or solution can yield significant savings in time and ultimately money.  The current practice is not necessarily the best practice in your industry.  In manufacturing, trivial or minute differences in methods deployed become more apparent during mass production or as volume and demand increases.  The same is true for software solutions and both are subject to continual improvement and the relentless pursuit to eliminate waste.

Using Excel to demonstrate certain aspects of Lean is ideal.  Numbers are the raw materials and formulas represent the processes or methods to produce the final solution (or product).  Secondly, most businesses are using Excel to manage many of their daily tasks.  Any extended learning can only help users to better understand the Excel environment.

The Model:

We recently created a perpetual Holiday calendar for one of our applications and needed an algorithm or procedure to calculate the date for Easter Sunday and Good Friday.  We adopted an algorithm found on Wikipedia at that produces the correct date for Easter Sunday.

In our search for the Easter Algorithm, we found another algorithm that uses a different method of calculation and provides the correct results too.  Pleased to have two working solutions, we initially did not spend too much time thinking about the differences between them.  If both routines produce the same results then we should choose the one with the faster execution time.  We performed a simple time study to determine the most efficient formula.  For a single calculation, or iteration, the time differences are virtually negligible; however, when subjected to 5,000,000 iterations the time differences were significant.

This number of cycles may seem grossly overstated, however, when we consider how many automobiles and components are produced each year then 5,000,000 approaches only a fraction of the total volume.  Taken further, Excel performs thousands of calculations a day and perhaps even as many more times this rate as numbers or data are entered on a spreadsheet.  When we consider the number “calculations” performed at any given moment, the number quickly grows beyond comprehension.


As a relatively new student to John Walkenbach’s book, “Excel 2003 Power Programming with VBA“, speed of execution, efficiency, and “Declaring your Variables” have entered into our world of Lean.  We originally created two (2) routines called EasterDay and EasterDate.  We then created a simple procedure to run each function through 5,000,000 cycles.  Again, this may sound like a lot of iterations but computers work at remarkable speeds and we wanted enough resolution to discern any time differences between the routines.

The difference in the time required to execute 5,000,000 cycles by each of the routines was surprising.  We recorded the test times (measured in seconds) for three separate studies as follows:

  • Original EasterDay:  31.34,  32.69,  30.94
  • Original EasterDate:  22.17,  22.28,  22.25

The differences between the two methods ranged from 9.17 seconds to 8.69 seconds.  Expressed in different terms, the duration of the EasterDay routine is 1.39 to 1.46 times longer than EasterDate.  Clearly the original EasterDate function has the better execution speed.  What we perceive as virtually identical systems or processes at low volumes can yield significant differences that are often only revealed or discovered by increased volume or the passage of time.

In the Canadian automotive industry there are at least 5 major OEM manufacturers (Toyota, Honda, Ford, GM, and Chrysler), each producing millions of vehicles a year.  All appear to produce similar products and perform similar tasks; however, the performance ratios for each of these companies are starkly different.  We recognize Toyota as the high velocity, lean, front running company.  We contend that Toyota’s success is partly driven by the inherent attention to detail of processes and product lines at all levels of the company.


We decided to revisit the Easter Day calculations or procedures to see what could be done to improve the execution speed.  We created a new procedure called “EasterSunday” using the original EasterDay procedure as our base line.  Note that the original Wikipedia code was only slightly modified to work in VBA for Excel.  To adapt the original Wikipedia procedure to Excel, we replaced the FLOOR function with the INT function in VBA.  Otherwise, the procedure is presented without further revision.

To create the final EasterSunday procedure, we made two revisions to the original code without changing the algorithm structure or the essence of the formulas themselves.  The changes resulted in significant performance improvements as summarized as follows:

  1. For integer division, we replaced the INT (n / d) statements with a less commonly used (or known) “\” integer division operator.  In other words, we used “n \ d” in place of “INT( n / d)” wherever an integer result is required.  This change alone resulted in a gain of 11 seconds.  One word of caution if you plan to use the “\” division operator:  The “n” and “d”  are converted to integers before doing the division.
  2. We declared each of the variables used in the subsequent formulas and gained yet another remarkable 11 seconds.  Although John Walkenbach and certainly many other authors stress declaring variables, it is surprising to see very few published VBA procedures that actually put this to practice.


The results of our Time Tests appear in the table below.  Note that we ran several timed iterations for each change knowing that some variations in process time can occur.

EasterDay = 31.34375 Original Code uses INT( n / d ) to convert Division Results
EasterSunday = 20.828125 1.  Replaced INT ( n / d) with (n \ d)
EasterDate = 22.28125 Original Code – Alternate Calculation Method
Re-Test to Confirm Timing
EasterDay = 30.9375 Original Code uses INT( n / d ) to convert Division Results
EasterSunday = 20.921875 1.  Replaced INT ( n / d) with (n \ d)
EasterDate = 22.25 Original Code – Alternate Calculation Method
Re-Test to Confirm Timing
EasterDay = 30.90625 Original Code uses INT( n / d ) to convert Division Results
EasterSunday = 21.265625 1.  Replaced INT ( n / d) with (n \ d)
EasterDate = 22.25 Original Code – Alternate Calculation Method
Re-Test to Confirm Timing
EasterDay = 31.078125 Original Code uses INT( n / d ) to convert Division Results
EasterSunday = 9.171875 2.  Variables DECLARED!
EasterDate = 22.1875 Original Code – Alternate Calculation Method
Re-Test to Confirm Timing
EasterDay = 31.109375 Original Code uses INT( n / d ) to convert Division Results
EasterSunday = 9.171875 2.  Variables DECLARED!
EasterDate = 22.171875 Original Code – Alternate Calculation Method

The EasterSunday procedure contains the changes described above.  We achieved a total savings of approximately 22 seconds.  The integer division methods used both yield the same result, however, one is clearly faster than the other.

The gains made by declaring variables were just as significant.  In VBA, undeclared variables default to a “variant” type.  Although variant types are more flexible by definition, performance diminishes significantly. We saved at least an additional 11 seconds simply by declaring variables.  Variable declarations are to VBA as policies are to your company, they define the “size and scope” of the working environment.  Undefined policies or vague specifications create ambiguity and generate waste.

Lessons Learned:

In manufacturing, a 70% improvement is significant; worthy of awards, accolades, and public recognition.  The lessons learned from this example are eight-fold:

  1. For manufacturing, do not assume the current working process is the “best practice”.  There is always room for improvement.  Make time to understand and learn from your existing processes.  Look for solutions outside of your current business or industry.
  2. Benchmarking a current practice against another existing practice is just the incentive required to make changes.  Why is one method better than another?  What can we do to improve?
  3. Policy statements can influence the work environment and execution of procedures or methods.  Ambiguity and lack of clarity create waste by expending resources that are not required.
  4. Improvements to an existing process are possible with results that out perform the nearest known competitor.  We anticipated at least being able to have the two routines run at the similar speeds.  We did not anticipate the final EasterSunday routine to run more than 50% faster than our simulated competitive benchmark (EasterDate).
  5. The greatest opportunities are found where you least expect them.  Learning to see problems is one of the greatest challenges that most companies face.  The example presented in this simple analogy completely shatters the expression, “If it ain’t broke, don’t fix it.”
  6. Current practices are not necessarily best practices and best practices can always be improved.  Focusing on the weaknesses of your current systems or processes can result in a significant competitive edge.
  7. Accelerated modeling can highlight opportunities for improvement that would otherwise not be revealed until full high volume production occurs.  Many companies are already using process simulation software to emulate accelerated production to identify opportunities for improvement.
  8. The most important lesson of all is this:

Speed of Execution is Important >> Thoughtful Speed of Execution is CRITICAL.

We wish you all the best of this holiday season!

Until Next Time – STAY Lean!

Vergence Analytics

At the onset of the Holiday project, the task seemed relatively simple until we discovered that the rules for Easter Sunday did not follow the simple rules that applied to other holidays throughout the year.  As a result we learned more about history, astronomy, and the tracking of time than we ever would have thought possible.

We also learned that Excel’s spreadsheet MOD formula is subject to precision errors and the VBA version of MOD can yield a different result than the spreadsheet version.

We also rediscovered Excel’s Leap Year bug (29-Feb-1900).   1900 was not a leap year.  The leap year bug resides in the spreadsheet version of the date functions.  The VBA date function recognizes that 29-Feb-1900 is not a valid date.