Tag: Cheating OEE

OEE for Batch Processes

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We recently received an e-mail regarding OEE calculations for batch processes and more specifically the effect on down stream equipment that is directly dependent (perhaps integrated) on the batch process.  While the inquiry was specifically related to the printing industry, batch processing is found throughout manufacturing. Our more recent experiences pertain to heat treating operations where parts are loaded into a stationary fixed-load oven as opposed to a continuous belt process.

Batch processing will inherently cause directly integrated downstream equipment (such as cooling, quenching, or coating processes) to be idle. In many cases it doesn’t make sense to measure the OEE of each co-dependent piece of equipment that are part of the same line or process. Unless there is a strong case otherwise, it may be better to de-integrate or de-couple subsequent downstream processes.

Batch processing presents a myriad of challenges for line balancing, batch sizes, and capacity management in general.  We presented two articles in April 2009 that addressed the topic of  where OEE should be measured.  Click here for Part I or Click  here for Part II.

Scheduling Concerns – Theory of Constraints

Ideally, we want to measure OEE at the bottleneck operation.  When we apply the Theory of Constraints to our production process, we can assure that the flow of material is optimized through the whole system.  The key of course is to make sure that we have correctly identified the bottleneck operation.  In many cases this is the batch process.

While we are often challenged to balance our production operations, the real goal is to create a schedule that can be driven by demand.  Rather than build excess inventories of parts that aren’t required, we want to be able to synchronize our operations to produce on demand and as required to keep the bottleneck operation running.  Build only what is necessary:  the right part, the right quantity, at the right time.

Through my own experience, I have realized the greatest successes using the Theory of Constraints to establish our material flows and production scheduling strategy for batch processes.  Although an in-depth discussion is beyond the scope of this article, I highly recommend reading the following books that convey the concepts and application through a well written and uniquely entertaining style:

  1. In his book “The Goal“, Dr. Eliyahu A. Goldratt presents a unique story of a troubled plant and the steps they took to turn the operation around.
  2. Another book titled “Velocity“, from the AGI-Goldratt Institute and Jeff Cox also demonstrates how the Theory of Constraints and Lean Six Sigma can work together to bring operations to all new level of performance, efficiency, and effectiveness.

I am fond of the “fable” based story line presented by these books as it is allows you to create an image of the operation in your own mind while maintaining an objective view.  The analogies and references used in these books also serve as excellent instruction aids that can be used when teaching your own teams how the Theory of Constraints work.  We can quickly realize that the companies presented in either of the above books are not much different from our own.  As such, we are quickly pulled into the story to see what happens and how the journey unfolds as the story unfolds.

Please leave your comments regarding this or other topics.  We appreciate your feedback.  Also, remember to get your free OEE spreadsheets.  See our free downloads page or click on the file you want from the “Orange” box file on the sidebar.

Until Next Time – STAY lean!

Vergence AnalyticsVergence Analytics
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OEE Calculation Errors

Database Errors

We agree that collecting and tracking OEE data is a task best suited for a database, however, all the bells and whistles of an OEE system don’t serve much purpose if the calculations are wrong.  Before you make a significant investment in your OEE data collection, tracking, and monitoring system, make sure the system you plan to purchase is calculating the OEE results correctly.

The ultimate system is one that supports automated data collection technology to minimize data entry costs, reduces the risk of entry errors, and provides reporting or monitoring of OEE in real time.  These solutions may be purchased “off the shelf” or customized to your specific process application.

Excel Spreadsheets

If a database is the best approach, you may ask why we use Excel spreadsheets to present our examples or why we supply templates to allow you to track and monitor OEE.  We have four primary reasons:

  1. Almost everyone is familiar with spreadsheets and most people have access to them on their computer.
  2. We determined that a customized database solution being used was not calculating the weighted OEE factors correctly and the overall OEE index was also wrong.  We found it necessary to develop a spreadsheet that made it easy to validate the database calculations.
  3. Database enhancements were easier to develop and demonstrate using a spreadsheet.  We encountered a production process that was equipped with automated data collection capability and provided an overwhelming amount of performance data in real time.  It was easier to perform database queries and use the power of PIVOT tables to develop the desired solutions.
  4. Spreadsheet templates allow you to start collecting and analyzing data immediately.  It also allows the users to get a “feel” for the data.  Although the graphs and drill downs offered by databases are based on predetermined rules, humans are still required to make sense of the data.

Recommendations

In summary, validate the software and its capabilities prior to purchase.  We have observed installations where the OEE data is used to monitor current production performance and the reports generated by the system are used to support the results – good or bad.

We have also evaluated a number of other free OEE spreadsheet offerings on the web and observed that some of these also fail to correctly calculate OEE where multiple machines or part numbers are concerned.  Take a look at our free spreadsheets offerings (see the sidebar).  Our tutorial provides an in depth explanation of how to calculate OEE for single and multiple machines or parts.

The purpose of measuring OEE is to ensure sustained performance with the objective to continually improve over time.  Don’t fall into the trap of setting up a system that, once installed, will only be used to generate reports to justify the current results.

Take the time to train your team and demonstrate how the results will be used to improve their processes.  Involve all of your employees from the very beginning, including the system selection process, so they understand the intent and can provide feedback for what may be meaningful to them while, in turn, they can support the company’s goals and objectives.

Reference Posts.

We encourage you to visit our previous posts showing how to calculate OEE for multiple parts and machines.

  1. Single Process – Multiple Shifts:  OEE

  2. Multiple Parts / Processes:  OEE

  3. Practical OEE

  4. Weighted Calculations:  OEE

  5. How to Calculate OEE

  6. Overall Equipment Efficiency

If you have any questions, comments, or wish to suggest a topic for a future post, please forward an e-mail to leanexecution@gmail.com

We appreciate your feedback.

Until next time – STAY Lean!

OEE and Overtime

A number of requests have been received recently that point to a lack of clarity in the definition of Overall Equipment Effectiveness (OEE).  One of the questions we were recently asked is:

How do you calculate OEE for Overtime hours?

Overtime and OEE

Our first response is quite simple.  OEE doesn’t care whether you are working overtime or straight time.  The basic OEE definition pertains to equipment effectiveness.  If the machine is scheduled to run production, the same basic calculations apply regardless of the day or hours worked.

If your machine is running two shifts and customer requirements increase to the point where you no longer have capacity to meet demand , two choices exist:  either work overtime or add an additional shift to make up for the shortfall.  In both cases, the production time would be scheduled.

If capacity should be available but simply isn’t because of extenuating circumstances such as poor quality (material or process) or equipment condition, the same rules still apply.  Production is scheduled, therefore machines must run to meet customer demand.  The difference in this case is not increased customer demand but rather the inability to produce parts due to extenuating equipment or process conditions.

While appropriate action should be taken to address the reason(s) for working overtime, the fact that you are working it should not change the method used to calculate OEE.

This question, like many others we receive, reinforce our recommendation to clearly state what is being measured and why.  We also stated in previous posts that OEE is relative for your organization – a standard industry wide definition for OEE exists only by way of calculation.  The elements and how they are to be considered for calculation purposes are subject to company policy.

We appreciate the feedback and look forward to hearing from our readers.  To submit your questions, comments, or suggest a topic for a future post, send an e-mail to LeanExecution@gmail.com

Until next time – stay LEAN.

OEE and Morale

Is employee morale impacting your OEE?  If so, how much of a concern is it?  As we wrote in one of our recent posts,  “Perhaps the greatest “external” influence on current manufacturing operations is the rapid collapse of the automotive industry in the midst of our current economic “melt down”.  The changes in operating strategy to respond to this new crisis are bound to have an effect on OEE among other business metrics.”  We would argue that these times of economic crisis demand, now more than ever, that Lean Practices must become even more prevalent in our manufacturing operations.

People are concerned about the state and stability of the company’s finances and the industries they serve.  The automotive industry has been devastated by the recent decline, or more accurately, collapse of the market.  Significant changes in operating strategy including lay offs and reduced production days have impacted all of the OEM’s including Ford, GM, Chrysler, Toyota, and Honda.  No one company is immune from the effects of the current economic conditions.

It is clear that the auto industry fell behind the “power curve” and crashed.  Did conditions change too quickly to avoid the inevitable?  Was it so big that, like the Titanic, the ultimate demise could be predicted but not avoided?  Toyota was the number one producer of automobiles in 2008 but failed to yield significant profits.  Conditions such as these were ripe for continued growth in years past.  It is clear that even the best of the Lean practitioners are not immune from the effects of the current economy.

A company’s agility will certainly be tested during times such as these.  Sustainability and viability are among the few significant objectives of Lean dynamics.  As such, Lean dynamics should be at the forefront of every business leader.  How adaptable is your business?  Are you reinventing your business in response to the changes of your industry?  The true Lean practitioner is certainly challenged to eliminate waste and variation beyond current means and traditional approaches.  As change is constant, we must continually seek out ways to redefine or “better” define our businesses.

At the most fundamental level, everyone is concerned about the state of the economy, however, individuals, at the personal level, are concerned about their jobs and careers.  We all want to preserve our current life style to some degree and, at a minimum, continue to pay our bills.  It would be a difficult task to estimate the lost productivity that occurs when someone’s state of mind is focused on their own personal situation versus that of the company.  We have observed first hand how employee morale has diminished as a result of the recent economic doom and gloom.  Nothing can come between an indivual and their prosperity – this is an instinctive, almost primitive, response mechanism – a self defense position.

Recommendations:

While you may not be able to change the economy, we would suggest that you can influence the “morale” of your employees.  People will understand that you didn’t cause the current economic crisis, however, they do expect that you will let them know what the impact is to your business and ultimately to themselves.

Be honest with your employees, let them know where you stand – where they stand.  They need to prepare for their futures too, whether it is working for you or someone else.  During times of crisis such as this, it is time for the executive leadership to stand behind their Vision and Mission statements and treat their employees – THE PEOPLE -the most important assets a company can have – with the dignity and respect they not only deserve but worked so hard to earn.  Be present and available to your team.

Our employees recognize that we only attract, retain, and hire the best employees.  Regardless of the economy, the standard remains and we take great pride in the strength of our people.  They know this intrinsically.

People come to companies to work for PEOPLE.  Their immediate supervisor or manager is, in their eyes, the company.  Arm your staff with the information they need so people can make informed decisions.  Believe it or not, people are motivated when they feel that they are part of the process and not regarded as part of the problem.  Reality check:  “People come to companies to work for themselves.”  How does this statement change your perspective?  Who do you work for?

How many times have you heard, “Our labour is just too high,  we need to cut back.”  Well, who made the decision to hire the people in the first place?  Look in the mirror.  Treat people like they are part of the team, part of the solution.  Get them engaged and focused on moving forward.  Will they be motivated?  They will be if they feel that they are valued players on the team, performing meaningful work that is contributing to the success of the company.  Times of crisis tend to bring teams closer together and, in the end, they become stronger for the cause.

A great business parable written by Patrick Lencioni, “The Three Signs of a Miserable Job”, may provide some useful insights to motivate your team and even grow your business into a more profitable venture despite the current economic crisis.

While people think they work for a company or other people, we ultimately believe that people work for themselves and we, as a company, are the beneficiaries of their efforts.

Conclusion

So how does all of this tie to OEE?  Weill, performance typically lags when people are not focused on the task at hand.  There is a sense that, no matter what they do, they can’t change the current circumstances so, “Why bother?”  Distractions of this magnitude are hard to ignore.  As the leadership of the company, it is your responsibility to be in tune with the morale of your team and workforce in general.  It is possible to mitigate the effects of low morale by addressing them early on and encouraging employees to be part of the turn around process.

This might be one of the few times in history where the term “CHANGE” will be viewed in a positive light and actually be embraced by your team.

We may just discover the 5S process for managing our economy with a real process in place to manage the fifth “S” Sustainability.  Another one of the “anomalies” that just don’t make sense is, “This is just part of the nautral cycle of the economy.  We were long overdue.”  Somehow, that doesn’t say much about our governments or industry leaders. Why?  Because it suggests we should have been more than prepared to deal with this a long time ago.  The current scramble suggests the contrary to be true.  Secondly, what is “natural” about the economy – it’s manmade – driven by the decisions of business leaders and governments around the globe.  Natural? Never.  A logical excuse that every one seems to accept as part of “nature”?  Maybe.

Until next time – STAY Lean!

OEE Measurement Error

How many times have you, or someone you know, challenged the measurement process or method used to collect the data because the numbers just “don’t make sense” or “can’t be right”?

It is imperative to have integrity in the data collection process to minimize the effect of phantom improvements through measurement method changes.  Switching from a manual recording system to a completely automated system is a simple example of a data collection method change that will most certainly generate “different” results.

Every measurement system is subject to error including those used to measure and monitor OEE.  We briefly discussed the concept of variance with respect to actual process throughput and, as you may expect from this post, variance also applies to the measurement system.

Process and measurement stability are intertwined.  A reliable data collection / measurement system is required to establish an effective baseline from which to base your OEE improvement efforts.  We have observed very unstable processes with extreme throughput rates from one shift to the next.  We learned that the variance in many cases is not always the process but in the measurement system itself.

We decided to comment briefly on this phenomenon of measurement error for several reasons:

  1. The reporting systems will naturally improve as more attention is given to the data they generate.
  2. Manual data collection and reporting systems are prone to errors in both recording and data input.
  3. Automated data collection systems substantially reduce the risk of errors and improve data accuracy.
  4. Changes in OEE trends may be attributed to data collection technology not real process changes.

Consider the following:

  1. A person records the time of the down time and reset / start up events by reading a clock on the wall.
  2. A person records the time of the down time event using a wrist watch and then records the reset /start up time using the clock on the wall.
  3. A person uses a stop watch to track the duration of a down time event.
  4. Down time and up time event data are collected and retrieved from a fully automated system that instantly records events in real time.

Clearly, each of the above data collection methods will present varying degrees of “error” that will influence the accuracy of the resulting OEE.  The potential measurement error should be a consideration when attempting to quantify improvement efforts.

Measurement and Error Resolution

The technology used will certainly drive the degree of error you may expect to see.  A clock on the wall may yield an error of +/- 1 minute per event versus an automated system that may yield an error of +/- 0.01 seconds.

The resolution of the measurement system becomes even more relevant when we consider the duration of the “event”.  Consider the effect of measurement resolution and potential error for a down time event having a duration of 5 minutes versus 60 minutes.

CAUTION!

A classic fallacy is “inferred accuracy” as demonstrated by the stop watch measurement method.  Times may be recorded to 1/100th of a second suggesting a high degree of precision in the measurement.  Meanwhile, it may take the operator 10 seconds to locate the stop watch, 15 seconds to reset a machine fault, and 20 seconds to document the event on a “report” and another 10 seconds to return the stop watch to its proper location. 

What are we missing?  How significant is the event and was it worth even recording?  What if one operator records the “duration” after the machine is reset while another operator records the “duration” after documenting and returning the watch to its proper location?

The above example demands that we also consider the event type:  “high frequency-short duration” versus “low frequency-long duration” events.  Both must be considered when attempting to understand the results.

The EVENT is the Opportunity

As mentioned in previous posts, we need to understand what we are measuring and why.  The “event” and methods to avoid recurrence must be the focus of the improvement effort.  The cumulative duration of an event will help to focus efforts and prioritize the opportunities for improvement.

Additional metrics to help “understand” various process events include Mean Response Time, Mean Time Between Failures (MTBF), and Mean Time To Repair (MTTR).  Even 911 calls are monitored from the time the call is received.  The response time is as critical, if not more so, than the actual event, especially when the condition is life-threatening or otherwise self-destructive (fire, meltdown).

An interesting metric is the ratio between Response Time and Mean Time To Repair.  The response time is measured from the time the event occurs to the time “help” arrives.  Our experience suggests that significant improvements can be made simply by reducing the response time.

We recommend training and providing employees with the skills needed to be able to respond to “events” in real time.  Waiting 15 minutes for a technician to arrive to reset a machine fault that required only 10 seconds to resolve is clearly an opportunity.

Many facilities actually hire “semi-skilled” labour or “skilled technicians” to operate machines.  They are typically flexible, adaptable, present a strong aptitude for continual improvement, and readily trained to resolve process events in real time.

Conclusion

Measurement systems of any kind are prone to error.  While it is important to understand the significance of measurement error, it should not be the “primary” focus.  We recommend PREVENTION and ELIMINATION of events that impede the ability to produce a quality product at rate.

Regrettably, some companies are more interested in collecting “accurate” data than making real improvements (measuring for measurements sake). 

WHAT are you measuring and WHY?  Do you measure what you can’t control?  We will leave you with these few points to ponder.

Until next time – STAY Lean!

Variance, Waste, and OEE

What gets managed MUST be measured – Including VARIANCE.

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

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

Measure with Meaning

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

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

VARIANCE:  the leading cause of waste!

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

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

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

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

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

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

Conflict Management and OEE

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

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

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

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

FREE Downloads

We are currently offering our Excel OEE Spreadsheet Templates and example files at no charge.  You can download our files from the ORANGE BOX on the sidebar titled “FREE DOWNLOADS” or click on the FREE Downloads Page.  These files can be used as is and can be easily modified to suit many different manufacturing processes.  There are no hidden files, formulas, or macros and no obligations for the services provided here.

Please forward your questions, comments, or suggestions to LeanExecution@gmail.com.  To request our services for a specific project, please send your inquiries to Vergence.Consulting@gmail.com.

We welcome your feedback and thank you for visiting.

Until Next Time – STAY Lean!

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Problem Solving with OEE – Measuring Success

OEE in Perspective

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

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

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

Problem Solving with OEE

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

Understanding and Managing Variance:

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

Availability

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

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

Performance

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

Quality

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

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

Standards

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

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

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

Caution

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

FREE Downloads

We are currently offering our Excel OEE Spreadsheet Templates and example files at no charge.  You can download our files from the ORANGE BOX on the sidebar titled “FREE DOWNLOADS” or click on the FREE Downloads Page.  These files can be used as is and can be easily modified to suit many different manufacturing processes.  There are no hidden files, formulas, or macros and no obligations for the services provided here.

Please forward your questions, comments, or suggestions to LeanExecution@gmail.com.  To request our services for a specific project, please send your inquiries to Vergence.Consulting@gmail.com.

We welcome your feedback and thank you for visiting.

Until Next Time – STAY Lean!

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