Tag: Lean Methods

Contingency Planning For Lean Organizations – H1N1 (Swine Flu) – Reference

In our first post on Contingency Planning For Lean Organizations, we made reference to the current situation regarding the H1N1 virus or Swine Flu. We also suggested that history may provide relevant information that can be used to aid in future crisis event planning.

Michael A. Roberto, author of “Know What You Don’t Know” copyright 2009 by Pearson Education Inc., presents a case surrounding the 1976 “swine flu” incident to exemplify how faulty analogies can have devastating effects. In his book, Michael Roberto cites research of Richard Neustadt and Ernest May.

An excerpt from the book, reference pages 77-78, reads as follows:

“In that situation, President Gerald Ford and his advisors drew an erroneous analogy to the infamous flu epidemic of 1918. The faulty analogy led them to dramatically overestimate the seriousness of the problem they faced. As a result, they embarked on a very comprehensive and unnecessary immunization program. Roughly five hundred people experienced a serious side effect that was linked to the immunizations, and twenty five people died.”

This may explain the slow, seemingly uncoordinated, pace of the government to address the current H1N1 outbreak. Many people remain skeptical as to whether the immunization process is safe. This skepticism may be warranted. Again, referencing Michael Roberto’s book “Know What You Don’t Know” (page 78), “More people died from the immunization than from the flu itself.”

The experts of the day advised that this could be another epidemic. Two prior non-swine flu outbreaks, one in 1957 and the other in 1968, caught the government off guard. It is more than noteworthy that an estimated twenty million people were killed world-wide by the virus during the epidemic of 1918.

With mounting pressure from the Centers for Disease Control to avoid history repeating itself and in an effort to be pro-active, immunizations were ordered and given to an estimated forty million people. What would you have done?

For more information we recommend reading “Knowing What You Don’t Know – How Great Leaders Prevent Problems Before They Happen” by Michael A. Roberto, copyright 2009 by Pearson Education Inc., publishing as Wharton School Publishing, Upper Saddle River, New Jersey 07458 (ISBN: 0-13-156815-9), Pages 202.

Also see Warner, J. “The Sky Is Falling: An analysis of the Swine Flu Affair of 1976.” http://www.harverford.edu/biology/edwards/disease/viral_essays/warnervirus.htm

Until Next Time – STAY Lean!


Lean Contingency Planning For Lean Operations – IT and the BSOD

Coincidentally, we are having a first hand experience with the Blue Screen of Death or BSOD with one of our laptops today.  The completely unexpected critical system error that renders Windows completely helpless.  If this isn’t on your list of IT concerns, it should be.

In our case the error appears to be video related – driver or card.  Most IT specialists know how to deal with these types of errors but for the average user, the message that appears is enough to make you sweat.  If the system can’t fix the error, you may very well end up staring at a Black Screen – just as we are.

How is it that we were still able to produce this POST?  Well, we are currently executing our contingency plan and using another system that is operated independently.  Most companies back up their data to prevent or minimize loss.  Another concern that is often overlooked is accessibility to that back up data in the event the system goes down.

What have we learned?

We are not the first to experience this problem.  We did a Google search using some brief terms such as “Computer Black Screen”, “Laptop Black Screen”, and we even Googled parts of the error message that appeared on the screen.  The result?  Thousands of people have experienced this same error.

The point of this post is to demonstrate that you do not have to re-invent the wheel to determine potential solutions or to discover problems that may occur.  Quite likely, they may already have happened and solutions are already developed and available.

There are two probable solutions to our video issue:

  1. Update the video device driver (Free)
  2. Replace the video card (Cost $)

Hopefully, the first solution is the answer to our problem.  Video cards are not sitting on our shelf and the downtime may be extended if we can’t find something locally.

It is noteworthy that we have not yet identified the root cause of this failure.  We haven’t loaded any new software or experienced problems in recent history.  This may be the topic for a future problem solving post.

Regardless of the outcome of our present dilemma, we have learned that it is a good idea to keep device drivers up to date.  As a planned activity, this may prevent some of you from having to experience the BSOD as we have today.

The loss incurred for this event is more than just the cost to repair.  This computer may be down for a few days.  How much is the down time worth?  Unless we play out the scenarios that may threaten or pose a risk to our business, we may never have the opportunity to prepare for the event until it actually happens.

Keep an open mind and use the resources available to you to help solve the problem.  In some cases a simple Google search could confirm your concern in a matter of seconds.

Until Next Time – STAY Lean!

Contingency Planning For Lean Operations (II)

Contingency Planning For Lean Operations – Part II

Putting together a contingency plan can be quite challenging when you consider all the things that could go wrong at any given point in time.  Contingency plans should not only be restricted to “things gone wrong” and are not limited to operations or process specific events.  All aspects of an operation are prone to risk.  As such, contingency planning must be an enterprise wide activity.

Failing to understand and assess the risks that may impact your operation is a recipe for future failure.  If you fail to plan then plan to fail.  The same is true for contingency plans.  Effective risk management and contingency planning are critical to minimize or eliminate the effects of failure.

Natural disasters (like we’ve never seen before) continue to plague us without prediction.  Yet, we are able to respond immediately and effectively.  If you get hurt or injured, someone is there to help you simply by dialing 911.  Emergency units are ever present and available to respond.

Unfortunately the same is not necessarily true for business.  The recent turn in the economy caused financial markets to tumble and decimated corporations on every scale.  Millions of people are affected by the fallout.  The government “loans” were not crafted after the event.  Did contingency plans exist to even consider this level of change in the economy?

Although history may be the best predictor of future events, it is not exclusive or exhaustive to predicting unforeseen future events.  Even if history did provide a glimpse of potential future failures, we may simply choose to ignore the probability of recurrence – this isn’t the first time the financial markets have crashed, yet we can’t seem to determine or understand what key indicators existed that could have prevented this current situation.

Certainly new variables are introduced as technologies continue to evolve and become more integral in our operations.

In Part I of this series we suggested that contingency plans should be prepared to address potential labour challenges and more specifically availability.  Certainly, the recent concerns regarding the H1N1 virus have heightened attention with respect to labour shortages.

  • Inclement Weather – Immediate effects of Snow Storm, Hurricane, Heavy Rain, Tornado.
    • Other considerations include:
      • Duration
      • Seasons
      • Cumulative Severity
      • Delayed Effects (flooding)
      • Property Damage.
  • Accident / Injury:  Personal versus Workplace
    • Long Term
    • Short term
    • Considerations to reduce or minimize impact to operations:
      • Early Return To Work
      • Modified Duty
      • Restricted Duty
      • Reduced Hours
  • Illness (Personal / Family / Extended Family)
    • Short Term
      • (Flu, Cold)
      • Emergency
    • Long Term
      • Surgical Care
      • Chronic Care
  • Sudden Premature Death
  • Parental Leave (Maternity Leave)
  • Bereavement – Immediate Family, Out of Country
  • Retirement / Attrition
  • Training
    • Onsite vs Offsite
    • Duration
  • Meetings – Department
    • Company Wide
    • On Site
    • Customer Site
  • Quality Disruption
    • Containment Activity
      • Sorting
      • Rework
  • Travel
  • Vacation Allowance / Timing
    • Customer Driven
    • Company Mandated
    • Personal Choice
    • Season
    • Duration
      • New Hires – Zero Weeks
      • Senior Employees – Per “X” Years of Service
  • Holidays
  • Absenteeism (Culpable)
  • Layoff and Recall
    • Short Term
    • Long Term
  • Supply Chain Disruptions – Raw Material or Part Supply
  • Planned Shutdown / Start Up Events – Holidays
  • Leave of Absence – Short Term / Long Term
  • Facilities
    • Loss of Utilities:  Water, Electricity
    • Fire, Suspended Services
    • Parking Availability
    • Locker Space
  • Equipment – Breakdown / Malfunction (Major)
  • Tooling – Breakdown  / Malfunction (Major)
  • Skill Levels Required – Non-Skilled, Semi-Skilled, and Skilled Labour
  •  Union – Strike
  • Customer Decreases
    • Shutdown (Reduced Volume)
    • Slow Down (Reduced Volume)
    • Reduced Work Week (4 vs. 5 days)
    • Shutdown (Planned)
  • Customer Increases:
    • Volume
    • Extended Work Days (Daily Overtime)
    • Extended Work Week (Saturday)

There are likely more areas of concern that may impact your labour pool, however, this does serve as a starting point.  Do all of the above elements require a contingency plan?  Not necessarily.  We still contend that it is good practice to document all potential concerns.  It is easier to add a note to document the reason for exclusion from the contingency plan by stating:

  • The following elements were discussed during the preparation of this plan, however, specific contingency plans were not considered necessary at the time of review:
    • Training – Scheduled Activity
    • Culpable Absenteeism – Progressive Discipline Program
    • Add Elements to the List as applicable

This latter task may seem somewhat trivial, but consider who else may be reading the report.  Defining the scope of the contingency plan and adding a list of exclusions supported with reason(s) clarifies any ommissions from the core plan, will minimize the time required for review, and eliminates any assumptions regarding unintended ommisions.

Our next post will address the elements to be considered when developing a contingency plan.

Until Next Time – STAY Lean!

Availability and OEE

What is Availability?

In its simplest form, availability measures the uptime of a machine or process against the planned production time.  As one of the factors of Overall Equipment Efficiency (OEE), Availability is expressed as a percentage.  The uptime is calculated by taking the difference between the planned production time and total duration of the downtime events that occurred during the planned production period.

We specifically address the “Availability” factor in this post for the simple reason that the definition of availability is likely to be one of the most debated and hotly contested topics of your OEE implementation strategy.  The reason for this, in many cases, is the lack of clarity in some of the most basic terminology.  The purpose of this discussion is to present some topics for consideration that will allow you to arrive at a clear definition that can perhaps be formed into a standard policy statement.

We will also demonstrate that it is possible to calculate the downtime by simply knowing the cycle time or process rate, the quantity of parts produced, and the planned production time.  We recommend using this technique to validate or reconcile the actual documented downtime.  We would argue that the first and foremost purpose of any machine monitoring or downtime event measurement system is to determine the “WHY and WHAT” of the downtime events and secondly to record the “When and How Long”.

You will learn that monitoring your processes to determine causes and duration of downtime events  is key to developing effective action plans to improve availability.  The objective of any machine automation, sensor strategy, or data collection and analysis is to determine methods and actions that will improve the availability of the equipment through permanent corrective actions, implementing more effective trouble shooting strategies (sensor technologies), improved core process controls, or more effective preventive maintenance.

Define the purpose of OEE

While it looks like we’re taking a step back from the topic of discussion, bear with us for just a paragraph or two.  A clear statement of purpose is the best place to start before executing your OEE implementation strategy:

To identify opportunities to improve the effectiveness of the company’s assets.

You will quickly realize that, when attempting to define the measurement criteria for the OEE factors, in particular Availability, your team may present rationale to exclude certain elements from the measurement process.  These rationalizations are typically predicated on existing policy or perceived constraints that simply cannot be changed.  People or teams do not want to be penalized for items that are “out of their control” or bound by current policy.  Continuous improvement is impeded by attempts to rationalize poor performance.

We understand that some of these “exclusions” present a greater challenge, however, we do not agree with the premise that they cannot be improved.  Again, it is a matter of “purpose”.  Limiting the scope of measurement will limit the scope of improvement.  Now it’s time to explore what could be the foundation for a sound definition of availability.

Availability Considerations

It may seem reasonable to assume that, at a minimum, the only planned down time events that should be excluded from the availability factor are  planned preventive maintenance activities, mandatory break periods, and scheduled “down” time due to lack of work.  We would argue and agree that the only justification for an idle machine is “Lack of Work”.

What would be the reason to settle for anything less?  If Preventive Maintenance is critical to sustaining the performance of your process, doesn’t it make sense to consider it in the measurement process?  The rationale that typically follows is that Preventive Maintenance must be done and it’s really out of our control – it is a planned event.  We would argue that the time to complete Preventive Maintenance can be improved.

Is it possible that the Mean Time Before Failure or Required Maintenance can be extended?  Is it possible to improve materials, components, or lubricants that could extend the process up time?  Is it possible to improve the time it actually takes to perform the required maintenance?  If so, what is the measure that will be used to show that additional capacity is available for production.

If set up times for die changes or tool changes can be improved from hours to minutes, could the same effort and devotion to improve Preventive Maintenance techniques yield similar results?  We think so.

One example is the use of synthetic oils and lubricants that have been proven to significantly extend the life of tools and components and also reduces the number changes required over the service life of the machine.  Quick change features that can assist with easy and ready access to service points on tooling and machines can also be implemented to reduce preventive maintenance times.

The other exclusion that is often argued is break times.  Labour laws require you to provide break times for your employees.  However, since automated processes are not subject to “Labour Laws”, the “mandatory break times” do not apply.  We would argue that methods should be pursued to reduce the need for human intervention and look for ways to keep the machine running.  Is it possible to automate some of the current processes or rotate people to keep the machine running?

Aside from this more obvious example, consider other organizational policies that may impact how your organization runs:

  1. Shift start-up meetings
  2. Employee Communication Meetings
  3. End of Shift clean up periods
  4. Quality first off approval process
  5. Shift first off versus Run first off
  6. Weld Tip changes – PM or Process Driven

 What is the purpose of the shift start-up meeting?  What is the purpose of the monthly employee communication meeting?  Could this information be conveyed in a different form?  What length of time is really required to convey the message to be shared?  Is the duration of the meeting actually measured or do you resort to the standard time allotted?

Clean up periods at the end of the shift  are also a common practice in many plants.  What is being cleaned up?  Why?  Is it possible to maintain an orderly workplace during the shift – clean up as it happens in real-time?  Again, do you record the actual clean up time or do you just enter the default clean up time allotted?

How much time is lost to verify the integrity of the product before allowing production to commence?  What process parameters or factors would jeopardize the quality of the product being produced?  No one wants to make scrap or substandard components, however, the challenge remains to determine what factors influence the level of quality.  If it is possible to determine what factors are critical to success in advance, then perhaps the quality verification process becomes a concurrent event.

Measuring Downtime.

 There are other factors that can impact availability including, but certainly not limited to, personnel (illness, inclement weather), material availability, other linked processes (feeder / customer), material changes, tool changes, quality concerns, and unexpected process, equipment, or machine faults.

It is possible to use manual or automated systems to collect various machine or process codes to record or document the duration and type of downtime event.  We recommend and support the use of automated data collection systems, however, they should be implemented in moderation.  One of the primary impediments to success is overwhelming volumes of data that no one has the time to analyze.

The Goal = 100% Up Time = ZERO Down Time = Zero Lost Time = Zero Defects = 100% Availability

The goal is to use the data and tools available to either permanently resolve the problem by implementing an effective corrective action or to assist the trouble shooting process by identifying the failure mode and to minimize the duration of the downtime event.

We have witnessed data collection strategies where an incredible number of sensors were installed to “catch” problems as they occur.  The reality was the sensors themselves became the greater cause of downtime due to wear or premature failure due to improper sensor selection for the application.  Be careful and choose wisely.

When used correctly, automation can be a very effective tool to capture downtime events and maintain the integrity of the overall measurement process.  With the right tools, trouble shooting your process will minimize the duration of the down time event.  Monitoring the frequency of these events will also allow you to focus your attention on real opportunities and circumvent nuisance faults.

The objective of collecting the “downtime event” history is to determine what opportunities are available to improve uptime.

Duration versus Frequency

The frequency of a downtime event is often overlooked as most of the attention is devoted to high duration downtime events.  Some sources suggest that short duration downtime events (perhaps as little as 30 seconds) are not worth measuring.  These undocumented losses are reflected, or more accurately hidden, by a corresponding reduction in the performance factor.

Be careful when setting what appears to be simple policy to document downtime.  A 20 second downtime event that occurs 4 times per hour could quickly turn into 10 minutes a shift, 30 minutes a day, 2.5 hours a week, 125 hours a year.  Rather than recording every event in detail, we recommend implementing a simple “tick” sheet to gain an appreciation for the frequency of failures.  Any repetitive events can be studies and reviewed for corrective action.

Verify the Downtime

One of the advantages of OEE is that it is possible to reconcile the total time – OEE should never be greater than 100%.  Of course this statement requires that the standard cycle time is correct and the total quantity of parts produced is accurate.  So, although all of the downtime events may not be recorded, it is very easy to determine how much downtime occurred.  This will help to determine how effectively downtime data is being recorded.

A perfect example to demonstrate this comes from the metal stamping industry.  Progressive dies are used to produce steel parts from coil steel.  The presses typically run at a fixed “predetermined” optimum run rate.  Depending on the type of part and press, progressive dies are capable running at speeds from as low as 10 strokes per minute up to speeds over 300 strokes per minute.

For ease of calculation, assume we have a press that was scheduled to run a part over an 8 hour shift having two 10 minute breaks.  The standard shift hours are 6:45 am – 3:15 pm and 3:30 pm – 12:00 am.  The company provides a 30 minute unpaid meal break after 4 hours of work.  The optimum press speed to run the part is 20 strokes per minute (spm).  If a total of 6200 parts were made – how much downtime was incurred at the press?

To determine the press time required (also known as earned time), we simply divide the quantity of parts produced by the press rate as follows:

Machine Uptime:  6200 / 20 = 310 minutes

Our planned production time was 8 hours or 480 minutes.  Assuming that company policy excludes break times, the net available time to run the press is 480 – (2 x 10) = 460 minutes.

Calculated downtime = Available – Earned = 460 – 310 =150 minutes

Availability = Earned Time / Net Available Time = 310 / 460 = 67.39%

We can see from the above example that it easy to determine what the downtime should have been and, in turn, we could calculate the availability factor.  This calculation is based on the assumption that the machine is running at the stated rate.

The Availability TWIST (1):

Knowing that press and die protection technologies exist to allow presses to run in full automatic mode, the two break periods from our example above do not apply to the equipment, unless company policy states that all machines or processes must cease operations during break periods.

Assuming that this is not the case, the press is available for the entire shift of 480 minutes.  Therefore, the availability calculations from above would be:

Calculated downtime = Available – Earned = 480 – 310 =170 minutes

Availability = Earned Time / Net Available Time = 310 / 480 = 64.58%

The Availability TWIST (2):

Just to expand on this concept just a little further.  We also indicated that the company provided an unpaid lunch period of 30 minutes.  Since meal breaks don’t apply to presses, the reality is that the press was also available to run during this period of time.  The recalculated downtime and availability are:

Calculated downtime = Available – Earned = (480 + 30) – 310 =200 minutes

Availability = Earned Time / Net Available Time = 310 / 510 = 60.78%

The Availability TWIST (3):

Finally, one last twist (we could go on).  We deliberately indicated that there was a 15 minute break between shifts.  Again, is there a reason for this?  Does the machine have to stop?  Why?

Availability – NEXT Steps

As you begin to look at your operations and policies, start by asking WHY do we do this or that?  The example provided above indicates that a significant delta can exist in availability (close to 7%) although the number of parts produced has not changed.  The differing results are related to policy, operating standard, or both.

If the performance (cycle time or production rate) and total quantity of parts produced data have integrity, the availability factor can be reconciled to determine the integrity of the downtime “data collection” system.  From this example it should also be clear that the task of the data collection system is to capture the downtime history as accurately as possible to determine the opportunities to improve availability NOT just to determine how much downtime occurred.

This example also demonstrates why effective problem solving skills are critical to the success of your lean implementation strategy and is also one of the reasons why programs such as six sigma and lean have become integrated as parallel components of many lean execution strategies.

The Goal:  100% uptime / Zero downtime / Zero lost time /100% availability

Regardless of the measurement baseline used, be consistent.  Exclusions are not the issue, it is a matter of understanding what is involved in the measurement process.  For example, maintenance activities performed during break periods may be a good management practice to improve labour efficiencies, however, the fact that the work was performed during a break period should not exclude it from the “downtime” event history.  We would argue that all activities requiring “equipment time” or “process time” should be recorded.

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!


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.


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.


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.


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).


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?


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!