Tag: Variance

Variance – OEE’s Silent Partner (Killer)

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I was recently involved in a discussion regarding the value of Overall Equipment Effectiveness (OEE).  Of course, I fully supported OEE and confirmed that it can bring tremendous value to any organization that is prepared to embrace it as a key metric.  I also qualified my response by stating that OEE cannot be managed in isolation:

OEE and it’s intrinsic factors, Availability, Performance, and Quality are summary level indices and do not measure or provide any indication of process stability or capability

As a top level metric, OEE does not describe or provide a sense of actual run-time performance.  For example, when reviewing Availability, we have no sense of duration or frequency of down time events, only the net result.  In other words we can’t discern whether downtime was the result of a single event or the cumulative result of more frequent down time events over the course of the run.  Similarly, when reviewing Performance, we cannot accurately determine the actual cycle time or run rate, only the net result.

As shown in the accompanying graphic, two data sets (represented by Red and Blue) having the same average can present very different distributions as depicted by the range of data, height of the curve (kurtosis), width or spread of the curve (skewness), and significantly different standard deviations.

Clearly, any conclusions regarding the process simply based on averages would be very misleading.  In this same context, it is also clear that we must exercise caution when attempting to compare or analyse OEE results without first considering a statistical analysis or representation of the raw process data itself.

The Missing Metrics

Fortunately, we can use statistical tools to analyse run-time performance to determine whether our process is capable of consistently producing parts just as Quality Assurance personnel use statistical analysis tools to determine whether a process is capable of producing parts consistently.

One of the greatest opportunities for improving OEE is to use statistical tools to identify opportunities to reduce throughput variance during the production run.

Run-Time or throughput variance is OEE’s silent partner as it is an often overlooked aspect of production data analysis.  Striving to achieve consistent part to part cycle times and consistent hour to hour throughput rates is the most fundamental strategy to successfully improve OEE.  You will note that increasing throughput requires a focus on the same factors as OEE: Availability, Performance, and Quality.  In essence, efforts to improve throughput will yield corresponding improvements in OEE.

Simple throughput variance can readily be measured using Planned versus Actual Quantities produced – either over fixed periods of time and is preferred or cumulatively.  Some of the benefits of using quantity based measurement are as follows:

  1. Everyone on the shop floor understands quantity or units produced,
  2. This information is usually readily available at the work station,
  3. Everyone can understand or appreciate it’s value in tangible terms,
  4. Quantity measurements are less prone to error, and
  5. Quantities can be verified (Inventory) after the fact.

For the sake of simplicity, consider measuring hourly process throughput and calculating the average, range, and standard deviation of this hourly data.  With reference to the graphic above, even this fundamental data can provide a much more comprehensive and improved perspective of process stability or capability than would otherwise be afforded by a simple OEE index.

Using this data, our objective is to identify those times where the greatest throughput changes occurred and to determine what improvements or changes can be implemented to achieve consistent throughput.  We can then focus our efforts on improvements to achieve a more predictable and stable process, in turn improving our capability.

In OEE terms, we are focusing our efforts to eliminate or reduce variation in throughput by improving:

  1. Availability by eliminating or minimizing equipment downtime,
  2. Performance through consistent cycle to cycle task execution, and
  3. Quality by eliminating the potential for defects at the source.

Measuring Capability

To make sure we’re on the same page, let’s take a look at the basic formulas that may be used to calculate Process Capability.  In the automotive industry, suppliers may be required to demonstrate process capability for certain customer designated product characteristics or features.  When analyzing this data, two sets of capability formulas are commonly used:

  1. Preliminary (Pp) or Long Term (Cp) Capability:  Determines whether the product can be produced within the required tolerance range,
    • Pp or Cp = (Upper Specification Limit – Lower Specification Limit) / (6 x Standard Deviation)
  2. Preliminary (Ppk) or Long Term (Cpk) Capability:  Determines whether product can be produced at the target dimension and within the required tolerance range:
    • Capability = Minimum of Either:
      • Capability Upper = (Average + Upper Specification Limit) / (3 x Standard Deviation)
      • Capability Lower = (Lower Specification Limit – Average) / 3 x Standard Deviation)

When Pp = Ppk or Cp = Cpk, we can conclude that the process is centered on the target or nominal dimension.  Typically, the minimum acceptable Capability Index (Cpk) is 1.67 and implies that the process is capable of producing parts that conform to customer requirements.

In our case we are measuring quantities or throughput data, not physical part dimensions, so we can calculate the standard deviation of the collected data to determine our own “natural” limits (6 x Standard Deviation). Regardless of how we choose to present the data, our primary concern is to improve or reduce the standard deviation over time and from run to run.

Once we have a statistical model of our process, control charts can be created that in turn are used to monitor future production runs.  This provides the shop floor with a visual base line using historical data (average / limits) on which improvement targets can be made and measured in real-time.

Run-Time Variance Review

I recall using this strategy to achieve literally monumental gains – a three shift operation with considerable instability became an extremely capable and stable two shift production operation coupled with a one shift preventive maintenance / change over team.  Month over month improvements were noted by significantly improved capability data (substantially reduced Standard Deviation) and marked increases in OEE.

Process run-time charts with statistical controls were implemented for quantities produced just as the Quality department maintains SPC charts on the floor for product data.  The shop floor personnel understood the relationship between quantity of good parts produced and how this would ultimately affect the department OEE as well.

Monitoring quantities of good parts produced over shorter fixed time intervals is more effective than a cumulative counter that tracks performance over the course of the shift.  In this specific case, the quantity was “reset” for each hour of production essentially creating hourly in lieu of shift targets or goals.

Recording / plotting production quantities at fixed time intervals combined with notes to document specific process events creates a running production story board that can be used to identify patterns and other process anomalies that would otherwise be obscured.

Conclusion

I am hopeful that this post has heightened your awareness regarding the data that is represented by our chosen metrics.  In the boardroom, metrics are often viewed as absolute values coupled with a definitive sense of sterility.

Run-Time Variance also introduces a new perspective when attempting to compare OEE between shifts, departments, and factories.  From the context of this post, having OEE indices of the same value does not imply equality.  As we can see, metrics are not pure and perhaps even less so when managed in isolation.

Variance is indeed OEE’s Silent Partner but left unattended, Variance is also OEE’s Silent Killer.

Until Next Time – STAY lean!

Vergence Analytics

Twitter:  @Versalytics

OEE For Manufacturing

We are often asked what companies (or types of companies) are using OEE as part of their daily operations.  While our focus has been primarily in the automotive industry, we are highly encouraged by the level of integration deployed in the Semiconductor Industry.  We have found an excellent article that describes how OEE among other metrics is being used to sustain and improve performance in the semiconductor industry.

Somehow it is not surprising to learn the semiconductor industry has established a high level of OEE integration in their operations.  Perhaps this is the reason why electronics continue to improve at such a rapid pace in both technology and price.

To get a better understanding of how the semiconductor industry has integrated OEE and other related metrics into their operational strategy, click here.

The article clearly presents a concise hierarchy of metrics (including OEE) typically used in operations and includes their interactions and dependencies.  The semiconductor industry serves as a great benchmark for OEE integration and how it is used as powerful tool to improve operations.

While we have reviewed some articles that describe OEE as an over rated metric, we believe that the proof of wisdom is in the result.  The semiconductor industry is exemplary in this regard.  It is clear that electronics industry “gets it”.

As we have mentioned in many of our previous posts, OEE should not be an isolated metric.  While it can be assessed and reviewed independently, it is important to understand the effect on the system and organization as a whole.

We appreciate your feedback.  Please feel free to leave us a comment or send us an e-mail with your suggestions to leanexecution@gmail.com

Until Next Time – STAY lean!

OEE and the Quality Factor

Many articles written on OEE (ours being the exception), indicate or suggest that the quality factor for OEE is calculated as a simple percentage of good parts from the total of all parts produced.  While this calculation may work for a single line part number, it certainly doesn’t hold true when attempting to calculate OEE for multiple parts or machines.

OEE is a measure of how effectively the scheduled equipment time  is used to produce a quality product.  Over the next few days we will introduce a method that will correctly calculate the quality factor that satisfies the true definition of OEE.  The examples we have prepared are developed in detail so you will be able to perform the calculations correctly and with confidence.

Every time a part is produced, machine time is consumed.  This time is the same for both good and defective parts.  To correctly calculate the quality factor requires us to start thinking of parts in terms of time – not quantity.

If the cycle time to produce a part is 60 seconds, then one defective part results in a loss of 60 seconds.  If 10 out of 100 parts produced are defective then 600 seconds are lost of the total 6000 seconds required to produce all parts.  Stated in terms of the quality factor, 5400 seconds were “earned” to make quality parts of the total 6000 seconds required to produce all parts (5400/6000 = 90%).  Earned time is also referred to as Value Added Time.

As we stated earlier, for a single line item or product, the simple yield formula would give us the same result from a percentage perspective (90 good / 100 total = 90%).  But what is the affect when the cycle times of a group or family of parts are varied?  The yield formula simply doesn’t work.

The quality factor for OEE is only concerned with the time earned through the production of quality parts.  Watch for our post over the next few days and we’ll clear up the seemingly overlooked “how to” of calculating the quality factor.

Until Next Time – STAY lean!

We appreciate your feedback.  Please feel free to leave a comment or send an e-mail with suggestions or questions to leanexecution@gmail.com

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SPC for OEE

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

Our simple answer is this:

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

Negative trends may have Positive returns

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

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

Scope of Analysis – Keep it Simple

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

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

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

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

Statistics on a larger scale

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

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

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

A final note

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

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

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

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

Until Next Time – STAY lean!

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

How to use the 5 WHY approach

Schema of the Process of problem solving. Base...
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The 5 WHY technique, developed by Sakichi Toyoda, is one of the core problem solving tools used by Toyota Motor Corporation and has been adopted and embraced by numerous companies all over the globe.  This technique is unconstrained, providing the team with a high degree of freedom in their thinking process.

As we suggested in our “How to Improve OEE” post, the 5 WHY system is simple in principle.  This simplicity may also be the downfall of this technique unless you take the time to understand and apply the process correctly.  Other problem solving tools, such as Cause and Effect diagrams, allow for the development of multiple solution threads, in turn creating the potential for multiple solutions.

Some root-cause analysis experts have correctly identified some of the short comings presented by the 5-WHY technique including:

  1. The approach is not repeatable – One problem, different teams, different solutions.
  2. The scope of the investigation is constrained by the experience of the team.
  3. The process is self directing based on the evolution of the “WHY + Answer” series.
  4. The TRUE Root Cause may never be identified – Symptoms may be confused for Root Causes.
  5. The inference that a root cause can be determined by a 5 tier “Why + Answer” series.
  6. The Problem Statement defines the Point of Entry. It is imperative to define where the real problem begins.

We would argue that any problem solving or root-cause analysis tool is subject to some short falls in one form or another.  Perhaps even in problem solving there is no definitive solution.  Different problems require different tools and perhaps even different approaches.  In the automotive industry, each customer has a different variation on the problem solving approach to be used and prescribe various tools to be used in the problem solving process.

For this reason, most companies do not rely on one single technique to approach their problem solving challenges.  We would also argue that most companies are typically well versed in their processes (equipment and machines), products, and applications.  As a result, having the right people on the team will minimize the experience concerns.  There is no reason not to include outside expertise in or outside of your current industry.

One concern that can be dismissed from the above list of short comings is the inference that the solution can be found by a 5 tier “Why + Answer” series.  There is no rule as to how many times the “Why + Answer” series should be executed.  Although five times is typical and recommended, some problems may require even deeper levels.  We recommend that you keep going until you have identified a root cause for the problem that when acted upon will prevent its recurrence.

The TRICK:

The technique that we propose in this post will at least provide a method to validate the logic used to arrive at the root cause.  Most 5-WHY posts, web sites, articles, or extracts on the topic seem to focus on a top-down or deductive “Why + Answer” logic sequence.  The challenge then is to have some way to check the “answer” to see if it actually fits.

A simple way to validate the top down logic is to read the analysis in reverse order, from the bottom up, substituting the question WHY with the words “Because” or “Therefore.”  To demonstrate the technique we’ll use an example based on a problem sequence presented in Wikipedia:

I am late for work (the problem):

  1. Why? – My car will not start. (The Real Problem)
  2. Why? – The battery is dead.
  3. Why? – The alternator is not working.
  4. Why? – The alternator belt is broken.
  5. Why? – The alternator belt was well beyond its useful service life and was never replaced.
  6. Why? – The car was not maintained according to the recommended service schedule. (Root Cause)

You probably noticed that we used a 6 “Why + Answer” series instead of 5.  We did this deliberately to demonstrate that 5 WHY is a guideline and not a rule.  Keep asking WHY until you find a definitive root cause to the problem.  We could keep going to determine why the car was not maintained and so on to eventually uncover some childhood fear of commitment but that is beyond the scope of our example.

The CROSS CHECK – Root Cause Analysis Validation

Root Cause: The car was not maintained according to the recommended service schedule.

  1. Therefore, the alternator belt was well beyond its useful service life and was never replaced.
  2. Therefore, the alternator belt is broken.
  3. Therefore, the alternator is not working.
  4. Therefore, the battery is dead.
  5. Therefore, the car will not start. (The Real Problem)
  6. Therefore, I will be late for work.

Does the reverse logic make sense to you?  It seems to fit.  Does it sound like the owner of the car needed to be a mechanic or at least know one?  When it comes to car trouble, we don’t seem too concerned about going to the outside experts (the mechanic) to get it fixed.  Why do some companies fail to recognize that experts also exist outside of their business as well?  In some cases, proprietary or intellectual knowledge would preclude calling in outside resources.  Barring that, some outside expertise can certainly bring a different perspective to the problem at hand.

Caution!  Stick to the Problem – Don’t Assign Blame

The original Wikipedia example identified the root cause as, “I have not been maintaining my car according to the recommended service schedule.”  It would be too easy for someone to say, “Aha, it’s entirely your fault.  If you only took better care of your things you wouldn’t have been in this predicament.”  For this reason, we presented the case based on the facts.  It’s not WHO it’s WHAT.  This approach also tempers emotions and keeps the team focused on the problem and the solution.

Where do we start? Problem Entry Points

You have likely noted that the problem statement is the key to establishing a starting point for the 5 WHY process.  A problem may have different entry points depending on what stage you become involved:

Entry Point Problem Statement
You Late For Work
Service Manager The car will not start
Mechanic The battery is dead
Belt Supplier The alternator belt is broken

Product Recalls and Warranty Returns are typical examples of where you may find multi-level 5 WHYs.  Ultimately the suppliers of most products, like the Belt Supplier in our example, will also complete a 5 WHY.  This is typically the case for most Tier I automotive suppliers.

WHY MAPS / TREES.

The one drawback or downfall of the 5 Why process as presented above and also used by most companies is the suggestion that a single “WHY + Answer” series will evolve into a neat single root cause.  Our experience suggests that this is far from reality.  We typically present the single series as part of the final solution, however, we can assure you that multiple root cause / solution threads were developed before arriving at the final result.

We use the WHY MAP (WHY TREE) as a tool that allows us to pursue multiple thought threads simultaneously.  Pursuing multiple threads also stimulates new ideas and potential causes.  In some cases the root cause analysis threads lead to the same or common root cause.  Then it is a matter of selecting the most likely root cause.

Tip:

Problem solving TREES come under many different names including Why-Tree, Cause Tree, Root Cause Tree, Causal Factor Tree, Why Staircase Tree, and Cause Map to name a few of them.  As you can see from the names, they all serve to create, stimulate, and propagate ideas.

Regardless of the tool you use, finding the true root cause and ultimately the solution to resolve it is the key to your problem solving success.

We trust this post will provide you with some insight to using the 5-WHY approach for problem solving and will serve as a useful tool to improve your OEE.

More on this series to follow in our next post.

Until Next Time – STAY lean!


OEE Integration: Can you fix it?

As we are all aware, inspecting or measuring parts does not change the quality of the product.   Likewise, measuring and reporting OEE alone does not solve problems or improve performance.  While it is fair to say that increased focus and measurement of any process usually results in some degree of improvement, these are typically attributed to changes in human behavior due to observation and not necessarily real process improvements.

Using OEE to identify opportunities in your operation is the equivalent of turning the light on in a dark room.  Although the room hasn’t changed, we certainly have a better understanding of what it looks like.  As such, OEE is a vantage point metric that can be used to illuminate our understanding of the process and identify opportunities to drive improvements.

It is essential for your team to develop and utilize effective problem solving skills to successfully identify systemic and process root causes for failure and to develop and execute permanent corrective actions to resolve them.  Our experience suggests that the lack of solid and proven problem solving skills coupled with poor execution is the leading cause of failure for new initiatives such as OEE.

We introduced an approach to improving OEE in our “Improve OEE:  A Hands On Approach“, post (03-Jan-09).  Although we identified some of the tools that could be used to solve of the problems, we didn’t spend much time going into the details.  Over the next few posts, we’ll discuss some of the ideas in a little more detail.

The real problem for most companies is identifying what the real underlying root cause of the current “failure” mode is.  Without a good understanding of the root cause, the solutions developed and implemented will not be effective, only serving to temporarily cure the immediate superficial symptoms.

Using effective problem solving skills to analyze the OEE data and to develop and execute permanent corrective actions will assure sustainable and ever improving performance.

Until Next Time – STAY lean!

Lean Culture

Background

Being in business for many years is, and can be, both a blessing and a curse.  On one hand, the company has established itself and is well rooted in its chosen industry.  On the other hand, it has established and well rooted “behaviors” built on a culture that have stood the test of time.  This could be for better or worse.

Today’s economic conditions are forcing many company executives to make extremely difficult decisions, including severe cutbacks (layoffs), bankruptcy, insolvency, sale, and even closure.  If your business is still viable at this point in time, congratulations may be in order, or, the impact of the current economy is simply lagging.

The automotive executives, particularly those seeking government assistance, are quickly learning that something must change and quickly.  In fact, the federal governments of both the Canada and the United States have mandated “change” as a prerequisite to receiving funding.

Entitlements and Accountability

We are not looking to assign blame or assert accountabilities for the current economic crisis.  We would strongly suggest however, that there is probably no better time to achieve a company wide “buy-in” to the concepts of lean manufacturing.

Sometime before and even during the “fall”, many seemed to be reluctant to recognize that changes were imminent.  No one person can be held accountable for the current economic climate. 

Unfortunately, rationalizing poor performance was simply attributed to the natural course of events and are consistent with historical economic cycles.  Several problems with this rationalization persist.  If the best predictor of future performance is past performance, then why didn’t “we” take the necessary steps to prepare for such a recurrence of events.  If this was predictable, then why did this seem to take everyone by surprise?  Who should be held accountable?

“Labour” was very reluctant to make any concessions until the government mandated a review and indepth analysis of competitor labour agreements.  The announcement of plant closures and significant “down” days has seemingly created a new perspective.

Engage for Success

To some, everything was fine … until you came along and suggested that “we need to change” in order to survive.  Could it be that the path of least resistance is also the path to peril?  Improvements are always easier to implement than “change”.

As Lean practitioners, we need to be cognizant of the individuals who may be impacted by the improvements that must be made.  We should also stress that the goal of Lean is not to displace people from your organization.  The ultimate goal of lean is to increase your value added activity that in turn will improve your competitive position and stimulate future growth.  Your competitors should be concerned about displacing employees.

A well designed situational assessment and the manner in which it is conducted may open the doors to improved performance as opposed to a wall of resistance to change.  The key to overcoming this resistance and preventing backlash is through effective communication and participation of all employees from the top down or bottom up.

Another important point to make is this.  Everyone likes to be involved in a successful venture.  This extends beyond your employees and the walls of your business.  Customers like to be associated with success too.

The Olympics present a good example of this.  Think of the extreme efforts companies take to be associated with Gold Medal athletes as part of their branding strategy.  Everyone wants to be associated with a winner.  Taking this one step further, consider the extreme efforts and personal sacrifices that the athletes themselves have taken to achieve this level of success.

Where does your company fit into the economy – leaders or losers?  Is everyone on board?  Today’s climate has likely created the opportunity to bring everyone on board without even having to ask.  The real breaking point occurs when company and self preservation become synonymous.

LEAN Strategy and Metrics

Once a sense of purpose has been established, your lean strategy can be developed and executed with the full support of your team.  As part of your implementation strategy, training should be at its core.  People will be more prepared to embrace the “new” way of doing business when they understand why they are doing what they do.

Naturally, people will want to know that the new way of doing business is making a difference and that they are contributing to this success.  Your training program should provide a clear definition of the metrics being used and how they correlate to the “success” of the company.  Posting charts on the wall is not going to sustain a lean culture for long unless the people understand what the charts mean.  Reading a foreign newspaper is of no use unless you understand the language.

Lean Strategy

Almost every article, book, or blog will tell you that the focus of Lean is the “ELIMINATION of WASTE”.  We recommend taking slightly different approach.  Our reasons for this come from our own successes at implementing lean.  Too many people don’t know what waste is or even looks like.  Yes, you can train people on the “7 or 8” categories of waste, and yet they still don’t recognize waste outside of the context that was used to explain it.

Our approach focuses on True Customer Value.  Does the activity add value from the customer’s perspective.  If NOT, it is waste.  This approach makes it much easier to discern waste in your company.

Again an example may serve us best.  When buying a pizza for home delivery, what do you, as a customer, perceive as value?  We would argue that since we are paying for the pizza, the quality of the pizza is where the value should be focused (taste, temperature, and so on).

Do we care what kind of car the delivery person was driving?  Do we care where the pizza box was purchased or how many were in inventory?  Do we care how many hours of overtime were worked or how many products had to be expedited in to meet customer orders?  Do we care that the “company” covers speeding tickets per driver?  Do you care if they cut their own wood to heat the stove or if they use gas or electricity?  Do we care how many speeding tickets the driver incurred?  The answer to all, if not most, of the above questions is likely a resounding NO.  I’m willing to pay a reasonable price for a great tasting pizza.

To put it plainly, if the customer doesn’t see value in it, then why should you?  Simple enough right?  From this perspective, waste is readily recognized and identified as any policy, practice, or activity that does not add or create value for our customer.

One definition of Lean that many could easily embrace as there own is “A systematic approach for delivering the highest quality, lowest cost products with the shortest lead-times through the relentless elimination of waste.”  Could the same objectives be achieved through the relentless pursuit of value?

The most difficult challenge will be convincing someone that what they are doing is “waste”.  Most people would be insulted or offended by any statement that suggests they have been wasting their time all these years.  We would recommend acknowledging the current level of success and thanking them for their efforts accordingly.  You could almost be in a position to apologize for the current way in which a task is being completed.  Keep in mind that, in many cases, we are only looking to change the method or the HOW.  In other words, the WHY remains the same.  What was being done was not a waste, it’s simply a matter of achieving the same end more efficiently and using our time more effectively.

Lean is a very effective means of sustaining a viable business enterprise even during these difficult times.  It is difficult for some to see how the current economic crisis can be opportunity knocking.  Those who are succeeding today have already responded during their times of crisis and envisioned what we now call Lean.

Until Next Time – Stay LEAN.

Upcoming OEE Topics – February 2009

The following topics will be featured in an upcoming post, we’ll try to squeeze them in before February 2009 rolls off the calendar.  If you have a topic that you would like to see featured on our site, send an e-mail to LeanExecution@gmail.com.

Capacity Planning with OEE:  By definition, it only makes sense to use OEE as an integral part of your capacity planning process.  We will cover the details to do this effectively.  Effective capacity planning naturally extends to improved resource management and effective production planning.

OEE, Value Streams, and COST:  Although some managers may rise to the challenge and volunteer, many are either assigned or designated to be project champions.  In many cases, unfortunately, the scope of the project is extremely limited or restricted and project managers simply become “metric managers”.  Who is in charge of OEE?  The answer is quite simple:  EVERYONE.  OEE is a multi-discipline metric and, like other sound lean strategies, requires seamless interaction among managers and departments.

OEE cannot and should not be managed as an independent metric.  Having said that, don’t get caught in the trap of “stand alone” OEE reviews.  While there may be a number of strategies for improving OEE, such as constrained capacity, we will present a model that explicitly ties operational costs to your processes.  When OEE data is sensitised by cost data, a completely different strategy for improvement will emerge.  If the ultimate goal is to improve your bottom line, then our Cost sensitisation model will bring the concept of OEE and your bottom line to a whole new level.

OEE and Lean Agility:  Can OEE be a leading indicator of your ability to respond to change?  Well we think so and happen to have a few ideas that will show you how and why.

Send us your questions or comments or simply suggest a topic for a future post or article.

Stay tuned for more!  We appreciate your feedback.

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!