Tag: Overall Equipment Effectiveness

Method Matters and OEE

English: This figure demonstrates the central ...
English: This figure demonstrates the central limit theorem. It illustrates that increasing sample sizes result in sample means which are more closely distributed about the population mean. It also compares the observed distributions with the distributions that would be expected for a normalized Gaussian distribution, and shows the reduced chi-squared values that quantify the goodness of the fit (the fit is good if the reduced chi-squared value is less than or approximately equal to one). (Photo credit: Wikipedia)

Tricks of the Trade

 

Work smarter not harder! If we’re honest with ourselves, we realize that sometimes we have a tendency to make things more difficult than they need to be. A statistics guru once asked me why a sample size of five (5) is commonly used when plotting X-Bar / Range charts. I didn’t really know the answer but assumed that there had to be a “statistically” valid reason for it. Do you know why?

 

Before calculators were common place, sample sizes of five (5) made it easier to calculate the average (X-Bar). Add the numbers together, double it, then move the decimal over one position to the left.  All of this could be done on a simple piece of paper, using some very basic math skills, making it possible for almost anyone to chart efficiently and effectively.

 

  1. Sample Measurements:
    1. 2.5
    2. 2.7
    3. 3.1
    4. 3.2
    5. 1.8
  2. Add them together:
    • 2.5+2.7+3.1+3.2+1.8 = 13.3
  3. Double the result:
    • 13.3 + 13.3 = 26.6
  4. Move the decimal one position to the left:
    • 2.66

To calculate the range of the sample size, we subtract the smallest value (1.8) from the largest value (3.2). Using the values in our example above, the range is 3.2 – 1.8 = 1.4.

 

The point of this example is not to teach you how to calculate Average and Range values. Rather, the example demonstrates that a simple method can make a relatively complex task easier to perform.

 

Speed of Execution

 

We’ve written extensively on the topic of Lean and Overall Equipment Effectiveness or OEE as means to improve asset utilization. However, the application of Lean thinking and OEE doesn’t have stop at the production floor.  Can the pursuit of excellence and effective asset utilization be applied to the front office too?

 

Today’s computers operate at different speeds depending on the manufacturer and installed chip set. Unfortunately, faster computers can make sloppy programming appear less so. In this regard, I’m always more than a little concerned with custom software solutions.

 

We recently worked on an assignment that required us to create unique combinations of numbers. We used a “mask” that is doubled after each iteration of the loop to determine whether a bit is set. This simple programming loop requiring this is also the kernel or core code of the application.  All computers work with bits and bytes.  One byte of data has 8 bit positions (0-7) and represents numeric values as follows:

 

  • 0 0 0 0 0 0 0 0 =   0
  • 0 0 0 0 0 0 0 1 =   1
  • 0 0 0 0 0 0 1 0 =   2
  • 0 0 0 0 0 1 0 0 =   4
  • 0 0 0 0 1 0 0 0 =   8
  • 0 0 0 1 0 0 0 0 =  16
  • 0 0 1 0 0 0 0 0 =  32
  • 0 1 0 0 0 0 0 0 =  64
  • 1 0 0 0 0 0 0 0 = 128

To determine whether a single bit is set, our objective is to test it as we generate the numbers 1, 2, 4, 8, 16, 32, 64 and so on – each representing a unique bit position in binary form . Since this setting and testing of bits is part of our core code, we need a method that can double a number very quickly:

 

  • Multiplication:  Multiply by Two, where x = x * 2
  • Addition:  Add the Number to Itself, where x = x + x

These seem like simple options, however, in computer terms, multiplying is slower than addition, and SHIFTing is faster than addition.  You may notice that every time we double a number, we’re simply shifting our single “1” bit to the left one position.  Most computers have a built in SHL instruction in the native machine code that is designed to do just that.  In this case, the speed of execution of our program will depend the language we choose and how close to the metal it allows us to get.  Not all languages provide for “bit” manipulation.  For this specific application, a compiled native assembly code routine would provide the fastest execution time.  Testing whether a bit is set can also be performed more efficiently using native assembly code.

 

Method Matters

 

The above examples demonstrate that different methods can be used to yield the same result.  Clearly, the cycle times will be different for each of the methods that we deploy as well.  This discussion matters from an Overall Equipment Effectiveness, OEE, perspective as well.  Just as companies focus on reducing setup time and eliminating quality problems, many also focus on improving cycle times.

 

Where operations are labour intensive, simply adding an extra person or more to the line may improve the cycle time.  Unless we change the cycle time in our process standard, the Performance Factor for OEE may exceed 100%.  If we use the ideal cycle time determined for our revised “method”, it is possible that the Performance Factor remains unchanged.

 

Last Words

 

The latter example demonstrates once again why OEE cannot be used in isolation.  Although an improvement to cycle time will create capacity, OEE results based on the new cycle time for a given process may not necessarily change.  Total Equpiment Effectiveness Performance (TEEP) will actually decrease as available capacity increases.

 

When we’re looking at OEE data in isolation, we may not necessarily the “improved” performance we were looking for – at least not in the form we expected to see it.  It is just as important to understand the process behind the “data” to engage in a meaningful discussion on OEE.

 

Your feedback matters

 

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

 

Until Next Time – STAY lean

 

 

 

Versalytics Analytics

 

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Its not what you know …

It’s not what you know but what you understand that matters most.  ~ Redge

Discerning  perceived knowledge from understanding is a challenge for many leaders. For example, it is possible for anyone to memorize facts and figures and to correctly answer related questions simply by recalling this same information from memory. Similarly, many of us “perform” simple multiplication from recall – without even thinking about the calculations involved.

Why this matters

Having knowledge of metrics is not necessarily the same as understanding what the metric is measuring or what it means. Consider that the formula for Overall Equipment Effectiveness, or OEE, is the product of three factors:  Availability, Performance, and Quality. After basic training, anyone can recite the formula and calculate OEE correctly. This basic knowledge does not necessarily equate to any real level of understanding what is actually being measured.

OEE measures how effectively an asset’s time was used to produce a quality part. Confusion as to what is really being measured typically occurs when the Quality factor is calculated. For a single run, numerous texts teach that we can calculate the quality factor as:

Quality Factor = (Good Parts Produced / Total Parts Produced) x 100.

While the calculation will yield the correct result for a single instance, the formula isn’t quite complete as presented and doesn’t work when attempting to calculate OEE for multiple parts running through the same machine. The Quality formula should actually be stated as:

Quality Factor = (Good Parts Produced x Cycle Time / Total Parts Produced x Cycle Time)

or

Quality Factor = Pure Time to Produce Good Parts / Pure Time to Produce ALL Parts.

When expressed this way, we can state how much time was spent producing good parts, total parts, and defective parts! The time lost to produce defective or scrap parts is given by the formula:

Lost Quality Time = Time to Produce ALL parts – Time to Produce Good Parts.

OEE is not complicated when we understand what it is we’re measuring. By way of example, assume a production shift consists of 435 minutes of scheduled production time where breaks and lunches have already been accounted for. For the sake of simplicity, we will assume the process is running at rate (performance = 100%).  A part having a cycle time of 2 minutes was scheduled to run for the entire shift where 160 good parts from a total of 180 parts were produced.

From this basic data and assuming the process was running at rate – (Performance = 100%) – we can derive the following:

Availability = Up Time / Total Time = ((180 x 2) / 435) x 100 = (360 / 435) x 100 = 82.76%

Performance = 100% (assuming run at rate) = 100%

Quality =Time to Produce Good Parts / Time to Produce ALL Parts

Quality = ((160 x 2) / (180 x 2)) x 100 = (320 / 360) x 100 =  88.89%

OEE = A x P x Q = 82.76% x 100% x 88.89% = 73.56%

Cross Check:  435 x OEE = 435 x 73.56% = 320

Before calculating the percent values for each factor, we can see that time is common to all factors. We can readily determine that we lost 40 minutes due to the production of defective parts (360 -320) and that we also lost 75 minutes due to unplanned downtime events.

To calculate OEE for a given machine, shift, department, or plant we can easily sum the total “time” based values for each factor and calculating the percentages accordingly.  These calculations are clearly conveyed in prior posts and in our free downloads (see our free downloads page or on the widget on the sidebar).

What you know is taught, what you understand is learned. ~ Redge

When we truly understand what is being measured, the data that forms the basis for our calculations becomes more meaningful too. We can even challenge the data before the calculations are made.  The greatest frustration occurs when the results are not what we expected and the reasons are either in the very data that generated them or worse, when someone doesn’t understand the calculation they’re actually performing.

Many years ago I recall reading a sign that stated, “The proof of wisdom is in the results“. While their is truth in this statement, the implication is that we understand the results too!

Your feedback matters

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

Until Next Time – STAY lean

Versalytics Analytics

Time Flies When You’re Having Fun

English: John leading Lean and Mean
English: John leading Lean and Mean (Photo credit: Wikipedia)

It’s hard to believe that four years have passed since we started blogging here on WordPress! We would like to thank our many subscribers and visitors for your many e-mails and comments, making this a fulfilling learning experience for all of us.

This blog was originally founded on the premise that very little information was available on the topic of Overall Equipment Effectiveness (OEE) with the exception of the most basic formula and it’s application for single machine operations. We advanced the application of OEE over numerous posts to include multiple machines, parts, shifts, divisions, and even corporate level reporting. We have also maintained that the intent of OEE is to serve as a tool to drive continuous improvements in your operations.

When integrated correctly, OEE provides feedback to operations management that enables further improvements to occur. From this perspective, leadership that empowers employees to implement improvements is a pre-requisite for manufacturing operations wanting to gain the most from their OEE initiative. In this regard, leadership recognizes and embraces lean thinking and instills lean principles throughout the organization. Where lean serves as the overarching strategy, OEE is an integral key performance indicator (KPI) that enables continuous improvements to occur.

We recognized that our initial offerings would serve and be of interest to a niche audience, however, after four years it is exciting to see that we have received more than 100,000 visitors from over 150 countries. The top 10 countries driving visits to our site – to date – are:

Country  Rank
United States FlagUnited States      1
India FlagIndia      2
United Kingdom FlagUnited Kingdom      3
Canada FlagCanada      4
Malaysia FlagMalaysia      5
Australia FlagAustralia      6
Germany FlagGermany      7
Philippines FlagPhilippines      8
Mexico FlagMexico      9
Brazil FlagBrazil     10

We are thankful for the feedback we have received and for the many people who have taken the time to express their thoughts and share their gratitude either in the comments or by the many e-mails we have received from around the world. Social media have certainly played a role in expanding our scope and our reach. We look forward to continuing our journey with you.

“Our goal is to deliver the highest quality product or service in the shortest amount of time at competitive prices on time and in full.”

“There’s always a better way and more than one solution”

“What you see is how we think”

Thanks again for reading and, to our US friends, have a Happy Thanksgiving!

Until Next Time – STAY lean

Vergence Analytics

Game On – Playing it Safe with Lean

An astronaut in training for an extra-vehicula...
Image via Wikipedia

Communicating a concept or methodology in a manner that doesn’t offend the current status quo is likely the biggest challenge we face as lean practitioners and consultants.  In all too many instances it seems that people are open to change as long as someone else is doing the changing.

To diffuse opposition and resistance to change, it is essential that everyone understands the concern or problem, the solution, inherent expectations, and consequences of remaining the same. Our objective then is to create a safe, non-threatening environment where new ideas and concepts can be explored without undermining the current infrastructure or the people and departments involved.  There are a number of options available to do just that:

  • I personally like to use analogies and stories to convey concepts or ideas that exemplify methods or processes that can be adapted to address a current situation, opportunity, or concern.
    • This is ideal for sharing the company vision, top-level ideas, and philosophies that help to explain the overall strategic direction or mission under discussion or of concern. 
    • Stories and analogies create opportunities to expand our thinking processes  and to look outside the immediate scope of our current business interests and circumstances.
  • I also recommend targeted books and selected reading that allow individuals to learn and understand at their own pace. Classics books include “The Goal” by Eliyahu Goldratt and Jeff Cox, “Velocity” by Dee Jacob, Suzan Bergland, and Jeff Cox , “Toyota Kata” by Mike Rother, and The High Velocity Edge by Steven Spear.
    • Offering a list of recommended books for individual study is likely the least intrusive, however, participation cannot be assured and does not promote interaction among team members.
    • The reader learns the thinking processes and solutions as developed by the authors. 
  • Formal classroom or in-house training may also be effective, however, it can be costly and is inherently exclusive to the participants.  It is also difficult for non-participants to become as knowledgeable or proficient with the material without attending the course or training for themselves.
    • Outside training is inherently more generic in nature due to the diverse range of companies and individuals that are represented in the class.
    • In-house training can be more effective to address a specific concern, however, it’s true effectiveness is limited to the participants.
    • The concepts and thinking processes are developed and conveyed as prescriptive solutions.
  • Interactive simulations that allow teams to work together to solve problems or participate in non-invasive / non-intrusive tasks.
    • Class sizes remain small, however, the process is repeatable across multiple classes.
    • Concepts can be tested and developed without disrupting the “real world” processes.
    • Simulations are accelerated models representing real-world conditions.
    • Simulations can be conducted internally with limited resources and is easily duplicated.
    • Unlike the other methods above, the “solution” evolves with the team’s experience.
Of the methods presented above, I find that interactive simulations tend to be the most effective.  Lean Simulations, an increasingly popular website, has amassed a wealth of free lean games, videos, and other lean tools that make this a real possibility.
More specific to the purpose of our discussion here is a post titled “Seven Benefits of Teaching Lean with Simulations” that offers shared insights to the benefits of using Simulations to train and teach lean principles to our teams.
Having a method to explore new ideas and develop concepts is only one hurdle that needs to be addressed.  The next task is establishing the need for change itself and instilling the sense of urgency that is required to engage the team and accomplish the necessary improvements.
The Need For Improvement Drives Change

Change is synonymous with improvement and must be embraced by employees at all levels of the organization.  Change and improvements are also required to keep up with competitors and to avoid becoming obsolete.  From another perspective, it is a simple matter of continued sustainability and survival. In this context, we recognize that businesses today are confronted with uncompromising pressures from:

  • Customers expecting high quality products and services at competitive or reduced prices, and
  • Internal and external influences that are driving operating costs ever higher.  Some of these influences include increased taxes, rising utility costs such as electricity and fuel, increased wages and benefits, increased material costs, and volatile exchange rates.

An unfortunate and sad reality is that any realized cost savings or loss reductions are quickly absorbed by these ever-increasing costs of doing business.  As a result, many of the “savings” do not find their way to the bottom line as most of us have been conditioned to expect.  While many companies are quick to post “cost savings”, I am surprised at how few post the “cost increases” that negate or neutralize them.

Some manufacturers, such as automotive suppliers to the Original Equipment Manufacturers (OEM’s), are expected to offer reduced prices year over year regardless of the current economic climate.  Unbelievably, “give backs” are expected for the full production life cycle of the vehicle and may even be extended to support service demand as well.  In today’s global economy, parts suppliers to the automotive OEM’s risk losing their business to competitors – especially those in low-cost labour countries – if attempts are made to increase prices.

My experience suggests that the best approach to establish a need for change is to work directly with the leadership and individual teams to understand and document the “current state” without bias or judgement. Our primary interest is to identify and assess “what is” and “what is not” working as supported by observations and objective evidence as gathered by the team.  To be very clear, this is not a desk audit. To understand what is really happening, an assessment can only be effective when it is conducted at the point of execution – the process itself.

We also need to understand the reasons why the current state exists as it does.  Is it the culture, system, processes, resources, resourcefulness,  training, methodologies, team dynamics, or some other internal or external influences? As a lean practitioner, I serve as a catalyst for change – helping leadership, teams, and individuals to see, learn, and appreciate for themselves what it means to be lean regarding culture, thinking, and best practices.

I believe that many lean initiatives fail for the simple reason that people have not been provided with a frame of reference or baseline (other than hearsay) that enables them to internalize what lean really means.

What’s Next?

The last thing we want to do is abandon current practices without having a sense of confidence that what we plan to do “in practice” will actually work. Secondly, we want to ensure that everyone understands the concept without jeopardizing current operations in the process.  As alluded to earlier, lean simulations allow us to do just that.

The main points of the article, “7 Benefits of Teaching Lean With Simulations“, as referenced earlier are summarized as follows:

  1. Simulations demonstrate lean principles in action,
  2. Games involve your audience,
  3. Games are perfect team building activities,
  4. Simulations are small and flexible,
  5. Games are confidence builders,
  6. Test real processes with simulations first,
  7. Give yourself a break.

Another benefit derived from simulations is that results are realized in a very short period of time due to the accelerated nature of the game.  As is often the case, real-time implementations may require days or even weeks before their effects are can be observed and felt within the organization.  Simulations can provide real world experiences without subjecting the company or the team to real world risks or consequences.

Finally, games allow participants to truly become involved in the process and present an opportunity to observe and assess team dynamics and individual strengths and weaknesses. A game is more than just an event. It is a memorable experience that involves all the senses, thinking processes, and emotions that engage the whole person.  To this extent the participants can and will internalize the concepts.  From this perspective, I say Game On …

Until Next Time – STAY lean!

Vergence Analytics
Twitter: @Versalytics

Integrated Waste: Lather, Rinse, Repeat

shampoo
Image via Wikipedia

Admittedly, it has been a while since I checked a shampoo bottle for directions, however, I do recall a time in my life reading:  Lather, Rinse, Repeat.  Curiously, they don’t say when or how many times the process needs to be repeated.

Perhaps someone can educate me as to why it is necessary to repeat the process at all – other than “daily”.  I also note that this is the only domestic “washing” process that requires repeating the exact same steps.  Hands, bodies, dishes, cars, laundry, floors, and even pets are typically washed only once per occasion.

The intent of this post is not to debate the effectiveness of shampoo or to determine whether this is just a marketing scheme to sell more product.  The point of the example is this:  simply following the process as defined is, in my opinion, inherently wasteful of product, water, and time – literally, money down the drain.

Some shampoo companies may have changed the final step in the process to “repeat as necessary” but that still presents a degree of uncertainty and assures that exceptions to the new standard process of “Lather, Rinse, and Repeat as Necessary” are likely to occur.

In the spirit of continuous improvement, new 2-in-1 and even 3-in-1 products are available on the market today that serve as the complete “shower solution” in one bottle.  As these are also my products of choice, I can advise that these products do not include directions for use.

Scratching the Surface

As lean practitioners, we need to position ourselves to think outside of the box and challenge the status quo.  This includes the manner in which processes and tasks are executed.  In other words, we not only need to assess what is happening, we also need to understand why and how.

One of the reasons I am concerned with process audits is that conformance to the prescribed systems, procedures, or “Standard Work” somehow suggests that operations are efficient and effective.  In my opinion, nothing could be further from the truth.

To compound matters, in cases where non-conformances are identified, often times the team is too eager to fix (“patch”) the immediate process without considering the implications to the system as a whole.  I present an example of this in the next section.

The only hint of encouragement that satisfactory audits offer is this: “People will perform the tasks as directed by the standard work – whether it is correct or not.”  Of course this assumes that procedures were based on people performing the work as designed or intended as opposed to documenting existing habits and behaviors to assure conformance.

Examining current systems and procedures at the process level only serves to scratch the surface.  First hand process reviews are an absolute necessity to identify opportunities for improvement and must consider the system or process as a whole as you will see in the following example.

Manufacturing – Another Example

On one occasion, I was facilitating a preparatory “process walk” with the management team of a parts manufacturer.  As we visited each step of the process, we observed the team members while they worked and listened intently as they described what they do.

As we were nearing the end of the walk through, I noted that one of the last process steps was “Certification”, where parts are subject to 100% inspection and rework / repair as required.  After being certified, the parts were placed into a container marked “100% Certified” then sent to the warehouse – ready for shipping to the customer.

When I asked about the certification process, I was advised that:  “We’ve always had problems with these parts and, whenever the customer complained, we had to certify them all 100% … ‘technical debate and more process intensive discussions followed here’ … so we moved the inspection into the line to make sure everything was good before it went in the box.”

Sadly, when I asked how long they’ve been running like this, the answer was no different from the ones I’ve heard so many times before:  “Years”.  So, because of past customer problems and the failure to identify true root causes and implement permanent corrective actions to resolve the issues, this manufacturer decided to absorb the “waste” into the “normal” production process and make it an integral part of the “standard operating procedure.”

To be clear, just when you thought I picked any easy one, the real problem is not the certification process.  To the contrary, the real problem is in the “… ‘technical debate and more process intensive discussions followed here’ …” portion of the response.  Simply asking about the certification requirement was scratching the surface.  We need to …

Get Below the Surface

I have always said that the quality of a product is only as good as the process that makes it.  So, as expected, the process is usually where we find the real opportunities to improve.  From the manufacturing example above, we clearly had a bigger problem to contend with than simply “sorting and certifying” parts.  On a broader scale, the problems I personally faced were two-fold:

  1. The actual manufacturing processes with their inherent quality issues and,
  2. The Team’s seemingly firm stance that the processes couldn’t be improved.

After some discussion and more debate, we agreed to develop a process improvement strategy.  Working with the team, we created a detailed process flow and Value Stream Map of the current process.  We then developed a Value Stream Map of the Ideal State process.  Although we did identify other opportunities to improve, it is important to note that the ideal state did not include “certification”.

I worked with the team to facilitate a series of problem solving workshops where we identified and confirmed root causes, conducted experiments, performed statistical analyses, developed / verified solutions, implemented permanent corrective actions, completed detailed process reviews and conducted time studies.  Over the course of 6 months, progressive / incremental process improvements were made and ultimately the “certification” step was eliminated from the process.

We continued to review and improve other aspects of the process, supporting systems, and infrastructure as well including, but not limited to:  materials planning and logistics, purchasing, scheduling, inventory controls, part storage, preventive maintenance, redefined and refined process controls, all supported by documented work instructions as required.  We also evaluated key performance indicators.  Some were eliminated while new ones, such as Overall Equipment Effectiveness, were introduced.

Summary

Some of the tooling changes to achieve the planned / desired results were extensive.  One new tool was required while major and minor changes were required on others.  The real tangible cost savings were very significant and offset the investment / expense many times over.  In this case, we were fortunate that new jobs being launched at the plant could absorb the displaced labor resulting from the improvements made.

Every aspect of the process demonstrated improved performance and ultimately increased throughput.  The final proof of success was also reflected on the bottom line.  In time, other key performance indicators reflected major improvements as well, including quality (low single digit defective parts per million, significantly reduced scrap and rework), increased Overall Equipment Effectiveness (Availability, Performance, and Quality), increased inventory turns, improved delivery performance (100% on time – in full), reduced overtime,  and more importantly – improved morale.

Conclusion

I have managed many successful turnarounds in manufacturing over the course of my career and, although the problems we face are often unique, the challenge remains the same:  to continually improve throughput by eliminating non-value added waste.  Of course, none of this is possible without the support of senior management and full cooperation of the team.

While it is great to see plants that are clean and organized, be forewarned that looks can be deceiving.  What we perceive may be far from efficient or effective.  In the end, the proof of wisdom is in the result.

Until Next Time – STAY lean!

Vergence Analytics
Twitter:  @Versalytics

Lean – A Race Against Time

The printer Benjamin Franklin contributed grea...
Image via Wikipedia

Background

If “Time is Money”, is it reasonable for us to consider that “Wasting Time is Wasting Money?”

Whether we are discussing customer service, health care, government services, or manufacturing – waste is often identified as one of the top concerns that must be addressed and ultimately eliminated.  As is often the case in most organizations, the next step is an attempt to define waste.  Although they are not the focus of our discussion, the commonly known “wastes” from a lean perspective are:

  • Over-Production
  • Inventory
  • Correction (Non-Conformance  – Quality)
  • Transportation
  • Motion
  • Over Processing
  • Waiting

Resourcefulness is another form of waste often added to this list and occurs when resources and talent are not utilized to work at their full potential.

Where did the Time go?

As a lean practitioner, I acknowledge these wastes exist but there must have been an underlying element of concern or thinking process that caused this list to be created.  In other words, lists don’t just appear, they are created for a reason.

As I pondered this list, I realized that the greatest single common denominator of each waste is TIME.  Again, from a lean perspective, TIME is the basis for measuring throughput.  As such, our Lean Journey is ultimately founded on our ability to reduce or eliminate the TIME required to produce a part or deliver a service.

As a non-renewable resource, we must learn to value time and use it effectively.  Again, as we review the list above, we can see that lost time is an inherent trait of each waste.  We can also see how this list extends beyond the realm of manufacturing.  TIME is a constant constraint that is indeed a challenge to manage even in our personal lives.

To efficiently do what is not required is NOT effective.

I consider Overall Equipment Effectiveness (OEE) to be a key metric in manufacturing.  While it is possible to consider the three factors Availability, Performance, and Quality separately, in the context of this discussion, we can see that any impediment to throughput can be directly correlated to lost time.

To extend the concept in a more general sense, our objective is to provide our customers with a quality product or service in the shortest amount of time.  Waste is any impediment or roadblock that prevents us from achieving this objective.

Indirect Waste and Effectiveness

Indirect Waste (time) is best explained by way of example.  How many times have we heard, “I don’t understand this – we just finished training everybody!”  It is common for companies to provide training to teach new skills.  Similarly, when a problem occurs, one of the – too often used – corrective actions is “re-trained employee(s).”  Unfortunately, the results are not always what we expect.

Many companies seem content to use class test scores and instructor feedback to determine whether the training was effective while little consideration is given to developing skill competency.  If an employee cannot execute or demonstrate the skill successfully or competently, how effective was the training?  Recognizing that a learning curve may exist, some companies are inclined to dismiss incompetence but only for a limited time.

The company must discern between employee capability and quality of training.  In other words, the company must ensure that the quality of training provided will adequately prepare the employee to successfully perform the required tasks.  Either the training and / or method of delivery are not effective or the employee may simply lack the capability.  Let me qualify this last statement by saying that “playing the piano is not for everyone.”

Training effectiveness can only be measured by an employee’s demonstrated ability to apply their new knowledge or skill.

Time – Friend or Foe?

Lean tools are without doubt very useful and play a significant role in helping to carve out a lean strategy.  However, I am concerned that the tendency of many lean initiatives is to follow a prescribed strategy or formula.  This approach essentially creates a new box that in time will not be much different from the one we are trying to break out of.

An extension of this is the classification of wastes.  As identified here, the true waste is time.  Efforts to reduce or eliminate the time element from any process will undoubtedly result in cost savings.  However, the immediate focus of lean is not on cost reduction alone.

Global sourcing has assured that “TIME” can be purchased at reduced rates from low-cost labour countries.  While this practice may result in a “cost savings”, it does nothing to promote the cause of lean – we have simply outsourced our inefficiencies at reduced prices.  Numerous Canadian and US facilities continue to be closed as workers witness the exodus of jobs to foreign countries due to lower labor and operating costs. Electrolux closes facility in Webster City, Iowa.

I don’t know the origins of multi-tasking, but the very mention of it suggests that someone had “time on their hands.”  So remember, when you’re put on hold, driving to work, stuck in traffic, stopped at a light, sorting parts, waiting in line, sitting in the doctors office, watching commercials, or just looking for lost or misplaced items – your time is running out.

Is time a friend or foe?  I suggest the answer is both, as long as we spend it wisely (spelled effectively).  Be effective, be Lean, and stop wasting time.

Let the race begin:  Ready … Set … Go …

Until Next Time – STAY lean!

Vergence Analytics

Twitter:  @Versalytics

Variance – OEE’s Silent Partner (Killer)

Example of two sample populations with the sam...
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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