Tag: SPC

The Point of No Return

The “Point of No Return” is a common expression that typically means you’ve reached an unrecoverable state if you continue to proceed with the current course of action.  When I clicked the “Publish” button for this post, I reached a point of no return (action).

From an accounting perspective, the term “Break Even” point is used to define the point where Total Costs equal Total Revenue.  The break even point translates to the quantity of parts that must be produced and sold to turn a profit.  Stock exchanges around the world serve as a constant reminder that investors are only concerned with PROFIT and return on investment (PROFIT).  In this context, a point of no return (profit) also exists.

Businesses exist for the customer or consumer.  Poor quality, missed delivery dates, short shipments, warranty returns, and poor customer service all lead to higher costs and may eventually cause customers to reach their “point of no return”.  Customers understand that the lowest price is not always the lowest cost option in the long run.  Business depends on repeat customers.

What’s the point?

In the simplest of terms, our actions must yield a return that is greater than the investment required to achieve it.  Delivering VALUE to the customer is one of the underlying principles of lean thinking and is measured by our ability to provide the highest quality products and services at the lowest possible cost, on time, delivered on time and in full.

This all sounds great on the surface but there will come a time where the cost to improve your systems and / or processes will exceed the return on investment – another point of no return.  Alternative, lower cost, solutions must be found to meet your continuous improvement objectives.

Where a significant capital investment is required, your company may require a payback period of one or two years.  A capital investment for a program that is soon to become obsolete is not a feasible option.  The point of no return (investment) is reached before any funding can even be considered.

The Bottom Line

Understandably, the team will become extremely frustrated when the very solution they proposed is rejected or declined.  While they may not doubt their own ability to provide viable solutions, they will doubt the company’s commitment to pursue excellence and continually improve.

For this reason, it is essential for the team to understand the reasons why.  It also underscores the need to identify and respond to improvement opportunities quickly and as early as possible during the launch cycle of any new system, process, or product.

Embrace Rejection

Rejection can sometimes be a gift.  As I have stated many times before, “There’s always a better way and more than one solution.”  Could it be that sometimes bad things happen for a good reason?

Rejection provides (forces) us with the opportunity to consider the present circumstances from a fresh perspective.  If the premise for the proposed solution was to “fix” the current system or process as it’s is now defined, perhaps a radically different and innovative system or process could better serve the company in the long term.

Is it possible that a new and lower cost alternative exists that could be at least as effective and perhaps even more efficient?  There are numerous examples of systems, processes, and technologies that exist today that were discovered by removing the limits that we unconsciously place on the scope of the problem that in turn limit the solutions we are able to develop.

The real problem with problem solving is the idea that the only solution is a “fix” to a system or process that is already be flawed from the onset.

Be Inspired

TED Talks are rife with examples of problem solving that yield radical and in some cases simple solutions.  The following TED Talks may serve to inspire you and your organization to look at problems and their solutions from a different perspective:

These TED Talks present problems on a different scope and scale than we may be accustomed to, however, the very discussion of alternatives alone should serve to inspire radical thinking that in turn inspires radical change.

You may have noticed from these TED Talks that some of the solutions presented were found outside of the context or circumstances from which the problem originated.  Is it possible that a “surrogate” solution exists elsewhere?

“Problems cannot be solved by the same level of thinking that created them.”

The point of no return is significant and literally requires “out of the box” thinking.  Many companies no longer grace our communities or employ our neighbours, losing business and opportunities for growth to lower cost manufacturers and distributors to continually emerging global economy.  The difference could very well be how we embrace the point of no return.

Consider that Toyota, as a new company to the North American automotive market, implemented innovative supply chain,  inventory management, and production techniques to remain competitive.  Radical change and innovation does not imply higher cost or investment.  At best it should simply imply “different”.

Other companies like Apple and GE managed to change their futures under the leadership of Steve Jobs and Jack Welch respectively.  Was it always pretty? Likely not from the books I’ve read.  However, the outcomes are undeniable.

The courage of Steve Jobs to solicit support from Microsoft’s Bill Gates was an extremely radical decision at the time.  This video, “Steve Jobs, Bill Gates and Microsoft – It’s Complicated“, clearly demonstrates the challenges faced in the relationship between Apple and Microsoft.  As for GE, I highly recommend reading “Straight From the Gut” by Jack Welch to best understand the radical changes in business and company culture during his tenure there.

Asking the right questions, open minds, radical thinking, and strong leadership coupled with a commitment to pursue excellence, continually improve, and solve problems may help everyone realize that the point of no return can be one of the greatest gifts you’ll ever receive.

To quote Albert Einstein, “A clever person solves a problem.  A wise person avoids it.” and so we … “look before we leap.”

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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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

 

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!

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