Posts Tagged ‘lean-six-sigma’

The Lean, Six Sigma, and Quality Triad

Tuesday, May 31st, 2011

In response to a message from a colleague asking about the relationship between Lean, Six Sigma and Quality, I wrote the following:

Both Lean and Six Sigma (and Lean Six Sigma, the combination of the two) are ways of improving operational excellence. Lean does this by improving flow through value streams, primarily focusing on the elimination of various forms of muda (waste.) Six Sigma does this by identifying what customers and other stakeholders want and delivering it with minimal waste, variation and errors.

The Lean and Six Sigma DMAIC disciplines focus on the processes for creating and delivering products and services that meet or exceed customer expectations. The Design for Six Sigma discipline focuses on the design of products or services that meet or exceed customer expectations. Quality is a discipline which focuses on identifying customer requirements and expectations, translating them into internal requirements, and assuring that the requirements are consistently met. Of course, these Quality activities provide input into both Lean and Six Sigma. It is the “Y” being solved for when waste is identified (Lean) or when searching for the root causes of waste, variation and errors (Six Sigma.)

Thus, Lean, Quality, and Six Sigma are all different aspects of excellence.

I welcome your comments on how you consider the three areas to be related. Or do you consider them to be unrelated ideas?

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Statistical Engineering

Monday, April 11th, 2011

In the movie “The Graduate,” the new graduate is told by a would-be mentor to remember only one word as he heads out into the world: Plastics. Times have changed. Hal Varian, the chief economist at Google says, ‘‘I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding.’’ Statistical methods are being used by a larger cross-section of people in a wider variety of industries than ever before. There are numerous reasons for this. Nearly everyone has what was once considered to be a supercomputer sitting on their desktop. Powerful statistical software is widely available, including popular packages like Minitab, JMP, SAS and SPSS, and extremely powerful free software. Oracle’s Crystal Ball software makes it possible to create a statistical distribution for any cell in a spreadsheet, making statistical simulation a snap. While becoming more sophisticated, the software is also becoming easier to use. Output is increasingly graphical and easier to explain to laypersons. The number of people trained in Lean Six Sigma methods is growing rapidly. There is an enormous amount of data saved in public and corporate data warehouses. The list goes on and on.

But perhaps the most important reason for the ballooning use of statistics is: it works.

If we take Aristotle’s logic as the historical starting point for rational analysis, and Galileo’s experimental method as the next major leap, then statistical methods might be viewed as the next step in applied analysis. Many problems don’t lend themselves to solution by pure logic nor by carefully planned and controlled experimentation. Most organizations, especially in the commercial sector, must deal with so many problems and such a dynamic external environment that they are forced to make quick decisions despite large uncertainty, then move on to the next problem. Statistical methods help these decision makers evaluate the evidence and make better decisions quickly. The tools and technology described in the first paragraph make this easier than ever before.

This situation is much more akin to engineering than it is to pure science. The approach has been termed “Statistical Engineering.” Authors Roger W. Hoerl and Ron Snee describe Statistical Engineering as follows:

“The statistical engineering discipline [is] the study of how to utilize the principles and techniques of statistical science for benefit of humankind. From an operational perspective we define statistical engineering as the study of how to best utilize statistical concepts, methods, and tools and integrate them with information technology and other relevant sciences to generate improved results. In other words, engineers—statistical or otherwise—do not focus on advancement of the fundamental laws of science but rather how they might be best utilized for practical benefit.

This definition goes beyond applied statistics. Statistical Engineering implies the application of statistics in a systematic framework that utilizes technology to create or improve products, processes and services that improve the lives of people. Disciplines such as Lean Six Sigma, Quality Engineering, Reliability Engineering, and others can be said to do this to some degree, but there are other ways to use Statistical Engineering, some quite unexpected. Billy Beane, general manager of MLB’s Oakland A’s and protagonist of Michael Lewis’s book Moneyball, had a problem: how to win in the Major Leagues with a budget that’s smaller than that of nearly every other team. Conventional wisdom long held that big name, highly athletic hitters and young pitchers with rocket arms were the ticket to success. But Beane and his staff, buoyed by massive amounts of carefully interpreted statistical data, believed that wins could be had by more affordable methods such as hitters with high on-base percentage and pitchers who get lots of ground outs. Given this information and a tight budget, Beane defied tradition and his own scouting department to build winning teams of young affordable players and inexpensive castoff veterans. Author Michael Lewis examines how in 2002 the Oakland Athletics achieved a spectacular winning record while having the smallest player payroll of any major league baseball team. Given the heavily publicized salaries of players for teams like the Boston Red Sox or New York Yankees, baseball insiders and fans assume that the biggest talents deserve and get the biggest salaries. However, argues author Michael Lewis, little-known numbers and statistics matter more.

Statistical Engineering is not limited to applied statistics, theoretical statistics have a place too. In a paper published in the April-June 2011 issue of the journal Quality Engineering author Philip R. Scinto offers this list of Statistical Engineering attributes:

  • Meets high-level needs of an organization
  • Work/study for the greater good
  • Use of statistical concepts and tools
  • Collaborative effort with other sciences
  • Integrated with other sciences
  • Documented protocol
  • Activity continuous with sustainable life
  • Improved results

It isn’t necessary that all items on the list be checked off, but the list is useful in evaluating whether an activity qualifies as Statistical Engineering or if it’s merely another clever use of statistics. The important thing isn’t the label we apply, but the improvement that can be achieved by properly using statistical methods along with science and technology to achieve a challenging goal.

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Pyzdek Institute Receives Accreditation

Friday, April 1st, 2011

PEOPLECERT Group,  the experts in certifying professionals, today announced the accreditation of Pyzdek Institute , the global online training company, as an Accredited Training  Organization for the International Association for Six Sigma Certification (IASSC) Lean Six Sigma exams.

PEOPLECERT Group offers independent, globally recognized certifications that evaluate competence, know-how and expertise and are key to today’s competitive, performance-driven business environment. PEOPLECERT operates worldwide, with 142 employees and 1,000 associates, through 8,850 global examination locations, including the extensive network of Pearson VUE.

Through its accredited program, instructors and curricula, Pyzdek Institute offers Lean Six Sigma training to its customers around the world. Students who complete Pyzdek Institute Lean Six Sigma Green Belt or Lean Six Sigma Black Belt training will be well-prepared for the PEOPLECERT certification exams.

PEOPLECERT Group is the only certification body to offer the IASSC certification on a global level, through a multi-year strategic partnership with IASSC. IASSC, the only independent third-party association in the Lean Six Sigma industry providing professional credentialing, has developed the Lean Six Sigma certification examinations, designed to measure a person’s knowledge of the Lean Six Sigma process. Practitioners can sit for the exam in order to test their skills against a globally recognized standard. The Pyzdek Institute’s curricula for Lean Six Sigma Black Belt and Lean Six Sigma Green Belt training has been accredited to IASSC standards by PEOPLECERT Group.

“Our Lean Six Sigma training and certification is rapidly becoming more popular as our clients are seeking both cost efficiency and process optimization for their organizations, and our students seek a valuable credential to enhance their opportunities.”Stated Thomas Pyzdek, President of the Pyzdek Institute. “We are proud to add the premier PEOPLECERT accreditation and testing services to our portfolio.”

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The Problem with Swiss Army Knife Control Charting

Thursday, March 31st, 2011

I’m an advocate of using the I-chart as the default control chart. If I am teaching statistical process control (SPC) and can only teach one chart, the I-chart is always the one that I teach. It’s the only control chart I cover in my Lean Six Sigma Green Belt training. It’s the only chart that I teach in Process Excellence Leadership training. It’s the only chart I use if the data I’m looking at are reasonably close to symmetric (note that I didn’t say “normal”,) unless I have some compelling need for greater sensitivity. I teach that the I-chart is the “Swiss army knife” of control charts.

But I still sometimes use other control charts.

The Problem

Organizations don’t do SPC for the fun of it. They do it because it helps them achieve their goals. Organizations exist to produce things of value for the benefit of customers, investors, and employees. They do this by transforming inputs into outputs of higher value via processes. They can do this better if they minimize variability of outcomes, which can best be accomplished by controlling the sources of variation in the inputs and processes. This is where SPC comes in. SPC is a methodology that uses statistical guidelines to help separate “special cause” and “common cause” variation. If a special cause of variation exists, it signals the need to act. Special cause variation is defined as a change of such a large magnitude that its cause can probably be identified if looked for at once. SPC operationally defines such a change as a measurement result more than 3 standard deviations from the process mean for whatever process metric is being monitored.

A problem might exist if the process generates measurements that are highly skewed, even when it is not being influenced by special causes of variations. Such processes are quite common in the real world. For example, nearly all measurements produced by geometric dimensioning and tolerancing are skewed, as are measurements of time-based phenomena such as those encountered in services industries including the healthcare and hospitality industries. Highly skewed distributions produce a relatively high percentage of results more than 3 standard deviations from the mean even if no special causes exist. In other words, they produce many “false alarms” that will trigger a search for a problem when there is no problem. The false alarms may even lead to tampering, thereby causing a stable process to become unstable.

I-Charts Don’t Solve the Problem

The skewed distribution problem is exacerbated by using I-charts. I-charts are relatively insensitive to moderate departures from normality, and very insensitive if the non-normality still produces a symmetric distribution. But for the data described above, this is not the case. If you use the I-chart for these data you will experience many false alarms. It’s just that simple.

The problem is to determine if a process is or is not being influenced by special causes of variation. A process distribution might appear as skewed because of special cause outliers, or because it naturally produces skewed data. The I-chart treats all data beyond 3 sigma as outliers; it doesn’t help you separate the natural, common cause process outcomes from special cause outcomes. Is the point beyond 3 sigma an outlying chicken, or a common cause egg? I.e., is the process being influenced by special causes, or only common causes? If the process data are naturally skewed you can’t answer this question using an I-chart.

A Simple Solution

The solution that I recommend is to begin your investigation with averages charts, also known as x-bar charts. Averages tend to have distributions that are approximately normal, even if the individual values are skewed. This means that, for a process with a skewed distribution that is not influenced by special causes, averages are much more likely to produce results that stay within 3 standard deviations of the mean than I-charts. It’s the best of both worlds: few false alarms, but still sensitive to special causes. If you have a nice run of subgroup averages without a special cause, plot a histogram of the data and see if the distribution looks skewed or symmetric. If the latter, you can use I-charts with confidence. If the former, stick with averages charts, or find a statistician or Master Black Belt to help you find a more advanced solution.

Stable Does Not Mean Normal

Before ending this article, I’d like to address another pet peeve of mine. I believe that too many teachers of SPC obsess on the need for normality. They confuse normality with the absence of special causes, also known as statistical control. I usually attribute this misunderstanding to a lack of experience with the real world, where normal distributions are so rare as to be virtually non-existent. By insisting on normality we encourage tampering and all of the problems associated with this approach to “process management.”

On the other hand, I am also impatient with people who insist that all non-normality be ignored. These individuals advocate using I-charts in all situations, regardless of the risk of false alarms. This attitude may also be due to a lack of experience. However, I’ve seen SPC lose its credibility when concerned process owners look for special causes over-and-over again without finding them. Like the boy who cried “Wolf!”, out-of-control signals become something to ignore. Eventually so does SPC.

My approach, which favors the I-chart but doesn’t make its use dogma, provides a rational middle ground.

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A Sampling Question

Monday, March 14th, 2011

A Six Sigma Green Belt student asked an interesting question about sampling. Here’s the question and my response.

QUESTION:
======================
Just a question that I thought I would run by you…
I work in the Automation industry, and am currently working on two production lines, and logging data for the parts being produced. One line is producing 60 parts per minute and I can thus log the data for every part. The other line is producing 240 parts per minute, and it is not possible to log the data for every part. I remember reading somewhere that in order to perform SPC you must take n consecutive samples (I think n was 5) every x number of cycles. What I need is definitive guidance on how to calculate n and x. I also need to know the statistical reason that n and x are used in order to explain this to the customer. Any feedback you can give me in relation to this would be greatly appreciated.

Regards,

AT in Irelend

RESPONSE:
======================
There is no rule that you need to sample n consecutive samples every x number of cycles. You are probably thinking of a technique known as PRE-Control, which is different than SPC. PRE-control also incorporates rules for deciding when to increase or decrease sampling frequency, stopping rules for processes, etc.. Personally, I don’t like PRE-Control for a variety of reasons, but if you have The Six Sigma Handbook, 2nd edition I discuss in starting on p. 661 or the 3rd edition starting on p. 465. My primary reason for disliking PRE-control is that it is a specification-based scheme (which I dislike in principle) and it will allow process variation to increase until it is as wide as the specs allow. SPC is all about reducing process variability to a minimum by identifying special causes of variation. When used in conjunction with Lean Six Sigma, SPC will also address common cause variation.

Instead of PRE-control I suggest that you consider using standard SPC control charts. I don’t know anything about your process so I can only offer general advice. If you’re already logging in metrics for 60 parts-per-minute I would be surprised if you’re not encountering problems like autocorrelation, which requires an adjustment to standard SPC such as using EWMA charts instead of classical control  charts. If you have autocorrelation and are not using the proper chart, then you will be experiencing a lot of “false alarms.” Processes seldom change by any meaningful amount in a matter of seconds, so you can probably extend the sampling interval. If you feel that you can economically sample 60 per minute, and that it is wise to do so, then you could sample this number of parts from the process running 240 parts per minute rather than checking every part. It would be best to choose the sample at random, rather than sampling every 4th part. Samples chosen using a fixed pattern are susceptible to problems if the process exhibits similar patterns. For example, if the process had 4 positions on a workstation then your 1-in-every-4 sample would always be sampling from the same workstation. Sometimes the patterns in the process are quite difficult to spot, and “Murphy’s Law” can strike at any time. Murphy’s Law states that anything that can go wrong, will go wrong.

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Lean Six Sigma Improvement and Work Design, Part 13

Monday, September 6th, 2010

This is the thirteenth and last post in a series taken from a lesson in Pyzdek Institute Lean Six Sigma Black Belt training. You can find all of the articles in the series by searching this site for the title.

Sustain

Sustain  is the name of the whole 5S game. You gain nothing by deploying the first 4 S’s, only to let things go back to business as usual in the long run. In fact, you probably create an attitude among workers and supervisors that management isn’t really serious about Lean Six Sigma.

Just why things tend to get worse unless we pay close attention to them is a debatable proposition. There is an analogous concept used in thermodynamics: entropy.  One definition of entropy is applied to human systems, “The inevitable and steady deterioration of a system or society.”[1] In physics entropy is inevitable in closed systems. These are systems where there is no additional input of energy. The same applies to Lean Six Sigma 5S systems: if no additional effort is put into sustaining the improved state, then deterioration is inevitable and steady. You simply have no choice. If you want to sustain the benefits of 5S you must put forth the required effort to do so. Here are some guidelines to help you do so.

  • Provide periodic refresher training on 5S.
  • Schedule the required time to perform 5S on a daily basis.
  • Create a standardized approach to 5S that clearly spells out how 5S will be implemented.
  • Have your Lean Six Sigma process owner acknowledge and accept ownership of 5S.
  • Create programs to recognize 5S efforts and reward compliance with standards.
  • Keep 5S fun! Think of creative ways to keep 5S from becoming drudgery. (5-Minute 5S contests anyone?)

Safety–The Real 1st S

A workplace where 5S is practiced is not only clean and well-organized, it is also safe. Clutter and unnecessary materials and equipment contribute to accidents. People can locate the tools and materials they need without searching among  unneeded objects and moving them out of the way. There are no oil spills where people can slip and fall. Adequate and clearly marked aisles make transportation safer. Marked storage areas which contain only what is needed are less likely to have excess inventory that can fall and injure people.


[1] http://dictionary.reference.com/browse/entropy

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Lean Six Sigma Improvement and Work Design, Part 12

Monday, August 30th, 2010

This is the twelfth post in a series taken from a lesson in Pyzdek Institute Lean Six Sigma Black Belt training. Future posts will continue the topic. You can find all of the articles in the series by searching this site for the title.

Standardized Cleanup

Standardized cleanup is used to maintain the 5S activities described so far. The definition is somewhat circular: when the 5S activities of Sort, Set in Order, and Shine are properly maintained, then you have standardized 5S. When 5S has been standardized you avoid backsliding.

Determine Responsibilities

The tools needed for standardized cleanup include the tools already introduced: 5S maps and 5S schedules. In addition you’ll need a new tool: the 5S Cycle Chart (see Figure 14.) To create such a chart you sort the duties into Sort, Set in Order, and Shine categories and use a letter code to identify the cycle period. The resulting 5S Job Cycle Chart can be used as a checklist by the personnel responsible for the various 5S activities.

Figure 14-5S Job Cycle Chart

 

 

5S Job Cycle Chart

5S Job Cycle Chart

 

 

 

Integrate Sort, Set in Order, and Shine with the Work Routine

Make these three 5S activities a part of the normal work done in the work cell. This integration will reinforce the idea that 5S isn’t something added on to the work being done, it is an integral part of it. One mechanism for implementing this is “Visual 5S.” As with the visual workplace in general, the purpose of visual 5S is to be able to tell at a glance that 5S activities are being done on an ongoing basis. For example, if Set in Order requires that  tools are kept on a pegboard, then the tool outlines on the pegboard will indicate which tools are currently in use. This means that any blank space observed on the pegboard at the start or end of the shift is an indication of a problem.

Another mechanism is 5-minute 5S. This is similar to the 5-minute shine described earlier, only the scope is the entire 5S program. Don’t get hung-up on the “5-minute” part of this activity, it’s just an easy to remember tag. However, think of it as something you do quickly. You may want to use a visual display to make it easier to track your 5-Minute 5S activities, such as that shown in Figure 15.

Figure 15-5 Minute 5S Signboard

 

 

5 Minute 5S Signboard

5 Minute 5S Signboard

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Lean Six Sigma Improvement and Work Design, Part 11

Monday, August 23rd, 2010

This is the eleventh post in a series taken from a lesson in Pyzdek Institute Lean Six Sigma Black Belt training. Future posts will continue the topic. You can find all of the articles in the series by searching this site for the title.

Shine

 

Cleaning Inspection

Cleaning Inspection

 

Shine can be thought of as the Lean Six Sigma version of housekeeping. It involves making sure that dirt, grease, and grime is eliminated from the work place. The goal is to make the workplace a safe and pleasant place to work. Shine also assures that items and equipment will be ready to use when needed. Shine is an ongoing activity, not a once-in-a-while “spring cleaning” type of event.

Cleaning and inspection go hand-in-hand. When you clean an area you automatically inspect the working surfaces, floor, equipment, parts, etc. that you are cleaning. This is a side-benefit of cleaning because it highlights issues and opportunities that would otherwise be overlooked. To get the full benefit from this you will need to incorporate a method for easily reporting any problems discovered.

 

Shine Steps

Identify the shine targets. What warehouse items (parts, raw materials, subassemblies, etc.,) equipment (machines, tools, worktables, desks and chairs, etc.,) and spaces (floors, work areas, beams, windows, shelves, lights, etc.) will be cleaned?

Assign responsibilities. Use the 5S map to create specific areas that will be assigned to individuals. Set up and post a schedule showing when each area is to be cleaned. Be sure that shine activities take place throughout each day.

Determine the shine methods. Start and end each shift with a shine inspection. Determine what will be cleaned and how it will be cleaned, including the cleaning supplies and equipment needed. Implement the “5-minute shine” drill. You will be surprised at how much can be done in an intense 5-minute effort. Develop standard cleaning procedures that assure that time is spent on actual cleaning rather than on preparation for the task.

Tools. Apply the Set In Order approach to your cleaning tools, thereby making them easy to find and easy to use.

Shine! Now it’s time to get to work on the targets. Have the responsible people follow the shine procedures and, using the proper tools, clean the work area to the required standards.

Deal with issues identified during cleaning. Finally, respond to any problems found during the shine process. When possible, fix things immediately. The standard cleaning procedure should include what steps to take to deal with problems that can’t be fixed at once. To whom should they be reported? What forms, etc. are needed? It is a good idea to attach a tag to any equipment where maintenance has been requested to remind workers and supervisors that maintenance is pending.

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Lean Six Sigma Improvement and Work Design, Part 10

Monday, August 16th, 2010

This is the tenth post in a series taken from a lesson in Pyzdek Institute Lean Six Sigma Black Belt training. Future posts will continue the topic. You can find all of the articles in the series by searching this site for the title.

Set in Order

Once the Sort phase has been completed, it is time to set the remaining needed items in order. Items are arranged and labeled so they are easy to find and use when needed. When this is done a great deal of waste is eliminated in production and office activity. For example, it will no longer be necessary to waste time searching for the needed item, nor will it be necessary to return an item because it wasn’t the item you actually needed. You’ll make fewer errors due to using the wrong tool or material or form.

Setting in order revolves around standardization, and, conversely, standardization revolves around setting things in order. The key principle is visual control. For example, Figure 10 makes it clear to the surgical team which instrument goes where by providing drawings and verbal descriptions. In factories, Lean Six Sigma teams often keep things simple by drawing outlines of the tools on simple pegboards, as shown in Figure 11. It is then easier to see which items are currently in use, as well as where a given item needs to go when it is returned. If possible, attach the tool to a retractable cord so it automatically returns to the correct location when released. Color-coding the tools helps reduce errors (Figure 12.)

 

Figure 10-5S Surgical Instruments Organizer

5S Surgical Instruments Organizer

To further simplify, teams should organize tools so they are presented in the order of use and are easily accessible to operators. Ideally operators will be able to get the needed tool without even looking at the tray or pegboard. This may require providing storage areas with additional space between tools to make it easy to reach them.[1]

As a general set in order rule, frequently used items are located nearer to the work cell than items used less frequently. Items that are seldom used are usually stored in a remote location to reduce clutter.

Figure 11-Pots and Pans Outlined on Pegboard

Pots and Pans Outlined on Pegboard

Figure 12-Engine Assembly Line in Poland with Color-Coded Overhead Tools on Retractable Cords

Engine Assembly Line in Poland with Color-Coded Overhead Tools on Retractable Cords

Locations

The locations where WIP, jigs, tools and other equipment are stored can be determined by evaluating the “5S Map,” such as that shown in Figure 7-Work cell Layout. This is done as follows:

  1. Draw the 5S Map on a floor plan, preferably drawn to scale. Indicate the location of WIP, fixtures, tools, etc. on the scale drawing.
  2. Draw a spaghetti diagram of the work flow on the 5S map. Identify wasted motion.
  3. Create alternative 5S maps which reduce or eliminate wasted motion.
  4. Simulate the work flows represented by the various 5S maps and choose the best alternative.
  5. Create the new work cell layout, including locating the WIP, tools, fixtures and jigs, etc..

Once the improved layout has been determined, create “signboards” to identify the locations for the various items needed in the work cell. This includes location indicators that show where the various items go, such as marking off floor areas with tape or paint. It also includes item indicators which show the specific items that belong in each location. Finally, you will need amount indicators to specify how many of each item are needed. Signboards are used to identify machine locations, locations for standard procedure displays, storage of equipment when it is not being used, location of WIP and finished goods inventory, racks and spaces within racks for various items, and named work areas.

Floor locations are often shown in places other than the work cell itself. For example, paint (or colored tape) is used to show aisles and aisle direction, door swing space, storage locations, zones which are off-limits for storage, hazardous areas, etc.. Additional information can be conveyed by the use of color-coded paint. For example, red might show off-limit areas, green might show operations areas, and yellow might indicate divider lines.[2] If you use color-coding, be sure that the color uses are standardized.

 


[1] In the case of the surgical instruments tray, a person normally hands the needed instrument to the surgeon.

[2] Color coding has other uses as well. For example, it can be used to show  which tools are used together, which equipment make up a “set” for producing a particular item, etc.. Be creative and use your imagination to identify how to use simple, visual means of conveying information at a glance.

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Six Sigma Project Presentations in a Nutshell

Saturday, August 14th, 2010

I’ve reviewed thousands of improvement projects. I’ve lost count of how many project presentations I’ve attended, either for certification purposes or for presentations to leaders. I’ve come to the conclusion that most Green Belts and Black Belts simultaneously present too much information, and not enough information. If I may speak to Green Belts and Black Belts on behalf of leaders and Master Black Belts everywhere, here’s what I’d like to say. What we’re asking is actually very simple, namely how did you apply the Six Sigma process to pursue a real opportunity? In other words, for your project just walk us through the L1 Six Sigma process shown in the figure, and do so in 45 minutes or less. I actually don’t even care if you use a PowerPoint template, or even if you have any slides whatever. I just want to hear a great Six Sigma success story.

Six Sigma L1 Map

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