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Our Executives are Too Busy for an “Executive Training”

“Our executives are too busy for an “Executive Training” … They support the effort, so do we really need the training?”

I can’t tell you how many times I have heard this when discussing a Lean Six Sigma deployment proposal. This is the first clue that the company’s efforts will most likely fail.

I also hear other excuses for not formally training the executives like:

  • “One of our executives is a “Black Belt” and will support our efforts so we don’t see the need to educate the other executives”
  • “I have been chosen by our company to champion the efforts and report to the executives so we don’t really need an executive training”
  • “We have given the executives a brief (less than 1 hour) overview of Lean Six Sigma so we can skip the executive training”

My team has one key metric to gauge the success of a deployment and that metric is the engagement of the Company Leaders. The more the leaders are engaged, supportive and accountable the better the chance of a sustainable deployment.

When executives do not “buy-in” to a Continuous Improvement Program and are not educated as to their role in Continuous Process Improvement, what is the result?

  • “Turf Wars” – When executives do not see a Continuous Process Improvement (CPI) program as important, the leader’s agendas will take precedence. When a Belt is engaged in solving a process problem and that problem impedes on a leader’s agenda, the company leader will trump the Continuous Process Improvement project. If the leaders go through an effective “Executives Training” they will learn that each Continuous Process Improvement project is prioritized to the company’s metrics of success (or KPI’s). When the leader impedes on a Continuous Process Improvement project they are impeding on improvement of KPI’s.
  • “Sub Optimization” – When executives do not see an “Executive Training” as important, they will not know how to identify Continuous Process Improvement projects. I have been in countless companies that have “Green Belts” and “Black Belts” that become the “new toys” to the company leaders. Projects are arbitrarily identified by the leaders based on where the present “pain” is experienced. Unfortunately, that “pain” in most cases is not the constraint in the overall system. The Belt may solve the problem in that step of the process but in the overall system, there is no improvement to company’s ability to produce.

An example of sub-optimization:

These affects (“Turf Wars” and “Sub Optimization”) quickly lead to the failure of a Continuous Process Improvement Program.

The Executive Training is the corner stone of the foundation for a sustainable Continuous Process Improvement Program. Without bought in and educated company leaders, the efforts will quickly lose focus when there is little ROI.

Do you belong to a company that has Change Agents (i.e. Green Belts, Black Belt, etc.) with company leaders that have not bought in to the Continuous Process Improvement program? If so, tell us your story. Are there other effects of not buying in?

The post Our Executives are Too Busy for an “Executive Training” appeared first on Sixsigma DSI.

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How Traffic and Process Capability (Ppk) Relate?

As a teacher and a mentor, i am constantly looking for new ways to make complex subjects less complex using games, simulations stories, analogies., etc. My colleagues and I have found that the topic of Process Capability and how to interpret the Long Term and Short Term capability indices tend to make our students “eyes glaze over”. 

What we came up with is a spin on other examples that i found on the web. We use an analogy of a car in a lane of a highway. The lane is bordered on both sides by concrete barriers. Cars are 60+ miles per hour in this lane.

Before we go into the analogy, we must first define what the CAR and the LANE represent.

  • The Width of the CAR is the Voice of the Process (VOP).
    • The Width of the Car (or VOP) is always equal to six standard deviations.wide.
    • VOP is a dynamic metric because as we add more data to the data set the standard deviation changes
  • The Width of the Lane (or VOC)
    • The Width of the Lane (or VOC) is also called the Tolerance because this is what the customer will tolerate
    • VOC is a static metric meaning that it only changes as the customer changes it

Now that we have a better understanding of how the analogy of the CAR and the LANE relate to VOP and VOC, we can use the CAR and the LANE example to explain the Process Capability Indices Ppk. 

We only explain the Capability Indices Ppk because it is considered “Real Capability” and will give you truer representation of you processes capability to meet the requirement set by the customer.  

Scenario:

You are in a city that has a road with one of two lanes under construction.

The are concrete barriers on both sides of the single lane.

Cars are going 60 MPH or more in this single lane

The Capability Indices “Pp” Compares the VOP (Car) to the VOC (Lane)

The Calculation for Pp = VOC / VOP

In this example Pp = 12/12 = 1

The Calculation for VOC = USL – LSL

The Calculation for VOP = 6 x StDev

“Pp” does not care whether the car is in the lane. It just compares the size of the lane to the size of the car

The Capability Indices “Ppk” not only Compares the VOP to the VOC but also tell you where the car is in the lane

Ppk is the Minimum of two Calculations Ppu (Pp Upper) and Ppl (Pp Lower)

The Minimum of the two calculations tell us what side of the Center of Lane the is the Center of the Car

The Calculation for Ppk = Min (Ppu, Ppl)

  • Ppu = (USL – Mean) / (3 x StDev)
  • Ppl = (Mean – LSL) / (3 x StDev )

Ppk = Min of Ppu and Ppl

Can Ppk be less than 1?

The Calculation for Ppk = Min (Ppu, Ppl)

  • Ppu = (USL – Mean) / (3 x StDev) (in this example: (12 – 3) / 5 = 1.80)
  • Ppl = (Mean – LSL) / (3 x StDev) (in this example: (3 – 0) / 5 = 0.6)
  •  

Ppk = Ppl (in this example: 0.6)

If the side of the car exceeds the lane then the Ppk is less than 1

If the center of the car has not exceeded the lane but the side of the car has then the Ppk is less than 1 and greater than 0

Can Ppk be 0 (Zero)?

The Calculation for Ppk = Min (Ppu, Ppl)

  • Ppu = (USL – Mean) / (3 x StDev) (in this example: (12 – 0 )/ 5 = 2.40)
  • Ppl = (Mean – LSL) / (3 x StDev) (in this example: (0 – 0) / 5 = 0.0)

Ppk = Ppl (in this example: 0.0)

If the middle of the car equals the lane then the Ppk is 0 (Zero)

Can Ppk be less than 0?

The Calculation for Ppk = Min (Ppu, Ppl)

  • Ppu = (USL – Mean) / (3 x StDev) (in this example: (12 – 13) / 5 = -0.2)
  • Ppl = Mean – LSL / 3 x StDev (in this example: (13 – 0 )/ 5 = 2.6)

Ppk = Ppu (in this example: -0.2)

If the middle of the car exceeds the lane then the Ppk is less than 0 (zero) or negative

What if the Car is in the Middle of the Lane?

The Calculation for Ppk = Min (Ppu, Ppl)

  • Ppu = (USL – Mean) / (3 x StDev) (in this example: (12 – 6) / 5 = 1.2)
  • Ppl = (Mean – LSL) / (3 x StDev) (in this example: (6 – 0) / 5 = 1.2)

Ppk = Ppu (in this example: 1.2)

The Calculation for Pp = VOC / VOP (In this example Pp = 12/10 = 1.2)

If the middle of my car equals the middle of the lane, then Pp, Ppu, Ppl and Ppk are the same

Feel free to use this analogy to teach others the concept of the Process Capability Indices Ppk. If this article has helped you or your colleagues to better understand these concepts please share with us in the comments below.

The post How Traffic and Process Capability (Ppk) Relate? appeared first on Sixsigma DSI.

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Are there Free Excel Alternatives to Minitab for my Lean Six Sigma Green / Black Belt Project?

We get asked this question a lot. Many companies or individuals do not want to incur the expense for Minitab or other costly statistical analysis tools. Another reason this question is often asked is because most people are more comfortable with Microsoft Excel.

The answer to their question is YES … there is are free Microsoft Excel alternatives to Minitab for statistical analysis. Excel has an add-on called the Data Analysis Toolpack. Plus there are many templates that have been developed and are available on the web.

This is a reference to Microsoft Excel alternative tools and templates to Minitab statistical analysis that are taught in our Lean Six Sigma Green Belt and Black Belt course. You will find YouTube videos and websites explaining the different tools.

These statistical analyses are created in MS Excel using tools other than the Data Analysis Tool (i.e., using MS Excel’s different default graphing functions)

These statistical analyses use the MS Excel Data Analysis Tool. We have also included an article explaining the different data analysis tool’s and how to load the MS Excel add-in.

I have attached MS Excel templates to perform the statistical analyses functions below in Excel that we teach in our Lean Six Sigma Green Belt course using Minitab. **These tools were not developed nor are maintained by any Six Sigma Development Solution, Inc. employee.

If you know of other free Excel alternatives to costly statistical analysis tools, please let us know in the comments.

The post Are there Free Excel Alternatives to Minitab? appeared first on Sixsigma DSI.

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Using “Range” as a Measure of Data Spread or Dispersion

A measure of dispersion tells you the spread of the data. This is important to know the spread of your data when describing your data set. Most describe a set of data by using only the mean or median leaving out a description of the spread. 

There are two main measures of spread or dispersion:

  • Range
  • Standard Deviation
In this article we will discuss the Range of the Distribution

The Range is the difference between the largest and the smallest observations in a set of data. It is a rudimentary measure of variability. Range does not give us an idea about the spread of the observations around some central value.

Advantages of using Range as a Measure of Spread or Dispersion
  • Range can be calculated easily.
  • It is a simplest measure of dispersion.
Limitations of using Range as a Measure of Spread or Dispersion
  • Range is not based on all the observations of the series. It takes into account only the most extreme cases.
  • It helps us to make only a rough comparison of two or more groups of variability.
  • The range takes into account the two extreme scores in a series.

Thus when the number of data in the data set is small or when there are large enough gaps in the distribution, range is unreliable.as measure of variability.

Do you have an example of using Range as a measure of spread during a Lean Six Sigma project or in a management meeting? If you do, let us know in the comments.

The post Using the Range as a Measure of Dispersion appeared first on Sixsigma DSI.

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Using “Median” as a Measures of Central Tendency

A measure of central tendency is a single value that describes the way in which a set of data cluster around a central value. In other words, it is a way to describe the center of a data set.

The three main measures of central tendency are:

In this article we will discuss the Median of the Distribution

The median is the literal middle of the data set, when arranged in order from smallest to largest. For an odd sized sample, the median is equal to the literal middle number. To find the median an even sized sample, add the two middle numbers and then divide the result by two.

The median of a distribution of data is determined as the 50th percentile point once the distribution has been defined.

For normal distributions, the median value occurs at or close to the mean of the distribution. This is also where the mode, or high point (most frequently occurring value), of the distribution occurs.

For distributions that are skewed to the left, both the median and mode occur to the left of (are smaller than) the average of the distribution.

For distributions that are skewed to the right, both the median and mode occur to the right of (are larger than) the average of the distribution.

Finding skewed distributions in a Lean Six Sigma project?

I normally find skewed distributions in data representing time. Foe example, a all centers time to resolve a level one help desk ticket. The call center is continuously trying to decrease the time to resolve a level one help desk ticket. The data set should show that most of the data should cluster closer to zero. This would represent skewed data that would have a central tendency best described by the median.

The post Using the Median as a Measure of Central Tendency appeared first on Sixsigma DSI.

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Using “Mean” as a Measures of Central Tendency

A measure of central tendency is a single value that describes the way in which a set of data cluster around a central value. In other words, it is a way to describe the center of a data set.

The three main measures of central tendency are:

In this article we will discuss the Mean (or Average) of the Distribution

The mean is a measure of central tendency that considers all of the values in the data set. One of the limitations of the mean as a measure of central tendency is, in order to calculate the mean, the data must be numerical. You cannot use the mean when you are working with nominal data, which is data on attributes or characteristics. For example, there cannot calculate the mean of defective loan applications.

The mean is also called the arithmetic center of a distribution, stemming from the method to calculate the mean: the sum of all numbers in the data set, divided by how many numbers there are in the data set.

For example, the mean of 1, 2, 3, 4, 5, 6 would be calculated:

 [1+2+3+4+5+6] / 6 = 3.5

The mean takes all numbers of a set of data into account, which could be deemed as a strength of the measure, but this also means that it is susceptible to skewing of the final calculated figure if the data features extreme values (or outliers).

For example, in a data set – 1, 2, 3, 4, 25 – the mean would be 7

i.e. [1+2+3+4+25] / 2). This calculation of the mean of the distribution of data could be argued as unrepresentative as most values in the data set are smaller than 7. In this case the data set may take on a different shape.

  It’s important to know the shape of your data …

The mean is used to define the central location in a normally distributed set of data (where the shape is a normal or bell curve). Therefore, it is important for to understand the shape of data before describing the mean as a measure of central tendency.

I have sat in countless management meetings of companies that my team is engaged with and heard reports of average turn around times, average days to delivery, average throughput, etc.. I often ask the person reporting if using the “average” is the right measure for the report? Are they reporting the correct numbers? Do they know the shape of the data used for the report? 

When we look at the data through a statistical tool like Minitab, we may find that the data is not shaped like a bell curve but it s positively or negatively skewed. These shapes will lead us to report the median as the correct central tendency. The median in most cases will give a much different number than the mean.

I challenge you to question using the mean (or average) to describe the central tendency. Use a statistical tool like Minitab to learn the shape of the data. If the data shows normality, then the mean (or average) is the correct central tendency.

The post Using the Mean as a Measure of Central Tendency appeared first on Sixsigma DSI.

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How to Use the Payoff Matrix to Prioritize Solutions

Are you new to the Process Improvement role?

Do you want to learn some tips from a Master Black Belt with over 20 years of experience?

In this article, I’m going to show you how to use a simple tool to prioritize the improvements generated from the Improve phase of your Lean Six Sigma project. The tool is called a Payoff Matrix and it helps prioritize your improvements according to their benefit if implemented and the resources needed to implement.

Below is an example of a completed Payoff Matrix:

 Before we can prioritize improvements …

Before we can prioritize the improvements, we must make a comprehensive list of the improvements. These improvements must be actionable items that mitigate the root causes that you uncovered in the analyze phase.  Actionable items are problems that we can implement a tangible fix.

Below is an example of an improvement list written on a flip chart during a Lean Six Sigma project team meeting:

  Setting the Stage for Improvement Prioritization

Before we can prioritize the improvements, we need to draw the the Payoff Matrix. I like to draw the Payoff Matrix on a flip chart to get engagement from the whole team.

Let me take you through the steps to drawing the Payoff Matrix:

Step #1, With a blue marker draw cross-hairs in the middle of the flip chart (like the one pictured below)

Step #2, With a red marker, draw cross-hairs in each of the four sections (like the one pictured below)

Step #3, Along the X axis draw the Low to High Resource line. Along the Y axis draw the Low to High Benefit line (like the one pictured below)

Now that the Stage is Set we can start Prioritizing Improvements

Now that we have drawn the Payoff Matrix, we can start populating and prioritizing improvements. Use your teams list of improvements. Start with improvement #1. Place the #1 on the payoff matrix according to the benefit of implementing the improvement and the amount of resource to implement. Move on improvement #2, #3, etc.

What is the Meaning of the Four Quadrants?

 

  • “Quick Win” Quadrant: These are improvements that are easy to implement and have a relatively high benefit. This is the quadrant that should be the priority when implementing Lean Six Sigma project improvements.
  • “Filler” Improvements: The improvements in this quadrant will individually have less effect but cumulatively could have a large effect.
  • Do we have the Time and Money?: The improvements in this quadrant are costly in time and/or expense. These improvements are were we have to evaluate the risk of giving up the resources and the reward gained.
  • “Kill It”: The improvements in this quadrant have little effect and are costly. These improvements are either tabled or we can decide they are not worth pursuing.
Below is an example of a Payoff Matrix on a flip chart:

What did you think? Did this article help you and your team to effectively prioritize your solutions? Please share your thoughts in the comments below.

The post How to Use the Payoff Matrix to Prioritize Solutions appeared first on Sixsigma DSI.

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The Movie “The Founder” is the best example of LEAN 3P

I was on an American Airlines flight heading to Washington DC to work with a Lean Six Sigma Green Belt course to company called CoStar. The following week I was to be engaged in a LEAN 3P event with a missile defense company called Aerojet Rocketdyne. Why are these details important, read on and I’ll tell you why…

While on the flight I was working on the curriculum that I was going to use for the LEAN 3P event. It was mostly complete. The curriculum was a “how-to” guide that the students could use to run 3P events in the future once we complete the initial event.

I was stuck on a simple way to explain 3P. I needed to clear my head because I had been thinking about this too long.

I put on my headphones and plugged them into the back of the small tablet attached on the back of the seat in front of me. I then scrolled through the list of the American Airlines free in-flight movies. Nothing looked interesting. I settled on a movie called “The Founder”. It was a movie about Ray Crock and the story behind the creation of McDonald’s. I thought “if it’s not good, it will at least be background noise to help me sleep”.

Fate was smiling down on me that afternoon. I was very interested with this movie because as an entrepreneur, I related to Ray Crock. The story of the founding of McDonald’s is full of lessons for a business owner. But, that wasn’t the reason fate was smiling on me …

In the movie, Ray met the original founders of McDonald’s , Dick and Mac McDonald in San Bernardino, California in 1954. He was enthralled by this small burger joint because of the speed that they could produce a burger, fries and a soda. It took less than a minute. He was used to a 20-30 minute wait at other restaurants.

The McDonald brothers go out to dinner with Ray and explain the history of McDonalds. This is where fate smiled down on me. During this conversation they explained how they designed their restaurant. Using chalk, a tennis court as a template and employees, they drew a mock-up of the kitchen on the tennis court. They then went through a number of scenarios creating imaginary hamburgers, fries, shakes and cokes from raw material to finished product in a bag given to the customer.

When I watched the McDonald’s brothers with their employees acting out multiple scenarios on the chalk mock-up they had drawn on the tennis court, I though “this has to be the best example of LEAN 3P that I have ever seen”.

Watch this YouTube video about the conversation between Ray Croc and the McDonald’s brothers. In this video is the scene with the McDonald’s brothers and their employee on a tennis court acting out multiple scenarios on a chalk drawing mock up of the McDonald’s kitchen.

Lean example from "The Founder" - YouTube

Allan Coletta, who is the author of a new book titled “The Lean 3P Advantage: A Practitioner’s Guide to the Production Preparation Process” said in an interview that “Lean 3P (Production Preparation Process) is a LEAN event-driven process for developing a new product concurrently with the people that will produce it.” That is exactly what the McDonald’s brothers were doing with the mock-up. With the help of their employees, they designed and re-designed a kitchen in a cost effective environment until they had a proven design with the most effective flow.

Do you have any examples of simple but effective ways to describe LEAN 3P? If you do, please share below and help other LEAN teachers and mentors.

The post The Movie “The Founder” is the best example of LEAN 3P appeared first on Sixsigma DSI.

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Using “Standard Deviation” as a Measure of Data Spread or Dispersion

A measure of dispersion tells you the spread of the data. This is important to know the spread of your data when describing your data set. Most describe a set of data by using only the mean or median leaving out a description of the spread. 

There are two main measures of spread or dispersion:

  • Range
  • Standard Deviation
In this article we will discuss the Standard Deviation of the Distribution

The standard deviation (s) is the most common measure of dispersion. Standard deviation tells you how spread out or dispersed the data is in the data set. It is a measure of how far each observed value in the data set is from the mean. In any distribution, theoretically 99.73% of values will be within +-3 standard deviations of the mean.

Why is Standard Deviation Important in Lean Six Sigma?

Let’s look at an example. Let’s say I owned a company that produced widgets as a sub-component for other parts. Every time a widget is produced, a system records the time it took from start to finish of the widget. The requirements for the time to complete a widget is no less than 6 minutes and no more than 18 minutes.

In the below example, we calculate the standard deviation as 1.5 minutes from a sample of 60 widgets.

You can see from this process that it can perform within the specification limits or the process tolerance.

What happens if the the standard deviation increases? Let’s see:

As the standard deviation of the process increases (which means that variation in the process is increasing), the width of the process grows  bringing it closer to exceeding the process tolerance.

These illustrations show that knowing the standard deviation and tolerance of a process can show the performance of the process. Knowing the performance of a process is critical in a Lean Six Sigma project.

The post Using the Standard Deviation as a Measure of Dispersion appeared first on Sixsigma DSI.

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