Six Sigma Yellow Belt Masters - Part 4: the ANALYZE phase | Valentin Ilicea | Skillshare

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Six Sigma Yellow Belt Masters - Part 4: the ANALYZE phase

teacher avatar Valentin Ilicea, Founder, VeryFastExcel

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Taught by industry leaders & working professionals
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Watch this class and thousands more

Get unlimited access to every class
Taught by industry leaders & working professionals
Topics include illustration, design, photography, and more

Lessons in This Class

7 Lessons (22m)
    • 1. Welcome!

    • 2. Master the Process analysis (Lean) tools

    • 3. Understand FMEA - one of the main Analysis tools

    • 4. Understand the four Root-Cause Analysis tools

    • 5. Master the difference between Common and Special Cause variation

    • 6. Perform Correlation analysis

    • 7. Congratulations! Thank you.

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About This Class

Lean Six Sigma Yellow Belt - the Training and Certification That Will Help You Win in Your Professional Life

This course focuses on the ANALYZE phase (DMAIC approach) and enables you to enhance your career with Lean Six Sigma in a three-stage high-level process:

  1. learn and apply the Analyze phase concepts, measures, and tools 

  2. see and use examples from a Real-World L6s project

  3. build L6s tools with downloadable files in a step-by-step process

This is the most straightforward path to learn and get certified for the Lean Six Sigma Yellow Belt.

We will cover: 

  • Master the Process analysis (Lean) tools
  • Perform Failure Mode and Effects Analysis ‚Äď FMEA
  • Understand Root-cause analysis: 5 Whys, process mapping, and others
  • Data analysis: basic distribution types ‚Äď Article
  • Master the difference between Common and Special Cause variation
  • Perform Correlation analysis
  • Regression ‚Äď Article
  • Hypothesis testing at a glance - Article

Learn in a practical way with downloadable materials, step-by-step demos, and a class project!

Master the Six Sigma Fundamentals & Experience Career Growth

Imagine for a minute what you can achieve by the end of this course: 

- you will become more productive

- you will get better projects and processes

- you will enjoy enhanced visibility to your senior stakeholders and management

- you will get more job opportunities having performed a Lean Six Sigma Yellow Belt Training

- you will become more competitive

- you will get a better pay

This is how you can win in your professional life.

About me:

My name is Valentin Ilicea, and I am a certified Lean Six Sigma Green Belt after a leading a project that enabled +200'000 USD savings per year in the business organization.

Using the Lean Six Sigma concepts and certification, I built a 12-year career in Advanced Data Analytics, team, and project management, in top multinational companies: HP, Ericsson, and INNIO (former part of General Electric).

This knowledge completely transformed the way I work and I'm so excited to have the possibility of sharing this with you!

So, let's get started with the Analyze phase. 

Meet Your Teacher

Teacher Profile Image

Valentin Ilicea

Founder, VeryFastExcel


· I am a seasoned Data Analyst and certified Lean Six Sigma professional with more than 12 years of experience specializing in Data Analytics, Data Quality, process and project management, acquired in top multinational companies like HP, Ericsson, and INNIO (former part of GE).

· I founded VeryFastExcel in 2019 to provide top-notch quality, affordable, great value learning in order for business professionals to develop, achieve more and reach their professional dreams.

See full profile

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1. Welcome!: Hi and welcome everybody. My name is voluntarily champ. And I build a 12 years career around advanced data analytics team and project management in three multinational companies. I'm a certified lean Six Sigma green belt as I lead the project with early savings of over $200 thousand. And it is my pleasure to welcome you today to this lean Six Sigma yellow belt training series. In this course, we will explore together Lean Six Sigma Yellow Belt analyze tools like FMEA, root cause analysis, data analysis, for example, correlation and regression. And in many under analyze tools. I am so excited to have you here. This knowledge has completely transformed my career and I'm looking forward to sharing this with you. I am confident that this actionable information, the demos and the tools that we will get together will have a significant positive impact on your professional life. So without further ado, let's get started. 2. Master the Process analysis (Lean) tools: In this lecture, we will cover the analyze phase objectives and fundamentals, also the five Ys and the value analysis. The goal of the make, Analyze phase, identify potential red causes for the process problem being addressed, and then confirmed actual root causes with data. Having completed the measure phase, the project team should have already established a clear problem statement which specifies what the problem is and under what circumstances it happens. They should have already gathered, collected, analyzed data to establish the baseline performance of the process. The question that the analyze phase six to answer is, why is this problem occurring? Another way to ask is, what is the cause of the problem? And it's not possible to make improvements in the process until causation has been identified. Potential root causes. In many cases, clues to the factors affecting performance are already available based on the work that was done in the previous project phases. Perhaps the team demonstrated how the problem is isolated to one group and they know that group is using older equipment. Or analysis of the process map may have been revealed some fairly obvious sources of inefficiency and delay in the process. However, this is not sufficient to confirm what is causing the problem for two reasons. One is that as in all phases of the DMAIC, suspicions and hypothesis must be confirmed and backed up by data. But only muster team confirmed that these factors are present. They must also confirm that changes in these factors greatly impact the outcome. The other is that the goal of analyses to determine root causes, which requires digging deeper than what is apparent. A 100 surface. 5s stands for SART, straightened, shine, standardize and sustain. And is based on the japanese concept for housekeeping. Say, say, say so, say gets some and she sued care. Let's explore examples for each of the five concepts. Sort is eliminate whatever is not needed by separating needed tools, part, and instructions from unneeded materials. Straighten also is known as setting order, organized whatever remains by neatly arranging and identifying parts and tools for for ease of use. Shine, clean the work area by conducting a cleanup campaign, standardized schedule, regular cleaning and maintenance, by conducting say it is a down and say so Daly. So using the first three concepts previously presented. Finally, sustained. Make five as a way of life by forming the habit of always following the first four as this process supports lean in its most basic form, maintaining a simplified and streamlined work environment helps you eliminate waste on a personal level. From here, you can apply the concepts and process and organizational levels. Lean 5S program benefits. Benefits to be derived from implementing Lean 5S program include improved safety, lower defect rates, reduce costs, increased production agility. Value analysis. Value analysis covers the topics of value stream mapping and also process value analysis. Value Stream Mapping, or VSM, is a popular tool used in lean methodology as an alternative approach to statistically identifying root causes. It is used to identify the types of wastes in the process. Vsm is holistic Matata to visually document the way in which value is getting built in the process. The types of wastes are generally referred to as non-value added tasks. In conditional Six Sigma, process value analysis is the process of associating every task in the process as either value adding, VA, non-value adding, and VA, or value enabling ie. 3. Understand FMEA - one of the main Analysis tools: In this lecture, we will explore an important analysis tool called FMEA, F and E. It's an acronym that stands for failure modes and effect analysis. Well, what exactly is FMEA? Let's dive right in. It's a step-by-step approach for identifying ONE possible failures. You know, designed a manufacturing or assembly process or a product or service. Failure modes means the ways or modes in which something might fail. Failures are any errors or defects, especially wants that affect the customer and can be potential or actual. Effect analysis refers to studying the consequences of those failures. Failures are prioritized according to how serious the consequences are, how frequently they occur, and how easily they can be detected. The purpose of the FMEA is to take actions to eliminate or reduce failures, starting with the highest priority ones. When to use FMEA. When a process, product or service is being designed or redesigned after quality function deployment. When an existing process product or services being applied new way. When improvement goals are planned for an existing process, product or service. When analyzing failures of an existing process, product or service. Also periodically throughout the life of the process, product or service. How do you perform an FMEA? Well, the tool varies a lot across the industries and scope. Here are some of the common high process steps. And please be aware that this is a simplified version. Number one, identify the functions of your scope. Asks, what is the purpose of this system, design process, or service? What do our customers are expected to do? Step number two, for each function, identify all the ways failure could happen. These are potential failure modes. Number three. For each failure mode, identify all the consequences on the output. Ask, what does the customer experience because of this failure? What happens when this failure occurs? That number four, determine how serious each effect is. This is the severity rating, or severity is usually rated on a scale from one to ten, where one is the minimum and the maximum. Number five for each failure mode determined all the potential root causes and list them on the FMEA form. And number six, for each cause, determined dy o Occurrence rating or oh, this rating estimates the probability of failure occurring for that reason during the lifetime of your scope. Now, you have captured on the basic FMI form, the failure modes, the effect analysis, also the severity and occurrence. And how do you perform step number five, determine the root causes. Let's move on to the next lecture and see. 4. Understand the four Root-Cause Analysis tools: Let's now explore together root cause analysis. Please check out this video lesson and the bonus material from the class project. There are four root cause analysis tools that we will cover. Let's jump in and see them in action. Five whys, process mapping, Force Field Analysis, and metrics charts. Five Whys analysis to result in fishbone diagrams. These are diagrams created by cower Ishikawa that showed the potential causes of a specific event. The most common use is to perform a deep dive and identify potential factors causing an overall effect. Each cause or reason for imperfection is a source of variation. Causes are usually grouped into, causes are usually grouped in two major categories. To identify and classify this sources of variation. Here I am sharing the fishbone diagram for a reorder greenbelt project. As you can see, the causes are grouped into these major categories. People, process, measurement systems, tools, other causes lead to the head of the fish, increased operational anaphoric. In our case, where we showed the problem statement. To perform these analyses, you start to identify the first cause of the problem. And from that answer is ask the team, why is this happening? And gradually asking, why? What is the root cause? Up to five times, you get to the real bottom of the issue. And we'll cover this diagram in more detail in the course section that covers the overview of the Lean Six Sigma real projects process mapping. Process mapping can be done using the flow chart. Flow chart is a type of diagram that represents a workflow of a process. Flow chart can also be defined as a diagrammatic representation of an algorithm step-by-step approach to solving the task. The flowchart shows the steps as boxes of various kinds and their order by connecting the boxes with arrows. Flow chart are used in analyzing, designing, documenting, or managing the process or programming multiple fields. Here I am sharing the flowchart from the Green Belt certification project. There are a couple of enhancements that are visible here and that you can also operate on a simple flowchart. First, you can split the teams who are involved in operating the process and to a different swimlanes, making it easy to see where the handovers are happening. Second, you can add the time spending operating each step. Make-up potential productivity improved one more targeted. Please check out the article from the class project to learn more about the remaining two root cause analysis tools. 5. Master the difference between Common and Special Cause variation: Let's now explore together common and special cause variation. Some degree of variation will naturally occur in any process. Common Cause Variation is the natural or expected variation in the process. Special cause variation is unexpected variation that results from unusual occurrences. It is important to identify and tried to eliminate special cause variation. In other words, common causes, also called natural patterns, are the usual historical quantifiable vibration system. While special causes are unusual, not previously observed, non-quantifiable variation, contrary, charts provide good illustrations of process capability or instability. A process must be stable before its capability is assessed or improvements are initiated. A process is stable if it does not contain any special cause variation. Only common cause variation is present. Out-of-control points and non-random patterns on a control chart indicate the presence of special cause variation. While it's important to avoid special cause variation, trying to eliminate all the common cause variation can make matters worse. It is called over correction of a process and this must be avoided. Let's now have a look together at some real examples of control charts and see if you can tell if it's common cause or special cause variation. Alright, so can you tell what this control chart indicate? First of all, you can see these lines. So let me explain to you the green one is the mean. Then you have the red line below and the red line above. Lcl and UCL. Ucl stands for upper control limit and LCL stands for a lower control limit. As you can see, there is no data point above the UCL and there is no data point also below the LCL. Also there is no trend of points below or above the mean. So if you said that this control chart indicates common cause variation, then you are right. Let's see the next example. It's basically the same chart. But now we have one data point outside the upper control limit. So can you say if this indicates common cause or special cause variation? Of course, it's special cause variation. Let's take one more example. Have a deeper look at this 1. First of all, you cannot notice any data points above the upper control limit or below the control limits. So this is the dotted line is the upper control limit and the dotted line below the lower control limit. However, there is a trend of data points more than 67 that are below the mean. So did this also indicates special cause variation? Now it's very important that before going and assessing the capability of the process and doing the improvements that you address. This special cause variation you need to investigate, first, discovered the root causes, and fix this special cause variation in your process have only common cause variation, and then only then improve the process and go forward. Alright, so this was special cause variation. One more example. And here actually we have three control charts, or maybe one control charts and three phases before improvement, transition and afterward improvement and the before improvement control chart we already saw in the first example. So this one indicates common cause variation than if you have a look at the transition and the after improvement. Can you tell us what they indicate? Alright, so it's common variation because we don't have any data points above or below the, the control limits, both of the cases. And also we don't have any big trends just here, maybe a couple of four data points. But you can see then we have also data points above the mean. And here also for your four data points, but then we also have data points above the mean and the final one is below the mean. All right, do you want to test your knowledge now with one more example. 6. Perform Correlation analysis: In this lecture, I will cover correlation. I'm gonna share with you a real-world example of a correlation analysis. What is correlation? Correlation is a technique for investigating the relationship between two quantitative continuous variables. Correlation is the degree of the relationship between two variables. And this can be in two ways. If the value of one variable increases, when the value of the other also increases, they can be positively correlated. If the value of one variable decreases, when the value of the other variable is increasing, they can be negatively correlated. However, if one variable does not affect the other, the archives that are not to be correlated. I am now sharing with you my correlation analysis done in a real greenbelt project. The objective is to give you a high overview of correlation and the tool used for that. On the other hand, we will cover more in this section of a represent the greenbelt project. Now we are using a scatterplot to test the correlation between two numerical and continuous metrics in the project that had two objectives. The first one was to identify the root causes and make improvements so that I can reduce the number of defects and wrong outputs in the system which led to overcompensation, overpaying, basically the distributors. And the second objective of the project was to reduce the time spent in operating the process almost by four times. In this correlation analysis, I am testing if there is a correlation between the two metrics or between improving One of the metric and having a positive effect on the other. In other words, I wanted to see if I would be able to improve the time spent an operation I reduce it. Will that also reduced the overcompensation? If I find a correlation that in that way I can use the process improvement ones just for one of the objectives or the secondary one, which was the time spent in operating the process to positively influence and reduce the overcompensation amount. In my case, I have tested using a scatter point, which is the primary charges for correlation testing. And in my case, as you can see, there is no obvious correlation. The data points are randomly distributed. So you cannot see are positive or non-negative. Visible correlation, let's say also the further analysis done in Minitab and for example, the correlation, Pearson correlation of time spent on or compensation which were the two lead truth that I had, you see has a low value minus 0 dot 221. The outcome was that I needed to tackling the root causes for both of dielectrics in order to achieve the success of the project. If you find the correlation patterning to analyze data and thus visible on the chart. It doesn't necessarily mean that the absurd lead tricks are correlated in the sense of causation, further investigation is necessary to prove causation. A famous example of correlation, but not causation, can be found with ice cream and murder. That is, the rates of violent crime and murder have been known to jump. When ice cream sales do. However, buying ice cream doesn't turn anybody into a killer, right? In a nutshell, correlation doesn't necessarily mean causation. 7. Congratulations! Thank you.: All right, congratulations on completing this course. For free to re-watch some of the lessons for better results. I'm confident that this knowledge will help you tremendously in your career. If you liked this course, then feel free to continue your Lean Six Sigma training with my next material on this topic. Thanks so much for watching, and I'll see you in the next class.