The Analyze phase of the DMAIC (Define, Measure, Analyze, Improve, Control) methodology is an important step in the Lean Six Sigma process. It is the stage at gathered data is analysed in order to comprehend the current state of a process and identify areas for improvement. The primary goal of this phase is to identify the underlying cause of the problem and understand how it affects the process. Understanding the underlying cause allows organisations to develop and implement effective solutions to improve the process.
Statistical process control, process mapping, and cause and effect diagrams are used in this phase to analyse the data and identify the root cause of the problem. As a Lean Six Sigma Yellow Belt, it is critical to understand the Analyze phase because it serves as the foundation for the subsequent DMAIC phases. This training course will teach you how to conduct a thorough process analysis, identify the root cause of problems, and make data-driven decisions to improve the process.
Root Cause Analysis (RCA) is a critical component of the Analyze phase of Lean Six Sigma’s DMAIC (Define, Measure, Analyze, Improve, Control) methodology. RCA’s primary goal is to identify the underlying cause of a problem or issue rather than simply treating the symptoms. Understanding the root cause allows organisations to implement effective and long-term solutions to improve processes and boost efficiency.
The purpose of this section of the course is to introduce the concepts and tools of RCA to support the Lean Six Sigma Yellow Belt methodology application. It will give you a thorough understanding of the significance of RCA in process improvement, as well as the various techniques and tools for identifying and analysing root causes. Participants will leave this course with the knowledge and skills needed to conduct a successful RCA and contribute to process improvement initiatives in their organisation.
Root Cause Analysis (RCA) is a technique for determining the root cause of a problem or issue. RCA seeks to understand the source of the problem rather than simply treating the symptoms. Organizations can implement effective and sustainable solutions that address the underlying issue rather than just treating the symptoms by identifying the root cause.
This can lead to improved processes, increased efficiency, and cost savings. Quality control, safety incidents, production problems, and customer complaints are all examples of where RCA can be used. RCA is used in a wide range of industries, including healthcare, manufacturing, service, and construction.It is a critical tool for process improvement and is frequently used in conjunction with other methodologies such as Lean Six Sigma, Total Quality Management, and ISO 9000.
RCA is a critical problem-solving technique that is especially popular among organisations that are new to implementing Lean Six Sigma. Because it is a logical, structured approach that all members of the organisation can easily understand, RCA is frequently one of the first tools adopted by organisations.
There are several reasons why Root Cause Analysis is an important approach to solving problems in the workplace, such as:
Improves processes: By determining the root cause of a problem, RCA allows organisations to develop and implement effective and long-term solutions that improve processes and increase efficiency.
Reduces costs: Addressing the root cause of a problem can prevent it from recurring, saving the organisation time and money.
Increases safety: RCA can be used to determine the root cause of safety incidents, allowing organisations to take corrective action to avoid similar incidents in the future.
Improves customer satisfaction: RCA can be used to identify the source of customer complaints, allowing businesses to take action to improve the customer experience.
Compliance: RCA can be used to determine the root cause of non-compliance issues and then implement corrective action to bring the process back into compliance.
Continuous improvement: RCA is a key tool in continuous improvement efforts as it helps organisations to understand the current state of a process and identify areas for improvement.
Data-driven decision making: Organizations can make data-driven decisions to improve processes and achieve their goals by identifying the root cause of a problem.
Overall, RCA is an important tool for process improvement because it can assist organisations in identifying and eliminating issues that are affecting their operations, allowing them to improve their performance and achieve their objectives.
There are several tools and techniques that can be used to conduct an RCA, each with their own strengths and weaknesses. We will go over some of the most common RCA tools and techniques, such as the Fishbone Diagram (Ishikawa Diagram or Cause and Effect Diagram), Pareto Chart, Scatter Plot, 5 Whys, Flowcharting, Fault Tree Analysis, Failure Modes and Effects Analysis (FMEA), and Statistical Process Control (SPC).
Each of these tools and techniques can be used to identify various root causes and provide valuable insights into the issue at hand. Other tools and techniques may be used to complete a comprehensive Root Cause Analysis, depending on the context, industry, and specific problem, these tools are also in the context of a Lean Six Sigma Yellow belt course, and you would learn more about Root Cause Analysis on a Lean Six Sigma Green Belt.
A Fishbone Diagram (also known as an Ishikawa Diagram or a Cause and Effect Diagram) is a tool for identifying potential causes of a problem. It is also known as an Ishikawa diagram or a cause and effect diagram. It is a graphical representation that helps in the organisation and identification of various factors that may be contributing to a problem.
The diagram is in the shape of a fish skeleton, with the problem or effect written at the head and potential causes branching off the spine. The various branches represent various categories of causes, such as equipment, people, processes, and the environment.
The Fishbone diagram is an effective tool for breaking down a complex problem into smaller, more manageable chunks. It helps in the organisation of data and the identification of various factors that may be contributing to the problem. It is also an excellent tool for team brainstorming and gathering feedback. It can be applied to any industry or problem.
To create a Fishbone diagram, first define the problem and write it at the fish’s head. Then, brainstorm the various cause categories, such as people, process, equipment, and environment, and write them as branches off the spine. Finally, you must brainstorm and write down specific causes that fall into each category.
Performing a Fishbone Root Cause Analysis (RCA) consists of several steps:
Define the issue: Define the problem you’re attempting to solve. This statement should be specific and measurable.
Identify the various causes: Identify the various causes that may be contributing to the problem. People, equipment, process, environment, materials, method, management, and measurement are all common categories.
Specific causes: For each cause category, brainstorm specific causes that fall under each category.
Make a Fishbone diagram: Make a Fishbone diagram of the specific causes. Write the problem or effect at the top of the fish, and the various causes as branches off the spine. Sub-branches are written for specific causes that fall into each category.
Identify the most likely cause(s): Use the Fishbone diagram to identify the problem’s most likely cause or causes.
Create a plan of action: Create a plan of action to address the problem based on the identified causes. This can include taking corrective actions, changing processes, or putting in place a new system or procedure.
Put the plan into action: Put the action plan into action and track the results.
Monitor and improve on a regular basis: Keep an eye on the process and look for any ways to improve.
Note: Sometimes a reason may seem like it fits in more than one category, in this instance, place it in the most relvant category.
The 5 Whys technique is a simple and efficient problem-solving method that can be applied in a wide range of industries and settings. It is especially useful in determining the root cause of complex and multifaceted problems because it helps to break the problem down into smaller, more manageable parts.
The key to the 5 Whys technique is to keep asking “why” until you find the root cause of the problem. It is critical to avoid making assumptions or jumping to conclusions, as this can lead to incomplete or inaccurate information. You can gain a deep understanding of the problem and identify the root cause by continuing to ask “why?” and drilling down to the underlying causes.
5 Whys is an extremely useful tool for organisations looking to improve their processes and increase efficiency, reduce costs, increase safety and customer satisfaction, compliance, and as a key tool in continuous improvement efforts. It promotes a problem-solving culture and can be used to identify and address issues before they become major issues. To gain a more comprehensive understanding of a problem, the 5 Whys technique can be used in conjunction with other problem-solving tools such as the Fishbone diagram or Flowcharting.
5 Whys is a very popular Root cause analysis tool in organisations for a varity of reasons such as:
Easy to understand and use: One of the main benefits of the 5 Whys technique is that it is easy to understand and use. The process is simple and does not necessarily require any special training or equipment. It can be used by individuals or groups, and it is an excellent tool for brainstorming and gathering team input. This makes it a useful and accessible tool for problem solving in a variety of contexts.
Encourages critical thinking: The 5 Whys technique encourages critical thinking by repeatedly asking “why,” which helps to surface underlying issues that are not immediately apparent. It encourages people to look beyond the surface of a problem and investigate deeper causes. This can lead to a better understanding of a problem and can assist in identifying and addressing issues before they become major issues.
Teamwork: Repeatedly asking “why” encourages collaboration and the exchange of ideas, which can lead to better problem-solving. It is an excellent tool for team brainstorming and gathering feedback. Different perspectives and expertise can be brought to the table by involving multiple people in the problem-solving process, leading to more comprehensive and effective solutions.
Continuous improvement: The 5 Whys technique is an excellent tool for organisations looking to improve their processes and increase efficiency, reduce costs, increase safety and customer satisfaction, and ensure compliance. It is also a key tool in continuous improvement efforts. It promotes a problem-solving culture and can be used to identify and address issues before they become major issues. It also assists organisations in determining areas for improvement and making data-driven decisions. It can also be combined with other problem-solving tools, such as the Fishbone diagram or Flowcharting, to gain a more comprehensive understanding of a problem and develop an effective plan of action.
Last but not least, Identifying the root cause: The goal of the 5 Whys technique is to repeatedly ask “why” to get to the bottom of a problem. Rather than addressing symptoms, this aids in determining the root cause of the problem. Organizations can develop effective and sustainable solutions that improve processes and increase efficiency, reduce costs, increase safety and customer satisfaction, compliance, and serve as a key tool in continuous improvement efforts by understanding the root cause. This is an important step in problem solving because it ensures that the correct problem is addressed and that the solution is long-term effective.
While conducting a 5 Whys analysis is a simple process, it’s important to remember that it’s not a one-size-fits-all solution. The key to successfully conducting a 5 Whys analysis is to be thorough and systematic in your approach.
Define the issue: Define the problem you’re attempting to solve. This statement should be specific and measurable. Ensure that the problem is well-defined and understood by all team members.
Collect data: Gather information about the problem. This includes things like observations, interviews, and measurements. It is critical to collect data from various sources to ensure that you have a complete understanding of the problem.
Begin with “why”: Begin by asking “why” the problem is occurring. Make a note of the answer to the first “why” question. When asking “why,” it is critical to be specific and avoid making assumptions or guesses.
Continue to ask “why”: Continue to ask “why” based on the answer to the previous “why” question until you have asked “why” five times. It is critical to be patient and persistent in asking “why” until you find the source of the problem.
Identify the root cause: The fifth “why” should reveal the problem’s root cause. When identifying the root cause, it is critical to be critical and ensure that the root cause identified is the actual cause of the problem.
Create a plan of action: Create a plan of action to address the problem based on the identified root cause. This can include taking corrective actions, changing processes, or putting in place a new system or procedure. Make certain that the action plan is specific, measurable, achievable, relevant, and time-bound.
Put the plan into action: Put the action plan into action and track the results.
Here’s an example of 5 Whys being applied to a problem in a manufacturing company:
Problem: The production line is experiencing frequent downtime
Analysis:
Root Cause: The company has not provided proper training for the maintenance team.
Plan of Action:
Implementation:
By using 5 Whys the company was able to identify the root cause of the problem, which was a lack of proper training for the maintenance team, and develop a plan of action to address it, which improved production and reduced downtime.
A Pareto chart is a graphical representation tool invented in the early twentieth century by Vilfredo Pareto, an Italian economist. Pareto discovered that 80% of the effects result from 20% of the causes, which became known as the Pareto principle. Pareto charts are used to identify and prioritise the most important factors influencing a problem or effect. It combines a bar chart and a line graph, with the bars representing the various factors and the line representing the cumulative total.
Pareto charts are used to identify the “vital few” factors that cause the majority of a problem and prioritise them for further investigation and improvement. For example in the picture above, the vital few factors are Turntable, blocker alignment and foil dispenser. They are frequently used in quality control, process improvement, and project management to identify and address the most serious issues. It’s a useful tool for visualising data distribution and identifying areas for improvement.
The key components of a Pareto chart are:
Using a pareto to analyze data and identify the most common root causes of a problem is a fairly simple process, especially if you have software such as excel.
2. List the categories in the first column
3. Input the frequency into the second column
4. Calculate the cumulative frequency in the third column, row one being =B2, row two being =B2+B3, row three being =B3+B4 and so on.
You should have something like the one below with the number increasing.
5. In the percent column calculate the percent of each category against the total. E.g. =B2/C14. This formula should be the frequency number divided by the total number from the cumulative frequency.
6. Now select the Categories and percent column and click insert, Recommended Charts, you should then have a screen like below, select Pareto and click Ok.
If you had any difficulties following these steps, don’t worry we got you covered with a free template of a Pareto in Excel below. However, it is useful to understand how these are created.
Download: Pareto Chart Template
Above is an example of a Pareto chart usef for Root cause analysis to help understand the main causes of customer complaints. Data was collected on the last six months to data of customer complaints to understand what the reasons for complaints where.
The data was then analyzed with a pareto chart which showed that 51% of complaints where due to customer service not being helpful and 32% due to customer waiting too long on hold.
Therefore 80% of the problem was cause by these two cateogories which need to be addressed with actions
Actions
Problem: Increasing number of customer complaints in relation to the customer service line.
Analysis:
Example of Pareto chart for customer complaints
The data was then analyzed with a pareto chart which showed that 51% of complaints where due to customer service not being helpful and 32% due to customer waiting too long on hold.
Root Cause: The Root cause for 80% of complaints is due to customer service not being helpful and customers waiting too long on hold.
Plan of Action:
By using the Pareto chart the company was able to identify the main causes of the problem, which was a poor customer service knowledge and long wait times on hold, and develop a plan of action to address it, which improved customer satisfaction and reduced customer complaints.
A scatter plot, also known as a scatter diagram or a scatter chart, is a type of plot that is used to show the relationship between two variables. It’s a graphical representation of two sets of data, with one variable on the x-axis and the other on the y-axis. Each scatter plot point represents a pair of (x,y) values that are used to plot the point on the graph. Scatter plots can be used for a variety of purposes, including data visualisation, trend analysis, and statistical modelling, and are useful for identifying correlations or patterns between two variables.
Scatter plots are especially useful for identifying outliers in data, which are data points that deviate significantly from the pattern of the rest of the data. Scatter plots can also be used to identify linear and nonlinear relationships between variables, as well as clusters or groups of data points.
Scatter plots are widely used in a variety of disciplines, including statistics, engineering, economics, and science. They can be made with software such as Microsoft Excel, R, Python, and MATLAB. Scatter plots are a powerful tool for examining the relationship between two variables, and they aid in understanding the relationship between variables, which is critical for making decisions and predictions.
When you want to investigate the relationship between two variables, you use a scatter plot. It’s especially useful when you want to:
Identify patterns: Scatter plots can be used to identify patterns and trends in data, such as linear or non-linear relationships, clusters, or groups of data points.
Investigate correlation: Scatter plots can be used to examine the relationship between two variables. A positive correlation indicates that as one variable increases, so does the other. A negative correlation means that as one variable rises, the other falls. A scatter plot can also be used to detect a lack of correlation between two variables.
Identify outliers: Scatter plots can be used to identify outliers, which are data points that fall far outside the pattern of the rest of the data.
Compare data groups: Scatter plots can be used to compare data sets. A scatter plot, for example, can be used to compare the relationship between two variables for different types of data.
Data visualisation: Scatter plots are a useful tool for data visualisation and can be used to present data in a clear and easy-to-understand format.
Statistical modelling: Scatter plots can be used as a tool for statistical modelling, helping identify the relationship between variables and predicting outcomes.
The line of best fit, also known as the trend line or regression line, is a line that is used in a scatter plot to represent the general pattern or trend of the data. The line of best fit is usually straight, but it can be curved if the data shows a non-linear relationship. Finding the line that minimises the difference between the data points and the line yields the best fit line. The least-squares method is used to accomplish this by minimising the sum of the squared differences between the data points and the line.
The best fit line can be useful in several ways:
Identifying correlation: A line of best fit can be used to identify the strength and direction of a correlation between two variables. A positive correlation has a best fit line that slopes upward from left to right, while a negative correlation has a best fit line that slopes downward from left to right.
Making predictions: A line of best fit can be used to forecast the value of one variable based on the value of another.
Missing data estimation: A line of best fit can be used to estimate missing data.
Identifying outliers: A line of best fit can be used to identify outliers, which are data points that fall far outside the pattern of the rest of the data.
Finding underlying patterns: A line of best fit can be used to find underlying patterns in data, such as linear or non-linear relationships.
It’s important to note that a line of best fit should not be used to make predictions outside of the data’s range or to extend trends indefinitely, because the line may not accurately represent the data’s pattern in those areas.
Content
A scatter plot can be used to identify the relationship between the number of defects in a manufacturing process and the temperature of the machinery used in the process as an example of root cause analysis (RCA).
Gather information: For each batch of products, collect data on the number of defects per batch and the temperature of the machinery.
Organize the data: Make a table with the number of defects on the y-axis and the temperature of the machinery on the x-axis.
Create the scatter plot: Using software such as Microsoft Excel, plot the data points on the graph to create a scatter plot.
Label the axes as follows: The x-axis should be labelled “Temperature (°C)” and the y-axis should be labelled “Number of Defects.”
Analyze the scatter plot: Examine the scatter plot for patterns and relationships in the data. Outliers, clusters or groups of data points, and linear or non-linear relationships should all be identified.
Draw a line of best fit: On the scatter plot, draw a line of best fit to represent the overall pattern of the data.
Interpret the scatter plot: Interpret the relationship between the number of defects and the temperature of the machinery based on the shape of the scatter plot and the line of best fit. The best-fit line demonstrates a positive relationship between the number of defects and the temperature of the system.
Conclusion: Based on the relationship between the number of defects and the temperature of the machinery, it is possible to conclude that the temperature of the machinery is a contributing factor to the number of defects in the manufacturing process.
Develop a plan of action: Develop a plan of action to address the problem of defects, such as implementing a cooling system for the machinery, to keep the temperature within a certain range.
Implement the strategy and keep a close eye on it: Put the action plan into action and monitor the results for ways to improve.
To summarise, part one the DMAIC Analyze phase is a critical step in the Lean Six Sigma process. It enables businesses to assess the current state of a process and identify areas for improvement. Root Cause Analysis (RCA) is an important part of this phase because it assists organisations in understanding the underlying cause of a problem and developing effective solutions to improve processes and increase efficiency. Fishbone diagrams, Pareto charts, Scatter plots, 5 Whys, Understanding and putting these tools and techniques to use is critical for anyone looking to improve processes and increase efficiency in their organisation.
Now we have covered the first part of Root cause analysis, the next section will cover part two focusing on Fault Tree Analysis, Failure modes and effect analysis (FMEA) and Statistical process control (SPC) for analysis in DMAIC.
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