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The 7 Quality Control Tools: A Comprehensive Guide for Quality Excellence

July 8th, 2024

Quality proves pivotal for organizational endurance and success. Whether a seasoned quality guiding hand or a newcomer to the field, the 7 Quality Control tools stand as treasured companions to advance one’s abilities.

Esteemed quality pioneer Kaoru Ishikawa unveiled these 7 tools amid Japan’s post-war awakening, fashioning statistical quality principles accessible for all experiences and enabling company-wide effectiveness.

Graphical techniques help pinpoint, unravel, and solve quality matters, the 7 tools offer structured, evidence-guided approaches for problem-solving, process refinement, and decision-making.

Teams thus steer confidently by comprehension over assumption or intuition.

For quality stewards dedicated to performance-boosting and relationship-building through shared knowledge, these classic tools remain trusted aids.

This discussion explores each technique’s nuanced gifts, illuminating their staying power for continual optimization wherever quality matters most.

Key Highlights

• Understand the historical context and significance of the 7 quality control tools, and how they revolutionized Japan’s industrial resurgence after World War II.
• Cause-and-Effect Diagram ( Fishbone/Ishikawa Diagram )
• Check Sheets (Tally Sheets)
• Control Charts (Shewhart Charts)
• Pareto Charts
• Scatter Diagrams
• Stratification (Flowcharts/Run Charts)
• Learn best practices for creating, interpreting, and effectively using each of these tools, with step-by-step guidance and industry-proven techniques.
• Explore case studies and success stories that showcase the powerful impact of integrating the 7 quality control tools.
• Discover strategies for seamlessly incorporating these tools into your organization’s problem-solving and continuous improvement efforts, fostering a data-driven culture of excellence.
• Gain insights into the future of quality control tools in the digital age and how they can be adapted to meet the evolving needs of modern businesses.

Introduction to the 7 Quality Control Tools

Where quality is the cornerstone of success, the 7 quality control tools stand as indispensable allies for organizations seeking to achieve and sustain excellence.

These tools, collectively known as the 7 QC tools, are a set of graphical techniques designed to simplify the intricate concepts of statistical quality control, making them accessible to professionals across various industries and backgrounds.

Definition and overview of the 7 quality control tools

The 7 quality control tools encompass a comprehensive suite of techniques that empower organizations to identify, analyze, and solve quality-related issues with precision and efficiency.

Each tool serves a specific purpose, providing a structured and data-driven approach to problem-solving, process improvement , and decision-making, enabling teams to make informed choices based on evidence rather than guesswork or intuition.

Historical background and importance

The origins of the 7 quality control tools can be traced back to the post-war era in Japan, where the esteemed Kaoru Ishikawa, a pioneer in the field of quality management , recognized the need to simplify the complex concepts of statistical quality control.

During this pivotal period, Japanese organizations were focused on training their workforce in these advanced techniques but soon realized that the inherent complexity could intimidate and deter many workers from embracing these methodologies effectively.

Ishikawa’s visionary solution was to introduce the 7 quality control tools, which distilled the essence of statistical quality control into a set of user-friendly, graphical techniques.

Benefits of using the 7 quality control tools

The adoption of the 7 quality control tools offers numerous benefits to organizations committed to continuous improvement and customer satisfaction.

These tools facilitate:

Effective Problem-Solving: By providing a structured framework for identifying root causes , analyzing data, and visualizing relationships, the 7 QC tools equip teams with the necessary insights to address quality issues effectively.

Process Improvement: Through data-driven analysis and monitoring, these tools enable organizations to identify areas for improvement, streamline processes, and eliminate inefficiencies, ultimately enhancing productivity and reducing waste.

Data-driven Decision Making: The 7 quality control tools empower teams to base their decisions on objective data and statistical analysis, minimizing the risk of biases or unfounded assumptions, and leading to more informed and effective decision-making processes.

The 7 Quality Control Tools Explained

1. cause-and-effect diagram (fishbone diagram).

The Cause-and-Effect Diagram, also known as the Fishbone Diagram or Ishikawa Diagram , is a powerful tool designed to facilitate root cause analysis and identify potential causes contributing to a specific problem or effect.

Named after its creator, Kaoru Ishikawa, this diagram visually represents the relationship between an effect and its potential causes, resembling the skeletal structure of a fish.

The primary purpose of the Cause-and-Effect Diagram is to systematically explore and organize the various factors that could potentially contribute to a particular issue or outcome.

How to create and use a cause-and-effect diagram

Creating an effective Cause-and-Effect Diagram involves the following steps:

• Define the problem or effect: Clearly state the issue or outcome you wish to analyze, which will be represented as the “fish head” on the diagram.
• Identify the main cause categories: Determine the primary categories or broad areas that could potentially contribute to the problem, such as materials, methods, machinery, environment, or personnel. These categories will form the “bones” or main branches of the fishbone diagram .
• Brainstorm potential causes: For each main category, engage in a structured brainstorming session to identify specific potential causes or contributing factors. These sub-causes will be represented as smaller “bones” branching off from the main categories.
• Analyze and prioritize causes: Once all potential causes have been identified, analyze the diagram to determine which causes are most likely to be contributing to the problem. Prioritize these causes based on their perceived impact or likelihood of occurrence.
• Develop and implement countermeasures: Based on the prioritized causes, develop and implement targeted countermeasures or corrective actions to address the root causes and mitigate the problem effectively.

2. Check Sheets (Tally Sheets)

Check sheets, also known as tally sheets , are straightforward yet powerful tools designed to facilitate the systematic collection and organization of data related to quality issues, defects, or process performance.

These sheets serve as a structured means of recording and tabulating data, enabling organizations to identify patterns, trends, and areas for improvement.

The primary purpose of check sheets is to streamline the process of data collection and analysis, allowing teams to gather quantitative or qualitative information consistently and efficiently.

Types of check sheets

Check sheets can be categorized into three main types, each serving a specific purpose:

• Defect Location Check Sheets: These sheets are designed to record the location or specific area where a defect or issue occurred, providing valuable insights into potential problem areas or hotspots within a process.
• Tally Check Sheets: As the name implies, tally check sheets are used to record the frequency or occurrences of specific events, defects, or phenomena. These sheets typically feature a simple tally or check mark system, making it easy to quickly capture and quantify data.
• Defect Cause Check Sheets: These sheets are particularly useful for identifying and categorizing the potential causes or contributing factors associated with observed defects or issues. By capturing this information, organizations can gain valuable insights into the root causes underlying quality problems.

How to create and use check sheets

Creating and utilizing check sheets involves the following steps:

• Identify the data to be collected: Determine the specific information or metrics that need to be captured, such as defect types, locations, frequencies, or potential causes.
• Design the check sheet: Based on the identified data requirements, create a structured check sheet with appropriate columns or sections for recording the relevant information. Ensure that the sheet is user-friendly and easy to understand for those responsible for data collection.
• Train data collectors: Provide clear instructions and training to the individuals responsible for collecting data, ensuring they understand the purpose of the check sheet and the proper methods for recording information.
• Collect data: Implement the check sheet in the relevant areas or processes, and consistently record data as it becomes available or as events occur.
• Analyze and interpret data: Once sufficient data has been collected, analyze the check sheet for patterns, trends, or areas of concern. Use the information gathered to identify opportunities for improvement or further investigation.

3. Control Chart (Shewhart Chart)

Control charts, also known as Shewhart charts, are powerful statistical tools used for monitoring and analyzing process performance over time.

Named after Walter A. Shewhart, a pioneer in the field of statistical quality control, these charts are designed to help organizations determine whether a process is stable and predictable, or if it is subject to undesirable variations that require intervention.

The primary purpose of control charts is to enable organizations to practice statistical process control (SPC) , which involves monitoring and controlling a process to ensure that it operates within predetermined statistical limits.

Components of a control charts

A typical control chart consists of the following key components:

• Control Limits
• Center Line (Mean)
• Data Points

How to create and interpret control charts

Creating and interpreting control charts involves the following steps:

• Collect data: Gather relevant data on the process characteristic or quality metric you wish to monitor, ensuring that the data is representative and collected under stable conditions.
• Calculate control limits and center line: Using statistical methods (e.g., X-bar and R charts , individuals, and moving range charts ), calculate the upper and lower control limits, as well as the center line (mean) for the process characteristic.
• Plot data points: Plot the collected data points or subgroup averages on the control chart, positioning them relative to the control limits and center line.
• Interpret patterns and signals: Analyze the control chart for patterns or signals that indicate potential issues or variations in the process . Common signals include points outside the control limits , runs above or below the center line, or unusual patterns or trends.
• Investigate and take action: When signals or patterns indicate a potential issue, investigate the root causes and take appropriate corrective actions to bring the process back within control limits and ensure consistent performance.

4. Histogram

A histogram is a powerful data visualization tool that graphically represents the frequency distribution of a set of data.

It is a type of bar chart that displays the number of occurrences or observations within specific ranges or intervals, providing a clear visual representation of how data is distributed.

How to create and interpret histograms

Creating and interpreting histograms involves the following steps:

• Collect data: Gather the relevant data that you wish to analyze and visualize.
• Determine bin ranges: Divide the range of data into intervals or “bins” of equal width, ensuring that each data point falls into one of the defined bins.
• Calculate frequencies: Count the number of data points that fall into each bin, representing the frequency of occurrences within that range.
• Construct the histogram: Plot the bins on the horizontal axis and the corresponding frequencies on the vertical axis, creating a bar for each bin with a height proportional to its frequency.
• Analyze the distribution: Interpret the shape, center, and spread of the distribution by observing the patterns and characteristics displayed in the histogram, such as skewness, modality, and outliers.

5. Pareto Chart

The Pareto chart, named after the Italian economist Vilfredo Pareto, is a powerful tool that helps organizations prioritize issues or factors based on their relative importance or impact.

It is based on the Pareto principle, also known as the 80/20 rule , which suggests that a majority of consequences (typically around 80%) are often influenced by a minority of causes (approximately 20%).

How to create and interpret Pareto charts

Creating and interpreting Pareto charts involves the following steps:

• Collect data: Gather data on the various factors or issues you wish to analyze, such as defect types, causes of customer complaints, or sources of waste.
• Categorize and rank data: Categorize the data into logical groups or factors, and rank them in descending order based on their frequency, impact, or importance.
• Construct the Pareto chart: On the left vertical axis, plot the frequency or impact of each factor using bars, arranged in descending order from left to right. On the right vertical axis, plot the cumulative percentage represented by a line graph.
• Identify the “vital few”: Analyze the chart to identify the factors or issues that contribute to a significant portion of the overall problem or outcome, typically around 80% or more. These are considered the “vital few” that should be prioritized.
• Prioritize and take action: Based on the identified vital few factors, prioritize and implement targeted improvement efforts or corrective actions to address the most significant contributors to the problem.

6. Scatter Diagram

A scatter diagram, also known as a scatter plot, is a graphical tool used to analyze and visualize the relationship between two variables.

It plots pairs of numerical data, with one variable represented on the horizontal (x) axis and the other variable on the vertical (y) axis, forming a collection of data points.

The primary purpose of a scatter diagram is to identify and understand the nature and strength of the relationship between two variables.

How to create and interpret scatter diagrams

Creating and interpreting scatter diagrams involves the following steps:

• Identify variables: Select the two variables you wish to analyze for potential relationships, typically an independent variable (x-axis) and a dependent variable (y-axis).
• Collect data: Gather pairs of data points representing the values of the two variables.
• Plot data points: On a coordinate plane, plot each pair of data points by representing the independent variable’s value on the x-axis and the dependent variable’s value on the y-axis.
• Positive correlation: Data points form an upward-sloping pattern, indicating that as one variable increases, the other tends to increase as well.
• Negative correlation: Data points form a downward-sloping pattern, indicating that as one variable increases, the other tends to decrease.
• No correlation: Data points are randomly scattered, indicating no apparent relationship between the variables.

7. Stratification (Flowchart, Run Chart)

Stratification, also known as a flowchart or run chart , is a quality control tool used to categorize and visually represent data or process steps in a structured manner.

It involves dividing or grouping data into distinct categories or strata based on specific characteristics or factors, enabling organizations to identify patterns, trends, or potential areas for improvement within each stratum.

The primary purpose of stratification is to enhance process understanding by revealing insights that may be obscured when data is analyzed as a whole.

How to create and use stratification

Creating and using stratification involves the following steps:

• Identify stratification factors: Determine the factors or characteristics that will be used to categorize the data, such as product type, manufacturing shift, supplier, or geographic region.
• Collect and categorize data: Gather relevant data and categorize it based on the identified stratification factors, ensuring that each data point is assigned to the appropriate stratum or category.
• Construct the stratification diagram: Visually represent the categorized data using a flowchart, run chart , or other suitable graphical representation, clearly distinguishing the different strata or categories.
• Analyze within strata: Examine the data within each stratum or category, looking for patterns, trends, or variations that may be specific to that particular group or factor.
• Compare across strata: Compare the patterns and trends observed across different strata to identify potential sources of variation or areas where improvements can be made.
• Implement targeted improvements: Based on the insights gained from the stratification analysis, develop and implement targeted improvement efforts or corrective actions tailored to specific strata or factors.

Integrating the 7 Quality Control Tools

While each of the 7 quality control tools serves a specific purpose, their true power lies in their integrated use for comprehensive problem-solving and process improvement efforts.

By combining the strengths of these tools, organizations can gain a holistic understanding of quality issues, identify root causes , and develop effective solutions.

By integrating the 7 quality control tools into a cohesive problem-solving framework, organizations can leverage their collective power, ensuring a comprehensive and data-driven approach to continuous improvement and quality excellence.

Incorporating the tools into quality management methodologies

The 7 quality control tools have become indispensable components of various quality management methodologies and frameworks, such as Lean, Six Sigma , and Total Quality Management (TQM) .

These methodologies provide structured approaches to quality improvement, and the 7 QC tools serve as essential techniques for data collection, analysis, and decision-making within these frameworks.

For instance, in the Six Sigma methodology, the 7 quality control tools are commonly used throughout the DMAIC (Define, Measure, Analyze, Improve, Control) cycle:

• Define: Flowcharts and cause-and-effect diagrams can be used to define the problem and identify potential root causes.
• Measure: Check sheets and stratification can be employed to collect and categorize data for analysis.
• Analyze: Histograms, Pareto charts, and scatter diagrams can provide insights into process performance, prioritize issues, and identify relationships between variables.
• Improve: Based on the analysis, targeted improvements can be implemented using the insights gained from the various tools.
• Control: Control charts can be used to monitor process performance and ensure sustained improvements.

These 7 quality control tools / companions emerge as invaluable allies across industries.

Born from Kaoru Ishikawa’s pioneering perceptiveness, they prove themselves repeatedly – empowering problem exposure, unraveling, and solving with sureness and efficiency.

Their true gift lies in simplicity and reach. Distilling statistical quality’s complexities insightfully, these graphical friends democratize quality’s pursuit, including diverse talents in continuous progress coordination.

Individual tools interconnect, a toolkit illuminating root causes, prioritizing concerns, and implementing targeted remedies.

Their integration further strengthens quality systems like Lean, Six Sigma , and Total Quality Management .

Whether a guiding veteran, up-and-coming practitioner, or business leader invested in operational excellence , embrace these seven gifts.

Foster opportunity and culture for constantly honing comprehension. Weave their methods wherever quality presides.

Steered thus, organizations stay on course addressing today’s and tomorrow’s challenges, and leadership in quality for decades ahead.

May shared insights propel all committed to thoughtful cooperation, service improvement and relationships uplifted through challenges met together.

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7 Basic Tools of Quality for Process Improvement

Japan is known worldwide for its quality products and services. One of the many reasons for this is its excellent quality management. How did it become so? Japan has Dr. Kaoru Ishikawa to thank for that.

Postwar Japan underwent a major quality revolution. Companies were focused on training their employees in statistical quality control. But soon they realized that the complexity of the subject itself could intimidate most of the workers; so they wanted more basic tools.

Dr. Kaoru Ishikawa, a member of the Japanese Union of Scientists and Engineers (JUSE), took it to his hands to make quality control easier for everyone – even those with little knowledge of statistics – to understand. He introduced the 7 basic tools of quality. They were soon adopted by most companies and became the foundation of Japan’s astonishing industrial resurgence after World War 2.

This post will describe the 7 basic quality tools, how to use them and give you access to templates that you can use right away.

Quality Tools: What Are They?

How can teams and organizations use the 7 basic quality tools, cause and effect diagram, scatter diagram, check sheets.

• Control chart
• Pareto chart

The 7 basic tools of quality, sometimes also referred to as 7 QC tools – represent a fixed set of graphical tools used for troubleshooting issues that are related to quality.

They are called basic quality tools because they can be easily learned by anyone even without any formal training in statistics. Dr. Kaoru Ishikawa played the leading role in the development and advocacy of using the 7 quality tools in organizations for problem-solving and process improvement.  

The 7 basic quality tools include;

• Cause-and-effect diagram
• Scatter diagram
• Check sheet

The 7 quality tools were first emphasized by Kaoru Ishikawa a professor of engineering at the University of Tokyo, who is also known as the father of “Quality Circles” for the role he played in launching Japan’s quality movement in the 1960s. During this time, companies were focused on training their employees in statistical quality control realized that the complexity of the subject could intimidate most of the workers; hence they opted for simpler methods that are easy to learn and use. 7 basic tools of quality were thus incorporated company-wide.

Quality tools are used to collect data, analyze data, identify root causes, and measure results in problem-solving and process improvement. The use of these tools helps people involved easily generate new ideas, solve problems, and do proper planning.

• Structured approach: They provide a systematic approach to problem-solving and process improvement, ensuring that efforts are well-organized and focused.
• Data-driven decision making: The tools enable data collection, analysis, and visualization, empowering teams to make informed decisions based on evidence.
• Improved communication and collaboration: Visual representations and structured tools facilitate effective communication and collaboration among team members, leading to shared understanding and alignment.
• Problem identification and prioritization: The tools help identify and prioritize problems or improvement opportunities, enabling teams to allocate resources efficiently and address critical issues first.
• Continuous improvement: By using these tools, teams can establish a culture of continuous improvement, as they provide a framework for ongoing monitoring, analysis, and refinement of processes.

7 Basic Quality Tools Explained with Templates

The 7 quality tools can be applied across any industry.  They help teams and individuals analyze and interpret the data they gather and derive maximum information from it.

Flowcharts are perhaps the most popular out of the 7 quality tools. This tool is used to visualize the sequence of steps in a process, event, workflow, system, etc. In addition to showing the process as a whole, a flowchart also highlights the relationship between steps and the process boundaries (start and end).

Flowcharts use a standard set of symbols, and it’s important to standardize the use of these symbols so anyone can understand and use them easily. Here’s a roundup of all the key flowchart symbols .

• To build a common understanding of a process.
• To analyze processes and discover areas of issues, inefficiencies, blockers, etc.
• To standardize processes by leading everyone to follow the same steps.

Real-world examples of usage

• Documenting and analyzing the steps involved in a customer order fulfillment process.
• Mapping out the workflow of a software development lifecycle.
• Visualizing the process flow of patient admissions in a hospital.

Enhances process understanding, highlights bottlenecks or inefficiencies, and supports process optimization and standardization efforts.

How to use a flowchart

• Gather a team of employees involved in carrying out the process for analyzing it.
• List down the steps involved in the process from its start to end.
• If you are using an online tool like Creately , you can first write down the process steps and rearrange them later on the canvas as you identify the flow.
• Identify the sequence of steps; when representing the flow with your flowchart, show it from left to write or from top to bottom.
• Connect the shapes with arrows to indicate the flow.

Who can use it?

• Process improvement teams mapping and documenting existing processes for analysis.
• Business analysts or consultants analyzing workflow and process optimization opportunities.
• Software developers or system designers documenting the flow of information or interactions in a system.

To learn more about flowcharts, refer to our Ultimate Flowchart Tutorial .

A histogram is a type of bar chart that visualizes the distribution of numerical data. It groups numbers into ranges and the height of the bar indicates how many fall into each range.

It’s a powerful quality planning and control tool that helps you understand preventive and corrective actions.

• To easily interpret a large amount of data and identify patterns.
• To make predictions of process performance.
• To identify the different causes of a quality problem.
• Analyzing the distribution of call wait times in a call center.
• Assessing the distribution of product weights in a manufacturing process.
• Examining the variation in delivery times for an e-commerce business.

Provides insights into process performance and variation, enabling teams to target areas for improvement and make data-driven decisions.

How to make a histogram

• Collect data for analysis. Record occurrences of specific ranges using a tally chart.
• Analyze the data at hand and split the data into intervals or bins.
• Count how many values fall into each bin.
• On the graph, indicate the frequency of occurrences for each bin with the area (height) of the bar.
• Process engineers or data analysts examining process performance metrics.
• Financial analysts analyzing expenditure patterns or budget variances.
• Supply chain managers assessing supplier performance or delivery times.

Here’s a useful article to learn more about using a histogram for quality improvement in more detail.

This tool is devised by Kaoru Ishikawa himself and is also known as the fishbone diagram (for it’s shaped like the skeleton of a fish) and Ishikawa diagram.

They are used for identifying the various factors (causes) leading to an issue (effect). It ultimately helps discover the root cause of the problem allowing you to find the correct solution effectively.

• Problem-solving; finding root causes of a problem.
• Uncovering the relationships between different causes leading to a problem.
• During group brainstorming sessions to gather different perspectives on the matter.
• Investigating the potential causes of low employee morale or high turnover rates.
• Analyzing the factors contributing to product defects in a manufacturing process.
• Identifying the root causes of customer complaints in a service industry.

Enhances problem-solving by systematically identifying and organizing possible causes, allowing teams to address root causes rather than symptoms.

How to use the cause and effect diagram

• Identify the problem area that needs to be analyzed and write it down at the head of the diagram.
• Identify the main causes of the problem. These are the labels for the main branches of the fishbone diagram. These main categories can include methods, material, machinery, people, policies, procedures, etc.
• Identify plausible sub-causes of the main causes and attach them as sub-branches to the main branches.
• Referring to the diagram you have created, do a deeper investigation of the major and minor causes.
• Once you have identified the root cause, create an action plan outlining your strategy to overcome the problem.
• Cross-functional improvement teams working on complex problems or process improvement projects.
• Quality engineers investigating the root causes of quality issues.
• Product designers or engineers seeking to understand the factors affecting product performance.

The scatter diagram (scatter charts, scatter plots, scattergrams, scatter graphs) is a chart that helps you identify how two variables are related.

The scatter diagram shows the values of the two variables plotted along the two axes of the graph. The pattern of the resulting points will reveal the correlation.  

• To validate the relationship between causes and effects.
• To understand the causes of poor performance.
• To understand the influence of the independent variable over the dependent variable.
• Exploring the relationship between advertising expenditure and sales revenue.
• Analyzing the correlation between employee training hours and performance metrics.
• Investigating the connection between temperature and product quality in a production line.

Helps identify correlations or patterns between variables, facilitating the understanding of cause-and-effect relationships and aiding in decision-making.

How to make a scatter diagram

• Start with collecting data needed for validation. Understand the cause and effect relationship between the two variables.
• Identify dependent and independent variables. The dependent variable plotted along the vertical axis is called the measures parameter. The independent variable plotted along the horizontal axis is called the control parameter.
• Draw the graph based on the collected data. Add horizontal axis and vertical axis name and draw the trend line.
• Based on the trend line, analyze the diagram to understand the correlation which can be categorized as Strong, Moderate and No Relation.  
• Data analysts exploring relationships between variables in research or analytics projects.
• Manufacturing engineers investigating the correlation between process parameters and product quality.
• Sales or marketing teams analyzing the relationship between marketing efforts and sales performance.

Check sheets provide a systematic way to collect, record and present quantitative and qualitative data about quality problems. A check sheet used to collect quantitative data is known as a tally sheet.

It is one of the most popular QC tools and it makes data gathering much simpler.

• To check the shape of the probability distribution of a process
• To quantify defects by type, by location or by cause
• To keep track of the completion of steps in a multistep procedure (as a checklist )
• Tracking the number of defects or errors in a manufacturing process.
• Recording customer complaints or inquiries to identify common issues.
• Monitoring the frequency of equipment breakdowns or maintenance needs.

Provides a structured approach for data collection, making it easier to identify trends, patterns, and areas for improvement.

How to make a checksheet

• Identify the needed information.
• Why do you need to collect the data?
• What type of information should you collect?
• Where should you collect the data from?  
• Who should collect the data?
• When should you collect the data?
• How should you measure the data?
• How much data is essential?

Construct your sheet based on the title, source information and content information (refer to the example below).

Test the sheets. Make sure that all the rows and columns in it are required and relevant and that the sheet is easy to refer to and use. Test it with other collectors and make adjustments based on feedback.

• Quality inspectors or auditors who need to collect data on defects or issues.
• Process operators or technicians responsible for tracking process parameters or measurements.
• Customer service representatives who record customer complaints or inquiries.

Control Chart

The control chart is a type of run chart used to observe and study process variation resulting from a common or special cause over a period of time.

The chart helps measure the variations and visualize it to show whether the change is within an acceptable limit or not. It helps track metrics such as defects, cost per unit, production time, inventory on hand , etc.

Control charts are generally used in manufacturing, process improvement methodologies like Six Sigma and stock trading algorithms.

• To determine whether a process is stable.
• To monitor processes and learn how to improve poor performance.
• To recognize abnormal changes in a process.
• Monitoring the variation in product dimensions during a manufacturing process.
• Tracking the number of customer complaints received per day.
• Monitoring the average response time of a customer support team.

Enables real-time monitoring of process stability, early detection of deviations or abnormalities, and prompt corrective actions to maintain consistent quality.

How to create a control chart

• Gather data on the characteristic of interest.
• Calculate mean and upper/lower control limits.
• Create a graph and plot the collected data.
• Add lines representing the mean and control limits to the graph.
• Look for patterns, trends, or points beyond control limits.
• Determine if the process is in control or out of control.
• Investigate and address causes of out-of-control points.
• Regularly update the chart with new data and analyze for ongoing improvement.
• Production supervisors or operators monitoring process performance on the shop floor.
• Quality control or assurance personnel tracking variation in product quality over time.
• Service managers observing customer satisfaction levels and service performance metrics.

Pareto Chart

The Pareto chart is a combination of a bar graph and a line graph. It helps identify the facts needed to set priorities.

The Pareto chart organizes and presents information in such a way that makes it easier to understand the relative importance of various problems or causes of problems. It comes in the shape of a vertical bar chart and displays the defects in order (from the highest to the lowest) while the line graph shows the cumulative percentage of the defect.

• To identify the relative importance of the causes of a problem.
• To help teams identify the causes that will have the highest impact when solved.
• To easily calculate the impact of a defect on the production.
• Analyzing customer feedback to identify the most common product or service issues.
• Prioritizing improvement efforts based on the frequency of quality incidents.
• Identifying the major causes of delays in project management.

Helps focus improvement efforts on the most significant factors or problems, leading to effective allocation of resources and improved outcomes.

How to create a Pareto chart

• Select the problem for investigation. Also, select a method and time for collecting information. If necessary create a check sheet for recording information.
• Once you have collected the data, go through them and sort them out to calculate the cumulative percentage.
• Draw the graph, bars, cumulative percentage line and add labels (refer to the example below).
• Analyze the chart to identify the vital few problems from the trivial many by using the 80/20 rule . Plan further actions to eliminate the identified defects by finding their root causes.
• Quality managers or improvement teams looking to prioritize improvement initiatives.
• Project managers seeking to identify and address the most critical project risks.
• Sales or marketing teams analyzing customer feedback or product issues.

What’s Your Favorite Out of the 7 Basic Quality Tools?  

You can use these 7 basic quality tools individually or together to effectively investigate processes and identify areas for improvement. According to Ishikawa, it’s important that all employees learn how to use these tools to ensure the achievement of excellent performance throughout the organization.

Got anything to add to our guide? Let us know in the comments section below.

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FAQs about 7 Basic Quality Tools

What is quality control, what are the common quality problems organizations face.

Quality problems in an organization can manifest in various forms and affect different areas of operations.

• Product defects: Products may have defects or non-conformities that deviate from quality specifications, leading to customer dissatisfaction, returns, or warranty claims.
• Service errors: Service errors can occur when services do not meet customer expectations, such as incorrect billing, delays in delivery, or inadequate customer support.
• Process inefficiencies: Inefficient processes can lead to delays, errors, or rework, resulting in increased costs, decreased productivity, and customer dissatisfaction.
• Poor design or innovation: Inadequate product design or lack of innovation can lead to products that do not meet customer needs, lack competitive features, or have usability issues.
• Supplier quality issues: Poor quality materials or components from suppliers can affect the overall quality of the final product or service.
• Ineffective quality management systems: Inadequate quality management systems, such as lack of quality standards, processes, or documentation, can contribute to quality problems throughout the organization.

What are the basic quality improvement steps?

The basic quality improvement steps typically follow a systematic approach to identify, analyze, implement, and monitor improvements in processes or products.

• Clearly articulate the problem or identify the area for improvement.
• Collect relevant data and information related to the problem.
• Analyze the collected data to identify patterns, root causes, and opportunities for improvement.
• Brainstorm and generate potential improvement ideas or solutions.
• Assess the feasibility, impact, and effectiveness of the generated improvement ideas.
• Develop an action plan to implement the chosen solution.
• Continuously monitor and measure the results of the implemented solution.
• Based on the monitoring results, evaluate the effectiveness of the implemented solution.
• Once the improvement is successful, document the new processes, best practices, or standard operating procedures (SOPs).
• Iterate through the steps to continuously improve processes and products.

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Amanda Athuraliya is the communication specialist/content writer at Creately, online diagramming and collaboration tool. She is an avid reader, a budding writer and a passionate researcher who loves to write about all kinds of topics.

What are the 7 basic quality tools, and how can they change your business for the better?

Reading time: about 6 min

What are the 7 basic quality tools?

• Check sheet (tally sheet)
• Cause and effect diagram (fishbone or Ishikawa diagram)
• Stratification
• Pareto chart (80-20 rule)
• Scatter diagram
• Control chart (Shewhart chart)

The ability to identify and resolve quality-related issues quickly and efficiently is essential to anyone working in quality assurance or process improvement. But statistical quality control can quickly get complex and unwieldy for the average person, making training and quality assurance more difficult to scale. 

Thankfully, engineers have discovered that most quality control problems can be solved by following a few key fundamentals. These fundamentals are called the seven basic tools of quality. 

With these basic quality tools in your arsenal, you can easily manage the quality of your product or process, no matter what industry you serve.

Learn about these quality management tools and find templates to start using them quickly.

Where did the quality tools originate?

Kaoru Ishikawa, a Japanese professor of engineering, originally developed the seven quality tools (sometimes called the 7 QC tools) in the 1950s to help workers of various technical backgrounds implement effective quality control measures.

At the time, training programs in statistical quality control were complex and intimidating to workers with non-technical backgrounds. This made it difficult to standardize effective quality control across operations. Companies found that simplifying the training to user-friendly fundamentals—or seven quality tools—ensured better performance at scale

7 quality tools

1. check sheet (or tally sheet).

Check sheets can be used to collect quantitative or qualitative data. When used to collect quantitative data, they can be called a tally sheet. A check sheet collects data in the form of check or tally marks that indicate how many times a particular value has occurred, allowing you to quickly zero in on defects or errors within your process or product, defect patterns, and even causes of specific defects.

With its simple setup and easy-to-read graphics, check sheets make it easy to record preliminary frequency distribution data when measuring out processes. This particular graphic can be used as a preliminary data collection tool when creating histograms, bar graphs, and other quality tools.

2. Cause-and-effect diagram (also known as a fishbone or Ishikawa diagram)

Introduced by Kaoru Ishikawa, the fishbone diagram helps users identify the various factors (or causes) leading to an effect, usually depicted as a problem to be solved. Named for its resemblance to a fishbone, this quality management tool works by defining a quality-related problem on the right-hand side of the diagram, with individual root causes and sub-causes branching off to its left.   

A fishbone diagram’s causes and subcauses are usually grouped into six main groups, including measurements, materials, personnel, environment, methods, and machines. These categories can help you identify the probable source of your problem while keeping your diagram structured and orderly.

3. Stratification

Stratification analysis is a quality assurance tool used to sort data, objects, and people into separate and distinct groups. Separating your data using stratification can help you determine its meaning, revealing patterns that might not otherwise be visible when it’s been lumped together. 

Whether you’re looking at equipment, products, shifts, materials, or even days of the week, stratification analysis lets you make sense of your data before, during, and after its collection.

To get the most out of the stratification process, consider which information about your data’s sources may affect the end results of your data analysis. Make sure to set up your data collection so that that information is included. 

4. Histogram

Quality professionals are often tasked with analyzing and interpreting the behavior of different groups of data in an effort to manage quality. This is where quality control tools like the histogram come into play. 

The histogram represents frequency distribution of data clearly and concisely amongst different groups of a sample, allowing you to quickly and easily identify areas of improvement within your processes. With a structure similar to a bar graph, each bar within a histogram represents a group, while the height of the bar represents the frequency of data within that group. 

Histograms are particularly helpful when breaking down the frequency of your data into categories such as age, days of the week, physical measurements, or any other category that can be listed in chronological or numerical order. 

5. Pareto chart (80-20 rule)

As a quality control tool, the Pareto chart operates according to the 80-20 rule. This rule assumes that in any process, 80% of a process’s or system’s problems are caused by 20% of major factors, often referred to as the “vital few.” The remaining 20% of problems are caused by 80% of minor factors. 

A combination of a bar and line graph, the Pareto chart depicts individual values in descending order using bars, while the cumulative total is represented by the line.

The goal of the Pareto chart is to highlight the relative importance of a variety of parameters, allowing you to identify and focus your efforts on the factors with the biggest impact on a specific part of a process or system. 

6. Scatter diagram

Out of the seven quality tools, the scatter diagram is most useful in depicting the relationship between two variables, which is ideal for quality assurance professionals trying to identify cause and effect relationships. 

With dependent values on the diagram’s Y-axis and independent values on the X-axis, each dot represents a common intersection point. When joined, these dots can highlight the relationship between the two variables. The stronger the correlation in your diagram, the stronger the relationship between variables.

Scatter diagrams can prove useful as a quality control tool when used to define relationships between quality defects and possible causes such as environment, activity, personnel, and other variables. Once the relationship between a particular defect and its cause has been established, you can implement focused solutions with (hopefully) better outcomes.

 7. Control chart (also called a Shewhart chart)

Named after Walter A. Shewhart, this quality improvement tool can help quality assurance professionals determine whether or not a process is stable and predictable, making it easy for you to identify factors that might lead to variations or defects. 

Control charts use a central line to depict an average or mean, as well as an upper and lower line to depict upper and lower control limits based on historical data. By comparing historical data to data collected from your current process, you can determine whether your current process is controlled or affected by specific variations.

Using a control chart can save your organization time and money by predicting process performance, particularly in terms of what your customer or organization expects in your final product.

Bonus: Flowcharts

Some sources will swap out stratification to instead include flowcharts as one of the seven basic QC tools. Flowcharts are most commonly used to document organizational structures and process flows, making them ideal for identifying bottlenecks and unnecessary steps within your process or system. 

Mapping out your current process can help you to more effectively pinpoint which activities are completed when and by whom, how processes flow from one department or task to another, and which steps can be eliminated to streamline your process. 

Learn how to create a process improvement plan in seven steps.

About Lucidchart

Lucidchart, a cloud-based intelligent diagramming application, is a core component of Lucid Software's Visual Collaboration Suite. This intuitive, cloud-based solution empowers teams to collaborate in real-time to build flowcharts, mockups, UML diagrams, customer journey maps, and more. Lucidchart propels teams forward to build the future faster. Lucid is proud to serve top businesses around the world, including customers such as Google, GE, and NBC Universal, and 99% of the Fortune 500. Lucid partners with industry leaders, including Google, Atlassian, and Microsoft. Since its founding, Lucid has received numerous awards for its products, business, and workplace culture. For more information, visit lucidchart.com.

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7 QC Tools: Your Ultimate Guide To Quality Improvement

Introduction to 7 QC tools

Quality management is an important aspect of any organization, and achieving it requires effective problem-solving strategies. In this regard, the 7 QC tools offer a comprehensive approach to problem-solving and quality improvement. These tools are designed to help organizations identify the root cause of problems, make data-driven decisions, and ultimately improve the quality of their products or services. In this post, we will explore the importance of the 7 QC tools, their history and evolution, how to select the right tool for quality control, and detailed explanations of each of the 7 QC tools.

Importance of 7 QC tools in quality management

The importance of 7 QC tools in quality management cannot be overstated. These tools help organizations to improve quality by providing a systematic approach to problem-solving. They enable organizations to analyze data, identify problem areas, and make data-driven decisions. By using these tools, organizations can reduce costs, increase productivity, and improve customer satisfaction. The 7 QC tools are widely used in various industries, including manufacturing, healthcare, and service sector. They are easy to use, cost-effective, and can be applied to various types of problems.

History and evolution of 7 QC tools

The history and evolution of the 7 QC tools can be traced back to the early 1920s when Dr. Walter A. Shewhart introduced the concept of statistical process control (SPC). Over time, additional techniques were added to the original seven, and the tools evolved to include Pareto charts, cause-and-effect diagrams, check sheets, histograms, scatter diagrams, and control charts. Today, the 7 QC tools are widely used in quality management and have become an integral part of Lean and Six Sigma methodologies.

How to select the right tool for quality control

Here are some points to consider when selecting the right tool for quality control:

• Identify the problem: Before selecting a tool, it is important to clearly identify the problem at hand. This will help determine which tool is best suited for the job.
• Understand the data: Understanding the data available is crucial for selecting the right tool. Some tools are better suited for qualitative data, while others work best with quantitative data.
• Determine the scope: Consider the scope of the problem and the level of detail required to solve it. Some tools are better suited for analyzing specific details, while others provide a more holistic view of the problem.
• Consider the complexity: Some problems are more complex than others, and require more sophisticated tools to solve. Consider the level of complexity and choose a tool that is appropriate for the problem at hand.
• Evaluate the strengths and limitations: Each tool has its own strengths and limitations. It is important to understand these before selecting a tool, so that you can choose one that is best suited for the problem at hand.
• Seek expert advice: If you are unsure which tool to use, seek advice from experts in the field. They can provide valuable insights and help you select the right tool for the job.

By considering these factors, you can select the right tool for quality control and ensure that your problem-solving efforts are effective and efficient.

7 QC Tools Explained

1. Pareto Chart

A Pareto chart is a graph that displays the relative frequency or size of problems in descending order of importance. It is a tool for identifying the most significant causes of a problem or the largest sources of variation in a process. The chart uses a vertical bar graph to show the frequency or size of each problem, with the bars arranged in order of decreasing importance. The chart also includes a cumulative percentage line that shows the cumulative percentage of problems accounted for by each cause. Pareto charts are useful for prioritizing problems and identifying the root causes that should be addressed to have the most significant impact on process improvement.

2. Cause-and-effect diagram

A cause-and-effect diagram, also known as a fishbone diagram or Ishikawa diagram, is a tool used to identify the root causes of a problem. It is a structured approach that helps to identify and categorize the possible causes of a problem, based on the various factors that could contribute to it. The diagram starts with a problem statement at the head of the diagram and uses a structured approach to identify the possible causes, grouping them into categories such as people, process, equipment, materials, and environment. Cause-and-effect diagrams are useful for identifying the root causes of a problem and for organizing and structuring the potential causes in a way that can be easily analyzed and addressed.

3. Check sheet

A check sheet is a tool used to collect data in a structured way. It is a simple form that is used to record data in a standardized format, making it easy to collect and analyze data across different processes or situations. Check sheets can be used to track defects or errors, record the frequency of events, or collect other types of data. They are useful for identifying patterns and trends in data, as well as for tracking progress and improvement over time.

4. Histogram

A histogram is a graph that shows the distribution of data. It is a visual representation of how frequently certain values occur within a set of data, using a series of vertical bars. The bars are grouped into categories or ranges of values, with the height of each bar representing the number of data points that fall within that category. Histograms are useful for identifying the shape of the distribution, including the mean and standard deviation, and for identifying outliers or unusual data points.

5. Scatter diagram

A scatter diagram also known as a scatter plot, is a graph that shows the relationship between two variables. It is a visual representation of how one variable changes in response to changes in the other variable. Each data point is plotted on the graph as a point, with one variable represented on the x-axis and the other variable represented on the y-axis. Scatter diagrams are useful for identifying correlations or patterns in data, and for identifying outliers or unusual data points. They are commonly used in quality control and process improvement to identify relationships between process variables and product quality or performance.

6. Control Charts

A control chart is a tool used to monitor and control a process over time. It is a graphical representation of data collected from a process, plotted against established control limits. The chart shows how the process is performing and alerts the user to any changes or variations that may occur. Control charts are useful for identifying trends, detecting shifts or changes in the process, and for identifying the sources of variation that may be causing problems. They can be used to monitor any process that produces data, from manufacturing to healthcare to financial services.

7. Flow Charts

A flow chart is a diagram that shows the steps in a process or system. It is a visual representation of the sequence of activities involved in a process, from start to finish. Flow charts are used to help understand a process, identify bottlenecks or inefficiencies, and to design or improve a process. The chart consists of boxes, symbols, and arrows that indicate the flow of the process. Boxes represent steps or actions in the process, while arrows represent the flow of materials or information between steps. Flow charts are useful for analyzing and improving any process, from simple to complex, and can be used in a variety of industries, including manufacturing, healthcare, and software development.

7 QC Tools: A Summary Table

These 7 QC tools are often used in combination with each other and with other quality management tools to improve quality and productivity, reduce costs and waste, and enhance customer satisfaction. The 7 QC Tools can be applied across various industries, including manufacturing, healthcare, finance, and service industries. These tools help to identify problems, analyze data, and improve processes, leading to better quality control and customer satisfaction. Knowing how and when to use each tool is essential to their effectiveness and achieving process improvement.

7 QC Tools Limitations:

While the 7 QC tools are widely used and effective for quality management, there are some limitations to their application. Here are some of the common limitations:

• Limited scope: The 7 QC tools are primarily focused on identifying and analyzing data related to quality issues and do not address other important aspects of quality management such as customer satisfaction, process improvement, and strategic planning.
• Lack of guidance: While the 7 QC tools provide a structured approach to data analysis, they do not provide guidance on how to implement solutions or make improvements based on the results.
• Data interpretation: The accuracy and usefulness of the data analyzed using the 7 QC tools depend on the quality and reliability of the data collected. Incorrect data or incomplete data can lead to incorrect conclusions and ineffective solutions.
• Limited application: The 7 QC tools are designed for use in manufacturing and industrial settings, and may not be as relevant or applicable in service industries or other non-manufacturing settings.
• Insufficient for complex problems: The 7 QC tools are useful for identifying and analyzing simple quality problems with a single cause or factor, but may be insufficient for more complex problems that have multiple causes and variables.
• Overreliance on data: The 7 QC tools rely heavily on data analysis and may overlook other important factors that contribute to quality, such as employee involvement, leadership, and culture.

Alternative Approach to 7QC Tools:

There are several other quality management tools and methodologies that organizations can use in addition to or instead of the 7 QC tools. Some of these alternatives include:

• Six Sigma: A data-driven approach to quality management that aims to minimize defects and variability in processes and products by using statistical methods and tools.
• Lean Manufacturing: A methodology focused on reducing waste and improving efficiency in manufacturing processes by eliminating non-value-added activities, streamlining production flows, and increasing responsiveness to customer demands.
• Root Cause Analysis (RCA): A problem-solving technique used to identify the underlying causes of a problem or failure, and develop solutions to prevent recurrence.
• Failure Mode and Effects Analysis (FMEA): A proactive risk management tool that helps identify and mitigate potential failures and defects in products or processes before they occur.
• Statistical Process Control (SPC): A method for monitoring and controlling a process by using statistical techniques to identify and correct deviations and abnormalities in the process.
• Kaizen: A continuous improvement philosophy that emphasizes small, incremental changes in processes and systems, and encourages employee involvement and empowerment.

These tools and methodologies can be used alone or in combination with each other, depending on the specific needs and goals of the organization.

In conclusion, the 7 QC tools offer a comprehensive approach to problem-solving and quality improvement. They are data-driven, cost-effective, and provide a systematic approach to quality management. By using these tools, organizations can reduce costs, increase productivity, and improve customer satisfaction. However, it is important to select the right tool for the problem at hand, and to understand the strengths and limitations of each tool. The 7 QC tools have a rich history and have become an integral part of Lean and Six Sigma methodologies, making them an essential tool for any organization that wants to improve the quality of its products or services.

References:

Goetsch, D. L., & Davis, S. B. (2014). Quality management for organizational excellence. Upper Saddle River, NJ: Pearson.

Ishikawa, K. (1985). What is Total Quality Control? The Japanese Way. Englewood Cliffs, NJ: Prentice Hall.

Batch vs. One Piece Flow Manufacturing: Which Is Right For Your Business?

Maximizing Quality And Efficiency: The Power Of Design For Six Sigma (DFSS)

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Revealing the Power of 7 QC Tools and Mastering Quality Excellence

Introduction:.

Quality control plays a crucial role in ensuring products and services meet the desired standards. It involves the systematic examination of processes to prevent defects and inconsistencies. The 7 QC tools are a set of techniques that are widely used to improve quality and productivity. By mastering these tools, individuals can streamline their operations, reduce waste, and enhance overall performance.

By utilizing the 7 QC tools, organizations can easily maintain product quality and sustain process variations. These tools serve as a practical and accessible way to analyze data and identify areas for improvement. From Pareto charts to histograms, each tool offers unique insights into different aspects of quality management.

The journey of 7 QC Tools began in post-war Japan, where the manufacturing industry was rebuilding. Recognizing the need for systematic problem-solving, 7 QC tools were developed to enhance decision-making and quality management. Today, 7 QC tools stand as a cornerstone in Lean Six Sigma methodologies, applicable across various industries and sectors.

Definition:

Let’s begin by making the term “7 QC Tools” easier to understand. The abbreviation stands for “Seven Quality Control Tools,” a set of instruments designed to identify, analyse, and solve quality-related issues within a process. These tools are fundamental in Lean Six Sigma, providing a structured approach to problem-solving and continuous improvement.

To get started with the 7 QC tools, it’s essential to learn about each tool’s purpose, application, and interpretation.

Each 7 QC Tools serves a specific purpose, ranging from data collection to trend analysis, offering a comprehensive toolkit for professionals aiming to enhance product or service quality.

7 QC Tools Templates:

Download these 7 QC tools templates and start using them by entering your own data.

Check Sheet

Pareto Chart

Cause-and-Effect Diagram (Fishbone/Ishikawa)

Scatter Diagram

Control Chart

Graphs and Charts

Key Details of 7 QC Tools :

        1. check sheet:.

• Purpose: The primary purpose of a Check Sheet is to facilitate systematic data collection. It acts as a simple and efficient tool for recording and categorizing data, providing a structured approach to understanding the frequency and distribution of specific events or defects.
• Application: Capturing and categorizing data for analysis. Check Sheets find applications in various scenarios, from tracking the number of defects in a manufacturing process to recording the types of customer complaints in a service-oriented setting.

Improved Data Accuracy: By standardizing data collection, Check Sheets minimize the chances of errors or inaccuracies.

Quick Visualization: Check Sheets allow for the quick visualization of data trends, aiding in identifying patterns.

2. Pareto Chart:

Pareto-Chart

• Purpose: The Pareto Chart serves the purpose of identifying the most significant factors contributing to a problem. It follows the Pareto Principle, suggesting that roughly 80% of effects come from 20% of the causes.
• Application: Prioritizing issues for effective problem-solving. When faced with a multitude of issues, the Pareto Chart helps prioritize efforts by focusing on the vital few rather than the trivial many.

Effective Prioritization: By visually representing the distribution of issues, the Pareto Chart allows teams to focus on the most critical areas for improvement.

Strategic Decision-Making: Prioritizing efforts based on the Pareto analysis ensures a strategic allocation of resources for maximum impact.

3. Cause-and-Effect Diagram (Fishbone/Ishikawa):

• Purpose: The Cause-and-Effect Diagram, also known as Fishbone or Ishikawa diagram, is designed to uncover the root causes of a problem. It provides a visual representation of potential causes and their relationships.
• Application : Teams use this tool to brainstorm and organize possible causes when faced with a specific problem, fostering a structured approach to problem-solving.

Systematic Problem Analysis: The Cause-and-Effect Diagram encourage a systematic exploration of potential causes, leading to a comprehensive understanding of the issue.

Cross-Functional Collaboration: Teams from various departments can collaborate on constructing the diagram, bringing diverse perspectives to problem-solving.

4. Histogram:

• Purpose: The Histogram serves the purpose of displaying the distribution of a dataset. It provides a visual representation of how often different values or ranges of values occur.
• Application: Teams use Histograms to understand the variability and distribution of data within a process, aiding in identifying patterns and trends.

Visual Data Representation: Histograms provide a clear visual representation of data, making it easier for teams to comprehend the distribution of values.

Identifying Data Patterns: By observing the shape of the histogram, teams can identify whether the data follows a normal distribution or if there are outliers.

5. Scatter Diagram:

• Scatter_Chart
• Purpose: The Scatter Diagram helps in identifying relationships between two variables. It provides a visual representation of how changes in one variable may correlate with changes in another.
• Application: Teams use Scatter Diagrams to analyse cause-and-effect relationships and identify patterns or trends in data.

Correlation Analysis: Scatter Diagrams assist in visually assessing whether there is a positive, negative, or no correlation between two variables.

Data Pattern Recognition: By plotting data points, teams can identify clusters, trends, or outliers, aiding in data pattern recognition.

6. Control Chart:

• Control_Chart
• Purpose: The Control Chart monitors process stability over time. It helps distinguish between common cause variation and special cause variation, aiding in identifying trends or shifts in a process.
• Application: Organizations use Control Charts to ensure that processes remain within acceptable limits and to predict future performance.

Early Detection of Issues: Control Charts provide a visual signal when a process is moving out of control, allowing for early intervention.

Predictive Analytics: By analysing patterns on the Control Chart, teams can make predictions about future process performance.

7. Graphs and Charts:

• Purpose: Graphs and Charts serve the general purpose of presenting data in a visual format. They are versatile tools that enhance the communication of information.
• Application: Teams use various types of graphs and charts based on the nature of the data and the message they want to convey. Common types include bar charts, line charts, and pie charts.

Clarity in Communication : Graphs and Charts offer a clear and concise way to communicate complex data to stakeholders.

Decision Support: Visual representations make it easier for decision-makers to grasp information quickly and make informed decisions.

• Example: Present the findings in a visually appealing way, using graphs and charts to communicate insights to stakeholders.

Benefits of Adopting 7 QC Tools:

7 QC tools are essential for businesses to maintain customer satisfaction, build a strong reputation, and drive continuous improvement. It helps organizations identify areas for enhancement, resolve issues efficiently, and make data-driven decisions.

The adoption of 7 QC Tools brings forth a multitude of benefits, including:

• Enhanced Problem-Solving: A systematic and structured approach to problem identification and resolution.
• Data-Driven Decision-Making: Utilization of data for informed decision-making, leading to improved processes.
• Efficiency Improvement: Targeting critical issues for resolution enhances overall process efficiency.
• Preventive Measures: Identification of root causes allows for the implementation of preventive measures.
• Continuous Improvement: The integration of 7 QC Tools supports the culture of continuous improvement within an organization.

Common Challenges:

While 7QC Tools are powerful, practitioners may face challenges:

• Data Accuracy: Overcoming issues related to inaccurate or incomplete data.
• Tool Selection: Choosing the right tool for a specific problem can be challenging for beginners.
• Interpretation Complexity: Understanding and interpreting the results of certain tools, such as control charts, might pose difficulties.

Integration with Lean Six Sigma:

7 QC Tools seamlessly integrate into the Lean Six Sigma methodology. They play a vital role in the “Improve” phase, providing the necessary instruments for data analysis, problem-solving, and decision-making. By incorporating these tools, organizations can achieve a higher level of efficiency and quality in their processes.

Conclusion:

Mastering the application of these 7 QC Tools is a journey toward achieving excellence in quality control and process improvement. Each tool brings a unique perspective and set of benefits to the table, contributing to the overall success of Lean Six Sigma initiatives.

Remember, the true power of 7 QC tools lies not just in their individual applications but in their collective use. Integrating them seamlessly into the problem-solving phases of the Lean Six Sigma methodology enhances the efficiency, quality, and continuous improvement culture within an organization.

Delve into the world of Lean Six Sigma and explore our collection of short notes on key topics. Click here to enhance your knowledge

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• Check Sheets are commonly used in manufacturing for defect tracking, in healthcare for patient symptom tracking, and in service industries for recording customer feedback.
• Design a Check Sheet by clearly defining the categories and criteria to be recorded. Ensure simplicity and ease of use for data collectors.
• Conduct a Pareto analysis by categorizing issues and calculating their frequency. Address the categories contributing to the majority of the problems.
• Absolutely. Pareto Charts are versatile and can be applied in personal productivity improvement or community problem-solving.
• Start by defining the problem on the right side of the diagram. Identify major categories of causes (like Man, Machine, Method, Material, Environment, Measurement) and branch out to specific causes within each category.
• Absolutely. By identifying and addressing root causes, organizations can implement preventive measures to avoid the recurrence of problems.
• Histograms are suitable for continuous data, such as time durations, weights, or temperatures.
• Outliers may appear as data points that significantly deviate from the main distribution in the Histogram.
• Scatter Diagrams primarily show correlation. While a strong correlation suggests a potential cause-and-effect relationship, further analysis is needed to establish causation.
• In marketing, Scatter Diagrams can help analyse the correlation between advertising spending and sales, for example.
• Control limits indicate the range within which the process is expected to perform consistently. Points outside these limits may signify special cause variation.
• Absolutely. Control Charts are applicable in service industries to monitor and improve processes such as customer service response times.
• Bar charts are often used to compare quantities, especially when the data is categorical.
• While visuals are powerful, there are cases where precise details or extensive information may be better conveyed through text, such as in detailed reports.

Harish Kumar Nayak is a dedicated Lean Six Sigma expert with over a decade of hands-on experience in the pharmaceutical industry. Holding a Bachelor’s degree in Business Administration and a Lean Six Sigma Green Belt certification, Harish has honed his skills in process improvement, efficiency enhancement, and quality control.

In his professional journey, Harish has served as an Assistant Manager, leading numerous successful projects. Notably, he has spearheaded initiatives aimed at improving Overall Equipment Effectiveness (OEE), boosting production line throughput and yield, and reducing changeover times for packaging lines. His work has consistently demonstrated his ability to drive significant operational improvements and deliver measurable results.

Beyond his professional achievements, Harish is passionate about making Lean Six Sigma tools and techniques accessible to a broader audience. He enjoys writing articles that break down complex concepts into simple, practical approaches, helping others understand and implement these powerful methodologies in their own work environments.

For insightful articles and practical advice on Lean Six Sigma, visit Harish’s blog at LeanSixSigmaTool.com, where he shares his knowledge and experience to help readers master the art of process improvement.

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Streamlining Six Sigma Projects with the 7 QC Tools

Published: September 16, 2024 by Ken Feldman

Think about a tool box for a moment. You have different tools for different jobs. A screwdriver makes a poor hammer, for example. Having the right tool for the right job is just the way things work. So, how do you implement the right tool for the job when streamlining your projects and processes? Thankfully, there are 7 quality control tools to get your project rolling without a hitch.

So, l et’s review the 7 QC tools that are most commonly used in Six Sigma , the benefits of those tools, and some best practices for using them.

Overview: What are the 7 QC tools?

It is believed that the 7 QC tools were introduced by Kaoru Ishikawa in postwar Japan, inspired by the seven famous weapons of Benkei. Benkei was a Japanese warrior monk who armed himself with seven weapons and was on a personal quest to take 1,000 swords from samurai warriors who he believed were arrogant and unworthy.

Ishikawa was influenced by a series of lectures on statistical quality control given by Dr. W. Edwards Deming in 1950 to a group of Japanese scientists and engineers. Unfortunately, the complexity of the subject intimidated most workers. As such, Ishikawa focused primarily on a reduced set of tools that would suffice for most quality-related issues.

The 7 QC tools are:

• Check sheet 
• Fishbone diagram (cause and effect diagram, or Ishikawa diagram)
• Pareto chart
• Control chart
• Scatter diagram

Stratification

A closer look at your 7 qc tools.

Now that you have a basic understanding of the tools at your disposal, let’s dig into how they function. Like any tool in your toolbox, understanding when you use it and where can make or break your current project.

Check Sheet

A check sheet is your most basic tool . You’re simply tallying up data for further analysis later in the process.

Image source:  techqualitypedia.com .

Fishbone Diagram

Fishbone diagrams are used to drill down to find the root cause of a problem. As the name implies, the diagram looks like the bones of a fish, where each main bone represents a specific category of possible root cause, and the subsequent drilling down is shown as smaller and smaller bones.

Image source:  asq.org .

This is a bar graph showing the frequency of a set of data, usually continuous data. The histogram allows you to see the center of the data, the range of the data, and the distribution of the data. It is a very useful snapshot. The downside is that you can’t see the sequence or order of the data.

Image source:  statisticsbyjim.com .

Pareto Chart

This chart is based on the 80/20 principle that says 80% of your effect is caused by 20% of your causes. For example, 80% of your sales come from 20% of your customers. Dr. Joseph Juran, who developed this chart, often referred to this principle as the vital few and trivial many . He later revised that to the vital few and useful many . The Pareto chart lists the causes in descending order of frequency or magnitude. It is used to prioritize what you should look at first to improve your process.

Image source:  www.automateexcel.com .

Control Chart

A control chart is a statistical tool that looks at your process data over time to distinguish between special cause and common cause variation.

Image source:  www.spcforexcel.com .

Scatter Diagram

These are also known as scatter plots. They’re used to show a graphical correlation between a set of paired data on an X and Y axis. Scatter diagrams are the graphical representation of what you would use for regression analysis.

Image source: www.spcforexcel.com .

This is a graph that shows data that has been stratified when the data comes from different sources. It is useful to view the data by certain strata such as shift, gender, geographic location, machines, or suppliers.

Image source: www.systems2win.com .

3 Benefits of the 7 QC Tools

These seven tools are easy to understand and apply and will help you understand what is going on in your process. 

Ease of Use

These 7 QC tools are easy to understand and implement yet powerful in identifying root causes, in discriminating between types of variation, and as a visual description of your data. A picture is truly worth 10,000 words (or statistical calculations). 

Flexibility and Adaptability

Gone are the days when you had to draw all of your graphs by hand. Many simple and cost-effective software packages will take your data and quickly produce graphs. 

The Pareto principle applies to the 7 QC tools as well. 80% of your quality issues can be addressed by using 20% of the most common tools.

Why Are the 7 QC Tools Important to Understand?

The key thing to understand is when to use each tool. Moreover, which one is appropriate for your specific situation?

Tools Address Different Issues

The more familiar you are with these common tools, the quicker you’ll be able to select the right one to help you solve your problem or answer your question. The Fishbone diagram is used to search for the root causes of your problem. A control chart is used to distinguish between common and special cause variation. A scatter diagram is used to look for a correlation or relationship between an X and Y variable. 

Graphs Don’t Tell the Whole Story

Graphs and diagrams are useful for providing an overview and directional indicator of your process. However, statistical analysis will provide greater confidence than a graph alone. 

Flexibility 

These seven tools can be used for different types of data and across any type of function. Their flexibility makes them useful in many situations and industries. As such, becoming familiar with them can be a wise investment.

3 Best Practices When Thinking About the 7 QC Tools

Use these tools for as many applications as is feasible. Keep it simple, and only use the more sophisticated and complex tools if you need additional information and analysis. 

Have a Clear Idea of What Question You’re Trying to Answer

Since each of the tools can be used to answer different data and process questions, be sure you’ve clearly defined the question you’re trying to answer. 

Use Them as Your Primary Presentation

Use the 7 QC tools and their accompanying graphs and diagrams as your primary presentation format. However, you can reserve the statistical analysis for questions that go beyond what’s answered in the graphs.

Make Sure They’re Self-explanatory

Your graphs need to be succinct and self-explanatory. People need to understand what you’re trying to tell them without the need for a long-winded explanation. You can save the details for further questions if needed.

Let’s Review What’s in Your Tool Belt

The 7 QC tools are basic graphical representations of your data. They can be used to answer a wide variety of questions about your data and your process. Use them as your primary presentation format when talking about what your data is telling you. While they are not a complete list of tools, they should be robust enough to address many of your improvement issues.

While basic, these 7 QC tools are foundational elements of Six Sigma. Their simplicity and versatility make them indispensable for professionals across industries. As businesses evolve and data becomes more integral to decision-making, the importance of these tools only grows.

Further, they bridge the gap between raw data and actionable insights. This allows teams to make informed decisions. The integration of modern technologies like artificial intelligence and machine learning can also make these tools more robust, yielding richer analysis as a whole.

However, the essence remains the same: understanding and improving processes through data visualization.

About the Author

Ken Feldman

7QCs: An Introduction to the Seven Basic Tools of Quality Control

Quality control. Of course it is important. When producing parts or products, the ability to monitor, troubleshoot, and adjust manufacturing processes is necessary for companies to remain efficient and competitive. If products are to be made consistently to a required standard, the methods of manufacturing must be measurable, adjustable, and repeatable.

In order to achieve these standards, logical, data driven approaches to finding acceptable solutions can be used, such as the 7QC tools, or the Seven Basic Tools of Quality Control. The 7QC tools are statistical tools that help individuals, organizations, and businesses resolve quality issues for products and processes. They are called basic tools because they are suitable for people with little formal training in statistics and because they can be used to solve the vast majority of quality-related issues.

7QC tools include:

Check Sheets

Check sheets are used to collect data in order to understand the qualitative and quantitative variables that can affect a process. When recording data on a check sheet, check marks or tally marks are used to indicate the amount of what is being collected, which helps in understanding the progress, defect patterns, and even causes for defects.

Control Charts

Control charts are graphs used to represent process performance over time. Subgroups of data points are collected and compiled together within a short interval of time. The average of the data points within a subgroup is represented as a single dot in the control chart. The amount of variation that exists within a sample data set is the standard deviation, which is used to determine the control limits. When the subgroups exist beyond the control limits or exhibit specific patterns or trends, then the process is said to be “out-of-control.”

Fishbone Diagrams

Fishbone diagrams, also referred to as cause and effect diagrams, are a quality control brainstorming tool used to help identify the root cause or causes of an issue by looking at all possible variables.

When using these diagrams, a central issue or focal point, such as a defect or quality problem, is placed at the head of the “fish.” The “bones of the fish” serve as a way to visually organize all possible variables, or causes, that may have caused the central issue, and sort ideas into categories to investigate further.

Histograms are a type of bar graph used to represent the frequency distribution, or how often each different value in a set of data occurs. It is created by grouping the data you collect into “cells” or “bins.” The histogram is the most commonly used graph to assess process behavior and demonstrate if the data follow a normal distribution, or bell-shaped curve.

Pareto Charts

Pareto charts are a combination of bar and line graphs that provide a visual representation of how often the various issues affecting a process are occurring. Pareto chart derives its name from the use of the Pareto Principle, which states “80% of the effect comes from 20% of the causes.” Using this chart, professionals can decide where to place priority and focus.

Scatter Diagrams

Scatter diagrams, also called scatter plots, are graphs used to visually represent the relationship between two variables in order to quickly identify the correlation between them.

This tool is used to determine the type of relationship that exists between the inputs to the process, or process characteristics, and the outputs from a process, or product characteristics.

Stratification

Stratification is a method of dividing data into subcategories and classifying data based on group, division, class, or levels that helps in deriving meaningful information to understand an existing problem.

To learn more about these Seven Basic Tools of Quality Control, and to learn how to apply these tools to solving quality problems by viewing examples, check out the online 7QC courses in the THORS Academy Library , brought to you by THORS eLearning Solutions.

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Scientific Problem Solving using the 7 Quality Tools

Master analytical techniques and quality improvement strategies, integrating the power of the 7 Quality Tools within scientific contexts, to identify, analyze, and solve problems efficiently

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Resources - 20

Duration - 3 hours

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Course Description

These tools help to identify, examine, and solve queries related to quality, process, and performance. They're simple to learn, but still effective, making them perfect for anyone involved in quality improvement. These tools are easy to use and understand, and they can be applied to a wide range of industries and situations.

This course includes:

Self-Paced Complete Video Tutorial

Downloadable & Customizable Templates 

PDF Tutorials

This course is designed to equip students with knowledge, and skills to:

• The Scientific Approach: A3 Thinking 
• The 7 QC Tools with video tutorials

Prerequisites

No prerequisites are required to take this course. 

Check out the detailed breakdown of what’s inside the course

Instructor Details

Ahmed Radwan

A seasoned professional with over 14 years of experience in lean manufacturing and operational excellence. Ahmed holds a Master of Business Administration and a deep understanding of various continuous improvement concepts, tools and methodologies. 

His credentials include:

• Certified Lean Six Sigma Black Belt (IASSC)
• Certified Lean Six Sigma Master Black Belt (CSSC)
• Certified Lean Bronze (Shingo Institute)
• Certified Professional Scrum Master (Scrum.org)
• Hoshin Kanri (Lean Enterprise Institute)
• Project Management Professional (PMI)

Ahmed's expertise extends beyond certifications. He brings a practical approach, honed through years of real-world experience, to ensure that our training translates into tangible results for your organization.

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Green Belt Academy

Helping You become a Certified Six Sigma Green Belt as Quickly and Painlessly as Possible!!

“A man and his tools make a man and his trade” – Vita Sackville-West

As a Six Sigma Green Belt, one of the most important skills you need is the ability to solve a problem or improve a process .

To do this successfully, you need the proper tools. In fact, there are 7 specific tools that you must know.

Kaoru Ishikawa once said “ As much as 95% of quality problems can be solved with seven fundamental quantitative tools ”.

These tools were first categorized as Quality Control Tools by Ishikawa in his book Introduction to Quality Control .

Improvements happen when we solve problems.

So, what are these 7 fundamental tools for problem solving & continuous improvement? Flow Chart

• Check Sheet
• Pareto Chart
• Cause & Effect Diagram
• Control Chart
• Scatter Diagram

Get The Free Quiz For The 7 QC Tools

So why are these seven tools so effective?

They all share two characteristics that make them very effective in problem solving (and continuous improvement).

First – they are all visual tools . You’ve heard the saying – a picture is worth a thousand words . These tools prove that point.

Second – they all deal with facts or data , not opinions or conjecture.

Problems are solved with facts and data .

Improvements are made with facts and data .

When we combine a fact-based approach with a visual tool, we are able to solve problems more easily .

The other comment I’ll make about these tools is that they are often used in combination with each other, and I’ll provide examples of that as we go through each tool.

Let’s get started with the flow diagram.

1. Flow Chart

A Flow Chart is a visual tool that depicts the flow or sequence of a process . This can include the flow of information, tasks, people, material or decision .

The Flow Chart’s value lies in its ability to visually communicate the steps and sequence of a process.

The Flow Chart makes the complex become simple, and promotes a common understanding of a process, which is the foundation for improvement.

The Flowchart is an excellent starting point in the Problem-Solving Process , as it allows your problem-solving team to see the entire process and identify improvements.

Flow Chart Example:

Let’s say we’re a manufacturer of toasters, and we’ve been asked to put together a high-level flow diagram of the entire manufacturing process.

Remember, each of these steps in the process could have its own more detailed flow chart.

2. Check Sheet

Solving problems and making improvements requires data . Period.

The check sheet is a simple tool for collecting, organizing and analyzing data .

A Check Sheet is normally a table with defined rows and columns where the data collected is usually 1 check mark within each category. However, you can modify this concept of a data collection tool to meet a variety of different needs.

The best check sheets contain something more than data, they contain meta data .

Meta data is data about the data – like who collected the data, when (date, shift or time) the data was collected, and where (location, line, equipment number) there data collection took place.

Without this meta data, the actual data can become ambiguous and lose its integrity ( think data integrity ).

Let’s say we go back to our toaster example and see what a check sheet for final assembly rejects might look like. This also includes the meta data and illustrations to go along with it.

This data can then be fed into a pareto chart to identify the “critical few” defects (Hint, it’s the electrical defect).

3. Pareto Chart

The Pareto Chart is a bar chart that allows for analysis of data in search of the Pareto Principle or the 80/20 rule .

The 80/20 rule was first identified by an Italian researcher, Vilfredo Pareto , who was studying wealth and land ownership in Europe, and found that 80% of the land in Europe was owned by 20% of the population.

The 80/20 rule was popularized by Joseph Juran , who names the Pareto Chart after Vilfredo Pareto.

Juran went on to say that the Pareto Chart helps us separate the vital few from the trivial many .

Mechanically, the Pareto Chart is simply a bar chart and the categories of data are typically arranged from greatest to least on the X-axis.

The Y-axis is a count of defects , but this number can be cost , or any other variable. Pareto Charts also frequently include a cumulative frequency line to assist in the analysis.

In this example , the top 3 defects (Defects A, B & C) make up only 20% (3 out of 15) of the defect conditions, however they contribute to 72% of the total number defects . If we could eliminate just these 3 defect conditions, we could eliminate 72% of the defects. That’s the Pareto Chart and the 80/20 Rule at work.

4. Cause & Effect Diagram

The Cause and Effect diagram is a visual tool to explore all the potential factors that may be causing or contributing to a particular problem .

This tool was popularized by Kaoru Ishikawa and allows you to graphically capture all the potential causes of a problem , then select those which require further investigation.

The Cause & Effect Diagram is also commonly referred to as the Fishbone Diagram, the Ishikawa Diagram, Cause & Effect Matrix, C&E Diagram or the C-E Diagram.

The Ishikawa diagram has 8 major categories (The 8M s) that might contribute to your problem which include:

• Mother Nature
• Measurements
• Maintenance

Cause & Effect Example:

Let’s say you’re a Toaster Manufacturer and you received a customer complaint for a toaster that is not toasting.

We can go through the brainstorming process using the 8M’s to identify potential causes and contributing factors that require further investigation.

You can see here we’ve excluded maintenance, machines and Management, and identified potential causes and contributing factors in other areas.

We can also prioritize the most likely contributing factors which should give the investigation actions to conclude the root cause of the problem.

5. Control Chart

A control chart is a statistically based tool that analyzes the variation of a process .

A control chart is a time-based line graph that reflects the behavior of a process over time including normal variation and any special cause variation.

A control chart can also be described as a visual communication tool that graphs analyzed data in real-time and reflects the stability of a process .

Remember: A good process is a stable process; we want stability.

The details of the control chart, including the various kinds, how to create them, and how to analyze them can be found in the Statistical Process Control (SPC)  chapter.

The control chart contains upper and lower control limits that are statistically based, which allow the user to identifying instances where the process appears to be behaving abnormally.

These control limits and centerline represent the “voice of the process” and are simply a reflection of the process – both the average value of your process and the natural variation of the process.

Using control charts allows you to proactively monitor your process , detect when a problem is occurring (or has occurred), which is the starting point for an improvement project.

A control chart is like a scoreboard . It can be used at the end of an improvement project to indicate if an improvement was successful or not.

6. Scatter Diagram

A Scatter diagram is a visual analysis tool that is meant to reflect the possible relationship between two variables .

The Scatter Plot visually plots pairs of data on an X-Y graph in order to reveal the relationship between the data sets.

The relationship between the two variables can be positive, negative or non-existent. The strength of the relationship can also be analyzed visually by how closely the points fall on the line of best fit.

The strength of that relationship can be expressed mathematically using the Pearson Correlation Coefficient , which is a number that ranges from a strong positive correlation (+1) to a strong negative correlation (-1).

The scatter plot is often used in the problem-solving process when we’re studying a process to understand which input variables (independent variables) are contributing to a negative outcome in a response variable (dependent variable).

FYI – below is a hypothetical situation. I’ve created this data as an example, however I believe the conclusions are likely accurate 😊.

Let’s say that I’m studying the various factors that affect performance on the CSSGB Exam .

I propose a hypothesis that there is a relationship between quiz scores and the ultimate exam score.

So I run an experiment where I work with 14 people and have them take a quiz before the exam to determine if a relationship exists between these two variables .

Ultimately, I’d like to be able to predict their exam score based on the quiz score .

So I’ve taken these pairs of data, with the Quiz Score as the Independent Variable (X), and the Exam Score as the Dependent Variable and analyzed them using a Scatter Diagram.

This scatter diagram indicates that a strong positive correlation exists between these two variables (r = 0.8).

If you do well on the quiz, you’re likely going to do well on the exam.

But can doing well on a quiz CAUSE you to do well on the exam. No.

This is a good opportunity to warn you about the difference between correlation and causation.

This is an example of correlation without causation.

These two variables highly correlate with each other because there are other factors like study time, study habits, or job performance that are CAUSING you to do well on both variables.

So, if you really want to do well on the exam, create healthy study habits, invest your time to study, reflect on what you’ve learned, put that into action and you will do well on the exam (and the quiz).

It’s not to say that this quiz is without value though. The quiz is an indicator of potential success on the exam .

7. Histogram

The Histogram is a tool used to visualize the distribution of continuous data .

More specifically, a Histogram is a type of Bar Chart that graphs the frequency of occurrence of continuous data and is a useful tool for displaying, summarizing and analyzing data .

Variation is all around us. Every process or product has some level of variation.

Every data set you collect will have variation in it, and this variation will exist in a “Pattern”.

And the best way to see or understand this Pattern of variation is to graph your data using a Histogram.

There are different patterns of variation that may be revealed in a Histogram. The most common distributions, and their analysis, are discussed within the Probability Statistical Distributions (Chapter 12) of the Green Belt Master Class .

Typically, a distribution can be characterized by the central tendency of the data (Mean, Median Mode), and the “ variation ” ( range, standard deviation, etc ) within the data.

The Normal Distribution is the most common type of statistical distributions.

The histogram is a visual tool you can use as a gut check to see if your data set is approximately normal .

As a Six Sigma Green Belt, one of the most important skills you can have, is the ability to solve a problem or improve a process .

To do this successfully, you need to be able to apply the 7 QC Tools.

These 7 tools combine a fact-based approach with a visual tool that makes solving problems easier.

Below is a quick and simple review of the definition for each of the 7 tools discussed within this chapter.

1. A Flow Chart is a visual tool that depicts the flow or sequence of a process . This can include the flow of information, tasks, people, parts, material , etc.

2. The check sheet is a simple tool for collecting, organizing and analyzing data .

3. The Pareto Chart is a bar chart that allows for analysis of data in search of the Pareto Principle or the 80/20 rule .

4. The Cause and Effect diagram is a visual tool to explore all the potential factors that may be causing or contributing to a particular problem (effect).

5. A control chart is a time-based line graph that reflects the behavior of a process over time including normal variation and any special cause variation.

6. A Scatter diagram is a visual analysis tool that is meant to reflect the possible relationship between two variables .

7. A Histogram is a type of Bar Chart that graphs the frequency of occurrence of continuous data and is a useful tool for displaying, summarizing and analyzing data .

Next: Kano Model

7 QC Tools for Process Improvement | PDF | Case Study

• What are 7 QC Tools?

→ 7 QC tools are systematic and scientific methods for problem-solving.

→ Also, they are used for product and process improvement.

→ They are used to solve almost 90% of shop floor problems very easily.

Table of Contents:

• History of 7 QC Tools

When to Use the Basic 7 QC Tools?

Why to use the 7 qc tools.

• The Basic 7 QC Tools For Process Improvement
• Cause and Effect Diagram (Fishbone or Ishikawa) 
• Pareto Chart
• Scatter Diagram
• Control Chart
• Benefits of 7 QC Tools
• Limitations of 7 QC Tools

History of 7 QC Tools:

→ The Basic 7 Quality Control Tools originated after World War II in Japan.

→ Dr. Edwards Deming has played an important role in introducing statistical quality control methods.

→ He recommends the use of statistical methods to improve manufacturing quality.

→ After his work, Japanese industries have improved a lot in quality and processes in manufacturing.

→ Primarily Kaoru Ishikawa introduced the 7 QC Tools.

→ Dr. Kaoru Ishikawa was a professor at the engineering college at Tokyo University.

→ Ishikawa is known for the “Democratizing (Visual Aids/Symbols) Statistics” .

→ Good visual representation makes statistical and quality control more comprehensive.

→ The Basic 7 QC Tools gained popularity for their simplicity and effectiveness across the world.

→ The Basic 7 Quality Control Tools are necessary for problem-solving and process improvement.

→ Each tool has its own specific applications and benefits.

⏩ Refer to the below-mentioned key points when we can use 7 QC tools:

• For identifying potential causes of a problem
• Useful during brainstorming sessions
• When collecting data in a structured manner
• For monitoring process trends or patterns over time
• During the identification of the distribution of data
• Prioritization of defects, causes, efforts, etc
• Identify or validate the correlation between two variable
• Process flow documentation, analysis, and improvement
• A graphical technique that is easily understood by all
• Most helpful in troubleshooting quality-related issues
• They are fundamental tools for process and product quality improvement
• This facilitates the organization to resolve basic problems
• The 7 QC tools are easy to understand and implement
• They do not require complex analytics and statistical skills
• The basic 7 QC tools are simple yet powerful
• We can get an 80% result by applying 20% of the effort

The Basic 7 QC Tools For Process Improvement:

➝ Now we will understand the Basic 7 QC Tools in detail.

⏩ The 7 QC Tools are:

Note: We are considering the Flow Chart as a part of the 7 Basic QC Tools.

Also, you can consider stratification as a part of this tool.

➡️  Sample Presentation File

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(1) Flow Chart:

→ "Flow Chart is a diagrammatic representation that illustrates the sequence of operation to be performed to get the desired result."

→ It is a visual representation tool for a sequence of operations, therefore, we can easily illustrate the Internal and external operational boundaries.

→ A flow chart includes the steps involved in a process, the decision points, and the flow of control from one step to the next.

→ A flow chart is also called a "Process Flow Diagram."

⏩ Purpose of Flowcharts:

• Visualize Processes
• Identify Inefficiencies
• Standardize Procedures

⏩ Basic Elements of the Process Flow Diagram are:

⏩ Basic Symbols Used in Flowcharts:

• Parallelogram

⏩ Steps to Create a Flowchart:

• Define the Process
• Select Symbols
• Draw the Flowchart
• Review and Revise

⏩ Benefits of Using Flowcharts:

• Improvement
• Communication

⏩ Use Cases:

• Process mapping in manufacturing and service industries
• Identifying areas for quality improvement in workflows
• Documenting procedures for training and compliance

👉 Read our detailed article on Different Types of Flow Charts Explained with Examples

(2) Cause and Effect Diagram (Fishbone or Ishikawa):

→ "Cause and Effect Diagram is a meaningful relationship between an effect and its causes."

→ It guides concrete action and tracks the potential causes during an investigation of the effort to determine whether the item significantly contributes to the problem or not.

→ The cause and effect tool is a very popular root cause analysis tool.

⏩ The Different Names of Cause and Effect Diagram are:

• Ishikawa Diagram
• Fishbone Diagram
• Herringbone Diagrams

→ A fishbone diagram can identify all possible cause(s) for the problem.

→ In this tool, we can find possible causes with the help of the 6M concept those are man, machine, material, method, measurement, and mother nature.

→ Also, we can use this tool in marketing as 8P analysis and service industry as 4S analysis.

⏩ Steps to Create a Cause and Effect Diagram:

• Define the Problem
• Identify Main Categories
• Brainstorm Causes
• Analyze and Prioritize

⏩ Benefits of Using a Cause and Effect Diagram:

• Visual Representation
• Team Collaboration
• Problem-Solving

⏩ Use Cases of Cause and Effect Diagram:

• Quality improvement projects
• Problem-solving in manufacturing
• Analyzing service delivery issues

👉 Check our detailed article on Cause and Effect Diagram Explained with Case Study

(3) Check Sheet:

→ "Check Sheet is a well-structured data sheet that is used for collecting and analyzing data."  

→ Data collected by the check sheet is used as input for the other tool and data can be collected based on asking a question by what, when, where, who, why, and how.

→ The purpose of a check sheet is to summarize the data and a tally count of event occurrences.

→ A check sheet counts the number of occurrences of an event, such as defects or non-conformance.

→ Hence the Check Sheet is also called a "tally sheet".

→ In many cases, a checklist will summarize countable data related to certain types of defects and will provide a rough graphical representation of wherein a part of the process, defects occurred.

⏩ Key Aspects of a Check Sheet:

• Structured Form
• Real-Time Data Collection
• Ease of Use
• Data Visualization

⏩ Uses of a Check Sheet:

• Defect Tracking
• Data Collection
• Process Improvement
• Quality Control

👉 Read our detailed article on Different Types of Check Sheets Explained with Example

(4) Histogram:

→ "Histogram is a type of bar graph representing the frequency distribution of the data."

→ Karl Pearson introduced the Histogram which is a bar graph representing the frequency distribution on every bar.

→ Histograms are used to show whether the output of our process matches the customer's requirements or not?

→ Also, we can make the decision based on the data patterns plotted on the Histogram.

→ With the help of the graph we can analyze whether the process is capable of meeting customer requirements or not?

→ A histogram is a pictorial representation of a set of data.

⏩ Key Aspects of a Histogram:

• Frequency Distribution
• Intervals (Bins)

⏩ Steps to Create a Histogram:

• Collect Data
• Determine the Range
• Choose Intervals (Bins)
• Count Frequencies
• Draw the Bars
• Interpret the Patterns

⏩ Uses of a Histogram:

• Understanding Data Distribution
• Identifying Patterns

⏩ Different Types of Histogram are:

• Normal Distribution
• Skewed Distribution
• Double-peaked or Bimodal
• Multipeaked or Multimodal
• Edge Peaked Histogram
• Truncated or Heart-cut histogram

👉 Also read a detailed article on Different Types of Histograms Explained with Case Study

(5) Pareto Chart:

→ "Pareto Chart is a bar graph arranged in descending order of height from left to right."

→ Pareto chart shows the order of the largest number of occurrences by item or by classes and the cumulative sum total.

→ The Pareto analysis helps us to prioritize where we can get more benefits by applying fewer efforts.

→ It is also very popular as a prioritization tool.

→ It communicates the principle of 80:20.

→ The Pareto Principle gives us information about the Vital few from Trivial many.

→ Hence,  It is known as the "Vital few from Trivial many tool".

→ It states that 80% of an effect comes from 20% of the causes.

⏩ Key Aspects of a Pareto Chart:

• Cumulative Line

⏩ Steps to Create a Pareto Chart:

• Identify Problems/Causes
• Measure Frequency or Impact
• Calculate Cumulative Percentages
• Draw the Chart

⏩ Uses of a Pareto Chart:

• Prioritizing Problems
• Resource Allocation

👉 Read our detailed article on Pareto Chart Explained with Case Study

[6] Scatter Diagram:

→ "Scatter Diagram is used to study and identify the possible relationship between two variables."

→ It is used to identify and visualize the relationship between two variables.

→ Mostly the scatter diagram is used to validate the cause-and-effect relationships between two variables.

→ This tool helps in decision-making during the problem-solving process.

→ Also it helps to determine the correlation between two variables.

→ Scatter Diagram is the best validation tool.

⏩ Different names of the Scatter Diagram:

• Scatter Plot
• Scatter Graph
• Correlation Graph
• Scatter Gram

⏩ Key Aspects of a Scatter Diagram:

• Data Points
• Correlation

⏩ Steps to Create a Scatter Diagram:

• Identify Variables
• Plot Data Points
• Analyze the Pattern

⏩ Types of Correlation:

• Positive Correlation
• Negative Correlation
• No Correlation

⏩ Uses of a Scatter Diagram:

• Identifying Relationships
• Predicting Trends

👉 Also visit our detailed article on Scatter Diagram Explained with Example

[7] Control Chart:

→ "Control Charts are used for studying the process variation over time."  

→ The control chart was invented by Walter A. Shewhart working for Bell Labs in the 1920s.

→ A control chart is also known as a Shewhart chart or process-behavior chart.

→ With the help of this tool, we can determine whether a manufacturing process or a business process is in control or not?

→ The control chart is a graph which is used to study process changes over time

→ Comparing the above tool this is the best forecasting tool.

⏩ Key Aspects of a Control Chart:

• Center Line (CL)
• Upper Control Limit (UCL)
• Lower Control Limit (LCL)
• Control Limits

⏩ Steps to Create a Control Chart:

• Calculate the Mean
• Calculate Control Limits
• Plot the Data
• Analyze the Chart

⏩ Types of Control Charts:

• X-bar Chart

⏩ Uses of a Control Chart:

• Monitoring Processes
• Identifying Variation

👉 Read our detailed article on Control Chart Explained with Case Study

Benefits of 7 QC Tools:

• Provides a structured approach for problem-solving
• Easy to understand
• Easy to implement
• A scientific and logical approach
• Improve the quality of products and services
• Identifying and analyzing problems
• Used for root cause analysis
• Enhance customer satisfaction

Limitations of 7 QC Tools:

• The accuracy of data collection depends on a person's skills
• Statistical interpretation requires highly skilled persons
• They are focused on identifying problems rather than preventing
• Reactive approach
• Focus on symptoms, not on root causes

Conclusion:

→ Seven QC tools are most helpful in troubleshooting issues related to quality

→ Different factors cause different effects on the process and make them unstable.

→ Those parameters cause variation in the process.

→ These tools are the most helpful for improving the process.

→ We can improve the efficiency and effectiveness of processes by using these tools.

Related Posts

23 comments.

very good presentation skill and to the point explaination

Thanks for your feedback and kind comment!!!

How to download???

You can check the individual articles!!!

Best in short... Great work Nikunj

Thank you very much for your kind comment!!!

Simply wonderful. Thanks very much!

this is a great initiative , well done

Thank you for your kind words!!!

Thanks and happy learning!!!

Nice teaching

Thank you and Happy Learning!!!

Great good initiative 👍 a How to Download

Thank you for your kind word!!

This is so helpful

Thanks for your feedback

HOW MAKE PARETO

You can go through with our article on pareto.

Sir can you please share process audit checklist

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7 Powerful Problem-Solving Root Cause Analysis Tools

The first step to solving a problem is to define the problem precisely. It is the heart of problem-solving.

Root cause analysis is the second important element of problem-solving in quality management. The reason is if you don't know what the problem is, you can never solve the exact problem that is hurting the quality.

Manufacturers have a variety of problem-solving tools at hand. However, they need to know when to use which tool in a manner that is appropriate for the situation. In this article, we discuss 7 tools including:

• The Ishikawa Fishbone Diagram (IFD)
• Pareto Chart
• Failure Mode and Effects Analysis (FMEA)
• Scatter Diagram
• Affinity Diagram
• Fault Tree Analysis (FTA)

1. The Ishikawa Fishbone Diagram IFD

The model introduced by Ishikawa (also known as the fishbone diagram) is considered one of the most robust methods for conducting root cause analysis. This model uses the assessment of the 6Ms as a methodology for identifying the true or most probable root cause to determine corrective and preventive actions. The 6Ms include:

• Measurement,
• Mother Nature- i.e., Environment

Related Training: Fishbone Diagramming

2. Pareto Chart

The Pareto Chart is a series of bars whose heights reflect the frequency or impact of problems. On the Chart, bars are arranged in descending order of height from left to right, which means the categories represented by the tall bars on the left are relatively more frequent than those on the right.

Related Training: EFFECTIVE INVESTIGATIONS AND CORRECTIVE ACTIONS (CAPA) Establishing and resolving the root causes of deviations, problems and failures

This model uses the 5 Why by asking why 5 times to find the root cause of the problem. It generally takes five iterations of the questioning process to arrive at the root cause of the problem and that's why this model got its name as 5 Whys. But it is perfectly fine for a facilitator to ask less or more questions depending on the needs.

Related training: Accident/Incident Investigation and Root Cause Analysis

4. Failure Mode and Effects Analysis (FMEA)

FMEA is a technique used to identify process and product problems before they occur. It focuses on how and when a system will fail, not if it will fail. In this model, each failure mode is assessed for:

• Severity (S)
• Occurrence (O)
• Detection (D)

A combination of the three scores produces a risk priority number (RPN). The RPN is then provided a ranking system to prioritize which problem must gain more attention first.

Related Training: Failure Mode Effects Analysis

5. Scatter Diagram

A scatter diagram also known as a scatter plot is a graph in which the values of two variables are plotted along two axes, the pattern of the resulting points revealing any correlation present.

To use scatter plots in root cause analysis, an independent variable or suspected cause is plotted on the x-axis and the dependent variable (the effect) is plotted on the y-axis. If the pattern reflects a clear curve or line, it means they are correlated. If required, more sophisticated correlation analyses can be continued.

Related Training: Excel Charting Basics - Produce Professional-Looking Excel Charts

6. Affinity Diagram

Also known as KJ Diagram, this model is used to represent the structure of big and complex factors that impact a problem or a situation. It divides these factors into small classifications according to their similarity to assist in identifying the major causes of the problem.

7. Fault Tree Analysis (FTA)

The Fault Tree Analysis uses Boolean logic to arrive at the cause of a problem. It begins with a defined problem and works backward to identify what factors contributed to the problem using a graphical representation called the Fault Tree. It takes a top-down approach starting with the problem and evaluating the factors that caused the problem.

Finding the root cause isn't an easy because there is not always one root cause. You may have to repeat your experiment several times to arrive at it to eliminate the encountered problem. Using a scientific approach to solving problem works. So, its important to learn the several problem-solving tools and techniques at your fingertips so you can use the ones appropriate for different situations.

ComplianceOnline Trainings on Root Cause Analysis

P&PC, SPC/6Sigma, Failure Investigation, Root Cause Analysis, PDCA, DMAIC, A3 This webinar will define what are the US FDA's expectation for Production and Process Control / Product Realization, the use of statistical tehniques, 6 sigma, SPC, for establishing, controlling , and verifying the acceptability of process capability and product characteristics, product acceptance or validation and other studies. Non-conformance, OOS, deviations Failure Investigations, and Root Cause Analysis, PDCA, DMAIC, and similar project drivers to improvement, A# and similar dash boards.

Accident/Incident Investigation and Root Cause Analysis If a major workplace injury or illness occurred, what would you do? How would you properly investigate it? What could be done to prevent it from happening again? A properly executed accident/incident investigation drives to the root causes of the workplace accident to prevent a repeat occurrence. A good accident/incident investigation process includes identifying the investigation team, establishing/reviewing written procedures, identifying root causes and tracking of all safety hazards found to completion.

Root Cause Analysis - The Heart of Corrective Action This presentation will explain the importance of root cause analysis and how it fits into an effective corrective and preventive action system. It will cover where else in your quality management system root cause analysis can be used and will give examples of some of the techniques for doing an effective root cause analysis. Attendees will learn how root cause analysis can be used in process control.

Addressing Non-Conformances using Root Cause Analysis (RCA) RCA assumes that systems and events are interrelated. An action in one area triggers an action in another, and another, and so on. By tracing back these actions, you can discover where the issue started and how it grew into the problem you're now facing.

Risk Management Under ISO 14971 ISO 14971:2019 is the definitive standard for risk management for medical devices and IVDs. The standard lays out a comprehensive approach to managing risks in the life sciences. The course will discuss practical approaches to complying with the standard.

Introduction to Root Cause Investigation for CAPA If you have reoccurring problems showing up in your quality systems, your CAPA system is not effective and you have not performed an in-depth root cause analysis to be able to detect through proper problem solving tools and quality data sources, the true root cause of your problem. Unless you can get to the true root cause of a failure, nonconformity, defect or other undesirable situation, your CAPA system will not be successful.

Root Cause Analysis and CAPA Controls for a Compliant Quality System In this CAPA webinar, learn various regulations governing Corrective and Preventive Actions (CAPA) and how organization should collect information, analyze information, identify, investigate product and quality problems, and take appropriate and effective corrective and/or preventive action to prevent their recurrence.

How to Design and Implement a Dynamic Control Plan This webinar training will discuss how to design a dynamic control plan that combines FMEA and the control plan by extending the FMEA to encompass the elements of the control plan and create a living document that helps to drive continual improvement.

An Easy to Implement Integrated Risk Management Approach Compliant with ISO 14971 This integrated risk management training for medical devices will discuss how to incorporate risk management as per ISO 14971 guidelines in all phases of medical device development. It will highlight the documentation needed to support the decisions made as part of the risk management process.

The Use and Mis-use of FMEA in Medical Device Risk Management The presentation will discuss the proper use of FMEA in risk management and how to recognize and avoid the traps associated with this tool in order to have a more efficient risk management process. Most medical device manufacturers use FMEA as a part of their risk management system. Most medical device manufacturers use FMEA as a part of their risk management system.

Root Cause Analysis for CAPA Management (Shutting Down the Alligator Farm) Emphasis will be placed on realizing system interactions and cultural environment that often lies at the root of the problem and prevents true root cause analysis. This webinar will benefit any organization that wants to improve the effectiveness of their CAPA and failure investigation processes.

Root Cause Analysis for Corrective and Preventive Action (CAPA) The Quality Systems Regulation (21 CFR 820) and the Quality Management Standard for Medical Devices (ISO 13485:2003), require medical device companies to establish and maintain procedures for implementing corrective and preventive action (CAPA) as an integral part of the quality system.

Strategies for an Effective Root Cause Analysis and CAPA Program This webinar will provide valuable assistance to all regulated companies, a CAPA program is a requirement across the Medical Device, Diagnostic, Pharmaceutical, and Biologics fields. This session will discuss the importance, requirements, and elements of a root cause-based CAPA program, as well as detailing the most effective ways to determine root cause and describing the uses of CAPA data.

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• All courses

Productivity

7 qc tools for quality improvement.

*Optional training delivery method for this course:  Remote Online Training (ROT) or Face-to-Face (F2F) ! 

COURSE DESCRIPTION

7 QC Tools For Quality Improvement course provides an in-depth study of the 7 Basic QC (Quality Control) Tools. They are scientific management tools, used worldwide by almost every organizations to collect and analyze data/facts for the purpose of quality improvements.  The 7 QC Tools are simple tools, low-cost and easy-to-use; but they are powerful tools that forms the critical foundation for all problem solving and quality improvement activities.   Once mastered, these tools will serve as the solid foundation for individuals’ and team’s root cause analyses, problem solving sessions, and continuous improvement projects.

Manufacturing and service industry professionals, quality technicians and auditors, and industrial engineers will all benefit from understanding these tools.  In addition to the foundational principles and concepts, this training examines practical real-world applications of the 7 QC Tools in the workplace. Participants will be provided with value-added MS Excel templates. These templates will help construct those QC Tools fast, easy and accurately.

This course is a MUST for all Quality Assurance staff such as Quality Engineers, Technicians, QC Leaders and also serve as a foundational course for Data Analysis & Lean Six Sigma practitioners.

CERTIFICATION

No certification.

Certificate of Achievement (for those who scored 80% and above for post-test) or Certificate of Attendance.

LEARNING OUTCOMES

At the end of the course, participants will be able to:

• Describe the benefits and power of each of the 7 QC Tools and its applications.
• Construct the QC tools accurately and fast using Microsoft Excel templates.
• Interpret , analyze and make accurate quality improvement decisions using the correct tools
• Perform data analysis and process monitoring using the QC tools
• Solve problems systematically and effectively.
• Experience the Quality Control Circle (QCC) team dynamics in solving problems.
• Make convincing presentations with the data collected from their workplace.

COURSE OUTLINE

Please see eBrochure above for more information.

To register, please Whatsapp : +60-19-502 2718  or email us at [email protected]

Course Features

• Skill level All level
• Language English
• Students 1632
• Assessments Yes

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 The 7 QC tools are simple graphical  and statistical tools that are  used for: • Problem Solving and Process Improvement

The check sheet is used for  collecting, recording, and analyzing  the data. data may be  numerical, observations and  opinions, etc., fishbone diagram  helps to identify all possible potential  causes  which contributes to  the problem/effect., a histogram is a pictorial representation of a set of data, and the most  commonly used bar graph for showing frequency distributions of  data/values., the pareto chart helps to narrow  the problem area or prioritize the  significant problems for corrective  measures. it gives us information about vital  few from trivial many, it is a graphical representation of  the collected information/data and  it helps to monitor the process  centering or process behavior  against the specified/set control  limits., a scatter graph is used to find out  the relationship between two  variables. scatter diagram is also known as  correlation chart, scatter plot, and  scatter graph., a technique used to analyze and  divide a universe of data into  homogeneous groups called - strata. it involves observing data,  splitting them into distinct classes  or categories to see a different  process for better analysis..