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The 7 Basic Quality Tools for Process Improvement

Quality Glossary Definition: Seven tools of quality

"The Old Seven." "The First Seven." "The Basic Seven."

Quality pros have many names for these seven basic tools of quality, first emphasized by Kaoru Ishikawa , a professor of engineering at Tokyo University and the father of "quality circles." Start your quality journey by mastering these tools, and you'll have a name for them too: indispensable.

Cause-and-effect diagram  (also called Ishikawa or fishbone diagrams): Identifies many possible causes for an effect or problem and sorts ideas into useful categories.

Check sheet : A structured, prepared form for collecting and analyzing data; a generic tool that can be adapted for a wide variety of purposes.

Control chart : Graph used to study how a process changes over time. Comparing current data to historical control limits leads to conclusions about whether the process variation is consistent (in control) or is unpredictable (out of control, affected by special causes of variation).

Histogram : The most commonly used graph for showing frequency distributions, or how often each different value in a set of data occurs.

Pareto chart : A bar graph that shows which factors are more significant.

Scatter diagram : Graphs pairs of numerical data, one variable on each axis, to look for a relationship.

Stratification : A technique that separates data gathered from a variety of sources so that patterns can be seen (some lists replace stratification with flowchart  or run chart ). 

Want more quality tools?

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• Fishbone Diagram
• Pareto Chart
• Scatter Diagram
• Check Sheet

7 Basic Quality Tool Templates

These templates will help you get started using the seven basic quality tools. Just download the spreadsheets and begin entering your own data.

• Cause-and-effect diagram template (Excel)
• Check sheet template (Excel)
• Control chart template  (Excel)
• Histogram template  (Excel)
• Pareto chart template  (Excel)
• Scatter diagram template (Excel)
• Stratification template  (Excel)

7 Basic Quality Tool Resources

You can also search articles , case studies ,  publications , and webcasts  for quality tool resources.

Innovative Control Charting

The Quality Toolbox

Pitch Perfect ( Lean & Six Sigma Review ) Learning the ins and outs of capability analysis by examining a baseball pitcher’s performance.

Fish(bone) Stories ( Quality Progress ) Today’s technology makes it easier than ever to communicate complex concepts more clearly, which is why older, "analog" quality methods should be digitized. The authors explore how digitizing one of the seven basic quality tools—the fishbone diagram—using mind mapping can significantly improve the tool.

Don't Misuse The Pareto Principle ( Six Sigma Forum Magazine ) Four commonly held misconceptions of the Pareto principle are discussed that have prevented some companies from realizing the true potential of the principle.

One Check to Rule Them All ( Quality Progress ) Check sheets were used to help an organization's Medicare Managed Care-focused operation gather data and pinpoint the specific problems, which helped them implement changes to eliminate rework and ultimately achieve almost $200,000 in labor efficiency.

Using Control Charts In A Healthcare Setting (PDF) This teaching case study features characters, hospitals, and healthcare data that are all fictional. Upon use of the case study in classrooms or organizations, readers should be able to create a control chart and interpret its results, and identify situations that would be appropriate for control chart analysis.

Certification

Excerpted from  The Quality Toolbox , ASQ Quality Press.

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7 QC Tools | 7 Quality Tools | Process Improvement Tools

7 QC Tools are also known as Seven Basic Quality Tools and Quality Management Tools. These graphical and statistical tools are used to analyze and solve work-related problems effectively.

The 7 Quality Tools are widely applied by many industries for product and process improvements, and to solve critical quality problems.

7QC tools are extensively used in various Problem Solving Techniques which are listed below:

• 8D Problem Solving Methodology.
• PDCA Deming Cycle for Continuous improvement in product and processes.
• Lean Manufacturing for 3M Waste elimination from processes.
• Various phases of Six Sigma-DMAIC to reduce process variations .

Table of Contents

WHAT ARE 7 QC TOOLS?

The 7 quality tools are simple graphical and statistical tools but very powerful in solving quality problems and process improvement.

These statistical tools are very easy to understand and can be implemented without any complex analytical competence or skills.

The 7 tools of quality are generally used by quality control and quality assurance engineers to solve product or process-related quality issues on a daily/weekly/monthly basis and to reduce/eliminate non-value-added activities like product rework, repair, and rejection.

7 QC Tools List | Quality Tools

The list of 7 QC tools are:

Check Sheet

Fishbone diagram, pareto chart, control chart, scatter diagram.

• Stratification Diagram (Some lists replace stratification with  Process Flowchart )

Click on the above links to Explore QC tools.

7 Tools of quality | Brief Explanation

The check sheet is used for collecting, recording, and analyzing the data. Data collection is an important activity in the problem-solving process as it provides a basis for further action. Data may be numerical, observations and opinions, etc.

Fishbone diagram is also called as Cause and Effect diagram and Ishikawa diagram . It helps to Identify all possible potential causes and select the real/best potential cause which contributes to the problem/effect. The brainstorming technique is used for potential cause identification.

In a brainstorming session, all 4M or 6M factors are taken into consideration to identify the potential causes. 4M or 6M factors are – Man, Machine, Method, Material, Measurement, and Mother nature also called Environment.

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. Histogram frequency distribution chart is widely used in Six Sigma problem solving process.

The Pareto chart helps to Narrow the problem area or prioritize the significant problems for corrective measures. The pareto principle is based on the 80-20 rule. It means that 80 percent of the problems/failures are caused by 20 percent of the few major causes/factors which are often referred to as Vital Few .

And the remaining 20 percent of the problems are caused by 80 percent of many minor causes which are referred to as Trivial Many . Hence, it gives us information about Vital few from Trivial many.

A control chart is also known as the SPC chart or Shewhart chart. 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 control chart is a very powerful tool to Investigate/disclose the source of Process Variations present in the manufacturing processes. Tells when to take necessary action to eliminate the Common or Random or Chance variations and Special causes of variations.

The control chart helps to measure and analyze the process capability and performance  ( Cp and Cpk and Pp and Ppk ) of the production process.

A Scatter diagram is also known as Correlation Chart, Scatter Plot, and Scatter Graph. A Scatter graph is used to find out the relationship between two variables. In other words, it shows the relationship between two sets of numerical data. Scatter graph shows a Positive or Negative correlation between two variables.

Independent variable data and dependent Variable data are customarily plotted along the horizontal X-axis and Vertical Y-axis respectively. Independent variable is also called controlled parameters.

Stratification Diagram

A technique used to analyze and divide a universe of data into homogeneous groups is called -Strata. Stratification tools are used when the data come from different sources or conditions, such as data collected from different shifts, machines,  people, days,  suppliers and population groups, etc.

Process Flow Chart

A  Process Flow Chart  (PFC) is a diagram of the separate steps of a operations/process in sequential order. PFC is also known as  process flow diagram  (PFD), and Process Map.

WHY DO WE NEED 7 QC TOOLS

We need Quality Tools for :

• Problem Solving – making decisions & judgments.
• For Process Measurement.
• For continual improvement in products, processes, and services.
• To improve Quality , Productivity, and Customer Satisfaction.

“95% of the problem is solved when clearly defined”

“95% of quality-related problems in the organization can be solved by using seven fundamental quantitative tools.”

7QC Tools benefits

The major benefits of QC tools are:

• To analyze and solve quality problems effectively.
• Improve product and process quality .
• Enhance customer satisfaction.
• Reduce cost due to poor quality.
• Helps in investigating the potential causes and real root cause of the problem for taking effective countermeasures.
• Check sheet helps in data collection and recording for quality problem analysis.  
• Identify and reduce the process variation using the SPC quality tool .
• The Pareto QC tool helps to narrow down the quality problem using the 80/20 rule.
• Helps in identifying the various sources of variations present in the process.
• Improve the employee’s analytical and problem-solving skills.

The new seven QC Tools are used for planning, goal setting, and problem-solving. These are explained below :

Affinity Diagram – KJ Method. This tool is used for Pinpointing the Problem in a Chaotic Situation and generating solution strategies.

Gathers large amounts of verbal data such as ideas, opinions, and issues, and organizes the data into groups based on natural relationships.

Tree Diagram – Also known as Systematic diagram or Dendrograms, Hierarchy diagram, Organisation chart, and Analytical Tree.

This diagram is used for systematically pursuing the best strategies for achieving an objective.

The advantages of the tree diagram are that it facilitates agreement among the team and is extremely convincing with strategies.

Relation Diagram – It is used for cause identification. For finding solutions strategies by clarifying relationships with Complex Interrelated Causes.

Allows for “Multi-directional” thinking rather than linear. Also known as Interrelationship diagrams.

Process Decisions Program Charts (PDPC) – Also called Decision Process Chart. It is used to produce the desired result from many possible outcomes.

The chart is used to plan various contingencies.

PDPC enables problems to be pinpointed.

Matrix Diagram – used for Clarifying Problems. It clarifies relationships among different elements.

Matrix Data Analysis – Matrix + Num. Analysis.

This can be used when the Matrix diagram does not give sufficient information.

This is used in various fields like process analysis, new product planning, market surveys, etc.

Arrow Diagram – Gantt Chart + PERT/CPM Chart.

An arrow diagram is employed for understanding optimal schedules and controlling them effectively.

This shows relationships among tasks needed to implement a plan.

This diagram is extensively used in PERT (Program Evaluation and Review Technique) and CPM (Critical Path Method).

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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.

Join over thousands of organizations that use Creately to brainstorm, plan, analyze, and execute their projects successfully.

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.

More Related Articles

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.

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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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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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Streamlining Six Sigma Projects with the 7 QC Tools

Published: September 23, 2024 by Ken Feldman

• The 7 QC tools are easy to use and understand.
• You can readily address most quality issues with just a handful of these tools.
• Different tools have different uses, so make sure you pick the right one for the job.
• Graphs and charts created need to be readily understood.
• You can utilize these tools as the driving force behind any presentation.

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.

Real-World Applications of the 7 QC Tools

Imagine for just a moment you’re the manager of an MSP for the tech industry. Your organization is keeps up with the workstations, servers, and other mission-critical elements of your clients. Lately, you’ve received complaints about workstations crashing at random points in the day.

Your first step should be to create an Ishikawa Diagram. You know the problem, so what are the root causes? These workstations are in your staff’s care, so something in your workflow is going awry.

You notice that some of these machines are lacking current system updates. Subsequently, this leads to broken drivers for the network interface card. Thanks to the Ishikawa Diagram, you can map out this issue effectively.

Afterward, any team lead would develop a check sheet. You’d have criteria to make sure machines are up-to-date with their updates. Using just a handful of tools, you’ve readily solved the issue, and those mission-critical machines can stay online.

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.

Additional Tools and Concepts

Quality control is just one aspect of managing your projects. Implementing quality management yields impressive results, as you can see in our comprehensive guide . Additionally, this strategy can readily implement the 7 QC tools covered throughout this guide.

Keeping your team on the same page is paramount in any stage of process improvement. Learning how to implement the 5Ps in your meetings increases engagement with your employees without wasting time.

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

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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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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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• Quality Management

Seven Basic Tools of Quality for Process Improvement

Quality improvement has become crucial for all organizations operating in various industrial sectors. Every organization aims to maintain and sustain the “quality” of the product and services throughout the production process. In addition, Customers are only willing to pay for high-quality products and services. Therefore, organizations should strive to meet their customer’s requirements by improving their business processes and delivering high-quality products and services. They can accomplish these tasks by using the 7 Quality Control Tools of Quality Management.

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History of 7 QC Tools of Quality Management

There are seven quality control tools in quality management for process improvement, which originated in post-war Japan. Kaoru Ishikawa, a professor of Engineering at Tokyo University, introduced these 7 QC Tools of Quality Management. Since these tools are basic and easy to use. This helped workers effectively improve the quality of the business process. As a result, Japan has become one of the first nations to produce high-quality products. The tool not only fulfilled the requirements of the customers but also helped generate a huge amount of profit for the organizations. Earlier, the quality improvement concept was used only in the manufacturing industries. Later, it extended to a wide range of other sectors, including finance, healthcare, government, and pharmaceuticals.

When the quality improvement concept reached the West, the automotive and manufacturing industries in America developed it further to match the high-quality production processes of their competitors in Japan. This resulted in the introduction of the concepts of Lean and Six Sigma, which became the fundamentals of Quality Management. However, the 7 QC Tools are still in practice along with the new process improvement methodologies of Lean, Six Sigma, TQM (Total Quality Management), and Lean Six Sigma . Hence, the seven quality control tools of quality management are believed to be the gold standards for process improvement.

7 Basic Quality Control (QC) Tools

The 7 Basic Quality Control (QC) Tools for Process Improvement in Quality Management are Cause-and-effect diagrams (Fishbone or Ishikawa diagram), Scatter diagrams, Histograms, Control charts, Pareto charts, Check sheets, and Stratification. With the use of these tools, professionals can derive the most information possible from the data they collect, analyze, and interpret.

Fishbone or Ishikawa Diagram

The Fishbone or Ishikawa Diagram is also known as the cause-and-effect diagram, introduced by Kaoru Ishikawa. It helps users to identify the causes or factors of the effect or problem and solve them accordingly. Ishikawa Diagram looks like a fish’s skeleton with the fish head to the right of the diagram and the bones branching off behind it to the left. It helps in identifying the root cause of the problem and improving the business process by finding the solution to the effect. 

Scatter Diagram

Scatter Diagram or scatter plot is a statistical quality control tool. The scatter diagram serves the same purpose as the fishbone diagram while establishing a relationship between the causes and the overall effect. These are depicted as variables – X and Y and are used by quality management professionals to analyze the relationship between the two variables. After establishing the relation, professionals can start working toward finding the solution to the problems. Professionals can use any number of independent and dependent variables to create a scatter plot.

Histogram was introduced by Karl Pearson. A histogram is a bar graph that shows the frequency distribution on each bar. It is used to study the areas of improvement that are needed at any given point in time in the production process. Quality professionals use this tool for studying various data like days of the week, physical measurements, the time needed to complete tasks, etc. Furthermore, Histogram helps in analyzing and interpreting the actions of various data sets to control quality. 

Control Chart

This quality control tool is also called the Shewhart chart after Walter A. Shewhart. A Control Chart is a statistical chart used to determine the stability and predictability of the process over time. It helps in reducing process variation by comparing the current data to historical data. Moreover, Control Chart helps in decision making for process improvement goals.

Pareto Chart

This QC Tool named after Vilfredo Pareto is used to determine the biggest contributors to a particular problem. Identifying these factors will help quality management professionals adjust to the causes and rectify the problem easily. Pareto chart is represented in a combination of bar graph and line graph. The 80-20 rule, which claims that 20% of the key factors account for 80% of an issue, is the foundation of the Pareto chart. The remaining 20% of the problem is caused by small elements that together account for 80% of the problem.

Check Sheet

Professionals utilize check sheets to collect quantitative or qualitative data. This quality control tool is called a tally sheet when it’s used for collecting quantitative data. The purpose of a check sheet is to list important data or information in a table and check their status. This helps in understanding the progress of the production process, find patterns, and solve problems.

Stratification

This quality control and assurance tool is also called a flowchart or run chart. Stratification divides data into categories and classifies them into groups. Depicting data in a visual representation helps quality professionals derive meaningful information from them, ultimately leading to process improvement. Moreover, Stratification improves understanding of the process, and identifies inefficiencies or bottlenecks.

Importance of 7 QC Tools of Quality Management

The 7 QC Tools can be used in collaboration with other quality management methodologies. For example, in the Define stage of the DMAIC phases of Lean Six Sigma, Flowcharts are used. Fishbone diagrams, Pareto charts, and Control charts are used in the Measure stage. Scatter diagrams, Histograms, and Check sheets are utilized to analyze data in the Analyze stage. Control charts are used in the Improve and Control phases of Six Sigma. 

Knowledge of seven basic quality control tools will help organizations improve their business processes and increase productivity. The 7 QC Tools are easy to understand. However, having in-depth knowledge of these tools will provide more opportunities for Quality Management professionals working on various Six Sigma projects. Therefore, candidates interested in gaining expertise in 7 QC tools can opt for Certified 7QC Tools Practitioner Certification Training . Training gives professionals knowledge and hands-on experience in understanding the 7 QC Tools of Quality Management. This will be very beneficial for quality management professionals while working in the organizations. 

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What Are The 7 Basic Quality Control Tools | 7 QC Tools Overview

The tools most often used for problem solving by organizations well versed in quality control are called the Seven QC Tools. In this guide, we give a general usage definition of the seven basic QC tools and detail how the 7QC tools can help your organization improve no matter what type of work you do.

7 Basic Tools Of Quality Control

Officially, the seven basic quality tools are a group of highly efficient proven tools developed by many different gurus. They are divided into:

1. Process Map The first QC tool is used to help employees and organizations understand the finite details and obtain a thorough description of the process that’s being applied to. The other very important aspect of a process map is to visually identify the process or system that it’s being used on officially. The process map can take or be defined as a diagram of the flow of production process or service process through a system. Standard symbols are often used to designate processing flow directions, branching decisions and inputs and outputs. There are various ways to formulate a process map, some include gathering a team of subject matter experts and analysts to use post-it notes individually listing the process steps and laying them out in sequence on a wall. Another way would be to use digital applications.

2. Pareto Chart Pareto Charts classifies problems and defects by type in the order of quantities and shows the cumulative total. Now Pareto Charts are often used for checking a problem or defect to be highlighted for solution. In other words, Pareto Chars often help us identify where the problem is.

3. Cause And Effect The cause and effect diagram is one of the most common and well-known quality tools. It’s a method of linking and organizing factors that affect an issue or problem being investigated. The ultimate goal of the cause and effect diagram is to identify all of the possible causes of an effect and investigate further with the intention of discovering root causes. All in all, the cause and effect diagram systematically arranges the results of effects and the factors that influence them. With this information, we’re able to classify causes and their potential influence on the problem at hand.

4. Check Sheet Check sheets are tables used to arrange data by type. They are simple and easy tools for summarizing occurrences of specific events through tally counting, checking if jobs are completed without problems and for preventing mistakes from happening at all. One important thing to keep in mind when creating a check sheet is that the team members who are creating the check sheets have ample amounts of time to create and gather accurate data.

5. Histogram The next tool in our set of seven basic quality tools is the histogram. It’s a graphical display of numerical data in the form of upright bars such as temperature wait and oftentimes dimension in a process. With histograms, we’re able to learn many things including how much variation or spread, a data set contains.

6. Control Chart Control chart is a tool used for judging the situation of quality values against control limits in order to check the stability of a process. It also help us to under stand whether we’re dealing with common cause or special cause variation.

7. Scatter Diagram This QC tool is used to show the relationship between two variables by plotting corresponding date. In other words, scatter diagrams help us examine the relationships between two variables and whether or not they’re associated, or correlated, with on another.

Benefits of the 7 Quality Control Tools

The 7 basic quality tools provide practitioners with a structured path for using statistical analysis to identify and solve problems that directly impact the quality of your products, processes, and services. The main benefit offered by the 7QC tools is that they make it easy to make quality control activities a routine part of your continuous improvement practice. Long-term, QC efforts support sustained improvements in customer satisfaction and brand reputation, and cut costs associated with errors, delays, and poor complaint-handling.

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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“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

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CQE Academy

Helping You Become a Certified Quality Engineer!!

Quality Control Tools

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

As a Quality Engineer 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 tool 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 .

Does it seem odd that we’re in the Continuous Improvement section talking about Quality Control & Problem Solving ?

It shouldn’t – problem solving is continuous improvement .

Improvements happen when we solve problems.

So, what are these 7 fundamental tools for problem solving & continuous improvement:

Flow Charts

Check sheets, pareto charts, cause & effect diagrams, control charts, scatter diagrams.

Within this chapter, we will discuss when to use each tool along with how to construct and analyze them.

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.

Lastly, I wanted to provide a link to a Youtube Playlist for the 7 QC Tools .

Let’s get started with the flow diagram.

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 Charts are also powerful in their application. You can make them super detailed, or you can stay at a high level, depending on the goal of the flow chart.

One common mistake with flow charts is that they are often created in a conference room, away from where the process actually occurs.

There will always be a difference between the “theoretical” process that you believe is occurring, and the actual process that’s occurring.

You must go and see for yourself (Gemba), to truly understand the actual process.   Go and talk with the folks who actually work the process to truly understand the process.

Common Flow Chart Symbols

To facilitate the communication process, it’s generally acceptable to use standard symbols with your flow charts. This will ensure consistency and reduce miss-communication.

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.

Solving problems and making improvements requires data . Period.

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

I would argue that when you combine the simplicity of the check sheet with the potential value associated with the collected data that the check sheet is the most powerful QC tool.

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 example above is very simply. Almost too simple.

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 ).

A good check sheet is designed to have clear, unique & unambiguous data collection categories . If necessary , standard work (work instructions) can be created and distributed with the check sheet to ensure data is collected appropriately.

This can include illustrations to go along with the check sheet.

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).

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.

What Pareto did not realize is that this 80/20 rule is a universal principle , and can be applied to a lot more than wealth distribution.

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 .

Essentially, the pareto chart is a prioritization tool that allows us to focus on the issues that are causing the biggest problem, and thus maximize our impact.

Mechanically, the Pareto Chart is simply a bar chart that displays data that from various discrete categories . This data might come from a check sheet .

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.

Let’s analyze this our Pareto Chart quickly. There are 15 total defect conditions (A-N).

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.

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).

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 cause and effect diagram can be completed as part of a 3-step process.

Step 1 is to agree on the problem statement, this is the negative “effect” you’re experiencing. This might seem simple, but it’s important to align on the problem statement prior to continuing.

Step 2 is the brainstorming process which is facilitated by the 8M’s of the fishbone process (below) , and should be used with a process flow chart and 5-Why technique to truly identify causes, and not simply stop at symptoms.

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

• Man – How do Humans interact with your product/process/equipment and how could that contribute to your problem.
• Machine – What type of equipment or machinery are used in your process and how could a deviation here contribute to your problem.
• Method – What type of process/procedure do you follow and what potential issues might contribute to your problem.
• Materials – What type of material is used and how could any material deviations contribute to your problem.
• Mother Nature – How does the environment interact with your product/process in a way that might contribute to your problem.
• Measurements – What type of measurements and measurement equipment do you use and how might this relate to the problem.
• Management – What are the attitudes, outlooks & priorities of management and how could this be contributing to your problem.
• Maintenance – What type of maintenance/calibration activities are being performed on your machines or measurement equipment that could be contributing to your problem.

Once you’ve brainstormed and created a list of potential causes and contributing factors , you can move on to Step 3.

Step 3 is to prioritize an action plan of investigation steps that will help confirm or exclude the potential causes and factors.

Another underrated characteristic of the Cause & Effect Diagram is its effectiveness as a communication aid . Especially when you’re dealing with a very complex issue.

Let’s go through a quick example

Cause & Effect Example

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

Step 1 in the Cause and Effect process is to agree on a problem statement : The Toaster is not toasting.

With more data we could refine this problem statement to improve the brainstorming, but for now we will leave it generic.

We can always refine the problem statement as the investigation progresses.

Then 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.

For example, we agree that the most likely root cause is a faulty heating element, and we will focus our investigation here first.

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.

An unstable process is unpredictable and results in both problems, and is a clear opportunity for improvement.

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 chapter.

This section is a high-level summary of the control chart , along with how it can be used to solve problems and improve processes.

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.

The primary benefit of a control chart is its unique ability to separate the normal variation within your process from the special cause variation.

Special cause variation causes problems .  It represents an opportunity for improvement .

Normal cause variation can also be an opportunity for improvement, however reducing normal cause variation can be difficult because it can often require making substantial changes to the process itself.

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.

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.

This section will summarize the scatter diagram at a high level, and the Relationship Between Variables Chapter in Statistics will cover this topic in detail.

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).

Scatter Plots require pairs of data , one set of data in the pair is normally referred to as the Independent Variable (X) with the second half of the data set being your observed measurement also known as the Dependent Variable (Y).

When a Positive Correlation exists between two variables a positive increase can be expected from the dependent variable (Y) when the intendent variable (X) increases.

The opposite is true for the Negative correlation . A negative correlation means that when the independent variable (X) increases, the dependent variable (Y) will decrease.

No Correlation results when the two variables have no measurable effect on each other. That is a change in X, does not impact Y.

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).

This chart is fairly easy to create using tools like excel or other statistical analysis packages, we can collect data using a check sheet, and we’re specifically collecting paired data.

Let’s take a look at an example of how we could use a scatter diagram to analyze two variables and assess the relationship between them.

Scatter Diagram Example

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 CQE Exam .

I 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.

Each pair of data represents a dot on the Scatter Plot, and I’ve included a linear regression line to reflect the relationship between these variables.

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 .

You might have indicators like this in your process that perhaps do not relate back to quality or cost, but they can indicate if a problem exists.

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 Distribution section of Statistics , and 3 examples are shown below.

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.

Within the next section on Statistics , you’ll learn that many concepts and tools assume that data is normally distributed .

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

Lastly, in terms of creating a histogram, this can be done in excel, and many statistical software packages will create histograms for you, so I won’t go into that detail here.

As a Quality Engineer 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.

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.

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 Chapter: Continuous Improvement Techniques

The 7 Basic Quality Tools for Process Improvement

Enhancing Efficiency and Excellence in Business

Written By: Rei Takako Proofread By: MSI Staff

In the fast-paced and competitive world of business and manufacturing, the pursuit of excellence is not just an ambition but a necessity. Quality and efficiency are the cornerstones of this pursuit, and mastering the art of process improvement is crucial for any organization aiming to thrive. This is where the “7 Basic Quality Tools for Process Improvement” come into play, serving as essential instruments in the toolkit of quality management professionals.

Originating from the foundational practices of Total Quality Management (TQM) and Six Sigma, these tools are not just tools but beacons that guide businesses through the complexities of process optimization. They are revered for their simplicity, versatility, and profound impact. Whether it’s a multinational corporation or a small startup, these tools are universally applicable, transcending industry boundaries and scaling to fit various operational scopes.

The beauty of these tools lies in their ability to transform complex, abstract problems into tangible, manageable components. They enable teams to dissect issues, analyze data, and craft strategic solutions. By implementing these tools, organizations can identify and rectify inefficiencies and foster a culture of continuous improvement and strategic foresight.

The 7 Basic Quality Tools are more than methodologies; they build a resilient, agile, and quality-focused business environment. As we delve into each of these tools, it becomes evident how they collectively form a powerful arsenal for driving process improvement, enhancing product quality, and ensuring customer satisfaction in today’s dynamic business landscape.

1. Cause-and-Effect Diagram (Ishikawa or Fishbone Diagram)

The Cause-and-Effect Diagram , also known as the Ishikawa or Fishbone Diagram, is a fundamental tool in the quality management arsenal. It is named after its creator, Kaoru Ishikawa. Its primary function is to facilitate the systematic exploration of potential causes for a specific problem or issue. The diagram’s unique fishbone structure visually organizes the causes into various categories, making complex problem-solving more manageable and structured.

How it Works

The diagram typically starts with a problem statement, placed at the “head” of the fish. Branching out from this problem statement are the “bones,” representing different categories of potential causes. Common categories include Methods, Machinery, Materials, Manpower, Measurement, and Environment, though these can vary depending on the problem’s specific context.

Application in Business

In a business context, the Cause-and-Effect Diagram is a powerful brainstorming tool. It encourages teams to consider all possible aspects of a problem, avoiding a narrow focus on the most apparent causes. For example, suppose a manufacturing company is facing a decline in product quality. In that case, the diagram can help the team explore various potential causes such as equipment malfunctions (Machinery), untrained staff (Manpower), inconsistent raw materials (Materials), or even environmental factors like humidity or temperature (Environment).

Comprehensive Analysis : It ensures a thorough exploration of all potential causes of a problem, not just the most apparent ones.

Team Collaboration : It fosters team collaboration and collective problem-solving, as various team members can contribute insights from their expertise.

Visual Clarity : The visual nature of the diagram makes complex problems more understandable and manageable.

Root Cause Identification : It aids in identifying the root causes of problems, which is crucial for developing effective solutions.

Over-Complexity : The diagram can sometimes become overly complex if too many potential causes are considered.

Misidentification of Causes : There is a risk of incorrectly identifying causes, leading to ineffective solutions.

Overall, the Cause-and-Effect Diagram is a versatile and effective tool for identifying, categorizing, and exploring the potential causes of problems in business processes. Its ability to break down complex issues into manageable parts makes it an invaluable quality and process improvement tool.

2. Check Sheet (Tally Sheet)

The Check Sheet, often referred to as a Tally Sheet, is a fundamental data collection tool in quality management. Its simplicity belies its power in capturing, organizing, and analyzing data, which is crucial for any process improvement initiative.

Nature and Functionality

A Check Sheet is a structured, prepared form for collecting and analyzing data. This customizable tool allows users to record and compile data systematically in real-time. It typically consists of several rows and columns, where each row represents a category or specific item to be observed, and each column is often used to tally the occurrences or measure other relevant data.

Diverse Applications

In a business context, Check Sheets serve various purposes, such as tracking defects’ frequency, monitoring events’ occurrence over time, or even conducting simple surveys. For instance, a Check Sheet might be used in a manufacturing setting to record the types and frequencies of machine breakdowns. In customer service, it could track the nature and number of customer complaints.

Ease of Use : Its simple format makes it easy for anyone to use without extensive training.

Real-Time Data Collection : It facilitates on-the-spot recording, reducing the likelihood of errors and omissions.

Versatility : It can be customized for various data collection needs.

Visual Representation : When analyzed, the data from Check Sheets can be easily transformed into other quality tools like histograms or Pareto charts for further analysis.

Subjectivity in Data Recording : The effectiveness of a Check Sheet can be compromised if the data recording is not standardized or if there’s ambiguity in what is being recorded.

Limited to Quantitative Data : It primarily collects quantitative data, and might not be suitable for capturing more nuanced, qualitative information.

Implementation Tips

Clear Definition : Ensure each category or item on the Check Sheet is clearly defined to avoid ambiguity.

Training : Train staff on how to use the Check Sheet effectively.

Review and Adaptation : Regularly review the Check Sheet for its relevance and adapt as necessary to meet changing needs.

The Check Sheet is a versatile and straightforward tool in the quality management toolkit. When used effectively, it can provide invaluable insights into process performance, thereby laying the groundwork for more detailed analysis and improvement strategies.

3. Control Charts

Control Charts, a pivotal tool in statistical process control, are crucial in monitoring and improving process performance over time. Developed by Walter A. Shewhart in the 1920s, these charts are fundamental for ensuring that processes are stable and predictable, a key aspect in maintaining consistent quality.

Understanding Control Charts

A Control Chart is a graphical representation used to monitor the variability and performance of a process. It typically consists of points plotted in time order, a central line for the average, an upper control limit, and a lower control limit. These limits are calculated based on the data and represent the threshold at which the process is considered in or out of control.

Applications in Various Sectors

In manufacturing, Control Charts can track production processes to detect any deviations from the norm, such as variations in product dimensions. In service industries, they might monitor transaction times or service quality. Essentially, any process that can be measured over time can benefit from the use of Control Charts.

Early Detection of Problems : They help identify process variations before they escalate into more significant issues.

Process Optimization : By monitoring process stability, they aid in identifying opportunities for process improvement.

Reduced Variation : They assist in maintaining process consistency, which is crucial for quality assurance.

Data-Driven Decision Making : Decisions based on Control Charts are grounded in concrete data, enhancing the reliability of the decisions.

Potential Challenges

Misinterpretation of Data : Misunderstanding the data or control limits can lead to incorrect conclusions about process stability.

Setting Inappropriate Limits : Inaccurately set control limits can either fail to detect real problems or signal problems where none exist.

Over-Reliance on the Tool : While Control Charts are powerful, they need to be used as part of a broader quality management approach.

Effective Usage

Regular Monitoring : Regularly update and review the Control Charts to keep track of the process performance.

Training : Ensure that staff responsible for monitoring and interpreting the charts are adequately trained.

Integration with Other Tools : Combine Control Charts with other quality tools, like Pareto Charts or Cause-and-Effect Diagrams, for comprehensive process analysis.

Control Charts are indispensable in the quality management toolkit, especially for maintaining and improving the stability of processes. Their ability to provide visual and statistical analysis of process variations makes them essential for organizations striving for excellence in their operations.

4. Histogram

A Histogram is a statistical tool that plays a critical role in quality management and process improvement. It is essentially a bar chart representing the distribution of numerical data. By showing the frequency of data points within successive intervals, histograms provide a clear visual snapshot of data variation and distribution, which is vital for understanding and improving processes.

Fundamentals of Histograms

Histograms display data in columns, where each column represents a range or bin of values, and the height of the column indicates the frequency of data points within that range. This representation makes it easy to see patterns such as skewness, the presence of outliers, and whether data is evenly or unevenly distributed.

Application Across Fields

In manufacturing, histograms can be used to analyze the consistency of product dimensions, like the diameter of a batch of bearings. In service industries, they might be utilized to understand customer wait times or service delivery times. This versatile tool can be applied to any process where quantifiable data is collected.

Visualization of Data Distribution : Histograms clearly visualize how data is distributed across different ranges.

Identification of Patterns and Anomalies : They help in identifying common patterns, outliers, or anomalies in the data.

Facilitation of Comparative Analysis : Histograms allow for the comparison of data distributions over different periods or under different conditions.

Informing Process Improvements : Organizations can make informed decisions to streamline and improve processes by understanding data distribution.

Data Misinterpretation : Without proper statistical knowledge, there’s a risk of misinterpreting what the histogram represents.

Selection of Bins : Choosing inappropriate bin sizes or ranges can lead to misleading data representations.

Over-Simplification : While histograms are great for displaying distribution, they don’t show everything, such as the relationship between two variables.

Best Practices

Appropriate Bin Size : Carefully determine the range and size of bins to accurately reflect the distribution of data.

Contextual Analysis : Always analyze histogram data in the context of other relevant data and information.

Integration with Other Tools : Combine the insights from histograms with other quality tools like Control Charts and Pareto Charts for a more comprehensive analysis.

Histograms are invaluable in the quality manager’s toolkit, offering a simple yet effective means to visualize and analyze data distribution. This insight is essential for identifying potential areas for process improvement and ensuring that decisions are data-driven and focused on enhancing quality and efficiency.

5. Pareto Chart

The Pareto Chart is a vital tool in the quality management field, embodying the principle that a small number of causes are often responsible for a large percentage of the effect – a concept known as the Pareto Principle or the 80/20 rule. This tool is crucial for prioritizing problem-solving efforts and focusing on the changes that will have the greatest impact.

Overview of Pareto Charts

A Pareto Chart is a visual tool that combines both a bar graph and a line graph. The individual values are represented in descending order by bars, and the cumulative total is represented by the line. This format helps in identifying the most significant factors in a dataset.

Applications in Different Sectors

In manufacturing, Pareto Charts can be used to identify the most common sources of defects or production delays. In service industries, they can help pinpoint the most frequent types of customer complaints or service bottlenecks. They are valuable in any scenario where prioritizing resources and efforts can lead to significant improvements.

Focuses Efforts on Key Issues : By identifying the most critical factors contributing to a problem, Pareto Charts help in focusing efforts where they can make the most difference.

Data Visualization : They provide a clear visual representation of data, making it easier to understand and communicate issues.

Decision-making Aid : Pareto Charts are powerful tools for decision-makers, guiding them in allocating resources effectively.

Over-Simplification : While Pareto Charts are useful for highlighting major issues, they may oversimplify complex situations where multiple interrelated factors contribute to a problem.

Data Interpretation : Misinterpretation of data can lead to incorrect conclusions about what the key issues are.

Effective Implementation

Accurate Data Collection : Ensure the data used is accurate and comprehensive.

Regular Updates : Update the Pareto Chart regularly to reflect the current state of the process or problem.

Combine with Other Tools : Use in conjunction with other quality tools, such as the Cause-and-Effect Diagram, to delve deeper into the root causes of the issues identified.

Pareto Charts are essential in the toolkit of quality improvement methodologies. They guide teams to focus on the ‘vital few’ rather than the ‘trivial many’, ensuring that efforts and resources are channeled towards making the most impactful improvements. As a result, they play a pivotal role in enhancing the efficiency and effectiveness of business processes.

6. Scatter Diagram

The Scatter Diagram, also known as the scatter plot, is an indispensable tool in quality management and process improvement, primarily used for analyzing the relationship between two variables. This tool is crucial for identifying patterns, correlations, or potential cause-and-effect relationships, providing invaluable insights for decision-making and process optimization.

The Essence of Scatter Diagrams

A Scatter Diagram plots pairs of numerical data, with one variable on each axis, to look for a relationship or trend between them. Each point on the graph represents an individual data point. The pattern of these points can indicate whether and how strongly two variables are related.

Application Across Various Domains

Scatter Diagrams are widely used in numerous industries. In manufacturing, they might be used to examine the relationship between machine settings and product defects. They can analyze the correlation between advertising spend and sales revenue in marketing. These diagrams are versatile and can be applied to any scenario where understanding the relationship between two variables is beneficial.

Identifying Correlations : Scatter Diagrams are excellent for identifying whether a relationship exists between two variables and how strong that relationship is.

Visual Clarity : They provide a clear visual representation that can often reveal trends and patterns more effectively than numerical statistics.

Hypothesis Testing : They can be used to test hypotheses about cause-and-effect relationships.

Data Exploration : Scatter Diagrams are useful for initial exploration of data, guiding further detailed analysis.

Causation vs. Correlation : A common pitfall is mistaking correlation (how variables are related) for causation (one variable causing the other).

Over-interpretation : There’s a risk of over-interpreting the data without proper statistical knowledge.

Complex Relationships : They may not effectively reveal complex relationships involving more than two variables.

Use with Other Tools : For a comprehensive analysis, combine Scatter Diagrams with other tools like the Cause-and-Effect Diagram to explore underlying causes.

Statistical Expertise : Seek statistical expertise when necessary to interpret the diagrams correctly.

Continual Refinement : Continuously refine and explore data with additional scatter plots as more variables and data are considered.

In summary, Scatter Diagrams are a powerful tool in the quality improvement toolkit, providing clarity and insights into the relationships between variables. By effectively utilizing this tool, organizations can uncover hidden patterns and relationships, leading to more informed decisions and improved processes and products.

7. Flow Chart

The Flow Chart is a fundamental tool in process improvement, offering a clear and systematic visual representation of a process from start to finish. It is instrumental in understanding, analyzing, and optimizing complex processes, thereby playing a critical role in enhancing efficiency and effectiveness in various business operations.

Basics of Flow Charts

A Flow Chart is a diagram that depicts the steps of a process through a series of shapes connected by arrows. Each shape represents a different type of action or decision point, and the arrows show the flow and sequence of these steps. This tool is essential for mapping out processes in a way that is easy to understand and communicate.

Wide-Ranging Applications

In manufacturing, Flow Charts can be used to detail the production process, from raw material handling to finished product. In services, they can map out customer service protocols or administrative procedures. Their versatility makes them applicable in virtually any industry where processes need to be understood and improved.

Clarifies Complex Processes : Flow Charts make it easier to understand even the most complex operations by visually breaking down a process.

Identifies Redundancies and Inefficiencies : They help pinpoint redundant or inefficient steps, paving the way for streamlining and optimization.

Facilitates Communication : They are an excellent tool for communicating processes and changes within a team or organization.

Enhances Problem-Solving : By providing a clear view of the process, Flow Charts aid in identifying areas for improvement and problem-solving.

Over-Simplification : There’s a risk of oversimplifying complex processes, which might lead to missing out on important nuances.

Maintenance : As processes evolve, Flow Charts need to be regularly updated, which can be time-consuming.

Best Practices for Implementation

Detailing Each Step : Ensure that every step of the process is clearly and accurately represented.

Involving Stakeholders : Include input from all stakeholders involved in the process to get a comprehensive view.

Regular Review and Update : Continually review and update the Flow Chart to reflect any changes in the process.

Use in Conjunction with Other Tools : Combine Flow Charts with other quality tools, like Pareto Charts or Control Charts, for a holistic approach to process improvement.

Flow Charts are invaluable in the quality management toolkit, offering a structured and clear methodology for dissecting and understanding processes. Their use facilitates a deeper insight into operational workflows, aiding businesses in refining and optimizing their processes for greater efficiency and effectiveness.

The 7 Basic Quality Tools for Process Improvement are foundational in any quality improvement initiative. They are versatile and can be applied in various industries and processes. Organizations can significantly improve quality, efficiency, and overall performance by effectively utilizing these tools. These tools help in problem-solving and foster a culture of continuous improvement and strategic thinking within the organization.

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Table of Contents

Introduction & Why use the 7 QC Tools?

The 7 QC tools help to analyze the data and are most helpful in problem-solving methods. It is the fundamental tool to improve our product and process quality by identifying and analyzing the problems.

As per the Deming chain to achieve the organizational goal, we must tackle the product & process-related problems, and analyze these problems we get help from 7 QC tools. These 7 QC tools give us the analytical and statistical competence to solve the problems .

What are 7 QC tools?

7 Basic Quality techniques

• Pareto Charts
• Cause and Effect Diagrams
• Check sheet
• Scatter Diagrams
• Control Charts
• Flow Charts

Pareto Chart

• Prioritize problems.
• Pareto Charts are used to apply the 80/20 rule of Joseph Juran which states that 80% of the problems are the result of 20% of the problems. A Pareto Chart can be used to identify 20% of route causes of problems.

How is it done?

• Create a preliminary list of problem classifications.
• Tally the occurrences in each problem classification.
• Arrange each classification in order from highest to lowest
• Construct the bar chart

• Pareto analysis helps graphically display results so the significant few problems emerge from the general background
• It tells you what to work on first

To know the detail of What Pareto Principle is?, How to Make Pareto in Excel?

Cause & Effect Analysis

• Graphical representation of the trial leading to the root cause of a problem
• It’s a diagram that demonstrates the relationship between Effects and the categories of their causes
• The Arrangement of the Diagram lets it look like a fishbone it is therefore also called a fish-bone diagram
• Decide which quality characteristic , outcome, or effect you want to examine (may use a Pareto chart)
• Backbone –draw a straight line
• Ribs – categories
• Medium-size bones –secondary causes
• Small bones – root causes

• Breaks problems down into bite-size pieces to find the root cause
• Fosters teamwork
• A common understanding of the factors causing the problem
• Road map to verify picture of the process
• Follows brainstorming relationship

To learn in detail How to create a cause and effect diagram (Fishbone diagram)?

• A Histogram is a bar graph
• To determine the spread or variation of a set of data points in a graphical form
• usually used to present frequency

• Collect data, 50-100 data point
• Determine the range of the data
• Calculate the size of the class interval
• Divide data points into classes Determine the class boundary
• Count # of data points in each class
• Draw the histogram
• Allows you to understand at a glance the variation that exists in a process
• The shape of the histogram will show process behavior
•  Often, it will tell you to dig deeper for otherwise unseen causes of variation.
• The shape and size of the dispersion will help identify otherwise hidden sources of variation
•  Used to determine the capability of a process
• The starting point for the improvement process

Check Sheet

• Tool for collecting and organizing measured or counted data
• Data collected can be used as input data for other quality tools
• Collect data in a systematic and organized manner
• To determine the source of the problem
• To facilitate the classification of data (stratification).

Scatter Diagram

• To identify the correlations that might exist between a quality characteristic and a factor that might be driving it
• A scatter diagram shows the correlation between two variables in a process.
• These variables could be Critical to Quality (CTQ) characteristic s and a factor affecting it two factors affecting a CTQ or two related quality characteristics.
•  Dots representing data points are scattered on the diagram.
• The extent to which the dots cluster together in a line across the diagram shows the strength.
• Decide which paired factors you want to examine. Both factors must be measurable on some incremental linear scale.
• Collect 30 to 100 paired data points.
• Find the highest and lowest value for both variables.
• Draw the vertical (y) and horizontal (x) axes of a graph.
• Plot the data
• Title the diagram

The shape that the cluster of dots takes will tell you something about the relationship between the two variables that you tested.

You may occasionally get scatter diagrams that look boomerang- or banana-shaped.

• To analyze the strength of the correlation, divide the scatter plot into two sections.
• Treat each half separately in your analysis
• Helps identify and test probable causes. 
• By knowing which elements of your process are related and how they are related, you will know what to control or what to vary to affect a quality characteristic.

Control Chart

• The primary purpose of a control chart is to predict expected product outcomes.
• Statistical tool, showing whether a process is in control or not.
• Taking samples of a process and detecting the possibility of the process being out of control

How does it Work?

• Define Upper limit, lower limit, and medium value
• Draw Chart.
• Gather values and draw them into the chart
• Predict process out of control and out of specification limits
• Distinguish between specific, identifiable causes of variation
• Can be used for statistical process control

Strategy for eliminating assignable-cause variation:

• Get timely data so that you see the effect of the assignable cause soon after it occurs.
• As soon as you see something that indicates that an assignable cause of variation has happened, search for the cause.
• Change tools to compensate for the assignable cause.

Strategy for reducing common-cause variation:

• Do not attempt to explain the difference between any of the values or data points produced by a stable system in control.
• Reducing common-cause variation usually requires making fundamental changes in your process
• Visual illustration of the sequence of operations required to complete a task.
• Schematic drawing of the process to measure or improve.
• The starting point for process improvement
• A potential weakness in the process is made visual.
• Picture the process as it should be.
• Way of representing a Procedure using simple symbols and arrows
• List major steps
• Write the process step inside each symbol
• Connect the Symbols with arrows showing the direction of the flow
• List sub-steps under each in the order they occur

• Identify process improvements
• Understand the process
• Shows duplicated effort and other non-value-added steps
• Clarify working relationships between people and organizations
• Target specific steps in the process for improvement.
• Simplest of all flowcharts
• Used for planning new processes or examining an existing one
• Keep people focused on the whole process
• Show what happens at each step in the process
• Show what happens when non-standard events occur
• Graphically display processes to identify redundancies and other wasted efforts

Benefits of all – Tool-wise

Related posts.

Cost of Quality | Quality engineer essential guide

Total Quality Management (TQM) Fundamentals

SIPOC – The complete guide

Root cause analysis

Control Charts & Types of control chart

7 Management and Planning Tools

TECHIEQUALITY

7qc tools for problem solving | what are 7 qc tools.

7QC Tools for Problem Solving techniques are generally used in manufacturing, Non-manufacturing industries, and service sectors to resolve problems.

Download 7-QC Tools Template/ Format

Definition and History:-

The 7QC Tools (Also Known as “Seven Basic Tools of Quality”) originated in Japan. First emphasized by Kaoru Ishikawa, a professor of engineering at Tokyo University and the father of “quality circles”. These tools are used to solve critical quality-related issues. You can use the 7 basic tools of quality to help understand and solve problems or defects in any industry. With the help of Excel, you can plot the graphs / Diagrams to resolve the daily quality problems. I will help you to understand the basic ideas and knowledge of 7QC Tools and their usage.

For solving problems seven QC tools are used Pareto Chart, Cause & Effect Diagram, Histogram, Control Charts, Scatter Diagrams, Graphs/Process Flow Diagram, and Check Sheets. All these tools are important tools used widely in the manufacturing field to monitor the overall operation and continuous process improvement. seven QC tools are used to find out the Root cause of the problem and implement the action plan to improve the process efficiency.

7QC tools are:-

• Pareto Chart
• Cause and effects diagram
• Scatter Diagram
• Control Chart
• Check Sheet
• PFD(Process Flow diagram)/Graphs

  Benefits of 7QC Tools:-

• Improve management decisions.
• Simple and easy for implementation
• Continuous quality improvement
• Quick results
• Enhances customer satisfaction through improved quality product
• Reduce cycle time and improve efficiency
• Control cost of poor quality / Cost of quality
• Reduce defects and optimize the production
• Reduce variations and improve the quality of Products
• Encouragement of teamwork and confidence
• Enhancement of customer focus.

Pareto Chart:-

A Pareto Chart is named after the Italian Economist Vilfredo Pareto. It is a type of chart that contains both bars and a line graph, where the individual values are represented in the bar graph in descending order (largest to smallest value) and the cumulative percentage is represented in the line graph.

Click here to learn “How to Plot Pareto Chart In Excel”.

Understanding the Pareto Chart principle (The 80/20 rule):  

The Pareto principle is also known as the 80/20 rule derived from the Italian Economist Vilfredo,

The principle is understood as –

20% of the input creates 80% of the results

80 % of the effects come from 20% of the causes.

In the above Pareto Chart[Figure-1], we can see the cumulative% in the line graph, According to the Pareto Chart principle 80/20 rule, the 80% cumulative in the line graph is filling under the low hardness, which means BH, Damage, SH and Low hardness defers are coving the 80% of contribution over total types of defects. And those 80 % of contributions were due to the 20% caused.

  Histogram:-

The histogram is one of the 7QC tools, which is the most commonly used graph to show frequency distribution.

Helps summarize data from a process that has been collected over a period of time.

Click here to know the “How to Plot Histogram in Excel:

Fish-bone  Diagram/Cause and Effects /Ishikawa Diagram:-

The cause and Effects Diagram looks like a fish that’s why it’s called Fish-bone Diagram, also called the Ishikawa diagram.

It’s a visualization tool for categorizing the potential causes of a problem in order to identify its root causes.

CFT members are identifying the potential cause through the Brainstorming process of individuals and together.

  The Potential cause is related w.r.t below as

• Environment
• Measurement

Scatter diagram:-

The scatter diagram graphs pairs of variable data, with one variable on each axis, to look for a relationship between them. If the variables correlate, the points will fall along a line or curve. The better the correlation, the more points will strongly cluster to the line. It generally gives the idea of the correlation between the variables.

In the above figure-4, the positive and Negative correlation is only due to the direction, and in both the correlation, points are clustered to the line but in the last figure in figure-4, Points are not clustered to the line but spread over the X and Y-axis.  

Control Chart:-

A line on a control chart is used as a basis for judging the stability of a process. If the observed points are beyond a control limit then it is evidence that special causes are affecting the process.

Control Charts can be used to monitor or evaluate a process.

There are basically two types of control charts, those for variable data and those for attributes data.

Click here to learn more about the Control Chart and Statistical Process Control.  

Benefits: -Higher Quality, Lower Unit Cost, Higher effective Capability, etc.

Selection of Control Charts based on Attribute / Variable Type Data:-

Calculation of Average and Range Charts-

Click here to know the details.

The formula of the Attributes Control Chart:-

Click here to learn the formula and calculation.

Nomenclature of Control Chart:-

Check Sheet:-

Check Sheet is a simple document used for collecting data in real time. Variable or Attribute type data is collected through a Check sheet. A check sheet generally helps to make the decision on the basis of a fact and to collect the data for analysis and evaluation.

Sample check Sheet:-

Process Flow diagram/Graphs:-

A process flow diagram is a diagram used to indicate the general flow of plant processes and equipment.

The 7QC tools are the most commonly used tool in the industry for improvement, With the help of the 7QC tools you can understand the process/activities, analyze the data, and interpret the result/graph/output.

Which are the 7 QC tools?

The seven QC tools are

• Fishbone diagram
• PFD(Process Flow diagram)/Graphs /Stratification

Useful Article:

why why analysis methodology | 5-why analysis step by step guide

Rework vs Repair |IATF Requirement for Control of Reworked/ Repaired Product

How to plot the Run Chart in Minitab

Run Chart Example | Concept & Interpretation of Result with Case Study | Industrial Example:

Thank you for reading..keep visiting Techiequality.Com

I hope the above article “7QC Tools for Problem Solving” is useful to you…

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The Author is an Expert in Quality Management System, Operation Management, Business Excellence, Process Excellence, IATF 16949, ISO 9001, ISO 14001, ISO 45001, ISO 17025, TQM, TPM & QA. He is Certified as an IA for ISO 9001, IATF 16949, ISO 14001, ISO 17025 & ISO 45001 Standard.

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Seven Basic Tools of Quality Control: The Appropriate Techniques for Solving Quality Problems in the Organizations

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