The 7 quality tools are a set of proven techniques used in quality management to identify problems, analyze root causes, and improve manufacturing processes.
Although they were developed more than 50 years ago, the seven quality tools remain the foundation of methodologies such as Lean Manufacturing, Six Sigma, and continuous improvement.
In this article, you’ll learn what the 7 quality tools are, what each one is used for, see practical examples of how they are applied, and discover how an MES system like Mapex can help you implement them more effectively in a modern manufacturing environment.
What are the 7 quality tools?
The seven quality tools are a collection of statistical and visual techniques designed to help manufacturers analyze data, identify quality issues, monitor process performance, and make data-driven decisions.
Originally popularized by Kaoru Ishikawa, these quality management tools were created to make continuous improvement accessible to everyone within an organization—not just quality specialists or statisticians.
The 7 quality tools are:
- Check Sheet
- Histogram
- Pareto Chart
- Cause-and-Effect Diagram (Ishikawa Diagram or Fishbone Diagram)
- Scatter Diagram
- Stratification
- Control Chart
Although each tool can be used independently, they are often applied together to identify a problem, analyze its root causes, monitor process performance, and verify the effectiveness of the improvements implemented.
What are the 7 quality tools used for?
The primary purpose of the 7 quality tools is to turn production data into actionable insights that help improve product quality and manufacturing performance.
Manufacturers use these quality management tools to:
- Identify recurring defects.
- Determine the root causes of quality issues.
- Reduce waste and rework.
- Monitor and maintain process stability.
- Analyze trends and process variation.
- Prioritize improvement initiatives.
- Make data-driven decisions instead of relying on assumptions.
Today, the seven quality tools are just as relevant as they were decades ago. They continue to be widely used across virtually every manufacturing sector, from automotive and aerospace to food and beverage, pharmaceuticals, and consumer goods, supporting continuous improvement and operational excellence.
The 7 quality tools explained
| Tool | What Is It Used For? | Example of Use |
|---|---|---|
| Check Sheet | Collect data in a structured way. | Record defects by production shift. |
| Histogram | Analyze the distribution of data. | Variation in product weight. |
| Pareto Chart | Prioritize the most significant problems. | Identify the most frequent defect. |
| Ishikawa Diagram (Fishbone Diagram) | Identify the root cause of a problem. | Analyze why scrap is increasing. |
| Scatter Diagram | Identify relationships between variables. | Temperature vs. rejection rate. |
| Stratification | Compare data by categories. | Analyze defects by machine or production shift. |
| Control Chart | Monitor process stability. | Track critical dimensions. |
The following sections explain each of the 7 Quality Tools, including their purpose, when to use them, and how they help improve manufacturing processes.
1. Check Sheet
A Check Sheet is a quality tool designed to collect data in a systematic and organized way.
Its purpose is to record events exactly as they occur, avoiding subjective interpretations and making subsequent data analysis more reliable.
It is primarily used to track defects, incidents, customer complaints, or any other recurring event within a manufacturing process.
By converting observations into measurable data, a Check Sheet helps identify patterns and provides the foundation for applying other quality tools, such as the Pareto Chart and the Histogram.
For example, during a production shift, an operator records every welding defect detected. At the end of the shift, the manufacturer has a reliable dataset to determine which defect occurs most frequently.
2. Histogram
A Histogram is a graphical representation of the distribution of a dataset.
It allows manufacturers to visualize how process measurements are distributed and identify excessive variation, outliers, or deviations from the expected process performance.
It is particularly useful when working with continuous variables such as weight, temperature, diameter, or cycle time, helping determine whether a process is consistent or exhibits variation that could affect product quality.
A common application is analyzing the weight of a sample of packaged products produced during a shift to verify that production remains within the specified tolerances.
3. Pareto Chart
A Pareto Chart ranks problems or causes from most frequent to least frequent, making it easier to prioritize improvement actions.
It is based on the Pareto Principle, which states that a relatively small number of causes often account for the majority of the effects.
This quality tool answers a simple but essential question: Where should we start?
Rather than trying to solve every problem at once, a Pareto Chart helps organizations focus their resources on the issues that have the greatest impact on quality.
For example, if a manufacturer discovers that two defect types account for 75% of all rejected products, eliminating those defects will have a much greater impact than addressing less frequent issues.
4. Ishikawa Diagram
The Ishikawa Diagram, also known as the Cause-and-Effect Diagram or Fishbone Diagram, is used to identify the possible causes of a problem.
Instead of looking for a single explanation, it organizes different hypotheses into categories, making root cause analysis more structured and effective.
The potential causes are typically grouped according to the 6Ms: Machine, Method, Material, Manpower, Measurement, and Mother Nature (Environment).
If the percentage of defective parts increases, the team reviews each of these categories to identify the factors that may be contributing to the problem before implementing corrective actions.
5. Scatter Diagram
A Scatter Diagram shows the relationship between two variables to determine whether a potential correlation exists.
Although it does not prove a cause-and-effect relationship, it helps identify trends that can serve as the starting point for further investigation.
A common example is comparing process temperature with the defect rate to determine whether both variables increase together.
6. Stratification
Stratification consists of dividing data into homogeneous groups to identify patterns that might remain hidden if all data were analyzed together.
Data can be grouped according to different criteria, such as machine, production shift, operator, supplier, raw material batch, or manufactured product.
For example, an analysis may reveal that most rejected products are not affecting the entire production process, but only a specific production line during the night shift.
7. Control Charts
A Control Chart is used to monitor process performance over time and determine whether the process remains stable or exhibits variations that require corrective action.
It plots process data together with control limits, making it possible to distinguish between normal process variation and abnormal variation that may indicate a quality issue.
It is one of the fundamental tools of Statistical Process Control (SPC) and is particularly valuable for detecting process anomalies before they result in defective products.
In a machining operation, for example, a Control Chart can be used to monitor the diameter of machined parts on a daily basis, allowing deviations to be identified before production falls outside the specified tolerances.
How to apply the 7 quality tools with an MES system
The 7 quality tools remain the same, but the way they are applied has evolved.
In many manufacturing companies, production data is still recorded manually on paper forms or spreadsheets, making it difficult to obtain accurate, reliable, and up-to-date information.
In a digital manufacturing environment, an MES system centralizes data from machines, operators, quality inspections, and production equipment, making it much easier to apply the seven quality tools effectively.
| Quality Tool | Traditional Application | Application with an MES System |
|---|---|---|
| Check Sheet | Paper-based records. | Automatic data collection. |
| Pareto Chart | Manually created charts. | Automatic defect reports. |
| Histogram | Manual data export. | Real-time data distributions. |
| Stratification | Manual data filtering. | Comparison by machine, shift, operator, or batch. |
| Ishikawa Diagram | Hypothesis-based analysis. | Traceable, data-driven analysis. |
| Scatter Diagram | One-time analysis. | Continuous comparison between variables. |
| Control Chart | Manual updates. | Continuous process monitoring and alerts. |
Thanks to this real-time production data, manufacturing teams can identify process deviations earlier, reduce analysis time, and make data-driven decisions based on objective information.
Practical example
Imagine a company that manufactures metal components. During each production order, the MES system automatically records:
- The machine used.
- The operator.
- The raw material batch.
- The cycle time.
- The process temperature.
- Rejected parts and the reason for rejection.
With this data, manufacturers can:
- Automatically generate a Check Sheet.
- Use a Pareto Chart to identify the most frequent defects.
- Monitor process stability with Control Charts.
- Determine whether there is a relationship between process temperature and the rejection rate using a Scatter Diagram.
- Discover that quality issues occur only on a specific machine during a particular production shift through Stratification.
- Use all this information to perform a root cause analysis with an Ishikawa Diagram.
Instead of spending days collecting and organizing production data, the team has access to the information it needs almost in real time.
The 7 quality tools: the foundation of continuous improvement
The 7 quality tools remain just as relevant today as they were when they were first introduced.
The difference is that they can now be supported by Industry 4.0 technologies, which make it possible to automatically collect, analyze, and leverage large volumes of production data.
As a result, continuous improvement no longer relies solely on the experience of the people working on the factory floor. Instead, it is driven by accurate, reliable, and up-to-date data.
In this context, MES systems such as Mapex do not replace the seven quality tools—they enhance them by simplifying data collection, improving traceability, and enabling real-time analysis.
This allows production teams to spend less time gathering information and more time improving manufacturing processes.
FAQs about the 7 quality tools
Who created the 7 quality tools?
The seven quality tools were popularized by Kaoru Ishikawa as part of the continuous improvement philosophy applied to quality control.
Are the 7 quality tools still used today?
Yes. They remain a standard reference across industries such as automotive, food and beverage, pharmaceuticals, machinery manufacturing, and any manufacturing environment where process control and continuous improvement are essential.
Which quality tool is the most widely used?
It depends on the objective. The Pareto Chart is commonly used to prioritize quality issues, while the Ishikawa Diagram is one of the most widely used tools for root cause analysis.
Can an MES system replace the 7 quality tools?
No. An MES system does not replace the seven quality tools. Instead, it provides the production data needed to apply them faster, more accurately, and with greater traceability.
