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As a quality management professional, you are asked to identify and resolve quality issues as efficiently as possible, no matter what industry you are in.
Fortunately, there are tools that can help you and your organization in that task. The seven basic tools of quality management were developed in post-World War II Japan and are still considered the gold standards today when it comes to evaluate a variety of issues in quality. They are often used in conjunction with popular process improvement methodologies such as Six Sigma, continuous process improvement, Total Quality Management, and Lean process management.
The History of the 7 Quality Tools
The use of the seven basic quality tools was pioneered by Kaoru Ishikawa, a professor of engineering at Tokyo University who is often known as the Father of Japanese Quality. The tools were first implemented by the Japanese as part of their post-WWII industrial training program. The goal was to apply basic, user-friendly statistical tools to quality analysis that could be used without much training by workers with varied backgrounds and at any skill level. Since that time, the use of these practical tools and other precepts developed by Ishikawa has been recognized and spread globally.
The 7 Quality Tools with Examples
Each of these seven important statistical quality tools has a distinct form and purpose.
Histogram
Histograms are graphs that are commonly used to show frequency distribution, or how often each “value” (or range of values) occurs in different sets of data. Each bar represents a data group, while the height of the bar demonstrates the frequency with which a value appears in that group. Histograms can break down frequency for any group or category that is numerical such as age, days of the week, physical measurements, etc.
Example: You can easily visualize the rejection levels of manufactured parts by plotting the number of parts rejected in weekly sample batches.
Stratification
Stratification is used to separate data gathered from multiple sources (using different colors) and sort it into distinct groups. It can reveal patterns that help you determine the meaning of your data.
Example: If purity is a recurring problem, stratifying data from different labs may reveal the problem is concentrated mostly within one lab.
Check Sheet (also known as a Tally Sheet)
A check sheet is a generic form that is structured for collecting and analyzing various types of data, either quantitative or qualitative. (It’s known as a tally sheet when used for quantitative data.) A check sheet’s simple format records data as checks or tally marks that indicate how many times a particular value has occurred.
Example: You can use a check sheet to gather data on the number of times over the course of a week that labels are found missing on a product.
Pareto Chart (also known as the 80-20 rule)
A pareto chart is a special form of a bar graph. It highlights the relative importance of a variety of factors, enabling you to focus on factors with the biggest contribution to the cumulative total of the effect. This approach leverages the 80-20 rule, which posits that 80% of a process’ problems are caused by 20% of the major factors.
Example: To deal with a high level of customer complaints, it’s helpful to chart them and see which problems in the design, production, and/or delivery are cited most commonly.
Control Chart
Control charts study how a process changes over time. They apply historical data to help you determine if a process is predictable, consistent, and stable, or unpredictable and out of control. Control charts are a very flexible and effective way of performing control activities on virtually any kind of process, from discrete to continuous production, from quantitative to qualitative attributes, from services to manufacturing industries.
Example: A hospital might use a control chart to track readmissions, in order to determine if there is a special cause variation currently occurring that requires investigation.
Scatter Diagram (Also known as the Shewhart Chart)
Scatter diagrams depict the relationship between two variables. This helps in the identification of cause-and-effect relationships. Pairs of numerical data are graphed, one variable on each axis. If the variables are correlated, the points will fall along a line or curve. The stronger the correlation, the tighter the points will hug the line and the stronger the relationship will be between variables.
Example: The color of a product might be graphed against the temperature at which it was prepared. A strong correlation would mean that the temperature of preparation likely affects the final product color.
Cause and Effect diagram (also known as a Fishbone or Ishikawa Diagram)
First developed by Kaoru Ishikawa and sometimes known as a Fishbone because of its shape, a cause and effect diagram is one of the more complicated statistical tools used in quality management. It aims to identify multiple potential causes for a problem and sort them into useful categories. Probable causes and sub-causes are usually grouped into six main groups—methods, materials, measurements, machines, personnel, and environment.
Example: If you are having delivery problems, a fishbone diagram could help you sort out potential causes such as vehicle breakdowns, bad weather, mislabelling, faulty estimation of delivery times, inadequate driver training, etc.
Effective Quality Management Software Can Increase Compliance and Agility
Identifying and resolving quality issues quickly is one critical aspect of a quality management system that can be improved with the use of an effective cloud-based Quality, Health, Safety and Environment (QHSE) management software solution like Veeva’s QualityOne.
QualityOne’s configurable reports and dashboards automatically track and trend quality data for products or processes and enable information to be easily shared with the team and external stakeholders.
QualityOne is intuitive and easy to use, unlike cumbersome on-premise legacy programs, so your staff will be able to easily and effectively use it to manage document control, training, quality processes, and HSE events.
- Reduce the cost of quality management
- Quickly and easily find and fix quality issues
- Access your files & dashboards from any device
- Visualize all product or quality data in one system
- Ensure your suppliers meet your quality standards
- Be audit-ready, anytime an auditor calls
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