What is Quality Control Charts?
308 reads · Last updated: December 5, 2024
A quality control chart is a graphic that depicts whether sampled products or processes are meeting their intended specifications. If not, the chart will show the degree by which they vary from specifications. A quality control chart that analyzes a specific attribute of a product is called a univariate chart, while a chart measuring variances in several product attributes is called a multivariate chart. Randomly selected products are tested for the given attribute(s) the chart is tracking.
Definition
A quality control chart is a graphical tool used to display whether sampled products or processes meet their expected specifications. If they do not meet specifications, the chart shows the degree of deviation from the specifications. By analyzing specific attributes of a product, quality control charts help identify and correct issues in the production process.
Origin
The concept of quality control charts originated in the early 20th century, developed by Walter A. Shewhart in the 1920s. While working at Bell Labs, he first proposed using statistical methods to monitor and control the quality of production processes.
Categories and Features
Quality control charts are mainly divided into univariate and multivariate charts. Univariate charts are used to analyze specific attributes of a product, while multivariate charts measure variations in multiple product attributes. The advantage of univariate charts is their simplicity and ease of use, suitable for monitoring single attributes; multivariate charts are suitable for complex product quality analysis but require more data and complex analysis.
Case Studies
Case 1: Toyota Motor Corporation uses quality control charts on its production lines to monitor the dimensional accuracy of car parts. By real-time monitoring and analysis, Toyota can quickly identify and correct deviations in the production process, ensuring product quality. Case 2: Samsung Electronics uses multivariate quality control charts in its semiconductor manufacturing process to monitor multiple key parameters, such as temperature and pressure. This approach helps Samsung improve product yield and production efficiency.
Common Issues
Common issues include how to choose the appropriate type of control chart and how to interpret anomalies in the chart. A misconception might be that all anomalies need immediate correction, whereas some may be random fluctuations that do not require action.
