Types of control charts

Control charts are graphical representations used in quality control to monitor and measure the performance of a process over time. They are used to determine whether a process is in statistical control, meaning it is producing results within a predictable range and is free from special-cause variation. Control charts provide a visual representation of process stability and performance. Common types of control charts include X-bar and R charts, individual and moving range charts, CUSUM charts, and EWMA charts. Each type of chart focuses on different aspects of a process, such as mean and range, or cumulative sums of deviations from a target.

Example of control charts

• X-bar and R charts: X-bar and R charts are used to monitor the mean and range of a process over time. They are used to check for significant changes in the mean and range of a process, as well as to detect out of control points. X-bar and R charts can be used to ensure that a process is stable and to identify when a process needs to be adjusted. A real life example of an X-bar and R chart could be the analysis of blood pressure readings taken from a patient over time.
• Individual and Moving Range Charts: Individual and Moving Range Charts are used to monitor the mean and variability of a process over time. They are used to detect and quantify changes in the process. A real life example of an Individual and Moving Range chart could be the analysis of glucose levels in a patient’s blood over time.
• CUSUM Charts: CUSUM Charts are used to monitor the cumulative sum of deviations from a target or baseline. They are used to identify when a process is no longer in control and is significantly different from the target. A real life example of a CUSUM chart could be the analysis of patient satisfaction scores over time.
• EWMA Charts: EWMA (Exponentially Weighted Moving Average) Charts are used to monitor the mean of a process over time. They are used to identify when a process is no longer in control and is significantly different from the baseline. A real life example of an EWMA chart could be the analysis of blood sugar levels over time.

When to use control charts

Control charts are an important tool for quality control and process monitoring. They can be used to identify process problems and changes in process performance, as well as to monitor process stability over time. The following are some of the most common types of control charts and when they should be used:

• X-bar and R charts are used to monitor the mean and range of a process over time. They are best used to detect small, consistent shifts in the process mean, and to detect out-of-control conditions such as large, sudden shifts.
• Individual and moving range charts are used to monitor the variation of individual measurements and the range of the measurements over time. These charts are often used in conjunction with X-bar and R charts, and are best used to detect small changes in process variability.
• CUSUM charts are used to monitor the cumulative sum of deviations from a target. They are used to detect shifts in process performance in a timely manner, and are especially useful for processes with slow, gradual shifts.
• EWMA charts are used to monitor the average of a process over time. They are often used to detect small changes in the process average, and are best used in combination with X-bar and R charts to provide a fuller picture of process performance.

Advantages of control charts

Control charts offer a variety of advantages for monitoring and controlling processes in quality assurance. Below are the advantages of the major types of control charts:

• X-bar and R charts are used to identify systematic changes in a process mean or range. They allow for quick identification of trends and changes in the process and provide a way to track the process over time.
• Individual and moving range charts are used to detect random changes in the process, such as outliers or transient effects. These charts allow for quick identification of instability and provide a way to track the process over time.
• CUSUM charts are used to detect small and gradual changes in the process. They allow for quick identification of trends and changes in the process and provide a way to track the process over time.
• EWMA charts are used to detect changes in the process mean or standard deviation. These charts provide a way to track the process over time and allow for quick identification of trends and changes in the process.

Other approaches related to types of control charts

In addition to the commonly used control charts, there are other approaches related to types of control charts. These include:

• Statistical Process Control (SPC) which is a set of techniques and tools used to monitor and analyze process performance. It helps to identify and reduce variability in the production process.
• Process Capability Analysis (PCA) which is used to assess the ability of a process to meet a given set of requirements. It measures the process capability index (Cpk) which is the ratio of the process's natural variability to the amount of variability allowed by the customer.
• Process Flow Analysis (PFA) which is used to identify areas of improvement in the manufacturing process. It looks at the entire process from start to finish and examines how each step affects the overall efficiency and effectiveness of the process.
• Design of Experiments (DOE) which is a systematic approach to evaluating the effects of different factors on the output of a process. It helps to identify the most important factors that affect the process and to identify which combinations of these factors yield the best results.

In summary, there are various approaches related to types of control charts that can be used to monitor and improve process performance, such as Statistical Process Control, Process Capability Analysis, Process Flow Analysis and Design of Experiments.

Types of control charts — recommended articles

Run chart — P chart — Attribute control chart — Critical-to-quality tree — Statistical process control — Common cause variation — Control chart — Np chart — Quality control

References

• Bersimis, S., Psarakis, S., & Panaretos, J. (2007). Multivariate statistical process control charts: an overview. Quality and Reliability engineering international, 23(5), 517-543.