An assignable cause, also known as a special cause, is a factor that causes a process to deviate from its expected performance. In the context of control charts, an assignable cause is a factor that causes a data point to fall outside the control limits of the chart.
Control charts are statistical tools used to monitor and control processes in manufacturing, service, and other industries. They consist of a plotted line or curve that represents the average performance of a process over time, as well as upper and lower control limits that mark the boundaries within which the process is expected to operate. Data points that fall outside the control limits are considered to be outside the normal range of variation and may indicate the presence of an assignable cause.
There are many potential assignable causes that can affect the performance of a process, including changes in raw materials, equipment malfunctions, and operator error. It is important to identify and address these causes in order to maintain the stability and reliability of the process.
One of the key benefits of using control charts is that they allow for the detection and correction of assignable causes before they lead to significant deviations from the desired process performance. This can help to improve the quality and consistency of the output, as well as reduce waste and costs.
To identify and correct assignable causes, it is important to use a structured problem-solving approach. This may involve collecting and analyzing data to identify patterns or trends, brainstorming potential root causes, and testing and implementing solutions. It may also involve involving team members and stakeholders in the process to ensure that all relevant perspectives are considered.
In summary, assignable causes are factors that can cause a process to deviate from its expected performance. Control charts are useful tools for detecting and correcting these causes in order to maintain the stability and reliability of a process. By identifying and addressing assignable causes, organizations can improve the quality and consistency of their output, as well as reduce waste and costs.