Trend, shift, and pattern
Trend, shift, and pattern are three key factors that are often analyzed in control charts, which are statistical tools used to monitor and control processes in manufacturing, healthcare, and other industries. These factors are essential in ensuring that processes are operating at their optimal levels, and they can also be used to identify problems and take corrective action.
In this article, we will explore the definitions and significance of trend, shift, and pattern in control charts, as well as provide some scientific references for further reading.
Trend refers to the long-term direction or movement of a process over time. This could be an upward or downward trend, and it can be influenced by a variety of factors such as changes in raw materials, equipment, or processes.
Trend analysis is important in control charts because it helps identify if a process is improving or deteriorating over time, and allows for the implementation of corrective action if necessary.
According to the International Journal of Production Economics, trend analysis can be performed using a variety of statistical techniques, including linear regression and time-series models (Azar et al., 2014).
Shift refers to a sudden change or deviation in a process from its normal or expected behavior. This could be a positive shift, where the process is performing better than usual, or a negative shift, where the process is performing worse than usual.
Shift analysis is important in control charts because it helps identify if a process has been affected by a sudden change or event, and allows for the implementation of corrective action if necessary.
According to the Journal of Quality Technology, shift analysis can be performed using statistical process control (SPC) techniques, such as control charts and process capability indices (Montgomery, 2010).
Pattern refers to the repetition or consistency of a process over time. This could be a stable pattern, where the process is performing consistently, or an unstable pattern, where the process is performing erratically.
Pattern analysis is important in control charts because it helps identify if a process is operating predictably or unpredictably, and allows for the implementation of corrective action if necessary. According to the Journal of Manufacturing Systems, pattern analysis can be performed using data mining techniques, such as clustering and classification algorithms (Wei et al., 2014).
