Control charts, commonly referred to as Shewhart charts, are statistical control methods that determine if a process is on the right track or has dragged out of control. They are used in understanding the variations that occur during the execution of a process. Control charts are also critical in informing project managers when their processes are going astray ( Chakraborti & Graham, 2019) . They can also be used in the prediction of future performances based on what is attained from the current recordings. These charts can also be used in the generation of new ideas to improve the quality of processes, based on one’s analysis of the current process trends.
There are various ways to identify the out-of-control signal in the control chart. The first indication is when seven points lie in a row, above the average. The out-of-control signal can also be identified when seven points in the chart are below the average ( Du Nguyen et al., 2020) . The other criterion is checking the trending of the graph, whereby when seven points lying in a row, trend up or down. Out-of-control signals affect the general mean and variance of the process.
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As a quality control manager, if the project is out of control, I would examine the assignable cause of the problem. In this case, identification of the problem will help in finding the suitable cause of action to take in correcting the problem. For instance, if a certain department is the cause of the drifts in quality, then it would be easier for me to summon the departmental heads and request them to improve their process quality ( Du Nguyen et al., 2020) . In essence, out-of-control signals are essential in relaying areas of weakness in an organization, as they enable leaders to find the specific causes of their quality drops. For this case, my role as a quality manager demands that I go point by point, checking the possible origin of the misalignment and finding ways of correcting it.
References
Chakraborti, S., & Graham, M. A. (2019). Nonparametric (distribution-free) control charts: An updated overview and some results. Quality Engineering , 31 (4), 523-544.
Du Nguyen, H. et al. (2020). On the effect of the measurement error on Shewhart t and EWMA t control charts. The International Journal of Advanced Manufacturing Technology , 107 (9), 4317-4332.
