Modern business operates in a very competitive environment. As such, every business will need to improve on its operations in order to remain competitive. One of the ways that a business will attempt to retain its competitive advantage is ensuring that the products that are produced meet the highest standards and are produced in the most efficient way. In order to meet this target, the business must have strong production controls that guarantee on quality and efficiency. Naturally, whenever there are controls, there must be a suitable tool that is used to ensure that the process does not have deviations from the specifications and the process does not have excessive variabilities. As such, this makes control chart one of the suitable statistical control measures.
There are two basic types of control charts. The first type is the control chart that is referred to as the univariate control. The univariate control is a chart that displays the chart for one quality characteristics. The graph has three lines that are used to make the decisions namely the mean, upper control line and the lower control line where upper control and lower control lines lie on the respective side of the mean line (O'Donnell, Fagan and Colette 46). For a process that is in control, all the points are expected to fall within the two limits. On the other hand, the second chart is referred to as the multivariate control chart which is a chart that displays a summary of more than one characteristic of the process. The multivariate has similar lines as univariate.
Control charts controls have a given probability limit say 0.001. Therefore, if chance was the only factor affecting the process, the probability that an item will fall above the lower control limit is one in one thousand items while the probability of falling above the upper control is one in a thousand. Therefore, since the probability of having an item outside the control limit is very small, the chart gives the appropriate assurance that the production process has high chances that it will meet the set targets. However, it does not mean that the outliers will not be considered. The firm should be seeking to understand the reason for the existence of the outlier thus assigning a reason to this variation. The occurrence of even one outlier means that the process is not in control thus measures should be taken to restore the process to be in control (Stamatis 177).
It does not mean that when all the points are within the control limits that the process is outright in control. Therefore, one must check for elements that support random distribution of the points. If two in every three consecutive points are on the same side of the center line and these points are more than two sigma from the center line, it shows that the process is not in control. Also, if four in every five consecutive points are on the same side of the center line and these points are further than one sigma from the centerline, it shows that the process is not in control. Furthermore, if eight consecutive points out of ten are on the same side of the center line, the process is not in control (Stamatis 170).
If the process is still found to be in control, the in control chart can further be analyzed using the zone test. These tests enhance the information that can be deduced from the chart since it provides the organization with the ability to detect small shifts. Since the space between centerline and the upper limit is divided into three equal spaces each one sigma apart from each other (3 sigma limit), each space is called a zone, the distribution of points within this space helps to monitor the small shift by observing the sequence of consecutive points (Webber and Wallace 180).
It is evident that control chart is a friend to industrial process. Therefore, very firm that aims at to ensuring that their processes have no deviation from the specification and their process lacks any excessive variabilities should consider the use of this statistical control if the process can be served by a C chart.
Works Cited
O'Donnell, Colm P., Colette Fagan, and Peter Colette Fagan. Process Analytical Technology for the Food Industry. Springer Verlag, 2014. Print.
Stamatis, D. H. Six Sigma and Beyond: Statistical Process Control. Boca Raton, FL: St. Lucie, 2003. Print.
Webber, Larry, and Michael Wallace. Quality Control for Dummies. Indianapolis, IN: Wiley Pub., 2007. Print.
