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Control chart for given values is used when the process is fixed and the mean and standard deviation are known. The control chart, also known as the Shewhart Chart, is a statistical tool for monitoring a business or manufacturing process in order to maintain or improve quality. It is one of the vital methods of Statistical Process Control (SPC), which assures the business process to be in a state of statistical stability.
The control chart is considered as one among the available seven fundamental and effective tools of quality control that is used in the application of six sigma in conjunction with pareto chart, check sheet, histogram, flowchart, scatter diagram cause-and-effect diagram. These tools are meant for effective control over business and manufacturing or to notice any important causes of variation. A control chart is an important tool used to monitor and develop quality. It is one of the efficient seven basic statistical tools often applied in Six Sigma along with other tools. Control charts usually permits easy recognition of events that is diverged from originality. This process may be difficult where there is continual variation in process characteristic. On detecting the change, it becomes important to identify the cause and eliminate it. If the values of a process are given then they are easy to plot and find the results. Controlling charting is the device for monitoring processes which helps them to keep in control or stable. A control chart usually carries the following components:
• Central Line: A central line is drawn at the process characteristic calculated from the data.
• Upper Warning Limit: An upper warning limit is sketched as two different lines above the line in the centre and both are standard deviations
• Upper Control Limit: An upper control limit is drawn above the centre line.
• Lower Warning Limit: Below the centre line two standard deviations is drawn.
• Lower Control Limit: It is drawn below the centre line and is three types of standard deviations. Control charts allow finding of events that may undergo changes from originality. To detect such changes, control chart provides statistical norm of change. If all the points will be plotted within the control limits, the process is said to be “In Control”, on the other hand if all the points will plot outside the control limit, the process is termed as “Out of Control”. The process necessary for constructing control chart are as follows: • You have to select the process that you want to chart.
• Settle on the process sampling plan.
• Data must be collected from the process done by you.
• Make estimate for control chart statistics.
• Gauge the control limits.
• Create control chart of your own.
To find out if the entire manufacturing or business processes in control, all the points will be plotted within the control limits. In the case of variation, the points in the chart will be seen plotted outside the control limits. The control chart alarmed the occurrence of certain problem which needs urgent investigation. If the error is detected, the solution to the problem can easily be carried out.
In the measurement of control limits the standard deviation that is required is that of the common cause variation in the process. Assignable causes are beyond control limit as it is the important factors of a process. The factor ‘unassignable causes’ or ‘chance causes’ are likely to occur by chance. The causes are normal and projected within a process for being inevitable. Control charts exclude condition or targets because of the trend associated with the process. Control charts use distribution chart or histogram and X bar as well as R bar. These two charts are often matched together. The X-Bar chart depicts the centerline, calculated using the grand average, and the upper and lower control limits. The R-chart designs the average range along with the limits of the range. In case of unusual circumstances, it can be depicted by assessing the change that had occurred in the mean or the variation of the course in question. Control chart is criticized by many writers. This criticism is drawn on the field that it violates likelihood principle. However, the principle is contentious and followers of control charts further argue that, in general, it is not always possible to specify a likelihood function for a process not in statistical control below the centre line. Control chart is criticized by many writers. This criticism is drawn on the field that it goes against the principle of likelihood. The principle, however, is contentious and followers of control charts involve in further arguments that, in general, it is not always possible for a process to denote a likelihood function that is not in statistical control.
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