The purpose of the "Robust Capability Analysis" is to give information about the stability and capability of the process, given a set of data. The method does not demand statistical knowledge from the user, there is no need of normality assumptions, goodness test or transformations. The proposed method is easy to be used, robust and many experiments have evidenced its quality.  The tool is implemented in VBA Excel, so it does not require any installation and it has a friendly interface.                What is expected from a Control Chart ? Predict the process ability to meet service expectations. Differentiate common cause from special cause. Use it effectively to take operational decisions on the floor.        Why is difficult to deploy an effective statistical process control on the floor? Advanced statistical knowledge Statistic software I- Chart or p-Chart ? How to measure the Stability and Capability of the process? What is the probability of missing the target? What to do if the distribution is not normal? Does it matter?           The interface of the tool is seen below in the figure 1. Figure 1           The figure 1 shows the control chart (in this case a P-Chart). It is plotted the data points, the control limits (red lines), the mean (green line), the specification limits (the blue line, in this example there is only the lower specification limit). In the right side there are some results: the risk (probability of missing the targets), a confidence level and confidence interval for the accuracy of the risk. Naturally it depends of the quantity of data points. It is also calculated an stability index, the capability index (lowest CPk). There is also an important field with notes and recommendations that helps the user to interpret the results (this is done because one purpose of the tool is to delivery understandable results regardless the statistical knowledge of the user).         The figure 2 gives the control chart using the well-known statistical software "Minitab". So it is possible to compare the charts.                                                                   Figure 2           The figure 2 is very similar to figure 1 and it evidences the quality of the results provided by the tool.  The Robust Control Chart gives additional, simple and useful information. It is easy to be used because the user does not need to know which type of control chart he must use (it depends on the type of the data). The tool is able to recognize automatically if it should be used a P-Chart or I-Chart. The user only needs to populate the yellow cells of the figure 1 and the data as showed in the figure 3 below.                                Figure 3         It is necessary to populate the 3 columns and press the button, the macro will build the correct type of Control Chart. If the is no denominator for the data, just use the value 1. The next figure gives an example for a case with continuous variable, using I-Chart.                                                                                                                               Figure 4                                                                    Figure 5         There are many other types of control charts, but the goal here is to provide a simple and flexible tool that is able to plot automatically P-Chart or I-Chart without demanding statistical knowledge from the user. The information provided by the Robust Control Chart is useful in many practical situations to evaluate the stability, capability and the risk of the process.            You can buy the tool for only US$ 3.00. The file will be sent to you via email  in 2 days. For additional support, submit the form by this link.