Polyhedrika

Design of Experiments (DOE) is a very useful process improvement methodology.  Microsoft Excel has some powerful data analysis tools, which I have, successfully, used for DOE. When it comes to analyzing cause and effect, we want to know what process factors affect our outputs.   Correlation and Regression could give us an indication of the main factors, and the extent they affect the outputs. In some cases, there may be interactions among the different factors, or the mathematical relation between factors and outputs may not be linear. In these cases, it is useful to run a systematic set of experiments, to test all possible combinations of factors, to relate them to the outputs. Factorial Experiment Example We want to minimize process loss, and after some brainstorming among the process specialists, we concluded that 5 factors may affect loss. Based on current factor levels, we have selected the following levels to experiment with:    Download this Excel file DOE_with_Excel.xlsm fro

  Statistical Process Control (SPC) is used to control a critical process output acting on the factors affecting it only when required. The purpose is to differentiate  between a statistically significant change in the process and common cause variation because the actions required will be different. Real Time Statistical Process Control enables the operators running the process to know when action is required, as described in    Real Time SPC There are two extremes to avoid:            Over-reaction (adjusting after every output) makes the process worse: dispersion is increased. Under-reaction is just as bad: a slow process degradation passes undetected until it is too late. SPC Charts for Variables and Attributes We will now analyse some of the charts used for variables and attributes. SPC Chart Interpretation SPC uses some rules, developed by the Western Electric company, to detect symptoms of  statistically significant  variation. The center line is the average of all values and th