Polyhedrika

  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. Process stability is a prerequisite for any further analysis. If a process is not stable, any conclusions you draw, can't be assumed to hold in the future. With SPC we check for process stability in the future. Control over-reaction A case of over-reaction is illustrated by the following example: Someone is shooting at a target, and based on the deviation of the impacts, he adjusts the gun site after each shot. The result will be an increase of the dispersion of the impacts; therefore, the adjustments will make the process worse. The correct way is, of course, to fire 5 or 6 shots without adjustments, and then decide if adjustment is required, based on the center of the impacts. ...

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_w...