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. ...

Understanding variation is key to interpret the value stream behavior.  Not all variation is the same. Variation These are some factors which may add variation to the process of cooking a turkey.  Every process has variation. Some causes of variation may be identified and acted upon. We can use two metrics for variation which complement each other: Manual Dice Throwing This simple exercise can help to experience process variation and understand the difference between a process change and inherent process variation. This understanding is key on management decisions to avoid both overreaction and lack of reaction. To run the exercise with actual dice print the form: Exercise: You will need a printed form and 4 dice for each team Throw 4 dice and add the outcomes Record the result in the Run Chart Repeat 50 times Join the dots in the Run Chart with a line Build the Histogram by counting the total number of dots on each group of 3 values Run this Exercise with a Simulator Downloa...

Design of Experiments is a useful methodology for process improvement. The purpose is to find a relationship between process variables we can control and key process outputs in order to increase process capability . You can use Excel statistical analysis tools, Solver, Pivot charts, etc. to plan and analyse the results of these experiments. The first approach is to look for a linear relationship as shown in:   Excel DOE But in some cases this relationship may not be linear, in which case we will try a quadratic model with Response Surface DOE . We will use an example in this Excel file you can download: Download file   ExcelResponseSurface.xlsm   from OneDrive to your PC. In this example we are trying to maximize process yield acting on the critical factors pH , Temperature and Time . We will run the experiments in the Experiments simulation sheet using coded values.   Code pH Temperature Time -1 2 120 7 1 12 150 15 Factorial Experiments We start by ru...