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

Optimize both Yield and Cost with Minitab Design of Experiments Design of Experiments is a powerful technique to optimize process outputs in complex processes where multiple factors affect the outputs. When we want to optimize more than one output the problem becomes more complex. With this example we want to follow the steps, using Minitab, to optimize both process yield and cost by acting on 3 critical factors which have been identified by previous experiments. Process Improvement Steps DEFINE: The first step in process improvement is to map the current process as it is really happening with a  Value Stream Map . MEASURE: Then we need to  collect process data  in the line. MEASURE VARIATION: To understand the process dynamic behavior we will need statistically significant data to enable  simulation     CONTROL: The first data analysis is to check for stability with  Statistical Process Control MEASURE CAPABILITY: Once we insure the process is stable we want to know to what extent i