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

    A still picture of a Value Stream Map may not fully explain the behavior of the Value Stream. In some cases we need to understand its dynamic behavior. To do this we will convert our Excel Value Stream Map into a Monte Carlo Simulator. In Excel Value Stream Map we saw how a complex Value Stream could be mapped using Excel. We saw the importance of mapping feedback loops such as test-repair loops because they often become the bottleneck for the complete value stream. In this situation not all items flow along all branches so we need to calculate the % flowing along each branch. This enabled us to calculate the cycle for each workstation based on the staffing. Finally we were able to calculate the ideal staffing to balance the line: insure no workstation cycle is above the average inter-arrival time (takt time). This we did using Solver. Value Stream Map: Still Picture Vs Dynamic Behavior Mapping the current value stream with a VSM is a good starting point to understand how it behav