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hey everyone it's Mike from the companion development team here to show you how easy it is to run a Monte Carlo simulation and companion to get started we'll add the Monte Carlo simulation tool to the roadmap and label it will use this simulation to determine the force generated by a spring in an assembly first we'll enter an input choose a distribution and enter parameter values then we'll add more inputs because we're modeling at the stack up that determines the compressed length of the spring will enter a response labeled spring cap use the equation editor to enter it manually if you have specification limits you can enter them here if not you can add them later as you add inputs and specify output equations you'll see representations of each model here a quick glance lets you know if you set up your simulation correctly that's helpful when working with multiple or complex models when setting up your models sometimes it's easier to separate functions into groups especially if you have multiple equations the function we already set up represents the assembly that holds the spring let's create a group called assembly by clicking create group after you create a group you can view just the inputs and outputs of your model or you can view it as the function next let's create a group called spring pre length and spring rate not sure of an input distribution companion can help you choose use data to decide and then open the CSV file that contains spring rate data from a sample of Springs measured in the lab select the column you want to analyze and click recommend distribution while many distribution fit actions are possible companion shows up to three with the largest one being the best tip when you accept a distribution companion enters estimated parameters now let's add a response per spring force which is the spring constant multiplied by the deflection of the spring in the final assembly group lets you see how the inputs and outputs from different groups relate to each other again you can choose between two views of your model click the tabs to view the individual groups simulate now to perform the simulation results of the initial simulation appear with additional details available when you expand the more results section individual models click all groups to see the results from every model in one place based on your results companion recommends an appropriate follow-up action in the next step section if you perform the simulation without specification limits companion will suggest you add them as a next step if you included specification limits and process capability is below accepted levels companions suggest proceeding with parameter optimization if capability is above one point six seven companions suggest a sensitivity analysis if you want to change assumptions or if you made an error just click Edit model and rerun the analysis context-sensitive help is available if you need additional guidance for the best course of action companion handles parameter optimization parameter optimization identify settings for the controllable inputs that can improve your process depending on your goal first define your goal such as to target a particular mean value minimize % out of spec your DPM ol or to maximize CPK then to find the search range based on your process specify settings for an input parameter so companion won't suggest settings that you can't achieve for example you might not be able to set an oven thermostat below 100 degrees or above 500 degrees check noise if an input is out of your control for example outdoor temperature after entering all of the inputs click optimize parameters companion identifies optimize input settings and shows you the improvement you can expect to see if you use them after you perform a parameter optimization companion adds the results to the roadmap next we'll take a look at companion sensitivity analysis a sensitivity analysis shows the effect of changing input variation on your response it can help you decide when it's worth the time and money to reduce the variability of an input and when it isn't this graph shows the effect of changing the variation of a single input while holding the others constant and then estimating the effect on the response why the x-axis represents the percent of change in variation the y-axis represents the standard deviation or the percent out of spec if you have specification limits for the response each line represents an input to view a single input click here for example you can see that if you were able to reduce the standard deviation of this input you would also substantially reduce the percent out spec by contrast this input is essentially a flat line indicating that changes in the variation of the input would have little effect on process capability for better or for worse change any of the input settings you like and perform the simulation again with these new parameters as with parameter optimization sensitivity analysis shows you the improvement you can expect to see if you use the new settings and it adds the results to the roadmap you can export your simulation by right-clicking the simulation in the roadmap to export the results and graphs choose the PDF option to export the simulated data ensure that the results are showing in the workspace and then choose the CSV file option for more information and videos and companion go to WWE team calm