In this model, we can see how @RISK is used to analyse the quality of electrical
circuits produced. The Power Draw on this circuit must fall within a specified
range in order to function properly. @RISK will show us how frequently the Power
Draw does not meet this specification, and which variables are leading to these
results.
Below is a screen shot of the @RISK model in Microsoft Excel. Click
here to download the model. You must have @RISK installed on your computer for
the model to function. To download a free trial version of
@RISK, click here.
Explanation
This simple DC circuit consists of two voltage sources — one independent and one dependant — and two resistors. The independent source specified by the Design Engineer has an operational power range of 5,550 W + 300 W. If the power draw on the independent voltage source is outside of the specification, the circuit will be defective. The design performance results clearly indicate that the design is not capable of performance with a percentage of the circuits failing on both the high and low end of the limits. The PNC values identify the Percent of Nonconforming units expected on the upper and lower ends of the specification.
The basic logic for this model follows:
The model calculates the standard deviation for each component based on known information and the following assumptions within this model: 1) The mean of the component values are centered within the tolerance limits. 2) The component values are normally distributed. Note that @RISK can be used to fit a probability distribution to a data set or to model other types of probability distributions, if needed.
The
@RISK Sensitivity Analysis identifies the input variables driving variation in the
output. The sensitivity shows that the two voltage sources are the main contributors
to the variation in power consumption. Armed with this information, the engineering
team can focus their improvement efforts on the voltage sources instead of the resistors.
The model can be used to test different components and tolerances, performances and
yields can be compared, and the optimal solution can be selected to maximise yield
and reduce cost.
Download
Model: Electrical Circuit Analysis
@RISK:
The Perfect Six Sigma Companion
Model: Design of Experiments
See how @RISK can be used to ensure the quality of experimental design.Model: Design of Experiments with Optimisation
See how @RISK simulation and RISKOptimizer optimisation ensure the quality of experimental design.Case Study: DecisionTools Safeguards Precious Metal Refiner
Met-Mex Peñoles simulates manufacturing processes to conserve materials and reduce errors.
More
About @RISK, The World's Leading Tool for Risk Analysis