Monte Carlo Analysis in SPICE simulation is a quite useful tool for many things relating to manufacturing yield, field issues, customer returns, and performance issues.
One of my first real exposures to how this might apply went something like this — I was out test flying a radio installation with a customer, and we flew over a patrol car — the radio transmission from the car (it was a law enforcement helicopter) was unintelligible. The communications officer onboard mentioned they needed to swap out that radio (the radio in the car) and get it repaired. I noticed as the day warmed up to 110 degrees on a later test flight that the companies own receiver design in this install did not sound too good on the secondary receiver.
Eventually, I got a decent SPICE simulator after a very large corporation bought the division, and I was able to model the circuit. At first in Monte Carlo it looked OK — it was a 10th order high-pass filter set to strip off the signaling tones that was implemented with inductors and capacitors; the parts were precision devices. The design was one that was re-used from several very old designs dating back to possibly the early 1970s.
However, once I studied the datasheets more closely, I determined that the devices being used did not show specs over temperature. The data sheets also did not have info for the effects of ageing, humidity, shock, vibration, etc. — it was a long list. I was able to get the information from the vendors eventually. Then my manager pointed out that these 2 percent parts that were used were likely from the same production line as the 1 percent parts. The 1 percent parts were “cherry picked” off of the line first, and the remaining parts, as long as they met the 2 percent spec, were sold as such. This is proper, but it results in a skewed distribution for the 2 percent devices — flatter distribution for the spread (rather than bell-curved). With all this information, I kicked off a simulation run that was set up just to step through the worst case limits on everything. This did not really show anything especially terrible. Still curious, I switched the simulator to a run of 200 on Monte-Carlo type simulation and let the workstation run overnight until it was done.
Low and behold, a few real stinkers popped out. It was time to look at the economics of buying the better parts. The simulator gave me the percent of units that would have issues, and I gave the manager the results to try and help run up the intangible cost of customer service calls, repairs, etc.
Have you seen similar issues with Monte Carlo analysis and datasheets these days?