In reply to shaun.

Hi Shaun, sure you can still compute CV for intermittent demand, but your question raises an interesting issue. Many traditional forecasting performance metrics (such as MAPE) are inappropriate for intermittent data -- they become undefined due to 0 in the denominator. So it would become necessary to select an appropriate accuracy metric to compare CV to on a comet chart.

I personally do not consider intermittent demand a forecasting problem, but rather an inventory management problem. Yes, there are methods developed to forecast intermittent demand, but they are unlikely to result in highly accurate forecasts. So why spend the time on a futile endeavor? I would rather admit that we can't "solve" intermittent demand through forecasting, and instead focus on making good inventory decisions to meet customer needs in a fiscally responsible manner.

Another issue is raised by Steve Morlidge, who argues against using CV to measure the volatility of a series for forecasting purposes. He rightly points out that two series can have the same CV yet, because of the time sequence of the data points, have wildly different "forecastability" (my word).

For example, if 10 periods of demand are 1,2,3,...9,10 in that order, that looks like a steady upward trend. Compare that to the same 10 demands ordered randomly over the 10 periods, with no pattern at all.

Morlidge suggests computing the error of the (one period ahead) naive forecast over the data as a more useful indicator of volatility. Depending on the sequence of the data over time, this can give very different results from CV.