B is the way to go. If the demand is mostly random, then a slow-responding forecast can help filter out the noise and give a more accurate prediction. Although, it does sound a bit like a Magic 8-Ball approach to forecasting.
Hmm, I'm torn between A and D. Seasonal and cyclical patterns both seem like they'd benefit from a slow-responding forecast. Maybe I should just roll a dice to decide?
I think the answer is D. A forecasting method that responds slowly would be best for a cyclical demand pattern, as it can smooth out the ups and downs.
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