In previous blog posts I have discussed how to go about the process of identifying forecasting bias: systematic under- or over-forecasting. CONTINUE READING
Unfortunately, there are more ways of getting this wrong than there are of getting it right and I would like to share one that I came across recently in a major blue chip company.
So we learned from the last blog post that the apparently simple matter of spotting bias – systematic under or over forecasting – can get surprisingly tricky in practice if our actions are to be guided by scientific standards of evidence – which they need to be if we are actually going to improve matters. CONTINUE READING
The average level of MAPE for your forecast is 25%. CONTINUE READING
Is it good or bad? Difficult to say.
If it is bad, what should you do? Improve…obviously. But how?