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?
As far as I know we are not legally required to forecast. CONTINUE READING
So why do we do it?
My sense is that forecasting practitioners rarely stop to ask themselves this question. This might be because they are so focussed on techniques and processes. In practice, unfortunately, often forecasting is such a heavily politicised process, with blame for ‘failure’ being liberally spread around, that forecasters become defensive and focus on avoiding ‘being wrong’ rather than thinking about how they can maximise their contribution to the business.
‘…of course it is not possible to have zero forecast errors’ CONTINUE READING
So went a conversation with a potential client some years ago. As usual when somebody says something that, from your world view, is so obviously wrong that you have never thought of what you might say to counter it, I was left open mouthed and speechless.
The word ‘feedback’ is in common usage, and people often talk about ‘closing the loop,’ but few people realise what these terms really mean…and how important they are. CONTINUE READING
Forecasting products where demand is intermittent (ID) has long been a problem for forecasters. CONTINUE READING
The reason for this is that, where the historic record contains many periods with zero demand, it is difficult for forecasting algorithms to pick up the correct demand signal to drive the forecast. And this is a non-trivial problem.