Go to the homepage


So your forecasts are higher than the actuals…but is over-forecasting the real problem?



Read IBF's interview with Steve Morlidge on Forecast Value Added

Click here to read this interview with Steve Morlidge on the practical application of Forecast Value Added, conducted by Mike Gilliland of SAS on behalf of the IBF (Institute of Business Forecasting and Planning). 


How not to tackle bias

In previous blog posts I have discussed how to go about the process of identifying forecasting bias: systematic under- or over-forecasting.

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. 


Why size matters. Tackling forecast bias Part 2

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.   


Signals and noise. Tackling forecast bias: Part 1.

The average level of MAPE for your forecast is 25%.

So what?

Is it good or bad? Difficult to say.

If it is bad, what should you do? Improve…obviously. But how? 


Why bother with forecasting? From error and ‘accuracy’ to adding value

As far as I know we are not legally required to forecast.

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.  


‘Good enough forecasts’: the limit of our ambition?

‘…of course it is not possible to have zero forecast errors’

‘Why not?’

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. 


Closing the loop on forecasting

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. 


Is it possible to measure the quality of Intermittent Demand forecasts?

Forecasting products where demand is intermittent (ID) has long been a problem for forecasters.

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.  


Why MAPE doesn't work

When practitioners are asked what forecast accuracy they use the usual response is ‘MAPE’, which is short for Mean Absolute Percentage Error.