In 1965's Subterranean Homesick Blues, Bob Dylan taught us: You don't need a weatherman / To know which way the wind blows In 1972's You Don't Mess Around with Jim, Jim Croce taught us: You don't spit into the wind By combining these two teachings, one can logically conclude that:
Our tradition from Foresight’s birth in 2005 has been to feature a particular topic of interest and value to practicing forecasters. These feature sections have covered a wide range of areas: the politics of forecasting, how and when to judgmentally adjust statistical forecasts, forecasting support systems, why we should
We're entering the busy season for forecasting events, and here is the current calendar: Analytics2014 - Frankfurt The European edition of Analytics2014 kicks off tomorrow in Frankfurt, Germany. Five hundred of the leading thinkers and doers in the analytics profession hook up for two full days of interaction and learning.
As we saw in Steve Morlidge's study of forecast quality in the supply chain (Part 1, Part 2), 52% of the forecasts in his sample were worse than a naive (random walk) forecast. This meant that over half the time, these companies would have been better off doing nothing and
As we saw last time with Steve Morlidge's analysis of the M3 data, forecasts produced by experts under controlled conditions with no difficult-to-forecast series still failed to beat a naive forecast 30% of the time. So how bad could it be for real-life practitioners forecasting real-life industrial data? In two words:
The Spring 2014 issue of Foresight includes Steve Morlidge's latest article on the topic of forecastability and forecasting performance. He reports on sample data obtained from eight business operating in consumer (B2C) and industrial (B2B) markets. Before we look at these new results, let's review his previous arguments: 1. All
Here is editor Len Tashman's preview of the new Spring 2014 issue of Foresight. In particular note the new article by Steve Morlidge of CatchBull, reporting on an analysis of eight B2B and B2C companies, which we'll discuss in a separate post. An organization’s collaboration in forecasting and planning has
Q: How would you set the target for demand planners: all products at 0.7? All at practical limit (0.5)? A: In principle, forecasts are capable of being brought to the practical limit of an RAE of 0.5. Whether it is sensible to attempt to do this for all products irrespective
Q: How important is it to recognize real trend change in noisy data? A: It is very important. In fact the job of any forecast algorithm is to predict the signal – whether it is trending or not – and to ignore the noise. Unfortuantely this is not easy to
Q: Do you think the forecaster should distribute forecast accuracy to stakeholders (e.g. to show how good/bad the forecast is) or do you think this will confuse stakeholders? A: This just depends what is meant by stakeholders. And what is meant by forecast accuracy. If stakeholders means those people who
