If the popularity of one's blog can be measured by the number of comments received, then The BFD has become quite popular.
Many of the comments are quite flattering, such as:
- Hello, I check your blog like еvery week. Үour writing style is wittу, keep doing ωhat you're doing!
- Vеry shortly this site will be famοus аmong all blogging and ѕitе-building users, ԁue to it's pleasant posts
- Oh my goodness! Incredible article dude! Many thanks
Some are more tempered in their enthusiasm:
- Looking at this article reminds me of my previous roommate!
- The idea reminds myself a small amount of lipstick removal that's another account wholly.
A few are quite harsh in their critique:
- I used to be able to find good information from your blog posts.
And one was just difficult to interpret:
- I have read not one article on your blog. You’re a big lad.
(Any idea what that is supposed to mean?) I just hope our newly installed spam filtering software doesn't intercept all these...I'll miss them.
Q:When Do You Stop Trying to Improve Forecast Accuracy?
Despite the popular exhortations to never quit, never give up, and never stop improving, there may be some good reasons to stop trying to improve your forecast, and focus resources elsewhere. Some rules of thumb:
1. Is your forecast accuracy good enough to meet your business needs? If so, don't waste resources building fancier models or developing a more elaborate process. If forecast accuracy is not constraining your overall performance, move on to the next problem.
2. Have you considered the consequences of a less-than-perfect forecast? If the costs and consequences are small, why waste time trying to get great forecasts? Or at least focus any improvement efforts on those forecasts that have the most impact on your business.
On the other hand, if you've conducted a rudimentary FVA analysis and determined that you are forecasting worse than a naive model, then this is no time to quit trying. The most fundamental objective of the forecaster is "First, do no harm." If all your people and software and elaborate processes are performing worse than a naive model, then there is room for improvement.
More rules of thumb in the next installment...