Should one forecast at the SKU-Customer-Location level and aggregate the results up? Or should one forecast at the product-family-region level and dis-aggregate the results down? Or should one do both and try to triangulate the results? Which method gives the best results to the business? Let us look at this together.
I was recently exposed to the idea of thinking like a hedgehog (one big idea) versus the fox (a lot...
In 1994, the IBM Micro-electronics Division, itself a fortune 100 size firm, put in place a major effort to create...
In one of my previous posts, I wrote about using coefficient of variation (CV) as a predictor of forecastability. In...
Key Point: Coefficient of Variation is not a perfect measure of forecastability. However, if used properly, it can add value...
You have a favorite forecast accuracy metric(s) you’ve been practicing within the organization for a while, and now you think...
I saw this news article on CNN (here) about our planet's earth bigger, older cousin. Quite an interesting discovery if...
I spent some time discussing MAPE and WMAPE in prior posts. In this post, I will discuss Forecast BIAS. Forecast BIAS can...
In our line of work at Arkieva, when we ask this question of business folks: What is your forecast accuracy?...
Key Points on MAPE: Mean Absolute Percent Error (MAPE) is a useful measure of forecast accuracy and should be used...
Sales is a fast-paced business; people who aren't focused on the here and now usually lose out on sales. As...
In our last post, Sujit discussed the importance of gathering input from participants as a way to mitigate biases. In...
Please tell us a little bit about yourself to help us better assist you.
