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 of different ideas). So what happens when people who are more like hedgehogs in their line of work, interact with people who think like a fox? I just happen to know two such groups: Business leaders
In 1994, the IBM Micro-electronics Division, itself a fortune 100 size firm, put in place a major effort to create best in class supply chain planning process and software including demand planning(DM), central planning, available to promise, et al. I was fortunate to be an original member and had the opportunity to work extensively on
In one of my previous posts, I wrote about using coefficient of variation (CV) as a predictor of forecastability. In this post, I will talk about how it can be used to indicate a sensitivity of lead time towards the safety stock calculations. To quickly remind the reader first: The formula for CV = StdDev
Key Point: Coefficient of Variation is not a perfect measure of forecastability. However, if used properly, it can add value to a business’s forecasting process. In the world of forecasting, one of the key questions to consider is the forecastability of a particular set of data. For example, a salesman might consistently be better at
You have a favorite forecast accuracy metric(s) you’ve been practicing within the organization for a while, and now you think you are ready to bring it to the Sales and Operations Planning (S&OP) meeting as a Key Performance Indicator (KPI) of your demand planning process. But you are not sure exactly how to go about
I saw this news article on CNN (here) about our planet’s earth bigger, older cousin. Quite an interesting discovery if you ask me. However, it got me thinking about the family tree of Mean Absolute Forecast Error (MAPE), a subject that I am a little bit familiar with. A few weeks ago, I wrote about
I spent some time discussing MAPE and WMAPE in prior posts. In this post, I will discuss Forecast BIAS. Forecast BIAS can be loosely described as a tendency to either Forecast BIAS is described as a tendency to either over-forecast (meaning, more often than not, the forecast is more than the actual), or under-forecast (meaning, more often
In our line of work at Arkieva, when we ask this question of business folks: What is your forecast accuracy? Depending on who we ask in the same business, we can get a full range of answers from 50% (or lower) to 95% (or higher). How is this possible? Imagine a management team being given
Key Points on MAPE: Mean Absolute Percent Error (MAPE) is a useful measure of forecast accuracy and should be used appropriately. Because of its limitations, one should use it in conjunction with other metrics. While a point value of the metric is good, the focus should be on the trend line to ensure that the