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
Sales is a fast-paced business; people who aren’t focused on the here and now usually lose out on sales. As mentioned in Jelle’s blog, sales persons would rather be out in the field selling, which is the reason why they feel it would be a waste of their resources sitting in their office forecasting. Yet,
In our last post, Sujit discussed the importance of gathering input from participants as a way to mitigate biases. In this post, I will explain why the sales team should be required participants and what their influence is on the forecasting process.
Recently, someone forwarded a Live Science web link to me. The web page had information on how human brains are able to read words and make sense of them even when the letters are jumbled up. The website suggested that it was possible for a human brain to make sense of the words as long
When businesses try to predict their future demand, they often begin with a mathematics based statistical forecast. Once this is established, some users are asked to provide overrides that can make this statistical forecast better. Typically, this is asked of users who have a client facing role.
Developing an accurate as possible forecast is very important in running a business. Demand planners spend countless hours trying to create a better forecast so that they can help their company be more efficient. A key ingredient in the creation of the final forecast is the forecast generated by a computer program, which is based in statistics. In this