Most organizations measure the quality (i.e., forecast accuracy and bias) of the end result of the demand planning process; often referred to as the “final” or “consensus” forecast. However, these metrics alone don’t capture the value added throughout the process. For instance, you might be satisfied with an 80% forecast accuracy, but if the baseline forecast accuracy was 85%, you’ve invested valuable time and resources only to diminish the forecast quality. This example highlights the importance of tracking the Forecast Value Add (FVA) of enrichment: that is, to what extent are demand planners improving the baseline forecast.
Monitoring only the FVA, however, isn’t enough. Imagine a scenario where the FVA is 10 percentage points, indicating that demand planners are excelling at improving the baseline forecast. Does this mean the overall demand planning process is flawless? Not necessarily. It might be that your forecast engine is underperforming; leading demand planners to spend considerable effort on enrichments that a higher-quality forecast engine could have handled more efficiently. In other words, if the forecasting engine were better, the planners wouldn’t need to spend as much time on adjustments. This example emphasizes the need to also evaluate the quality of your forecast engine by comparing it to a simple benchmark (e.g., by comparing the forecast accuracy of your forecast engine to a naïve forecast).
In short, to assess the effectiveness of our overall demand planning process, we must evaluate the quality of all the individual components. This app aims to provide a simple and intuitive way to do just that.
The app is hosted on Koyeb and can be accessed here. Note, the intended use for this app is for demonstration purposes and not for processing and analyzing big datasets. That being said, have fun playing around with it!
Yes it’s a typo, but it’s a typo with a purpose. It’s a playful nod to the mythological twins, Castor and Pollux. These two heroes were inseparable, known for their adventures, including the rescue of Helen of Troy. Alone, they were impressive, but together, they were unstoppable. This myth resonates with the essence of our app. In the world of supply chain demand forecasting, the final forecast often consists of two parts: a statistical or machine learning baseline and manual enrichments. Just like Castor and Pollux, both elements need to work in harmony to achieve effective demand planning. Yes, it's a bit cheesy, but hey, who doesn't love a good mythological reference?