Are all stages of your demand planning process adding value?

By Rijk van der Meulen 

A few weeks ago, we had the opportunity to attend the International Symposium on Forecasting (ISF) in Dijon. It was great to engage with forecasting experts from industry and academia and to gain fresh perspectives on supply chain demand forecasting. One of the things that makes ISF stand out is its holistic approach to this topic. Yes, there is a significant focus on the technical aspects, such as new model architectures. But we all know that the forecast generated by a forecast engine is seldom the final forecast that ends up being used as input to make decisions within a business. Demand planners play an important role in enriching this baseline forecast by (if done well) adding information that was not captured in the model. This aspect was also given considerable attention at ISF thanks among others to ongoing research by Robert Fildes and Paul Goodwin. 

At EyeOn we are also passionate about optimizing the demand planning process as a whole; making sure the overall process is efficient, and each component is adding value. In this blog, we will present our views on some effective best practices. 

Evaluating the quality of the entire demand planning process 

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. 

But there’s more to consider 

While having these basic insights is a good starting point, they may not necessarily offer guidance on how to improve the quality of your enrichments. For this, you need to track details of the enrichment process, such as the number and type (e.g., direction, magnitude) of enrichments. This allows you to gain perspective on: 

  • Which type of enrichments have historically been associated with positive/negative value add 
  • Which parts of your product portfolio benefit most from demand planner interventions 
  • The time invested in the enrichment process 
  • Potential biases in enrichments

From insights to action 

Insights are valuable, but they should be in service of improving the process. Data-driven insights in the performance of the baseline forecast engine and the behavior of the demand planning team should provide concrete suggestions for process improvements, ultimately leading to: 

  • Improved quality of enrichments, resulting in higher forecast value add and a better demand plan 
  • Enhanced efficiency of your demand planning process through the adoption of a more targeted enrichment strategy
     

How EyeOn can support 

Making the value-add in forecasting tangible is at the core of what we do. And we offer multiple ways to get experience with it or even get started in a fast way.  

  • Play our Forecast Game: a business game where you compete against others to create the best possible forecast, applying best-practice principles around forecast enrichments. Measuring and learning on forecast performance is at the heart of the game set-up.  
  • Use our Smart-touch dashboard: a ready-to-use dashboard that provides all the insights discussed in this blog. Let’s connect your data and unlock direct insights to improve your forecasting and demand planning process.  
  • Get your copy of our Statistical Forecasting e-book: ‘Statistical Forecasting as your steppingstone towards AI’. In the e-book, we explain how to set up statistical forecasting to create an optimal foundation for machine learning-based demand planning. Download it here. 

If you’d like to learn more, feel free to reach out to Rijk van der Meulen or Erik de Vos.

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