{"id":18788,"date":"2024-07-29T10:06:41","date_gmt":"2024-07-29T08:06:41","guid":{"rendered":"https:\/\/eyeonplanning.com\/blog\/supply-chain-digital-transformation-copy\/"},"modified":"2024-07-29T10:18:08","modified_gmt":"2024-07-29T08:18:08","slug":"demand-planning-process","status":"publish","type":"post","link":"https:\/\/eyeonplanning.com\/blog\/demand-planning-process\/","title":{"rendered":"Are all stages of your demand planning process adding value?"},"content":{"rendered":"

By Rijk van der Meulen<\/a>\u00a0<\/em><\/p>\n

A few weeks ago, we had the opportunity to attend the International Symposium on Forecasting (ISF)<\/span> in Dijon<\/span>.<\/span> It was great to engage with forecasting experts from industry and academia and to gain fresh perspectives on <\/span><\/span>supply chain demand forecasting<\/span><\/span><\/a>.<\/span> One of the things that makes ISF stand out is its <\/span>holistic approach<\/span> to this topic.<\/span> Yes, there is <\/span>a <\/span>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 <\/span>business<\/span>.<\/span> Demand planners play <\/span>an important role<\/span> in enriching this <\/span>\u201c<\/span>baseline<\/span>\u201d<\/span> forecast by (if done well) adding information that was not captured in the model. This aspect <\/span><\/span>was<\/span><\/span><\/span> also given considerable attention<\/span> <\/span>at ISF <\/span><\/span><\/span>– <\/span>thanks <\/span>among others<\/span> to ongoing research by <\/span>Robert Fildes<\/span><\/a>\u00a0and <\/span>Paul Goodwin<\/span><\/a>.<\/span><\/span>\u00a0<\/span><\/p>\n

At <\/span>EyeOn<\/span> we are <\/span>also <\/span>passionate <\/span>about <\/span>optimizing the demand planning process as a whole; making sure <\/span>the overall process is efficient<\/span>,<\/span> and <\/span>each component <\/span>is adding value<\/span>.<\/span> In this blog, we will present our views on some effective best <\/span>practices<\/span>.<\/span><\/span>\u00a0<\/span><\/p>\n

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Evaluating<\/span> the quality of the <\/span>entire demand planning <\/span>process<\/span><\/span>\u00a0<\/span><\/h3>\n

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\u2019t 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 <\/span>Forecast Value Add (FVA)<\/span><\/b> of enrichment: that is, to what extent are demand planners improving the baseline forecast.<\/span>\u00a0<\/span><\/p>\n

Monitoring only the FVA, however, isn\u2019t 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 na\u00efve forecast). In short, <\/span>to assess the effectiveness of our overall demand planning process, we must evaluate the quality of all the individual components.<\/span>\u00a0<\/span><\/p>\n

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But <\/span>there’s<\/span> more to consider<\/span><\/span>\u00a0<\/span><\/h3>\n

While having these basic insights is a good starting point, they may not necessarily offer guidance on <\/span>how<\/span><\/i> 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:<\/span>\u00a0<\/span><\/p>\n