{"id":15438,"date":"2022-12-16T12:48:02","date_gmt":"2022-12-16T11:48:02","guid":{"rendered":"https:\/\/eyeonplanning.com\/?p=15438"},"modified":"2024-08-06T17:04:24","modified_gmt":"2024-08-06T15:04:24","slug":"demand-forecasting-machine-learning","status":"publish","type":"post","link":"https:\/\/eyeonplanning.com\/blog\/demand-forecasting-machine-learning\/","title":{"rendered":"How to eat an elephant: The recipe for machine learning in demand forecasting"},"content":{"rendered":"

Are you ready to steer your business based on more precise forecasts but don\u2019t know where to start? Creating a value-adding machine learning model for demand forecasting<\/a> can be challenging. In this blog, we talk about the hurdles companies might face when implementing driver-based forecasting<\/a> and how to overcome them.<\/p>\n

\"how<\/em>Now, let’s start with some facts: From a benchmark that we conducted in 2021 we know that while most companies are eager to start applying machine learning in the area of demand forecasting, only 10% are actually doing it. We further inquired what is stopping them: It turns out that tooling<\/strong>, knowledge<\/strong>, and having a solid business case<\/strong> are the biggest obstacles to moving forward.<\/p>\n

Machine learning in demand forecasting: slice the elephant<\/h3>\n

As a first step you should ask yourself: \u201cWhat will machine learning bring to my business?\u201d Preparing a proper business case is crucial.<\/p>\n

Luckily, that is something where EyeOn can be of help. We can build a proof-of-concept in a very short amount of time. Our data scientists build and apply machine learning models in demand forecasting in a very pragmatic and robust way. The proof-of-concept offers you:<\/p>\n