Applying machine learning to improve your forecast, is it possible? This whitepaper, a result of quantitative research by Supply Value with key clients, evaluates three forecasting models—Prophet, Neuralprophet, and LightGBM—in a real-life retail business context. The importance of a mature data architecture is emphasized, with pointers on drivers and barriers towards achieving this goal. Companies with mature data workflows demonstrate higher-quality datasets, emphasizing the significance of accessing the right data at the right time for accurate predictions.
The power and limitations of machine learning are explored through the pilot study, showcasing the promise of increased model complexity. However, a balance is crucial as complexity reduces interpretability. Your business context also determines model performance, with neural networks suitable for erratic demand patterns and LightGBM effective in rich datasets.
Are you interested in unlocking the benefits of Machine Learning driven forecasts through mature data management? Then download the whitepaper below.
