Solutions Accelerator Demo Demand Forecasting


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In this solution accelerator, we demonstrate how to use Apache Spark™ and Facebook Prophet™ to build dozens of time series forecasting models in parallel on the Databricks Lakehouse Platform.

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Webinaire à la demande : prévision précise de la demande à grande échelle

New Methods for Improving Supply Chain Demand Forecasting

Témoignage de client RB

Video transcript

Demand forecasting is an essential practice in most organizations. Our goal is to predict how much of individual products we need in specific locations, and at what times, so that we can maximize our sales and minimize costs. The most accurate forecasts are going to take into consideration location-specific patterns associated with the product. But this requires us to produce a large number of location- and product-specific forecasts. Legacy approaches struggle with this – they typically produce these forecasts one at a time. And it’s very difficult to get through all the forecasts that are needed in time to affect our operations. So instead, what a lot of organizations do is they will aggregate their stores, they’ll aggregate their products, they’ll forecast at the aggregate level and then allocate it back down. This process is overly simplified and turns around quickly – but substantially sacrifices accuracy.


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