Planning resource to pick, pack and dispatch online orders in fulfilment centres currently relies on a manual forecast with accuracy opportunities. We use data science to provide a more accurate demand forecast for online sales to reduce fulfilment costs and protect the customer delivery time. Interpretability is critical to explain machine learning models and to understand the accuracy of the findings, which we achieve by breaking down the forecast to display each features incremental impact.

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