Outlier

An outlier is a data point that differs significantly from other observations in a dataset, potentially skewing analytics. D365 includes built-in data cleaning tools to identify and handle these data anomalies to prevent inaccurate planning.

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How do you identify an outlier in D365 demand forecasting?

An outlier in your demand or sales data can completely derail your forecasting algorithms, as a single anomalous event – such as a massive, one-time bulk order – can be incorrectly interpreted by the system as a permanent shift in demand. D365 provides the data tools required to flag these anomalies, allowing you to exclude them from your planning models to maintain accuracy.

If your current forecasting models are consistently missing the mark or providing volatile results that force your planners to override the system, your outlier management is likely insufficient. We help you configure automated data cleaning and anomaly detection, ensuring that your planning algorithms work with high-quality, normalized data every time.

Maintaining accurate analytical models demands dedicated, proactive technology management. Engaging an experienced technical team under a professional Dynamics 365 managed services contract guarantees that your data quality remains fully performant.

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Who are we?

We're DeliveredSoft, an Microsoft Dynamics 365 Partner based in Europe. With experts in Poland, Denmark and Spain, we build custom solutions using Microsoft Dynamics 365 for clients across a range of industries.​

Our mission is to translate complex technology into real business results. We use our deep Dynamics 365 expertise to help organizations optimize their operations and logistics, drive digital transformation, and achieve sustainable growth. A key area of focus for us is retail commerce. We specialize in enhancing and connecting omni-channel experiences, improving the in-store digital customer experience, and developing powerful toolsets for in-store staff. We integrate robust commerce solutions to meet the dynamic needs of modern retailers.​