Enhancing Predictive Accuracy through the Use of Machine Learning

Forecasts can be compared across dimensions (for example, retail store location) or item-level metadata (for example, product brand, size, and color). You can use this data for the following:

• Optimize existing forecasts - Save time and retain compatibility with your legacy tools, or gain insight into over- and under-provisioning, with the p50 forecast.
• Meet variable customer demand - Provide high levels of customer satisfaction with the p90 forecast, where the true value is expected to be lower than the predicted value 90% of the time.
• Avoid over-provisioning - Save on costs and avoid over-provisioning with the p10 forecast, where the true future demand value is expected to be lower than the predicted value only 10% of the time.

1. An Azure Storage queue for Azure Forecast configuration where you specify configuration settings for your dataset groups, dataset predictors, and forecasts, as well as the datasets themselves.
2. An Azure Storage Event that triggers when new datasets are uploaded to the related Azure Storage queue.
3. An Improving Forecast Accuracy with Machine Learning Azure Logic App. This combines a series of Azure functions that build, train, and deploy your Machine Learning (ML) models in Azure AI. All Azure Logic app log to Azure Monitor.
4. An Azure Notification Hub email subscription that notifies administrative users with the results of the Azure Logic App.
5. An Azure ML notebook instance that data scientists and developers can use to prepare and process data, and evaluate Forecast output.
6. An Azure Data Factory combines raw forecast input data, metadata, predictor back test exports, and forecast exports into an aggregated view of your forecasts.
7. Azure Synapse can be used to query your forecast output using standard SQL queries.
8. Azure Synapse analyses can be created on a per-forecast basis to provide users with forecast output visualization across hierarchies and categories of forecasted items, as well as item level accuracy metrics. Dashboards can be created from these analyses and shared within your organization.