High-performance and detailed insights into model and forecast quality

The ability to accurately forecast future sales impacts all areas of an organisation - from strategic planning, purchasing and supply chain management to individual shops. Retailers face the challenge of accurately predicting which products will be needed in which shop at which time to ensure seamless availability of the desired products for their customers. Although theoretically all the data is available for an accurate forecast, the quality of the forecasts often falls short of expectations. This is due to unrecognised errors in the forecasting model, which affect the accuracy of the forecasts.

This is why retailsolutions has developed the SAP Analytics Suite add-on. In addition to SAP Unified Demand Forecast (UDF), this content suite developed specifically for the retail sector offers interactive dashboards that show the problem areas at a glance.  Based on SAP HANA and SAP Analytics Cloud, the suite visualises critical article-store combinations using various error KPIs. This highly aggregated view of the underlying issues enables users to take appropriate action to improve forecast quality and thus effectively respond to the changing needs of their customers.

Forecast Monitor

Monitoring the accuracy of specific products, locations or hierarchy elements. Supports the validation of forecast quality prior to implementation and ongoing maintenance.

Product hierarchy analysis

Analysis of the product hierarchy to direct planning to the products and locations with the lowest forecasting accuracy.

Analysis of the location hierarchy

Analysis of the location hierarchy to compare the genuineness of nodes with each other, with drilldowns in the product hierarchy.

Analysis of incidents

Analysis of model accuracy, especially across events, to analyse the accuracy of the model. Comparing multiple diagnostic IDs to validate the performance of UDF parameter settings.

Batch Monitor

Monitoring of UDF modelling, forecasting and HPR batch jobs and analysis of runtime, memory and CPU trends in comparison to each other. Enables proactive monitoring of system performance to detect potential problems, e.g. with growing data volumes, at an early stage.