Cash Flow Forecasting in SAP ERP Enhanced by UiPath Automation: A Predictive Analytics Approach

Abstract

Maintaining liquidity, mitigating financial risks, andmaking strategic business decisions in today’s enterprisesrequire accurate cash flow forecasting. Unfortunately, thenative forecasting features of the SAP ERP are oftenconstrained by outdated input streams, static data assumptions,and rigid model structures, severely impeding responsivenessand accuracy. This study proposes and evaluates theresults-focused integration of UiPath robotic processautomation (RPA) with predictive analytics to improve shortand medium-term cash flow forecasting in SAP environments.We automated real-time data extraction from SAP FI, FI-CA,and bank interface modules, then employed machine learningand deep learning models (regression trees, LSTM networks,and ensemble methods) to demonstrate substantial gains inforecasting accuracy, cycle time, and exception handling. Theframework was tested on large data sets from multi-currency,multi-business unit enterprises, achieving forecast accuracyimprovement estimates of 15% to 28% compared to SAP’sbaseline predictions. Aside from significantly reducing manualeffort associated with forecast preparation, automation alsoexpedited scenario-based liquidity analysis while enhancinggovernance through exception-based audit logging. Theseresults provide a proven scaling architecture for intelligentreal-time cash forecasting that is reliable and compliant, placingRPA and AI at the core of cash management operations of thefuture, and integrating deeply within ERP systems.