Contemporary chocolate manufacturers operating SAP S/4HANA alongside specialized demand planning and manufacturing execution platforms face a systemic planning dysfunction: the three operational layers — demand sensing, production costing, and shop floor execution — function as architecturally isolated silos with no near-real-time integration pathway. This paper presents the Forecast-to-Finance (F2F) framework, a formally specified three-tier integration architecture connecting Outperform demand planning, SAP S/4HANA PP/SD, and Ignition MES through OData REST API and OPC-UA communication protocols. A central data lake aggregates multi-source streams, feeding Python AI agent pipelines orchestrated by Apache Airflow, which drive a 10,000-iteration Monte Carlo simulation engine calibrated to six chocolate-specific stochastic variables including cocoa yield variability and seasonal demand dispersion. Simulation outputs are translated into SAP CO standard costing inputs enabling real-time Material Usage Variance and Labor Efficiency Variance tracking, mid-period rolling COGS variance forecasting, and probabilistic capital investment ROI distributions across three CapEx scenarios. Six-month production simulation demonstrated forecast accuracy of up to 97%, COGS variance reduction of 2.5 percentage points, and capacity utilization improvement of 9 percentage points. Among three investment scenarios, packaging automation produced the strongest risk-adjusted ROI of 6.00% at the P50 percentile. The F2F framework addresses a documented gap in simulation-integrated financial planning for process manufacturing and provides a transferable architecture applicable across food and beverage sectors.
 

SAP S/4HANA, Outperform demand planning,Ignition MES, OData REST API, OPC-UA,Monte Carlo simulation,cost variance analysis,forecast-to-finance integration