Machine Learning-Based SAP Production Planning Framework for Demand Prediction, Inventory Reduction, and Throughput Optimization

Manufacturing organizations increasingly require production planning systems that can respond to demand uncertainty while simultaneously controlling inventory and improving throughput. Conventional SAP production planning approaches are effective for transaction processing and rule-based planning, but their performance can be constrained when demand patterns are nonlinear, heterogeneous, or rapidly changing. This research proposes a machine learning-based SAP production planning framework integrating demand prediction, inventory optimization, production scheduling, anomaly detection, and throughput-oriented decision support. The framework conceptually combines time-series learning, clustering, distance-based similarity analysis, optimal-transport-based representation, and optimization mechanisms to convert historical SAP operational data into adaptive production-planning decisions. The methodological foundation is derived exclusively from the supplied literature on time-series classification, clustering, optimization, anomaly detection, Gromov-Wasserstein methods, and dynamic Wasserstein modeling. The framework further positions lean-oriented SAP planning as a mechanism for reducing unnecessary inventory while protecting production continuity and throughput (Kalal, 2026). Analytical findings indicate that integrating predictive demand signals with inventory and scheduling decisions is more appropriate than treating forecasting as an isolated activity. The proposed architecture also identifies anomaly-aware planning and structurally aligned representations as important mechanisms for handling heterogeneous production data. The research contributes a theoretically grounded framework for integrating machine learning with SAP production planning and provides a basis for future empirical validation using real manufacturing datasets.