Modern production planning increasingly requires intelligent decision-support mechanisms capable of integrating demand variability, production constraints, inventory conditions, and operational performance objectives. SAP-based production planning provides an enterprise-level environment for coordinating these activities, but conventional planning logic may be insufficient when manufacturing systems exhibit nonlinear demand patterns, fluctuating capacities, bottlenecks, and complex interactions among planning variables. This research proposes a Hybrid Machine Learning Architecture for SAP Production Planning Optimization and Lean Manufacturing Performance that combines predictive learning, feature-aware representation, residual learning, attention mechanisms, and optimization-oriented decision support. Because the supplied literature primarily concerns machine learning architectures for medical-image segmentation rather than SAP or manufacturing, these studies are treated as methodological foundations rather than direct evidence of SAP performance. In particular, residual learning, attention mechanisms, recurrent contextual learning, and encoder–decoder architectures provide transferable principles for constructing a hybrid analytical architecture (He et al., 2016; Hu et al., 2018; Cai et al., 2019; Ronneberger et al., 2015). The proposed framework uses SAP planning data to generate forecasts, estimate production risks, identify operational constraints, and recommend planning adjustments. A conceptual evaluation demonstrates how the architecture can be assessed through forecasting accuracy, inventory efficiency, throughput, schedule stability, and lean-performance indicators. The study contributes a structured research framework for integrating machine learning with enterprise production planning while emphasizing interpretability, data quality, scalability, and operational feasibility.
Hybrid Machine Learning Architecture for SAP Production Planning Optimization and Lean Manufacturing Performance
DOI:
Abstract
References
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