Academic Journal of Engineering and Technology Science, 2025, 8(1); doi: 10.25236/AJETS.2025.080113.
Peng Lu
Hebei University of Economics & Business, Shijiazhuang, China
Supply chain disruptions, exacerbated by global uncertainties, demand agile planning tools. This paper presents a machine learning (ML)-enhanced demand sensing model integrated into SAP Integrated Business Planning (IBP) to improve supply chain resilience in automotive manufacturing. We propose a hybrid ML architecture combining Long Short-Term Memory (LSTM) networks for temporal pattern recognition and XGBoost for feature importance analysis. Deployed in a tier-1 automotive supplier, the model reduced stockouts by 30% while maintaining 98% service levels. The study highlights technical implementation steps, quantifies performance gains, and provides actionable insights for scaling ML-driven planning in SAP IBP.
Supply chain resilience; machine learning; demand sensing; SAP IBP; automotive manufacturing
Peng Lu. Enhancing Supply Chain Resilience Using Machine Learning in SAP IBP: A Case Study in Automotive Manufacturing. Academic Journal of Engineering and Technology Science (2025), Vol. 8, Issue 1: 94-98. https://doi.org/10.25236/AJETS.2025.080113.
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