Artificial Intelligence Driven Intelligent Lifecycle Management for Shelf-Life Optimized Outbound Logistics in Food Supply Chains Using SAP S/4HANA
Keywords:
artificial intelligence, ERP automation, food supply chain, lifecycle management, predictive analytics, SAP S/4HANA, shelf-life optimizationAbstract
The food industry operates within one of the most logistically demanding supply chain environments, driven by strict perishability constraints and evolving regulatory requirements. Global food loss along the supply chain reached 13.3 percent in 2023, with distribution-stage inefficiencies representing a primary contributing factor. Traditional enterprise resource planning systems manage the lifecycle through rule-based mechanisms such as First Expiry First Out rotation that cannot adapt to the dynamic variability of real-world logistics conditions. This article examines how Artificial Intelligence capabilities integrated within SAP S/4HANA, deployed through SAP Business Technology Platform, can transform lifecycle governance in food supply chains by enabling dynamic shelf-life optimization, predictive demand forecasting, and intelligent outbound logistics planning. Through structured analysis of AI integration architectures, machine learning mechanisms, and empirical evidence from recent peer-reviewed literature, the article demonstrates how AI-driven lifecycle management reduces food spoilage risk, improves inventory utilization, ensures regulatory traceability compliance, and strengthens supply chain resilience. Findings indicate that AI-driven lifecycle management within SAP ERP environments delivers measurable improvements, including food waste reductions of approximately 14.8 percent per deployment, demand forecasting accuracy improvements of 20 to 30 percent, and planning cycle time reductions of 50 to 70 percent, representing a strategically significant advancement over static inventory rotation approaches for food industry organizations.
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