Supply Chain Analytics: A Data-Driven Approach
Modern supply chain networks increasingly rely on real-time data to navigate structural uncertainties, market volatility, and operational disruptions. This manuscript bridges the gap between statistical data-driven learning and robust decision-making frameworks in logistics and operations management. We present a comprehensive, mathematically rigorous treatment of supply chain analytics, moving from empirical demand forecasting to optimal inventory and network control under uncertainty. Key topics explored include sample minimization, dynamic programming recursions for time-varying inventory replenishment, network fulfillment frameworks, and advanced distributionally robust optimization (...
| Authors | Elioth Sanabria |
|---|---|
| Primary category | math.OC |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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