I am conducting a study as part of my academic research on how companies manage safety stock across multiple echelons in the supply chain. This survey is not a data-gathering exercise for commercial purposes, nor is it intended for monetization. Instead, it aims to uncover real-world challenges, industry best practices, and the potential for AI/ML-driven optimization, particularly through Reinforcement Learning (RL).
Your insights will contribute to a research-driven project designed to explore innovative solutions for improving inventory efficiency, reducing stockouts, and optimizing working capital. Once the research findings are validated and the solution proves effective, a prototype of the tool will be distributed for free to industry professionals for testing and feedback. No proprietary or sensitive business data will be shared or used commercially.
Survey: Unlocking the Potential of Multi-Echelon Inventory Optimization with Reinforcement Learning for Safety Stock Management
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| Survey: Unlocking the Potential of Multi-Echelon Inventory Optimization with Reinforcement Learning for Safety Stock Management |
| Reinforcement Learning and Its Role in Safety Stock Optimization Reinforcement Learning (RL) is a branch of Artificial Intelligence (AI) that enables systems to learn optimal decision-making strategies through trial and error. Unlike traditional inventory models that rely on fixed formulas and historical data, RL continuously adapts to changing demand patterns, supply chain disruptions, and multi-echelon dependencies. |
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Bhubalan Mani
Olathe KS
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