论文信息 - Big Data-Driven Simulation Analysis for Inventory Management in a Dynamic Retail Environment

Big Data-Driven Simulation Analysis for Inventory Management in a Dynamic Retail Environment

Inventory management is one of the most important factors in logistics operations. However, real-world inventory systems are complexly intertwined with related elements, and determining the optimal parameters and identifying the determining factors that influence inventory changes are complex problems. In this paper, using real POS data, we propose a simulation-based algorithm to optimize automated refreshment systems in a retail environment. The inventory system is modeled and simulated, which then returns the performance functions. The expectations of these functions are then estimated by an algorithm and the optimal combination result is obtained. Based on the sensitivity analysis, the determining factor that influences inventory changes is identified. The results show that the proposed simulation-based algorithm is powerful and effective.

Haixia Sang | Shingo Takahashi | Rie Gaku

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