Discrete-event simulation modelling of inventory turnover under supply chain financial collaboration

This study addresses the critical gap between operational and financial objectives in supply chain inventory management by proposing a novel framework that integrates discrete-event simulation with deep reinforcement learning.We formulate a dual-objective reward function incorporating both traditional costs (holding, shortage, ordering) and the financial metric of cash conversion cycle.Trained and tested on the real-world M5 forecasting accuracy dataset, our model, cognitive load dynamic assessment model-proximal policy optimisation, demonstrates superior performance.Results show it achieves a total cost of 285.4 8.7 (in thousands), significantly lower than state-of-the-art baselines (p < 0.01), while maintaining a 98.2% service level and reducing cash conversion cycle to 35.2 days.This result highlights the framework's effectiveness in achieving operational-financial synergy, offering a data-driven decision-support tool for enhancing both efficiency and financial health in dynamic supply chain environments.