Article: Discrete-event simulation modelling of inventory turnover under supply chain financial collaboration Journal: International Journal of Simulation and Process Modelling (IJSPM) 2026 Vol.23 No.1 pp.11 - 23 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Zhifeng Qu

Addresses: School of Management, Wuhan Technology and Business University, Wuhan, 430065, China

Abstract: 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.

Keywords: supply chain finance; deep reinforcement learning; DRL; inventory optimisation; cash conversion cycle; M5 forecasting accuracy dataset.

DOI: 10.1504/IJSPM.2026.152090

International Journal of Simulation and Process Modelling, 2026 Vol.23 No.1, pp.11 - 23

Received: 14 Oct 2025
Accepted: 18 Nov 2025
Published online: 06 Mar 2026 *

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

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