Dynamic Replenishment Policies for Vendor-Managed Inventory under Stochastic Demand: A Simulation-Based Comparative Study

Jamal Musbah; Ibrahim Badi; Dragan Pamucar

doi:10.14513/actatechjaur.00960

Authors

Jamal Musbah Mechanical Engineering Department, Libyan Academy-Misrata, 94H2+WXC, Misrata, Libya // College of Technical Sciences- Bani Walid, Bani Walid, Libya
Ibrahim Badi Mechanical Engineering Department, Libyan Academy-Misrata, 94H2+WXC, Misrata, Libya
Dragan Pamucar Department of Applied Mathematical Science, College of Science and Technology, Korea University, Sejong 30019, Republic of Korea // Faculty of Engineering, Dogus University, 34775 Umraniye, Istanbul, Turkey // Department of Operations Research and Statistics, Faculty of Organizational Sciences, University of Belgrade, Jove Ilića 154, 11000 Belgrade, Serbia

DOI:

https://doi.org/10.14513/actatechjaur.00960

Keywords:

Vendor-Managed Inventory (VMI), Inventory Routing Problem (IRP), Dynamic Replenishment, Simulation-Optimization, Genetic Algorithm, Supply Chain Resilience

Abstract

Vendor-Managed Inventory (VMI) is a pivotal strategy for optimizing supply chain performance, yet it poses a significant challenge in balancing operational costs against service levels under stochastic demand. While dynamic policies are gaining traction, literature lacks a systematic comparison of the underlying trigger logic (reactive vs. proactive). This study addresses this gap by providing a rigorous comparative analysis of static versus dynamic inventory replenishment policies within a VMI framework for a pharmaceutical distribution network. We design and evaluate four distinct policies: a traditional static (s,S) policy and three novel dynamic policies—reactive, proactive, and inertial—that adapt replenishment triggers based on real-time, system-wide demand signals. The novelty of this work lies in the formal design and first systematic comparison of these distinct dynamic trigger mechanisms, particularly the "Inertial" policy, which utilizes a smoothed urgency signal to enhance resilience. A high-fidelity simulation-optimization framework is developed, where policy parameters are optimized via a Genetic Algorithm to ensure each strategy operates at its peak potential. The results, analysed using ANOVA and Tukey’s HSD tests, reveal that while all policies can be optimized to a statistically similar total cost (p = 0.782), they differ significantly in their ability to maintain service levels. The proposed inertial policy, which utilizes a smoothed urgency signal, demonstrates superior performance, significantly reducing stockouts by 21.5% and 29.7% compared to static and reactive policies, respectively, without incurring a statistically significant cost increase. This demonstrates that integrating anticipatory, smoothed demand signals offers a robust pathway to enhancing service resilience without sacrificing economic efficiency.

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