Inventory Optimization in E-Commerce: A Dual-Sourcing Collaborative Replenishment Model Under Demand Uncertainty

Abstract

Purpose: This study aims to develop a cost-efficient inventory optimization model for e-commerce enterprises facing uncertain demand. The objective is to enhance operational responsiveness and service levels while minimizing total inventory-related costs through a dual-sourcing replenishment strategy that combines lean and flexible suppliers. Design/methodology/approach: A dynamic collaborative replenishment model is formulated using optimization theory and stochastic demand modeling. The model integrates dual sourcing and service-level constraints and is solved through nonlinear programming via a bounded objective method. Matrix Laboratory (MATLAB) is used for implementation and simulation, with a real-world case study from a Chinese e-commerce firm to validate the model. Findings: Simulation results demonstrate that the proposed model significantly reduces total inventory costs-by up to 36%-compared to traditional single-source or fixed-cycle replenishment strategies. The model also improves inventory responsiveness and service reliability, effectively balancing cost control and customer service in volatile demand environments. Practical implications: This model provides a practical decision-support tool for inventory managers in e-commerce. By dynamically allocating orders between lean and flexible suppliers based on real-time demand and service level targets, firms can reduce holding and stockout costs while improving overall supply chain performance, particularly in fast-paced, digitally driven markets.