Leveraging Additive Manufacturing for Inventory Optimization: A Dual-Sourcing Niodel for Cost and Performance Enhancement in Retail Supply Chains

Abstract

This study examines the integration of additive manufacturing (AM), specifically 3D printing (3DP), into retail supply chains to optimize inventory costs while maintaining high service levels (CSL >= 95%). A dual-sourcing inventory model is developed, balancing demand between traditional suppliers and in-house 3DP production. The model, solved using Microsoft Excel Solver, incorporates economic order quantity (EOQ), economic production quantity (EPQ), and reorder points to minimize total costs. Experimental results show that hybrid sourcing with 3DP reduces inventory costs, particularly at higher demand levels, while capacity constraints limit full adoption. Findings suggest that retailers should invest in AM expansion to maximize cost efficiency. This study provides a data-driven framework for hybrid inventory strategies and highlights future research directions in demand uncertainty, queueing effects, and advanced optimization techniques.