INVENTORY PLANNING FOR 3D-PRINTED SPARE PARTS UNDER UNCERTAIN DEMAND

Abstract

In recent years, additive manufacturing technologies have increasingly appeared in industrial spare parts logistics systems. Instead of maintaining large inventories of finished components, companies can increasingly rely on digital inventories and produce spare parts on demand using 3D printing. This shift creates new decision-making challenges related to the management of raw printing materials and the planning of production under uncertain demand. This paper proposes a two-echelon newsvendor model for inventory planning in additive manufacturing-based spare parts supply systems. In the proposed framework, the first decision stage determines the quantity of raw printing material to be stocked before demand realization, while the second stage determines the number of spare parts produced in response to stochastic customer demand. The model captures the trade-offs between material procurement cost, inventory holding cost, and shortage penalties. The mathematical formulation is developed as a two-stage stochastic optimization problem. Numerical experiments are conducted to analyze the relationship between raw material inventory levels and expected system cost. The results show that the cost function exhibits a well-defined minimum and that the optimal material inventory level strongly depends on shortage cost parameters. Sensitivity analysis further demonstrates how shortage penalties influence optimal inventory decisions. The findings highlight the strategic role of raw material inventory in additive manufacturing supply systems and provide practical insights for companies adopting 3D printing technologies in spare parts logistics.