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Optimizing inventory management in 3D printing using model predictive control
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AI
DOI：10.1080/23307706.2025.2556332.png)
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Abstract
En 中文
This paper develops a dynamic model to optimize 3D printing operations by analyzing the interactions among inventory depletion, demand adaptation, and production capacity. Utilizing a differential equations framework, the model captures essential system dynamics over a defined prediction horizon. Sensitivity analyses on key parameters demonstrate their significant impact on operational stability and profitability. Our findings highlight the necessity for strategic planning and balancing immediate responsiveness with long-term efficiency. Critical factors include inventory depletion rates and demand sensitivities, while parameters like demand feedback sensitivity enhance system resilience. This study provides practical strategies for managers to synchronize production with organizational objectives and lays the groundwork for applying optimal control methods to improve the sustainability of additive manufacturing.
Keywords:
3D printing
dynamic modelling
inventory management
production control
deterioration
sensitivity analysis
Journal
IF:
1.8
Papers:
160
Citations:
724
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