As market competition intensifies and technological advancements progress, the time and labor costs associated with individual components have risen accordingly. Simultaneously, corporate inventory levels have increased due to various factors, resulting in higher operational expenses. As a result, effective inventory management has become a critical concern for businesses. This paper presents a machine learning-based solution for inventory optimization, addressing the challenges faced by a specific company. By analyzing historical shipment data, evaluating sales representative performance, assessing client purchasing power, and forecasting product shipment trends, the solution effectively reduces inventory costs while optimizing production planning. The proposed model has been shown to deliver high accuracy and feasibility in both shipment forecasting and inventory management. 

Junli Shi, Youpeng Fan, Yue Lv. A Multi-Layer Machine Learning Approach for Inventory Optimization: Case Study of Product Shipment Trend Forecasting. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 2: 67-76. https://doi.org/10.25236/AJCIS.2026.090210.

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