An automobile part material warehouse management method, system and device

By using multi-cycle demand forecasting and dynamic location allocation, the problem of not considering the frequency and correlation of parts entering and leaving the warehouse in existing technologies has been solved, achieving high efficiency and precision in automotive parts warehouse management, and improving warehouse operation efficiency and supply chain resilience.

CN122243358APending Publication Date: 2026-06-19富日供应链科技有限公司
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
富日供应链科技有限公司
Filing Date
2026-03-12
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing technologies in automotive parts warehousing management do not consider the frequency and correlation of parts entering and leaving the warehouse, resulting in high-turnover parts being stored in remote locations, which reduces warehousing management efficiency.

Method used

By acquiring multi-dimensional warehouse data and conducting multi-cycle demand forecasting, an improved LSTM model is used to predict the demand for parts. Combined with dynamic outbound frequency and correlation, an optimization model is constructed to automatically allocate high-demand and highly correlated parts to storage locations that are close to the outbound outlet and adjacent to each other. A genetic algorithm is used to optimize the allocation of storage locations.

Benefits of technology

It significantly shortens the average walking distance and time for sorting operations, improves warehousing efficiency and space utilization, and achieves precise inventory control and supply chain responsiveness and resilience.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122243358A_ABST
    Figure CN122243358A_ABST
Patent Text Reader

Abstract

This application discloses a material storage management method, system, and apparatus for automotive parts, relating to the field of warehousing and logistics technology. It solves the technical problem that existing technologies often allocate storage locations based on the order of entry or simple classification, without considering the frequency and correlation of parts entering and leaving the warehouse. This results in high-turnover parts being stored in remote locations, leading to low efficiency in material storage management. The method allocates storage locations based on part data and warehouse location data to obtain optimal storage locations. By calculating the frequency and correlation of parts leaving the warehouse, an optimization model is established with the goal of maximizing outbound efficiency. High-frequency, highly correlated parts are automatically allocated to the optimal storage locations near the exit and adjacent to each other. This achieves continuous adaptive optimization of the warehouse layout, ensuring that frequently accessed parts and related parts that are often ordered simultaneously are closely grouped and located in convenient areas, shortening the average sorting distance and time, and improving the core efficiency of warehousing operations.
Need to check novelty before this filing date? Find Prior Art