Yarn Spindle Warehouse Management Using Predictive Delivery Allocation
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Solution Overview
Problem
The challenge in the chemical fiber industry is the difficulty in achieving intelligent management of stereoscopic warehouses for yarn spindles due to varying production and sales volumes, leading to inefficiencies in delivery and warehousing tasks.
Innovation Solution
A yarn spindle warehouse management method and apparatus that utilizes production data and historical sales data to predict the estimated delivery time of yarn spindles, allowing for the allocation of optimal storage locations within the stereoscopic warehouse, and sends warehousing tasks to stackers for execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If stereoscopic warehouses are used for yarn spindle storage, then space utilization is improved, but intelligent management becomes difficult due to varying production and sales volumes
Solution Approach 1:
The system performs preliminary actions by predicting future delivery times based on production and historical sales data before yarn spindles are stored. This allows the warehouse management system to pre-allocate optimal storage locations that consider future delivery requirements, enabling intelligent management in stereoscopic warehouses despite varying production and sales volumes
Solution Approach 2:
The system establishes a feedback mechanism that continuously monitors production data, historical sales data, and delivery patterns. This feedback loop allows the system to dynamically adjust storage location allocations and improve prediction accuracy over time, enabling adaptive intelligent management in the stereoscopic warehouse environment
2Device complexity
If yarn spindles are stored in stereoscopic warehouse without prediction, then warehousing process is simple, but delivery efficiency is reduced
Solution Approach 1:
The system performs preliminary prediction of delivery times using production data and historical sales data before storage operations. This allows the system to pre-determine optimal storage locations that will facilitate efficient future delivery, improving delivery efficiency without significantly complicating the warehousing process
Solution Approach 2:
The system introduces dynamic elements by using predictive algorithms that adapt to varying production volumes and sales patterns. The storage location allocation is dynamically optimized based on predicted delivery times, enabling the system to balance process simplicity with delivery efficiency through intelligent, data-driven decisions
Data Source
AI summary
Provided is a yarn spindle warehouse management method, relating to the field of chemical fiber intelligent technology. The method includes: in response to reception of a warehousing request of a yarn spindle, obtaining production data and historical sales data of the yarn spindle; inputting the production data and the historical sales data into a prediction model to obtain an estimated delivery time of the yarn spindle output by the prediction model; allocating a first target storage location for the yarn spindle based on the estimated delivery time and storage status information of each storage location of a stereoscopic warehouse; and sending a warehousing task carrying a position of the first target storage location to a first stacker, so that the first stacker executes the warehousing task based on the position, wherein the warehousing task is used to instruct to store the yarn spindle to the first target storage location.