Intelligent warehouse management system

Abstract

The invention discloses an intelligent warehouse management system which realizes fine management: a fine management target system is constructed in the directions of precise cooperation, precise decision, precise management and control and precise service; a value target system is constructed in the direction of reducing manpower, reducing cost, improving quality and improving efficiency; warehouse-in and warehouse-out are unmanned, warehouse-in and warehouse-out receipts are electronized, warehouse management is visualized, and material in and out are intelligent; the labor cost of warehouse management is reduced through unmanned operation; warehouse data is real-time, real and accurate, intelligent early warning of dull loss is supported, and the financial management risk of storage materials is reduced; warehousing data are shared in real time, order delivery is intelligently reminded, and delivery resource collaborative service quality improvement is achieved; finished product information is automatically collected, batch number one-key tracing is supported, and rapid management response is achieved; pC / tablet computer / mobile phone / large-screen multi-scene data intelligent interaction is achieved, warehouse materials are intelligently analyzed in real time, and efficient management and decision making are achieved.

Description

technical field [0001] The invention relates to the field of warehouse management, in particular to an intelligent warehouse management system. Background technique [0002] As the core resources, materials are very important to the development of enterprises. The modern (smart) supply chain is the specific practice and application of the ubiquitous power Internet of things in the field of materials. Modern (smart) supply chain is based on the three smart business chains of smart procurement, digital logistics and panoramic quality control to improve the professional operation capability of materials; it is supported by efficient internal and external collaboration to better provide customers with high-quality services; based on smart operation The core is to improve the ability of data mining and value creation in the whole process of the supply chain, and to better provide information services for the company and the society. [0003] At present, the management method of...

AI Technical Summary

Problems solved by technology

[0003] At present, the management method of the spare parts warehouse is still relatively traditional, mainly relying on the self-management of employees, the minimum quantity of spare parts cannot be determined in time, it is difficult to check items, and the phenomenon of missing items occurs from time to time: the specific problems and needs can be divided into the following two parts,

[0004] 1) Scenario problem requirements: Inbound and outbound paper document records, manual comparison and entry, the workload is heavy and cumbersome; inbound and outbound depends on the self-quality management of maintenance workshop employees, which is prone to loss and non-traceability; long-term sluggish materials are unmanaged, collectively The materials cannot be allocated effectively, and the warehouse locations are not efficiently utilized; the materials have not realized the "one item, one code" required for the consistency of "account", "card" and "item"; the storage locations are managed in EXCEL, and the location is manually marked to determine the degree of visualization. Low, it is difficult to confirm the position of the material, and the first-in first-out cannot be controlled

[0005] 2) Requirements for business problems: Warehouse management has many manual operations, and labor costs are high; manual data records are not authentic, accurate and timely, which can easily induce financial risks; warehouse data visualization is low, and delivery management resources have a collaborative service effect Poor; product traceability efficiency and accuracy are insufficient, responsibility definition is difficult, and management response efficiency is slow; warehouse analysis and decision-making data are mostly statistically analyzed manually, and data is prone to distortion and lag, resulting in slow decision-making efficiency and large deviations; inventory quantity cannot be guaranteed, which affects operation and maintenance

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