Inventory stagnant material reason tracing method and system based on big data

Abstract

The invention discloses a method and a system for tracing reasons for stock stagnation based on big data. The method comprises the following steps: acquiring original analysis data from an ERP (Enterprise Resource Planning) system and a PLM (Performance Label Management) system; importing the original analysis data into the established inventory stagnant material reason tracing model, and operating the model; the construction of the inventory stagnant material reason tracing model comprises the following steps: setting tracing levels, wherein each level corresponds to a stagnant material reason; setting inter-hierarchy tracing logic and intra-hierarchy tracing logic; performing stagnant reason analysis layer by layer according to the inter-hierarchy tracing logic, and executing the corresponding intra-hierarchy tracing logic in each hierarchy; and outputting the stagnation reason of the current material. According to the method, an enterprise can quickly and accurately locate the inaction reason of the materials, the core source and the responsible department of the inaction materials are determined, processing and optimization are carried out in time, and the inventory cost of the enterprise is greatly reduced.

Description

technical field [0001] The invention belongs to the technical field of big data processing, and in particular relates to big data inventory analysis, in particular to a method and system for tracing the causes of sluggish materials in inventory based on big data. Background technique [0002] When an enterprise finds an abnormal inventory sluggishness, it can usually only give the quantity and time of the sluggish material in the inventory, and it is difficult to quickly and accurately locate the cause of the sluggishness. [0003] The cause of sluggish inventory involves checking and tracing a large amount of data, including MRP type data, order point data, minimum batch data, safety stock settings, etc. The large amount of data traceability and many related factors make it very difficult to accurately locate the cause of sluggish inventory. It is cumbersome and inefficient, and can only locate the cause of sluggishness for a small number of sluggish materials (such as high...

AI Technical Summary

Problems solved by technology

[0002] When an enterprise finds an abnormal inventory sluggishness, it can usually only give the quantity and time of sluggish inventory materials, and it is difficult to quickly and accurately locate the cause of sluggishness

[0003] The cause of sluggish inventory involves checking and tracing a large amount of data, including MRP type data, order point data, minimum batch data, safety stock settings, etc. The large amount of data traceability and many related factors make it very difficult to accurately locate the cause of sluggish inventory. Troublesome and inefficient, and can only locate the cause of sluggishness for a small number of sluggish materials (such as high-value materials)

However, due to the inability to locate the cause of sluggish inventory in a timely and accurate manner, most companies cannot give accurate business improvement suggestions for sluggish materials, and the management of sluggish materials is often relatively extensive, which eventually leads to a large inventory backlog

[0004] After retrieval, the Chinese patent with publication number CN105069596A disclosed a method for analyzing dead stock on November 18, 2015. This method can quickly locate and accurately analyze dead stock / material information, and can quickly query the user's current dead stock status. However, It is impossible to give the reasons for sluggish materials in inventory, that is, it is difficult to clarify the source or responsible department of sluggish materials, resulting in the inability to control the occurrence of sluggish materials from the source, which is not conducive to reducing inventory costs for enterprises

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