Full-process perspective analysis and management system for finished product inventory assets of automobile part enterprises

Abstract

The invention discloses a whole-process perspective analysis and management system for finished product inventory assets of an automobile part enterprise. Asset management is carried out on the whole process from offline to delivery of finished products of automobile parts to customers according to the stages of a finished product temporary storage warehouse, a qualified product warehouse, an in-transit warehouse, a middle storage warehouse, a line side warehouse, inner three packages, outer three packages and a finished product repair warehouse; 2, through quasi real-time data refreshed every N seconds, performing perspective analysis and control on finished product assets of the automobile part enterprise according to a product series and a warehouse age dimension; comprising the following steps: (1) a data acquisition and cleaning process; (2) a data storage and layering process; (3) processing and calculating data; and (4) a data visualization process. According to the invention, an enterprise can conveniently manage the stock and value of finished products in each stage, and the corresponding relationship and change trend between the stock and value of each month of storage age.

Description

Technical Field

[0001] The present invention relates to the field of automobile parts production and manufacturing, and in particular to a full-process perspective analysis management system for finished product inventory assets of automobile parts enterprises. Background Art

[0002] In order to properly order parts that make up a product, it is necessary to know the current inventory accurately. Therefore, in the past, bar codes indicating information such as the type, delivery date, and quantity of parts were attached to the boxes that bundled the parts. During acceptance, the bar code information was input into a computer through a reader to manage the number of delivered parts, that is, the inventory.

[0003] From ordering parts to actual delivery and use in product manufacturing, the so-called lead time is generated due to the time required for logistics, etc. Therefore, in order to grasp more accurate inventory levels, it is best to manage inventory levels in a timely manner in the logistics path where parts are shipped from the parts supplier, provided to the parts orderer, and used in product manufacturing.

[0004] In addition, the logistics path from the parts supplier to the assembly plant (parts orderer) varies depending on the parts. For example, when the parts supplier is near the assembly plant, the parts are directly transported from the parts supplier to the assembly plant; when the parts supplier is far from the assembly plant, the parts are sometimes transported through a transit warehouse. Therefore, it is best to be able to perform appropriate inventory management regardless of the difference in the logistics path of the parts. Summary of the invention

[0005] The present invention provides a perspective analysis management system for the finished product inventory assets of automobile parts enterprises. The present invention facilitates enterprises to manage the inventory volume and value of finished products at each stage, the corresponding relationship between the inventory volume and value of each month's inventory age, and the change trend.

[0006] The technical solutions to the above problems are as follows: The whole process perspective analysis management system of finished product inventory assets of automobile parts enterprises includes the following steps: Step 1: Manage the entire process of finished auto parts production from the end of the production line to the delivery to customers according to the stages of finished product temporary storage, qualified product storage, in-transit storage, intermediate storage, line-side storage, internal three-package, external three-package, and finished product repair storage; Step 2: Through the quasi-real-time data refreshed every N seconds, the finished product assets of the automotive parts company are analyzed and controlled in perspective according to the product series and inventory age dimensions; including the following processes: (1) The process of data collection and cleaning; (2) The process of data storage and stratification; (3) The process of data processing and calculation; (4) The process of data visualization.

[0007] Furthermore, the data collection and cleaning process is as follows: Use data collection tools kettle and datax to extract inventory asset-related data from the ERP system and set the extraction frequency to once every N seconds; Clean the collected data, including removing duplicate data, processing missing values, converting data formats, etc., to ensure data quality and consistency.

[0008] Furthermore, the process of data storage and tiering is: Use Hadoop distributed file system as data storage system and Hive as data warehouse tool to design data warehouse in layers, as follows: ①ODS layer: stores data extracted from the business system to the data warehouse, and performs preliminary data cleaning; ②DWD layer: The data of the ODS layer is cleaned, converted, integrated, and standardized to form detailed data, which is divided into dimension table and fact table data; ③DWS layer: lightly summarize the detailed data in the DWD layer according to each dimension; ④ADS layer: aggregates the data in the DWS layer in a coarse-grained manner according to indicator objects and display requirements.

[0009] Furthermore, the process of data processing and calculation is: Use the big data processing framework Apache Spark to implement distributed parallel computing and processing of large-scale data sets, including data conversion, aggregation, and connection, to generate the data sets required for perspective analysis of the entire inventory asset process and store them at different levels of the data warehouse.

[0010] Furthermore, the data visualization process includes: extracting the data of the ADS layer in the data warehouse into the MySQL database, and using SQL statements to query the required data through the data visualization tool Fanruan Report, and displaying the inventory quantity, change trend, and cost distribution information on the report page in the form of charts and graphs.

[0011] The system of the present invention covers multiple stages from offline to delivery to customers, including temporary storage for finished products, qualified product warehouse, in-transit warehouse, intermediate storage warehouse, lineside warehouse, internal three packages, external three packages, finished product repair warehouse, etc., linking various types of data to achieve a mapping relationship between finished product inventory and its asset value. Through this system, enterprises can intuitively feel the connection between data, while also managing the changing trend of finished product asset value. To a certain extent, this system helps enterprises understand the changes in monthly finished product inventory and value, as well as the value of each product and the number of finished products stored in different warehouse ages. The application of this system greatly reduces the burden of manpower and improves work efficiency and accuracy.

[0012] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic diagram of the whole process perspective analysis management system for finished product inventory assets of an automobile parts enterprise of the present invention; Figure 2 This is a functional structure diagram of the whole process perspective analysis management system for finished product inventory assets of an automobile parts enterprise of the present invention; Figure 3 This is a technical structure diagram of the full-process perspective analysis management system for finished product inventory assets of an automobile parts enterprise of the present invention. DETAILED DESCRIPTION

[0014] like Figures 1 to 3 As shown, the automotive parts enterprise finished product inventory asset full process perspective analysis management system of the present invention includes the following steps: The whole process perspective analysis management system of finished product inventory assets of automobile parts enterprises includes the following steps: Step 1: Manage the entire process of finished auto parts production from the end of the production line to the delivery to customers according to the stages of finished product temporary storage, qualified product storage, in-transit storage, intermediate storage, line-side storage, internal three-package, external three-package, and finished product repair storage. The asset overview shows the overall and various stages of asset value and inventory in 30 seconds in real time, and also presents the year-on-year and month-on-month situation; Asset distribution and asset value trends at each stage of finished assets: ①Distribution of finished products at different stages: The finished product asset distribution is recorded by product series classification and refreshed in 30 seconds in near real time, including the corresponding asset value, asset value proportion, asset value month-on-month, finished product inventory, inventory age less than 1 month, inventory age 1-3 months, inventory age 4-6 months, inventory age 7-12 months, inventory age more than 12 months, and finished product distribution-warehouse data; at the same time, the finished product distribution-warehouse records the finished product distribution details, and records the asset value, inventory, inventory age: 1-3 months inventory, inventory age: 3-6 months inventory, inventory age: 6-12 months inventory, inventory age more than 12 months data corresponding to the product series according to the sub-warehouse classification.

[0015] ②Value trends of finished assets at different stages: Finished Asset Value Trend - Value | Inventory: The value corresponding to the monthly inventory; Finished product asset value trend - value (stock age): the value corresponding to the stock age at each stage each month; Finished product asset value trend - inventory (stock age): the inventory corresponding to the stock age at each stage each month; The data in the chart is refreshed in quasi-real time every 30 seconds.

[0016] The above involves the definition of related variables: Inventory: refers to the number of finished products stored in the warehouse.

[0017] Inventory age: refers to the time that finished products are stored in the warehouse.

[0018] Finished product storage warehouse: refers to the storage of assembled finished products.

[0019] Qualified product warehouse: refers to the place where good finished products without quality problems are stored.

[0020] In-transit inventory: inventory that has not yet reached the destination and is in transit or waiting for transportation and is stored in a means of transportation.

[0021] Intermediate storage warehouse: a place where goods need to be temporarily stored during transportation.

[0022] Lineside warehouse: The logistics warehouse of a manufacturing enterprise includes a regular warehouse and a temporary storage warehouse next to the production line.

[0023] Internal three guarantees: refers to the quality problems found in the finished products by ourselves.

[0024] Three guarantees outside: refers to when customers discover quality problems with the finished product.

[0025] Finished product repair warehouse: refers to the place where finished products with quality problems are stored.

[0026] Step 2: Through the quasi-real-time data refreshed every N seconds, the finished product assets of the automotive parts company are analyzed and controlled in perspective according to the product series and inventory age dimensions; including the following processes: (1) The process of data collection and cleaning: Use data collection tools kettle and datax to extract inventory asset-related data from the ERP system, and set the extraction frequency to once every N seconds. The value of N is preferably 30.

[0027] Clean the collected data, including removing duplicate data, processing missing values, converting data formats, etc., to ensure data quality and consistency.

[0028] (2) The process of data storage and stratification: The process of data storage and stratification is as follows: Use Hadoop Distributed File System (HDFS) as the data storage system and Hive as the data warehouse tool to perform a hierarchical design of the data warehouse as follows: ①ODS layer: stores data extracted from the business system to the data warehouse, and performs preliminary data cleaning; ②DWD layer: The data of the ODS layer is cleaned, converted, integrated, and standardized to form detailed data, which is divided into dimension table and fact table data; ③DWS layer: lightly summarize the detailed data in the DWD layer according to each dimension; ④ADS layer: aggregates the data in the DWS layer in a coarse-grained manner according to indicator objects and display requirements.

[0029] (3) The process of data processing and calculation; the process of data processing and calculation is: Use the big data processing framework Apache Spark (data engine) to implement distributed parallel computing and processing of large-scale data sets, including data conversion, aggregation, and connection, to generate the data sets required for perspective analysis of the entire inventory asset process and store them at different levels of the data warehouse.

[0030] (4) Data visualization process. The data visualization process includes: extracting the data of the ADS layer in the data warehouse into the MySQL database, and using the data visualization tool Fanruan Report to query the required data using SQL statements, and displaying the inventory, change trend, and cost distribution information on the report page in the form of charts and graphs.

[0031] Description of English terms involved in this patent: Kettle: Kettle is an open source ETL (Extract, Transform, Load) tool used for data extraction, transformation, and loading. It supports data migration and transformation between multiple data sources and is often used in the construction of data warehouses and the preprocessing stage of data analysis.

[0032] dataX: dataX is an open source offline synchronization tool for heterogeneous data sources developed by Alibaba. It supports efficient data synchronization of multiple data sources including MySQL, Oracle, HDFS, and Hive.

[0033] ERP: ERP (Enterprise Resource Planning) is an integrated management information system used to integrate various resources within the enterprise, including human, material, financial, information, etc., to achieve optimal resource allocation and automation of business processes.

[0034] Hadoop: Hadoop is a distributed system infrastructure developed by the Apache Foundation that allows distributed processing of large-scale data on large computer clusters using simple programming models.

[0035] HDFS: HDFS (Hadoop Distributed File System) is Hadoop's distributed file system, which is designed to store very large data sets (such as PB level) on commodity hardware.

[0036] Hive: Hive is a data warehouse tool based on Hadoop. It can map structured data files into a database table and provide SQL query functions.

[0037] ODS layer: ODS (Operational Data Store) layer, also known as the operational data storage layer, is a layer in the data warehouse architecture, used to store the original data of the enterprise information system. The ODS layer is mainly responsible for data integration and cleaning tasks, providing high-quality data sources for the data warehouse.

[0038] DWD layer: DWD (Data Warehouse Detail) layer, also known as the data warehouse detail layer, is a subject-oriented detailed data layer generated after cleaning (noise removal, null value processing, data type conversion, etc.) and converting the ODS layer data. The data in the DWD layer is usually more standardized and clean, and is suitable for further data analysis and mining.

[0039] DWS layer: DWS (Data Warehouse Summary) layer, also known as the data warehouse summary layer, is a layer that summarizes and aggregates data based on business needs based on the DWD layer. The data in the DWS layer is usually lightly summarized data to meet certain specific analysis needs.

[0040] ADS layer: ADS (Application Data Service) layer is the data layer for end users or applications. The data in the ADS layer is usually highly aggregated or complexly calculated data, which is directly used to support various reports and decision analysis.

[0041] Data engine: A data engine refers to a software component or system used to process and store data. It is responsible for operations such as reading, writing, querying, and analyzing data.

[0042] Spark: Spark is a fast, general, and scalable big data processing engine that provides richer computing models than Hadoop MapReduce, including batch processing, stream processing, interactive query, and machine learning.

[0043] Mysql: MySQL is an open source relational database management system that uses structured query language (SQL) for database management.

[0044] Report: FineReport is a business intelligence (BI) software used for designing, making, publishing and sharing data reports.

[0045] SQL: SQL (Structured Query Language) is a standard programming language for accessing and operating database systems. SQL allows users to perform operations such as data query, data update, data insertion, data deletion, and database management.

[0046] The above embodiments are part of the embodiments of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and intended to be used to explain the present invention, but cannot be simply understood as limiting the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

Claims

1. The whole process perspective analysis management system for finished product inventory assets of automobile parts enterprises is characterized by: The following steps are involved: Step 1: Manage the entire process of finished auto parts production from the end of the production line to the delivery to customers according to the stages of finished product temporary storage, qualified product storage, in-transit storage, intermediate storage, line-side storage, internal three-package, external three-package, and finished product repair storage; Step 2: Through quasi-real-time data refreshed every N seconds, the finished product assets of the automotive parts company are analyzed and controlled in a perspective manner according to product series and inventory age dimensions; include The following process: (1) The process of data collection and cleaning; (2) The process of data storage and stratification; (3) The process of data processing and calculation; (4) The process of data visualization.

2. The automotive parts enterprise finished product inventory asset full process perspective analysis management system according to claim 1 is characterized in that: The data collection and cleaning process is as follows: Use data collection tools kettle and datax to extract inventory asset-related data from the ERP system and set the extraction frequency to once every N seconds; Clean the collected data, including removing duplicate data, processing missing values, converting data formats, etc., to ensure data quality and consistency.

3. The automotive parts enterprise finished product inventory asset full process perspective analysis management system according to claim 1 is characterized in that: The process of data storage and tiering is: Use Hadoop distributed file system as data storage system and Hive as data warehouse tool to design data warehouse in layers, as follows: ①ODS layer: stores data extracted from the business system to the data warehouse, and performs preliminary data cleaning; ②DWD layer: The data of the ODS layer is cleaned, converted, integrated, and standardized to form detailed data, which is divided into dimension table and fact table data; ③DWS layer: lightly summarizes the detailed data in the DWD layer according to each dimension; ④ADS layer: summarizes the data in the DWS layer in a coarse-grained manner according to indicator objects and display requirements.

4. The automotive parts enterprise finished product inventory asset full process perspective analysis management system according to claim 1 is characterized in that: The process of data processing and calculation is: Use the big data processing framework Apache Spark to implement distributed parallel computing and processing of large-scale data sets, including data conversion, aggregation, and connection, to generate the data sets required for perspective analysis of the entire inventory asset process and store them at different levels of the data warehouse.

5. The automotive parts enterprise finished product inventory asset full process perspective analysis management system according to claim 1 is characterized in that: The data visualization process includes: extracting the data of the ADS layer in the data warehouse into the MySQL database, and using SQL statements to query the required data through the data visualization tool Fanruan Report, and displaying the inventory quantity, change trend, and cost distribution information on the report page in the form of charts and graphs.

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