Multi-dimensional analysis management system for automobile parts

Abstract

The invention relates to the field of enterprise asset management, in particular to a multi-dimensional analysis management system for automobile parts, which is characterized in that a plurality of functional modules are arranged on the basis of traditional information management software, and the modules are used for calculating and integrating data acquired from a plurality of information management systems, so that the information management software can be used for realizing multi-dimensional analysis and management of the automobile parts. Multi-dimensional and deep-level analysis of data is realized, the comprehensive management capability of enterprise managers for enterprise part production, sales and repair stages can be improved, effective asset management and analysis means for automobile parts are provided for enterprises, and the enterprises are helped to optimize inventory structures, improve the asset utilization rate and reduce the operation cost.

Description

Technical Field

[0001] The present invention relates to the field of enterprise asset management, and in particular to a multi-dimensional analysis and management system for automobile parts. Background Art

[0002] Auto parts companies usually involve many types of parts management (including parts management as raw materials) in the process of production, sales and repair. At present, auto parts companies often use parts management systems to manage the types and quantities of various auto parts. Traditional parts management systems only have the functions of recording and storing the inventory of various parts and simple inventory statistics.

[0003] As the development trend of auto parts increases year by year, the market demand continues to grow, and the types of parts are increasing, the demands of enterprise managers for parts management are getting higher and higher. It is not just a simple inventory quantity statistics, but also a multi-dimensional and in-depth data analysis of the specific situation of the entire enterprise's parts. The traditional parts management system alone cannot improve the comprehensive management capabilities of enterprise managers in the production, sales, and repair stages of the enterprise's parts.

[0004] Therefore, there is an urgent need for a system that can assist managers of auto parts companies in comprehensively managing the production, sales, and repair of their parts. Summary of the invention

[0005] The purpose of the present invention is to address the corresponding deficiencies in the prior art and to provide a multi-dimensional analysis and management system for automotive parts. By setting up multiple functional modules on the basis of the traditional information management system, the data collected from several information management systems involved in the production, sales and repair processes of automotive parts enterprises are calculated and integrated, thereby realizing a multi-dimensional and in-depth analysis of the specific conditions of the parts of the entire enterprise.

[0006] The purpose of the present invention is to adopt the following scheme to achieve: a multi-dimensional analysis and management system for automobile parts, including a data acquisition and processing module, a data analysis and processing module, an asset analysis module, a data storage and management module, and an analysis result display module, wherein:

[0007] The data collection and processing module is used to collect data from various information systems of the enterprise and perform data cleaning and conversion;

[0008] The data analysis and processing module is used to build a multi-dimensional analysis model, support combined analysis of multiple dimensions and indicators, and use big data analysis tools to perform distributed data processing;

[0009] The asset analysis module is used to perform multi-dimensional asset analysis on automobile parts;

[0010] The data storage and management module is used to store data in layers according to subject domains;

[0011] The analysis result display module is used to display the analysis results in the form of charts and dashboards through visualization tools.

[0012] Preferably, the asset analysis module includes a parts asset analysis module, a stock-to-sales ratio analysis module, a stock location analysis module, and a stock age analysis module, wherein:

[0013] The component asset analysis module is used to analyze component assets in multiple dimensions;

[0014] The inventory-to-sales ratio analysis module is used to analyze the inventory-to-sales ratio in multiple dimensions and to predict the inventory-to-sales ratio for this month;

[0015] The storage location analysis module is used to analyze the storage locations in the warehouse in multiple dimensions;

[0016] The inventory age analysis module is used to analyze the inventory age in multiple dimensions and identify obsolete inventory and slow-turnover parts.

[0017] Preferably, the component asset analysis module performs multi-dimensional analysis of component assets in the following manner:

[0018] 1-1) Define the indicators that require asset analysis, and determine the analysis frequency, value range, and analysis dimensions;

[0019] 1-2) Based on the indicators and analysis dimensions, use the data analysis and processing module to build a multi-dimensional analysis model for component asset analysis;

[0020] 1-3) Using the data acquisition and processing module to collect parts inventory data and dimension data from the information management system;

[0021] 1-4) Based on the set analysis dimensions and calculation indicators, combined with the multi-dimensional analysis model for component asset analysis constructed in step 1-2), the component asset analysis module is used to perform drill-down analysis, comparative analysis, trend analysis, and forecast analysis on various indicators, and generate various asset analysis reports;

[0022] 1-5) Use the analysis result display module to visualize the analysis report generated in step 1-4).

[0023] Preferably, the inventory-to-sales ratio analysis module performs a multi-dimensional analysis of the inventory-to-sales ratio in the following manner, and predicts the inventory-to-sales ratio for this month:

[0024] 2-1) Define the indicators that require inventory-to-sales ratio analysis, and determine the analysis frequency, value range, and analysis dimensions;

[0025] 2-2) Based on the indicators and analysis dimensions, use the data analysis and processing module to build a multi-dimensional analysis model for inventory-to-sales ratio analysis;

[0026] 2-3) Use the data collection and processing module to collect sales and inventory related data from the information management system and integrate them;

[0027] 2-4) Calculate the inventory-to-sales ratio according to the set analysis dimensions, combine the inventory-to-sales ratio analysis multi-dimensional analysis model constructed in step 2-2), identify the problem areas of high inventory and low sales, and predict the sales data of this month through forecast analysis, and generate various inventory-to-sales ratio analysis reports;

[0028] 2-5) Use the analysis result display module to visualize the analysis report generated in step 2-4).

[0029] Preferably, the storage location analysis module performs multi-dimensional analysis of storage locations in the warehouse in the following manner:

[0030] 3-1) Define the indicators that require location analysis, and determine the analysis frequency, value range, and analysis dimension;

[0031] 3-2) Based on the indicators and analysis dimensions, use the data analysis and processing module to build a multi-dimensional analysis model for inventory location analysis;

[0032] 3-3) Use the data acquisition and processing module to collect storage location data from the information management system;

[0033] 3-4) Combine the multi-dimensional analysis model of the storage location analysis constructed in step 3-2) to analyze the usage rate and utilization rate of each storage location, the storage location usage of each supplier, and the storage location usage of each type of parts, identify suppliers or parts that use storage locations inefficiently, and generate a storage location situation analysis report;

[0034] 3-5) Use the analysis result display module to visualize the analysis report generated in step 3-4).

[0035] Preferably, the inventory age analysis module performs a multi-dimensional analysis of inventory age in the following manner to identify obsolete inventory and slow-moving parts:

[0036] 4-1) Define the indicators that require inventory age analysis, and determine the analysis frequency, value range, and analysis dimension;

[0037] 4-2) Based on the indicators and analysis dimensions, use the data analysis and processing module to build a multi-dimensional analysis model for inventory age analysis;

[0038] 4-3) Real-time monitoring of the storage time and inventory data of each component in the information management system;

[0039] 4-4) Combined with the multi-dimensional analysis model of inventory age analysis constructed in step 4-2), calculate the inventory age of each component, identify inventory that has not been moved for a long time, and propose disposal suggestions for long-aged inventory, and generate a warehouse location analysis report;

[0040] 4-5) Use the analysis result display module to visualize the analysis report generated in step 4-4).

[0041] Preferably, the data storage and management module includes a source layer, a data detail layer, a data summary layer, and a data application layer. The data storage and management module adopts a four-layer data processing mode to store data in layers according to subject domains in the following manner:

[0042] 5-1) Use the data acquisition and processing module to collect source data from the information management system, and clean and convert the collected data;

[0043] 5-2) storing the data processed in step 1) into the source layer of the data warehouse;

[0044] 5-3) Convert the data in the source layer into fact tables, dimension tables, and detail wide tables, and store them in the data detail layer;

[0045] 5-4) Build a multidimensional analysis model based on the summary syntax of the multidimensional dataset and the detailed wide table data in the data detail layer, and store it in the data summary layer;

[0046] 5-5) According to the page display requirements of each indicator, the data in the data aggregation layer is processed and stored in the data application layer.

[0047] Preferably, in step 5-3), the data of the source layer is converted into a fact table, a dimension table, and a detail wide table, and stored in the data detail layer in the following specific manner:

[0048] 5-3-1) Clean, convert, integrate and standardize the data of the source layer to form detailed data;

[0049] 5-3-2) Divide the detailed data into fact tables and dimension tables;

[0050] 5-3-3) Associate the fact table with the dimension table to form a detailed wide table;

[0051] 5-3-4) Store fact tables, dimension tables, and detail wide tables in the data detail layer.

[0052] Preferably, the fact table includes an inventory snapshot fact table and an inventory transfer fact table, and the dimension table includes an organization dimension table, a material dimension table, and a time dimension table.

[0053] Preferably, the specific process of data visualization by the analysis result display module is as follows:

[0054] 6-1) Use data synchronization tools to synchronize data files in the data warehouse data application layer to the report database;

[0055] 6-2) Connect data visualization tools to databases;

[0056] 6-3) Query data in the database;

[0057] 6-4) Use visualization tools to intuitively display the data queried in step 6-3) in the form of charts and dashboards.

[0058] The beneficial effects of the present invention are as follows:

[0059] ① The present invention can well cope with the current upward trend of the types of domestic auto parts. Even if there are many types of parts, enterprise managers can use the present invention to effectively control the research and development, production, sales and other links of each auto part. On the basis of accurately counting the inventory quantity of various types of auto parts, it can also conduct multi-dimensional and in-depth data analysis on the specific situation of the parts of the entire enterprise, so as to improve the comprehensive management ability of enterprise managers in the production, sales and repair stages of the enterprise's parts;

[0060] ② The present invention can provide enterprises with effective asset management and analysis methods for automotive parts by performing multi-dimensional and in-depth analysis on data collected from traditional information management software, which helps enterprises optimize inventory structure, improve asset utilization and reduce operating costs (for example, enterprises can use the present invention to establish production plans that meet actual conditions and make overall inventory arrangements for raw materials for parts production, thereby improving the delivery capabilities of the enterprise and the market competitiveness of the enterprise in the industry).

[0061] Glossary

[0062] ERP: Enterprise Resource Planning, is an enterprise management system based on information technology, which aims to integrate and optimize various resources within the enterprise to improve the operational efficiency and benefits of the enterprise.

[0063] Drill-down analysis: refers to the process of gradually deepening from a higher level of data summary to more detailed, lower level data details for analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a schematic diagram of the functional module architecture of the present invention;

[0065] Figure 2 This is a functional structure diagram of a multi-dimensional analysis and management system for automobile parts in an embodiment of the present invention;

[0066] Figure 3 This is a schematic diagram of a business implementation of a multi-dimensional analysis and management system for automobile parts in an embodiment of the present invention;

[0067] Figure 4 The figure is a schematic diagram of data flow among a traditional business system, a data warehouse, and a database in an embodiment of the present invention. DETAILED DESCRIPTION

[0068] like Figures 1 to 4 As shown, a multi-dimensional analysis and management system for automobile parts includes a data acquisition and processing module, a data analysis and processing module, an asset analysis module, a data storage and management module, and an analysis result display module, wherein:

[0069] The data collection and processing module is used to collect data from various information systems of the enterprise and perform data cleaning and conversion;

[0070] The data analysis and processing module is used to build a multi-dimensional analysis model, support combined analysis of multiple dimensions and indicators, and use big data analysis tools to perform distributed data processing;

[0071] The asset analysis module is used to perform multi-dimensional asset analysis on automobile parts;

[0072] The data storage and management module is used to store data in layers according to subject domains;

[0073] The analysis result display module is used to display the analysis results in the form of charts, dashboards, etc. through visualization tools.

[0074] The asset analysis module includes a parts asset analysis module, a stock-to-sales ratio analysis module, a stock location analysis module, and a stock age analysis module, wherein:

[0075] The component asset analysis module is used to analyze component assets in multiple dimensions;

[0076] The inventory-to-sales ratio analysis module is used to analyze the inventory-to-sales ratio in multiple dimensions and to predict the inventory-to-sales ratio for this month;

[0077] The storage location analysis module is used to analyze the storage locations in the warehouse in multiple dimensions;

[0078] The inventory age analysis module is used to analyze the inventory age in multiple dimensions and identify obsolete inventory and slow-turnover parts.

[0079] like Figure 1As shown, in the present invention, the data input end of the data acquisition and processing module is connected to several information management systems, and the data output end of the data acquisition and processing module is connected to the data input end of the data analysis and processing module; the data output end of the data analysis and processing module is connected to the data input end of the parts asset analysis module, the data output end of the data analysis and processing module is connected to the data input end of the inventory sales ratio analysis module, the data output end of the data analysis and processing module is connected to the data input end of the warehouse location analysis module, and the data output end of the data analysis and processing module is connected to the data input end of the inventory age analysis module; the data output end of the parts asset analysis module is connected to the data input end of the data storage and management module; the data output end of the inventory sales ratio analysis module is connected to the data input end of the data storage and management module; the warehouse location analysis module is connected to the data input end of the data storage and management module; the inventory age analysis module is connected to the data input end of the data storage and management module; the data output module of the data storage and management module is connected to the data input end of the analysis result display module; the data output end of the analysis result display module is connected to the front-end page.

[0080] The above modules are used to perform multi-dimensional and in-depth analysis on the data collected from traditional information management software, as shown in the following examples:

[0081] like Figure 2 In this implementation, the component asset analysis module performs multi-dimensional analysis of component assets in the following manner:

[0082] 1-1) Define the indicators that require asset analysis, such as inventory volume, inventory value, etc., and determine the analysis frequency, value range (time value range), and analysis dimension (factory, part type, etc.);

[0083] 1-2) Based on the indicators and analysis dimensions, use the data analysis and processing module to build a multi-dimensional analysis model for component asset analysis;

[0084] 1-3) Use the data collection and processing module to collect parts inventory data and dimension data from the business enterprise ERP (Enterprise Resource Planning) system, including inventory quantity, enterprise organization, materials, material types, suppliers and other data;

[0085] 1-4) Based on the set analysis dimensions and calculation indicators, combined with the multi-dimensional analysis model for component asset analysis constructed in step 1-2), the component asset analysis module is used to perform drill-down analysis, comparative analysis, trend analysis, and forecast analysis on various indicators, and generate various asset analysis reports;

[0086] 1-5) Use the analysis result display module to visualize the various asset analyses generated in step 1-4):

[0087] 1-5-1) Use data synchronization tools to synchronize various asset analysis reports of the data warehouse data application layer to the report database;

[0088] 1-5-2) Connect the data visualization tool to the database;

[0089] 1-5-3) Query asset analysis data in the database;

[0090] 1-5-4) Use visualization tools to visually display the asset analysis data queried in step 1-5-3) in the form of charts, dashboards, etc.

[0091] In this implementation, the inventory-to-sales ratio analysis module performs a multi-dimensional analysis of the inventory-to-sales ratio in the following manner and predicts the inventory-to-sales ratio for this month:

[0092] 2-1) Define the indicators that require inventory-to-sales ratio analysis, and determine the analysis frequency, value range, and analysis dimensions (such as factory, part type, etc.);

[0093] 2-2) Based on the indicators and analysis dimensions, use the data analysis and processing module to build a multi-dimensional analysis model for inventory-to-sales ratio analysis;

[0094] 2-3) Use the data collection and processing module to collect sales and inventory-related data from the WMS (warehouse management system) and ERP (enterprise resource planning) systems, such as inventory status, enterprise organization, materials, material types, sales orders, customers, etc., and integrate them;

[0095] 2-4) Calculate the inventory-to-sales ratio according to the set analysis dimensions, combine the inventory-to-sales ratio analysis multi-dimensional analysis model constructed in step 2-2), identify the problem areas of high inventory and low sales, and predict the sales data of this month through forecast analysis, and generate various inventory-to-sales ratio analysis reports;

[0096] 2-5) Use the analysis result display module to visualize the inventory-to-sales ratio analysis report generated in step 2-4):

[0097] 2-5-1) Use the data synchronization tool to synchronize the inventory-to-sales ratio analysis report of the data warehouse data application layer to the report database;

[0098] 2-5-2) Connect the data visualization tool to the database;

[0099] 2-5-3) Query inventory-to-sales ratio analysis data in the database;

[0100] 2-5-4) Use visualization tools to intuitively display the inventory-to-sales ratio analysis data retrieved in step 2-5-3) in the form of charts, dashboards, etc.

[0101] In this implementation, the location analysis module performs multi-dimensional analysis of the locations in the warehouse in the following ways:

[0102] 3-1) Define the indicators that require location analysis, and determine the analysis frequency, value range, and analysis dimension;

[0103] 3-2) Based on the indicators and analysis dimensions, use the data analysis and processing module to build a multi-dimensional analysis model for inventory location analysis;

[0104] 3-3) Use the data collection and processing module to collect storage location data from the WMS (warehouse management system), such as inventory status, enterprise organization, materials, material types, storage location information, etc.;

[0105] 3-4) Combined with the multi-dimensional analysis model of the storage location analysis constructed in step 3-2), analyze the usage rate and utilization rate of each storage location, the storage location usage of each supplier, the storage location usage of each type of parts, and identify suppliers or parts that use storage locations inefficiently, and generate a storage location situation analysis report;

[0106] 3-5) Use the analysis result display module to visualize the inventory situation analysis report generated in step 3-4):

[0107] 3-5-1) Use the data synchronization tool to synchronize the inventory location analysis report of the data warehouse data application layer to the report database;

[0108] 3-5-2) Connect the data visualization tool to the database;

[0109] 3-5-3) Query the storage location analysis data in the database;

[0110] 3-5-4) Use visualization tools to visually display the inventory location analysis data queried in step 3-5-3) in the form of charts, dashboards, etc.

[0111] In this implementation, the inventory age analysis module performs multi-dimensional analysis of inventory age in the following ways to identify obsolete inventory and slow-moving parts:

[0112] 4-1) Define the indicators that require storage age analysis, such as the number of long storage ages, the value of long storage ages, etc., and determine the analysis frequency, value range, and analysis dimension;

[0113] 4-2) Based on the indicators and analysis dimensions, use the data analysis and processing module to build a multi-dimensional analysis model for inventory age analysis;

[0114] 4-3) Real-time monitoring of the storage time and inventory data of each component from the WMS (Warehouse Management System);

[0115] 4-4) Combined with the multi-dimensional analysis model of inventory age analysis constructed in step 4-2), calculate the inventory age of each component, identify the inventory that has not been moved for a long time, and propose disposal suggestions for the long-aged inventory, and generate an inventory age analysis report;

[0116] 4-5) Use the analysis result display module to visualize the inventory age analysis report generated in step 4-4):

[0117] 4-5-1) Use the data synchronization tool dataX to synchronize the data warehouse data application layer's inventory age analysis report to the report database mysql;

[0118] 4-5-2) Connect the data visualization tool to the MySQL database;

[0119] 4-5-3) Use SQL statements to query the inventory age analysis data in the database;

[0120] 4-5-4) Use visualization tools to visually display the inventory age analysis data obtained in step 4-5-3) in the form of charts, dashboards, etc.

[0121] like Figure 4 In this implementation, the data storage and management module uses Hadoop Distributed File System (HDFS) as the data storage system, Hive as the data warehouse tool, and uses the big data computing engine Spark for distributed data processing. The data storage and management module includes the ODS layer (source layer), DWD layer (data detail layer), DWS layer (data aggregation layer), and ADS layer (data application layer), and adopts a four-layer data processing model to store data in layers according to the subject domain in the following way:

[0122] 5-1) Use the data synchronization tool dataX to collect source data (including inventory status table, enterprise organization table, material table, material type table, supplier table, storage location table, sales order table, customer table, etc.) from the enterprise's ERP system and WMS system, and clean and convert the collected data;

[0123] 5-2) storing the data processed in step 5-1) into the source layer of the data warehouse;

[0124] 5-3) Convert the data in the source layer into fact tables, dimension tables, and detail wide tables, and store them in the data detail layer;

[0125] 5-3-1) Clean, convert, integrate and standardize the data of the source layer to form detailed data;

[0126] 5-3-2) Divide the detailed data into fact tables and dimension tables;

[0127] 5-3-3) Associate the fact table with the dimension table to form a detailed wide table;

[0128] 5-3-4) Store fact tables, dimension tables, and detail wide tables in the data detail layer.

[0129] 5-4) Based on the multidimensional data set summary syntax of GROUP BY CUBE provided by the structured query language SQL and the detailed wide table data in the data detail layer, a multidimensional analysis model is constructed and stored in the data summary layer;

[0130] 5-5) According to the page display requirements of each indicator, the data in the data aggregation layer is further processed and stored in the data application layer.

[0131] In this embodiment, the fact tables include an inventory snapshot fact table and an inventory transfer fact table, and the dimension tables include an organization dimension table, a material dimension table, and a time dimension table.

[0132] like Figure 3 In this embodiment, the component asset analysis module supports drill-down analysis (such as viewing the total inventory of a specific part at a certain time), comparative analysis, trend analysis, and forecast analysis by dimensions such as factory organization (such as one, two, and three factories, sub-production bases, and R&D organizations), asset types (such as corporate assets and supplier assets), part types (such as boxes, shift mechanisms, clutches, hydraulic modules, sensors, gear shafts, etc.), and time intervals (such as days, months, and years); the inventory-to-sales ratio analysis module can help enterprises optimize inventory levels and supports analysis by dimensions such as factory organization, part type, and time interval; the storage location analysis module can optimize storage location layout and utilization rate, and supports analysis by dimensions such as factory organization, storage location type, part type, and time interval; the inventory age analysis module supports analysis by dimensions such as factory organization, part type, and time interval.

[0133] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modification made to the present invention by those skilled in the art without departing from the spirit of the present invention shall fall within the protection scope of the present invention.

Claims

1. A multi-dimensional analysis and management system for automobile parts, characterized in that: It includes data collection and processing module, data analysis and processing module, asset analysis module, data storage and management module, and analysis result display module, among which: The data collection and processing module is used to collect data from various information systems of the enterprise and perform data cleaning and conversion; The data analysis and processing module is used to build a multi-dimensional analysis model, support combined analysis of multiple dimensions and indicators, and use big data analysis tools to perform distributed data processing; The asset analysis module is used to perform multi-dimensional asset analysis on automobile parts; The data storage and management module is used to store data in layers according to subject domains; The analysis result display module is used to display the analysis results in the form of charts and dashboards through visualization tools.

2. A multi-dimensional analysis and management system for automobile parts according to claim 1, characterized in that: The asset analysis module includes a parts asset analysis module, a stock-to-sales ratio analysis module, a stock location analysis module, and a stock age analysis module, wherein: The component asset analysis module is used to analyze component assets in multiple dimensions; The inventory-to-sales ratio analysis module is used to analyze the inventory-to-sales ratio in multiple dimensions and to predict the inventory-to-sales ratio for this month; The storage location analysis module is used to analyze the storage locations in the warehouse in multiple dimensions; The inventory age analysis module is used to analyze the inventory age in multiple dimensions and identify obsolete inventory and slow-turnover parts.

3. The multi-dimensional analysis and management system for automobile parts according to claim 2, characterized in that: The component asset analysis module performs multi-dimensional analysis of component assets in the following manner: 1-1) Define the indicators that require asset analysis, and determine the analysis frequency, value range, and analysis dimensions; 1-2) Based on the indicators and analysis dimensions, use the data analysis and processing module to build a multi-dimensional analysis model for component asset analysis; 1-3) Using the data acquisition and processing module to collect parts inventory data and dimension data from the information management system; 1-4) Based on the set analysis dimensions and calculation indicators, combined with the multi-dimensional analysis model for component asset analysis constructed in step 1-2), the component asset analysis module is used to perform drill-down analysis, comparative analysis, trend analysis, and forecast analysis on various indicators, and generate various asset analysis reports; 1-5) Use the analysis result display module to visualize the analysis report generated in step 1-4).

4. The multi-dimensional analysis and management system for automobile parts according to claim 2, characterized in that: The inventory-to-sales ratio analysis module performs a multi-dimensional analysis of the inventory-to-sales ratio in the following manner and predicts the inventory-to-sales ratio for this month: 2-1) Define the indicators that require inventory-to-sales ratio analysis, and determine the analysis frequency, value range, and analysis dimensions; 2-2) Based on the indicators and analysis dimensions, use the data analysis and processing module to build a multi-dimensional analysis model for inventory-to-sales ratio analysis; 2-3) Use the data collection and processing module to collect sales and inventory related data from the information management system and integrate them; 2-4) Calculate the inventory-to-sales ratio according to the set analysis dimensions, combine the inventory-to-sales ratio analysis multi-dimensional analysis model constructed in step 2-2), identify the problem areas of high inventory and low sales, and predict the sales data of this month through forecast analysis, and generate various inventory-to-sales ratio analysis reports; 2-5) Use the analysis result display module to visualize the analysis report generated in step 2-4).

5. The multi-dimensional analysis and management system for automobile parts according to claim 2, characterized in that: The storage location analysis module performs multi-dimensional analysis of storage locations in the warehouse in the following manner: 3-1) Define the indicators that require location analysis, and determine the analysis frequency, value range, and analysis dimension; 3-2) Based on the indicators and analysis dimensions, use the data analysis and processing module to build a multi-dimensional analysis model for inventory location analysis; 3-3) Use the data acquisition and processing module to collect storage location data from the information management system; 3-4) Combine the multi-dimensional analysis model of the storage location analysis constructed in step 3-2) to analyze the usage rate and utilization rate of each storage location, the storage location usage of each supplier, and the storage location usage of each type of parts, identify suppliers or parts that use storage locations inefficiently, and generate a storage location situation analysis report; 3-5) Use the analysis result display module to visualize the analysis report generated in step 3-4).

6. The multi-dimensional analysis and management system for automobile parts according to claim 2, characterized in that: The inventory age analysis module performs a multi-dimensional analysis of inventory age in the following manner to identify obsolete inventory and slow-moving parts: 4-1) Define the indicators that require inventory age analysis, and determine the analysis frequency, value range, and analysis dimension; 4-2) Based on the indicators and analysis dimensions, use the data analysis and processing module to build a multi-dimensional analysis model for inventory age analysis; 4-3) Real-time monitoring of the storage time and inventory data of each component in the information management system; 4-4) Combined with the multi-dimensional analysis model of inventory age analysis constructed in step 4-2), calculate the inventory age of each component, identify inventory that has not been moved for a long time, and propose disposal suggestions for long-aged inventory, and generate a warehouse location analysis report; 4-5) Use the analysis result display module to visualize the analysis report generated in step 4-4).

7. The multi-dimensional analysis and management system for automobile parts according to claim 1, characterized in that: The data storage and management module includes a source layer, a data detail layer, a data summary layer, and a data application layer. The data storage and management module adopts a four-layer data processing mode to store data in layers according to subject domains in the following manner: 5-1) Use the data acquisition and processing module to collect source data from the information management system, and clean and convert the collected data; 5-2) storing the data processed in step 1) into the source layer of the data warehouse; 5-3) Convert the data in the source layer into fact tables, dimension tables, and detail wide tables, and store them in the data detail layer; 5-4) Build a multidimensional analysis model based on the summary syntax of the multidimensional dataset and the detailed wide table data in the data detail layer, and store it in the data summary layer; 5-5) According to the page display requirements of each indicator, the data in the data aggregation layer is processed and stored in the data application layer.

8. The multi-dimensional analysis and management system for automobile parts according to claim 7, characterized in that: In step 5-3), the data in the source layer is converted into fact tables, dimension tables, and detail wide tables, and stored in the data detail layer in the following specific ways: 5-3-1) Clean, convert, integrate and standardize the data of the source layer to form detailed data; 5-3-2) Divide the detailed data into fact tables and dimension tables; 5-3-3) Associate the fact table with the dimension table to form a detailed wide table; 5-3-4) Store fact tables, dimension tables, and detail wide tables in the data detail layer.

9. The multi-dimensional analysis and management system for automobile parts according to claim 8, characterized in that: The fact tables include an inventory snapshot fact table and an inventory transfer fact table, and the dimension tables include an organization dimension table, a material dimension table, and a time dimension table.

10. The multi-dimensional analysis and management system for automobile parts according to claim 1, characterized in that: The specific process of data visualization by the analysis result display module is as follows: 6-1) Use data synchronization tools to synchronize data files in the data warehouse data application layer to the report database; 6-2) Connect data visualization tools to databases; 6-3) Query data in the database; 6-4) Use visualization tools to intuitively display the data queried in step 6-3) in the form of charts and dashboards.

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