Multi-dimensional product income analysis system for automobile part enterprise

Abstract

The invention discloses a multi-dimensional product income analysis system for an automobile part enterprise, and the system is characterized in that a data collection and processing module is used for carrying out the data collection and processing of key business indexes; the overview module counts the number of products, income and billing conditions, and analyzes the data from the time dimension and the income type; the detailed condition module is used for displaying the product quantity, the income and the specific condition of invoicing, and analyzing the data from the time dimension, the income type and the product type; the installation trend module displays the change trend of the product quantity, the income and the billing condition, and analyzes the data from the time dimension and the income type; the fund inventory module obtains bank deposit information, receivable bill information and inventory commodity information, and performs data analysis on the inventory commodity information from a time dimension. The system can meet the requirements of different managers of an enterprise, and can carry out the real-time monitoring and data analysis of the product income.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise management systems, and in particular to a multi-dimensional product revenue analysis method and system for automotive parts. Background Art

[0002] A car is a complex machine composed of many parts. Therefore, in companies that manufacture auto parts, the products they produce are often diverse. In the daily production management process of an enterprise, different managers often focus on different products. For example, senior managers often need to understand the revenue situation of the entire enterprise, business managers often need to understand the changes in product demand, and financial managers need to understand the capital and product inventory situation. At the same time, due to the wide variety of products produced by auto parts companies, this undoubtedly brings complexity to the management of revenue data, making it difficult to fully grasp the financial situation of the enterprise from a single perspective. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the technical problem to be solved by the present invention is: how to provide a multi-dimensional product revenue analysis system for automotive parts companies that can meet the needs of different managers of the company and can also monitor and analyze product revenue in real time.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions: A multi-dimensional product revenue analysis system for auto parts companies, including a data collection and processing module, an overview module, a detailed situation module, an installation trend module, and a capital inventory module; The data collection and processing module is used to collect data on key business indicators and process the collected data; The overview module obtains data from the data collection and processing module, performs statistics on product quantity, revenue and invoicing according to the obtained data, and analyzes the data from the time dimension and revenue type; The detailed information module obtains data from the data collection and processing module, displays the specific information of product quantity, revenue and invoicing according to the obtained data, and analyzes the data from the time dimension, revenue type and product type; The installation trend module obtains data from the data acquisition and processing module, displays the changing trends of product quantity, revenue and invoicing according to the obtained data, and analyzes the data from the time dimension and revenue type; The fund inventory module obtains data from the data acquisition and processing module, obtains bank deposit information, notes receivable information and inventory information based on the obtained data, and performs data analysis on the inventory information from a time dimension.

[0005] Preferably, the overview module analyzes and processes the acquired data, compiles statistics on key business indicators based on annual, previous year and monthly time dimensions, calculates the year-on-year growth rate of key business indicators, and visualizes the analysis results of these real-time data through report blocks and dashboards. Key business indicators include product output, output value, installed capacity, installed capacity revenue, invoicing volume, and invoicing revenue.

[0006] Preferably, the detailed situation module analyzes and processes the acquired data, statistically analyzes key business indicators based on the annual, previous year and monthly time dimensions, groups the analysis results of these real-time data by category, and visualizes them in a table form. The key business indicators include product output, output value, installed capacity, installed capacity revenue, invoicing volume, and invoicing revenue.

[0007] Preferably, the installation trend module analyzes and processes the acquired data, tracks and records the key business indicators of each month according to the annual and previous year time dimensions, and visualizes the analysis results of these real-time data in the form of bar charts and line charts. The key business indicators include product output, output value, installed capacity, installed revenue, invoicing volume, and invoicing revenue.

[0008] Preferably, the capital inventory module analyzes and processes the acquired data, systematically organizes and compiles statistics on key financial indicator information according to the annual and previous year time dimensions, and visualizes the analysis results of these real-time data in the form of pie charts, bar charts and line charts. The key financial indicator information includes cash, inventory, accounts payable, bills payable, and inventory.

[0009] Preferably, the data collection object of the data collection and processing module is Oracle's ERP system, and offline data collection is completed through DataX, realizing a timestamp-based incremental data synchronization strategy, using the timestamp field of the ERP system as a synchronization mark, and real-time data uses Flink CDC to monitor the change log of the database and capture the changed data as an event stream.

[0010] Preferably, the data synchronization strategy is: full data collection is used when the overall data volume is less than 1 million or the daily increment is less than 1,000; incremental extraction is performed based on the timestamp field when the overall data volume is greater than or equal to 1 million or the daily increment is greater than or equal to 1,000.

[0011] Preferably, the data acquisition and processing module uses a DataX plug-in to perform data quality checks, which include null value processing, data type conversion, and data format normalization, and uses DataX's MapReduce task to integrate custom cleaning logic.

[0012] Preferably, a star schema is used to model the data and construct fact tables and dimension tables; Fact table design: define the structure of the fact table. Each fact table contains at least one metric and a primary key. The metric fields correspond to the numerical indicators in the ERP system. Partition strategy: partition design based on natural days; Dimension table design: Design dimension tables, each of which includes descriptive attributes and a unique dimension key; Dimension table maintenance: Perform change management on dimension tables and use zipper algorithm technology to record the change history of dimension table attributes.

[0013] Compared with the prior art, the present invention has the following advantages: 1. The present invention provides a comprehensive and in-depth income analysis through the three key dimensions of time, income type and product type, and divides the four core modules into an overview module, a detailed situation module, an installation trend module and a capital inventory module. The big data technology is used to realize the details and changing trends of the output, output value, installed capacity, installed income, invoicing volume and invoicing income corresponding to each dimension. Among them, the overview module provides an entry for quickly browsing the overall income situation of the enterprise, which is convenient for senior managers to quickly grasp the overall situation; the detailed situation module deeply explores the specific output value, month-on-month and year-on-year performance of each product, and quickly identifies abnormal situations; the installation trend module reveals the changing trend of product demand; the capital and inventory module focuses on the financial status to ensure that the funds and inventory are reasonable. The system is a complete product income analysis system, which is an important indicator for evaluating the financial health of the enterprise. It is crucial for the business decision-making and strategic planning of the enterprise. Through real-time monitoring and data analysis of product income, it helps to make faster and more accurate decisions, and it can also reflect the market competitiveness and business expansion capabilities of the enterprise.

[0014] 2. Through detailed product revenue analysis, the present invention can not only clearly see the profitability of each product, but also identify the potential reasons behind revenue fluctuations, whether it is changes in market demand, adjustments in the competitive situation, or the level of internal operational efficiency. This insight provides powerful data support for automotive parts companies and helps management make more accurate strategic decisions, such as the rational allocation of resources or the optimization and adjustment of product lines, which helps companies maintain their competitiveness in a rapidly changing business environment.

[0015] 3. Based on the detailed analysis results of each module, the present invention can generate a series of comprehensive analysis reports. These reports use a variety of intuitive visualization methods such as charts and lists to vividly display the overall picture of product assets, such as trends in output and revenue. These reports not only help business departments fully understand the status of product assets, but also promote cross-departmental information sharing and collaborative work.

[0016] 4. The data of the overall visualization development design of the present invention comes from the three daily light summary tables of model table installation, invoicing, and production. The product type dimension is used as the association condition, and the daily data is dynamically summarized to ensure that the annual, monthly, detailed situation and other data are obtained according to the hierarchy. Design an intuitive user interface, filter data by controlling data through time selectors, and provide different visualization display methods such as line charts, bar charts, and tables to meet different data visualization needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of a multi-dimensional product revenue analysis system for an automobile parts enterprise according to the present invention; Figure 2 The present invention is a schematic diagram of constructing fact tables and dimension tables in a multi-dimensional product revenue analysis system for an automotive parts company. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0019] It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In the description of the present invention, it should be noted that the orientation or position relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, or the orientation or position relationship in which the invention product is usually placed when used, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance. In addition, the terms "horizontal", "vertical", etc. do not mean that the components are absolutely horizontal or suspended, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted. In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0020] A multi-dimensional product revenue analysis system for automotive parts companies, as shown in the attached Figure 1 As shown, it includes data collection and processing module, overview module, details module, installed capacity trend module and capital inventory module.

[0021] In this specific embodiment, the data collection and processing module is used to collect data on key business indicators and process the collected data; the data collection and processing module is mainly used to collect, clean, convert, and store data.

[0022] (1) Data collection The data collection object of the data collection and processing module is Oracle's ERP system. Offline data collection is completed through DataX, which implements the incremental data synchronization strategy based on timestamps. The timestamp field of the ERP system is used as a synchronization mark. Real-time data uses Flink CDC to monitor the change log of the database and capture the changed data as an event stream. The data synchronization strategy is: when the overall data volume is less than 1 million or the daily increment is less than 1,000, full data collection is used; when the overall data volume is greater than or equal to 1 million or the daily increment is greater than or equal to 1,000, incremental extraction is performed based on the timestamp field. For example: transaction_date>= to_date('${bizdate}', 'yyyymmdd').

[0023] Specifically, during the data collection stage, data is collected from the business enterprise ERP (Enterprise Resource Planning) system to automatically and accurately capture multiple data information related to components, including key business indicators such as output, output value, installed capacity, installed capacity revenue, invoicing volume and invoicing revenue.

[0024] (2) Data cleaning and conversion The data collection and processing module uses DataX's plug-in to perform data quality checks, which include but are not limited to null value processing, data type conversion, and data format normalization. It also uses DataX's MapReduce tasks to integrate custom cleaning logic to ensure data accuracy and consistency.

[0025] (3) Data storage Data storage selection: Considering the data access pattern and performance requirements, the offline part of the overall storage adopts columnar storage (ORC format) for batch reading optimization.

[0026] In actual application, the data collection and processing module can further define and refine the indicators, and perform in-depth indicator calculation and analysis on the core indicators. When defining the indicators, we can work closely with the business department to jointly clarify the goals and directions of the analysis, accurately define the indicators that need to be focused on, such as output, output value, etc., and further refine the frequency of analysis, the range of data values, and the specific analysis dimensions. For core indicators, we can also conduct in-depth analysis of the collected data based on multi-dimensional perspectives such as product type and time period, and use advanced statistical principles and data analysis models to reveal the inherent correlation and dynamic trends between the indicators. At the same time, the offline model data (T-1 day) is aggregated and transmitted to Mysql, and then the data left in the real-time batch yesterday is cleared and merged with the real-time data (T day) to ensure that the data is accurate and unique.

[0027] In this specific embodiment, the overview module obtains data from the data collection and processing module, performs statistics on product quantity, revenue and invoicing based on the obtained data, and analyzes the data from the time dimension and revenue type.

[0028] Specifically, the overview module analyzes and processes the acquired data, compiles statistics on key business indicators based on the annual, previous year and monthly time dimensions, calculates the year-on-year growth rate of key business indicators, and visualizes the analysis results of these real-time data through report blocks and dashboards. Key business indicators include product output, output value, installed capacity, installed capacity revenue, invoicing volume, and invoicing revenue.

[0029] In this specific embodiment, the details module obtains data from the data collection and processing module, displays the specific details of product quantity, revenue and invoicing based on the obtained data, and analyzes the data from the time dimension, revenue type and product type.

[0030] Specifically, the detailed situation module analyzes and processes the acquired data, combines efficient data processing technology, and statistically analyzes key business indicators based on annual, previous year and monthly time dimensions. The analysis results of these real-time data are grouped by category and visualized in a table form. Key business indicators include product output, output value, installed capacity, installed capacity revenue, invoicing volume, and invoicing revenue.

[0031] In this specific embodiment, the installation trend module obtains data from the data acquisition and processing module, displays the changing trends of product quantity, revenue and invoicing based on the obtained data, and analyzes the data from the time dimension and revenue type.

[0032] Specifically, the installation trend module performs refined data processing and in-depth data analysis on the acquired data, tracks and records the key business indicators of each month according to the annual and previous year's time dimensions, and visualizes the analysis results of these real-time data in the form of bar charts and line charts. The key business indicators include product output, output value, installed capacity, installed revenue, invoicing volume, and invoicing revenue.

[0033] In this embodiment, the fund inventory module obtains data from the data collection and processing module, obtains bank deposit information, notes receivable information and inventory information based on the obtained data, and performs data analysis on the inventory information from a time dimension.

[0034] Specifically, the capital inventory module analyzes and processes the acquired data, systematically organizes and compiles key financial indicator information according to the annual and previous year time dimensions, and visualizes the analysis results of these real-time data in the form of pie charts, bar charts and line charts. The key financial indicator information includes monetary funds (bank deposits, bills receivable, funds collection, deposits), inventory (inventory, raw materials and purchased parts), accounts payable, bills payable, and inventory.

[0035] In this embodiment, each module uses a star schema to model data and construct fact tables and dimension tables. Figure 2 As shown; Fact table design: Define the structure of the fact table according to business requirements. Each fact table contains at least one metric and a primary key (usually a composite key, consisting of the serial number of the auto parts or the sales order plus one or more dimension keys). The metric fields (such as output and output value) correspond to the numerical indicators in the ERP system. Partition strategy: To speed up query and facilitate real-time data writeback, partition design is performed based on natural days; Dimension table design: Design dimension tables, such as product dimension, time dimension, production line dimension, etc. Each dimension table includes descriptive attributes (such as product name, time description, production line location) and a unique dimension key; Dimension table maintenance: Perform change management on dimension tables, such as adding new products, changing product categories, etc., to ensure the timeliness and accuracy of dimension tables. Zipper algorithm technology is used to record the change history of dimension table attributes.

[0036] In this embodiment, the data for the overall visual development and design of each module comes from the three daily light summary tables of model table installation, invoicing, and production. The product type dimension is used as the association condition, and the daily data is dynamically summarized to ensure that the annual, monthly, detailed situation and other data are obtained according to the hierarchy. An intuitive user interface is designed to filter data by controlling data through time selectors and other methods, and different visual display methods such as line charts, bar charts, and tables are provided to meet different data visualization needs.

[0037] Compared with the prior art, the present invention provides a comprehensive and in-depth revenue analysis through three key dimensions: time, revenue type and product type, and divides the four core modules into an overview module, a detailed situation module, an installation trend module and a capital inventory module. The big data technology is used to realize the details and changing trends of the output, output value, installed capacity, installed revenue, invoicing volume and invoicing revenue corresponding to each dimension. Among them, the overview module provides an entry for quickly browsing the overall revenue situation of the enterprise, so that senior managers can quickly grasp the overall situation; the detailed situation module deeply explores the specific output value, month-on-month and year-on-year performance of each product, and quickly identifies abnormal situations; the installation trend module reveals the changing trend of product demand; the capital and inventory module focuses on the financial status to ensure that the funds and inventory are reasonable. The system is a complete product revenue analysis system, an important indicator for evaluating the financial health of an enterprise, and is crucial to the business decision-making and strategic planning of the enterprise. Real-time monitoring and data analysis of product revenue can help make faster and more accurate decisions, and can also reflect the market competitiveness and business expansion capabilities of the enterprise.

[0038] Through detailed product revenue analysis, the present invention can not only clearly see the profitability of each product, but also identify the potential reasons behind revenue fluctuations, whether it is changes in market demand, adjustments in the competitive situation, or the level of internal operational efficiency. This insight provides powerful data support for automotive parts companies and helps management make more accurate strategic decisions, such as the rational allocation of resources or the optimization and adjustment of product lines, which helps companies maintain their competitiveness in a rapidly changing business environment.

[0039] Based on the detailed analysis results of each module, the present invention can generate a series of comprehensive analysis reports. These reports use a variety of intuitive visualization methods such as charts and lists to vividly display the overall picture of product assets, such as trend changes in output and revenue. These reports not only help business departments fully understand the product asset situation, but also promote cross-departmental information sharing and collaborative work.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A multi-dimensional product revenue analysis system for automotive parts companies, characterized by: It includes data collection and processing module, overview module, details module, installed capacity trend module and capital inventory module; The data collection and processing module is used to collect data on key business indicators and process the collected data; The overview module obtains data from the data collection and processing module, performs statistics on product quantity, revenue and invoicing according to the obtained data, and analyzes the data from the time dimension and revenue type; The detailed information module obtains data from the data collection and processing module, displays the specific information of product quantity, revenue and invoicing according to the obtained data, and analyzes the data from the time dimension, revenue type and product type; The installation trend module obtains data from the data acquisition and processing module, displays the changing trends of product quantity, revenue and invoicing according to the obtained data, and analyzes the data from the time dimension and revenue type; The fund inventory module obtains data from the data acquisition and processing module, obtains bank deposit information, notes receivable information and inventory information based on the obtained data, and performs data analysis on the inventory information from a time dimension.

2. The multi-dimensional product revenue analysis system for automotive parts companies according to claim 1 is characterized in that: The overview module analyzes and processes the acquired data, compiles statistics on key business indicators based on the annual, previous year and monthly time dimensions, calculates the year-on-year growth rate of key business indicators, and visualizes the analysis results of these real-time data through report blocks and dashboards. Key business indicators include product output, output value, installed capacity, installed capacity revenue, invoicing volume, and invoicing revenue.

3. The multi-dimensional product revenue analysis system for automotive parts companies according to claim 1 is characterized in that: The detailed situation module analyzes and processes the acquired data, statistically analyzes key business indicators based on the annual, previous year and monthly time dimensions, groups the analysis results of these real-time data by category, and visualizes them in a table form. The key business indicators include product output, output value, installed capacity, installed capacity revenue, invoicing volume, and invoicing revenue.

4. The multi-dimensional product revenue analysis system for automotive parts companies according to claim 1 is characterized in that: The installation trend module analyzes and processes the acquired data, tracks and records the key business indicators of each month according to the time dimensions of the year and the previous year, and visualizes the analysis results of these real-time data in the form of bar charts and line charts. The key business indicators include product output, output value, installed capacity, installed revenue, invoicing volume, and invoicing revenue.

5. The multi-dimensional product revenue analysis system for automotive parts companies according to claim 1 is characterized in that: The funds inventory module analyzes and processes the acquired data, systematically organizes and compiles key financial indicator information according to the annual and previous year time dimensions, and visualizes the analysis results of these real-time data in the form of pie charts, bar charts and line charts. The key financial indicator information includes cash, inventory, accounts payable, bills payable, and inventory.

6. The multi-dimensional product revenue analysis system for automobile parts enterprises according to claim 1 is characterized in that: The data collection object of the data collection and processing module is Oracle's ERP system. Offline data collection is completed through DataX, and a timestamp-based incremental data synchronization strategy is implemented. The timestamp field of the ERP system is used as a synchronization mark. Real-time data uses Flink CDC to monitor the change log of the database and capture the changed data as an event stream.

7. The multi-dimensional product revenue analysis system for automobile parts enterprises according to claim 6 is characterized in that: The data synchronization strategy is: when the overall data volume is less than 1 million or the daily increment is less than 1,000, full data collection is adopted; when the overall data volume is greater than or equal to 1 million or the daily increment is greater than or equal to 1,000, incremental extraction is performed based on the timestamp field.

8. The multi-dimensional product revenue analysis system for automobile parts enterprises according to claim 1 is characterized in that: The data collection and processing module uses DataX's plug-in to perform data quality checks, which include null value processing, data type conversion, and data format normalization, and uses DataX's MapReduce task to integrate custom cleaning logic.

9. The multi-dimensional product revenue analysis system for automobile parts enterprises according to claim 6 is characterized in that: Use star schema to model data and build fact tables and dimension tables; Fact table design: define the structure of the fact table. Each fact table contains at least one metric and a primary key. The metric fields correspond to the numerical indicators in the ERP system. Partition strategy: partition design based on natural days; Dimension table design: Design dimension tables, each of which includes descriptive attributes and a unique dimension key; Dimension table maintenance: Perform change management on dimension tables and use zipper algorithm technology to record the change history of dimension table attributes.

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