Financial budget management method based on ERP financial system

Abstract

The application relates to the technical field of financial management methods, and discloses a financial budget management method based on an ERP financial system. The method integrates financial, business and external market data through an ERP interface to construct a dynamic model containing a basic budget value and a floating interval, collects data in real time, synchronizes the data to a budget module after cleaning and standardization, compares budget and actual execution data in real time based on a preset index system, triggers a hierarchical early warning according to a deviation threshold value, and pushes the responsibility department in a targeted manner to realize dynamic supervision of the whole budget execution process. After the early warning, the system automatically digs the deviation root cause, generates multiple schemes containing adjustment amplitude and risk coefficients in combination with the model, matches the approval process according to the scheme influence range, automatically updates the budget data after the approval, synchronizes the budget data to the business module, regularly reviews the data to optimize the model parameters, strengthens the budget execution control, significantly improves the dynamic adjustment and risk response capability, and efficiently meets the enterprise budget management demand.

Description

Technical Field

[0001] This invention relates to the field of financial management methods, and in particular to a financial budget management method based on an ERP financial system. Background Technology

[0002] In the context of the new era's market economy, improving corporate financial budget management significantly enhances overall operational efficiency. By leveraging financial budget management tools, companies can efficiently control various internal information and data, achieving long-term development. In the context of rapid market economic development, companies seeking management breakthroughs must undergo corresponding reforms in their financial management methods, concepts, and philosophies. This involves refining existing financial budget management mechanisms, processes, and methods, referencing corporate development strategies, and improving the formulation, planning, and control of financial management plans. Effectively connecting various functional departments within the company is of great practical significance for enhancing the level of modern corporate management.

[0003] For example, patent CN113538121A discloses a financial budget management method, which includes three main modules: budget preparation, control, and analysis. In the preparation phase, administrators need to set up basic budget information and form approval workflows. Employees then enter and submit budgets, which are then approved to form the budget amount. In the control phase, the system determines whether a document uses the budget based on the budget amount; if there is a balance, the document is approved, and the system then processes the budget retention amount, the amount incurred, and updates the balance. In the analysis phase, the system provides administrators with multi-dimensional viewing methods based on budget execution, helping them to grasp budget dynamics.

[0004] Regarding the aforementioned and existing related technologies, the inventors believe that the following shortcomings often exist: On the one hand, the lack of an effective supervision mechanism and strict control measures in the budget execution process makes it impossible to track and regulate key situations such as actual progress and resource consumption in real time. Even if the execution deviates from the budget target, it is difficult to detect and correct it quickly. On the other hand, the method is insufficient in terms of dynamic budget adjustment and risk response. It has neither built a flexible budget adjustment system for changes in the internal and external environment nor formed a sound risk response strategy. As a result, when the actual situation changes or potential risks occur, the budget cannot be adjusted in a timely manner, making it difficult to effectively resist the impact of risks on the budget execution effect. Summary of the Invention

[0005] The technical problem to be solved by this invention is that existing financial budget management methods lack effective supervision and control of budget execution, as well as dynamic adjustment and risk response capabilities. To address this, we propose a financial budget management method based on an ERP financial system.

[0006] To achieve the above objectives, this application adopts the following technical solution: a financial budget management method based on an ERP financial system, comprising the following steps: S1, constructing a dynamic budget model: A unified data warehouse is built by real-time access to business system data, including finance, procurement, production, and sales, through the ERP system interface. A multi-dimensional dynamic budget model is established based on historical financial data, business data, and external market data from the ERP system. The dynamic budget model sets a basic budget value and a floating range parameter. S2, real-time data acquisition and synchronization: Business execution data, financial data, and external environment data are collected in real-time through the ERP system interface. A data cleaning and standardization mechanism is established to ensure real-time data synchronization to the budget management module. S3, intelligent monitoring and early warning: Based on a preset monitoring indicator system, the ERP system compares the budget value with the actual execution data in real time. When a deviation reaches the first threshold, a first-level warning is automatically triggered. The system provides warnings and pushes them to the corresponding business departments. When the deviation reaches the second threshold, a second-level warning is automatically triggered and synchronized to the financial management department; S4, Automated Analysis and Adjustment Suggestions: The ERP system performs automated root cause analysis on the warning items, generates multiple budget adjustment plans in conjunction with the dynamic budget model, and the adjustment plans include the adjustment range, scope of impact, and implementation path, and calculates the risk coefficient and expected benefits of each plan; S5, Hierarchical Approval and Execution: Based on the scope of impact and risk coefficient of the adjustment plan, the ERP system automatically matches the corresponding approval process. After approval, the system automatically updates the budget data and synchronizes it to the relevant business modules to achieve seamless execution of budget adjustments; S6, Closed-Loop Feedback and Model Optimization: The ERP system regularly analyzes the data of the entire budget execution process, evaluates the accuracy of the budget model and the effectiveness of the adjustment mechanism, automatically optimizes the parameter settings of the dynamic budget model, and forms a closed-loop management mechanism for continuous improvement.

[0007] Preferably, the specific implementation steps for building the dynamic budget model in S1 are as follows: S11. Determine the data collection scope and interface configuration: Based on the enterprise's business architecture, identify the core business systems and external data sources to be accessed. Configure the data transmission protocol through the ERP system's preset API interface or ETL tools, and determine the collection fields and frequency; S12. Multi-source data cleaning and standardization: Perform quality checks on the raw data, use tools to remove duplicate records, correct format errors, and fill in missing values. Form consistent field definitions according to the enterprise's unified standards using standardized mapping. Load the processed data into a unified data warehouse deployed in the cloud architecture or locally and store it by theme; S13. Determine the core dimensions of the budget model: Combine the enterprise's strategic goals and business characteristics to define time, group... The model uses organizational, business, and operational dimensions as analysis dimensions, employing dimensional modeling tools to establish hierarchical relationships and constraints between dimensions; S14, Establishing variable correlation and quantification models: Analyzing historical financial data to identify the fluctuation patterns and growth trends of key financial indicators, establishing regression relationships between business drivers and financial indicators, incorporating external market variables into the model variable pool, and training the quantification relationship between variables and budget targets through machine learning algorithms to form a multi-dimensional dynamic budget model framework; S15, Setting basic budget values ​​and floating range parameters: Combining the company's annual operating targets, historical budget execution deviation rates, and business volatility characteristics, determining the basic budget values ​​for each dimension, and setting floating range parameters for the basic budget values ​​based on the prediction range of external market variables, business elasticity coefficients, and risk management requirements.

[0008] Preferably, the dynamic budget model in S1 is calculated as follows: Let the adjusted budget value be Y, the basic budget value be B, and the sum of the absolute values ​​of the historical execution deviations over the past three periods be... If the average volatility of current business drivers is F, the average change of external market variables is M, and the average deviation improvement rate after budget adjustments in the previous two periods is G, then: .

[0009] Preferably, the specific implementation steps for real-time data acquisition and synchronization in S2 are as follows: S21. Determine the data types and interface requirements for real-time acquisition: Based on the functional requirements of the budget management module, sort out the three types of data that need to be collected in real time: business execution, finance, and external environment, and clarify the corresponding ERP system interface types and transmission protocols; S22. Configure interface connection and real-time transmission parameters: Configure the interface connection parameters of each data source through the ERP interface management module, set the data push frequency or subscription trigger conditions, and test the interface connectivity to verify whether the data can be transmitted to the ERP system middleware cache in real time; S23. Establish a data cleaning rule base: Define cleaning rules for the three types of real-time data characteristics, remove duplicate order records, verify the legality of amounts, and filter out expired market information; S24. Perform data standardization processing: Convert the cleaned data into a standardized format according to the enterprise's unified standards, unify the formats of customer names, production equipment numbers, dates, and amounts, and add data source tags to the market price field; S25. Real-time synchronization to the budget management module and monitoring of transmission status: Push the standardized data to the budget management module database in real time through the ERP system data bus or message middleware, configure a transmission monitoring dashboard to display transmission indicators, and automatically retry and alarm when an anomaly occurs.

[0010] Preferably, the specific implementation steps of intelligent monitoring and early warning in S3 are as follows: S31. Construct a monitoring indicator system: Combining corporate strategic goals and business pain points, identify three core monitoring indicators: financial, business, and external environment, clarifying the definition, calculation method, monitoring frequency, and responsibility of each indicator; S32. Configure tiered early warning thresholds: Based on historical data fluctuation range, corporate management tolerance, and business characteristics, set two levels of early warning thresholds (mild and severe) for each monitoring indicator, inputting them through the ERP system parameter configuration module and supporting dynamic adjustment; S33. Obtain real-time actual execution data: Extract relevant actual execution data and corresponding budget values ​​for monitoring indicators from a unified data warehouse, and obtain external data through third-party interfaces. Latest environmental indicator data; S34, Deviation calculation and verification: The ERP system automatically calculates the deviation rate according to the monitoring frequency, verifies the validity of the data, and marks and suspends the warning when the data is abnormal; S35, Triggering and pushing hierarchical warnings: When the deviation rate reaches the first threshold, a level 1 warning is triggered and pushed to the responsible department. When it exceeds the second threshold, a level 2 warning is triggered and pushed to the finance department simultaneously. The warning notification includes speculation on the cause of the deviation and impact assessment, and the abnormal indicators are highlighted on the visualization dashboard; S36, Warning handling tracking and closed loop: The business department provides feedback on the level 1 warning handling plan within 24 hours, and the finance department intervenes in the level 2 warning and puts forward rectification suggestions within 48 hours. The system records the handling progress until the warning is resolved and a closed loop is formed.

[0011] Preferably, the specific implementation steps for automated analysis and adjustment suggestions in S4 are as follows: S41. Extract relevant data for early warning items: Extract historical, actual execution, business-driven, and external environment data related to early warning indicators from the unified data warehouse to form a special dataset; S42. Automated root cause analysis execution: Call the root cause analysis module of the ERP system to identify deviation-related factors, determine the main influencing factors through correlation analysis, and locate the root cause in combination with business rules; S43. Dynamic budget model parameter linkage: Input the root cause analysis results into the dynamic budget model to trigger parameter updates, simulate the impact of different adjustment directions on budget targets, and generate an initial adjustment direction list; S44. Generate multiple version adjustment plans: Set the adjustment range for the initial adjustment direction according to the degree of root cause impact and management tolerance, clarify the scope of impact and implementation path, and generate multiple version adjustment plans; S45. Calculate the risks and benefits of the plan: Calculate the risk coefficient of each plan through the model risk prediction module, and calculate the expected benefits in combination with historical data and market forecasts to generate an assessment report including risks, benefits, and implementation difficulties.

[0012] Preferably, the formula for calculating the risk of the proposed solution in S45 is as follows: In the formula: R represents the risk coefficient; x represents the standard deviation of external market volatility; y represents the absolute value of business execution deviation; z represents the scope of adjustment impact; and t represents the historical risk frequency.

[0013] Preferably, the specific implementation steps of hierarchical approval and execution in S5 are as follows: S51, Matching approval process rules: Based on the scope of impact and risk coefficient of the adjustment plan, match the corresponding approval process template from the ERP system's preset approval rule library; S52, Generating and pushing approval requests: The system automatically generates an electronic approval request containing details of the adjustment plan based on the template, pushes it to the first approval node according to role permissions, and records relevant information; S53, Multi-level approval node processing: Each approval node reviews according to permissions, the business manager verifies the business matching, the finance manager verifies the financial logic, senior management assesses the impact on the overall target, and rejections must be stated with reasons. S54. Approval Result Confirmation and Archiving: After all nodes are approved, an electronic approval form is generated and the plan is marked "Approved". If supplementary materials are required, they are completed and resubmitted. The approval result is archived by the document management module. S55. Automatic Budget Data Update: After approval, the system calls the budget management module interface to write the adjustment parameters into the budget master table, triggers data verification to ensure consistency, and completes the budget data update. S56. Synchronize to Business Modules for Execution: The adjusted budget value is pushed to the relevant business systems through the enterprise service bus or API interface, the synchronization information is recorded, and "Executing" is displayed on the visual dashboard. Business departments carry out business accordingly.

[0014] Preferably, the rule base is pre-configured according to the "Enterprise Budget Management Measures": high-risk adjustment plans that cross three levels of organizations trigger the "Group CEO + Board of Directors" approval process, medium-risk adjustment plans that are within a single business unit trigger the "Business Unit General Manager + Chief Financial Officer" approval process, and low-risk adjustment plans that are localized to a specific business trigger the "Department Head" approval process.

[0015] Preferably, the specific implementation steps of closed-loop feedback and model optimization in S6 are as follows: S61. Determine the analysis cycle and data extraction scope: Set the analysis cycle according to the enterprise's budget cycle and business characteristics, and extract the full-cycle budget execution, adjustment records, external environment, and model prediction historical data from the unified data warehouse to form an analysis dataset; S62. Set the evaluation index system: Based on budget management goals and business needs, define evaluation indicators for model accuracy and adjustment mechanism effectiveness; model accuracy indicators include key indicator prediction deviation rate and budget execution consistency; adjustment mechanism effectiveness indicators include adjustment plan adoption rate, adjustment response timeliness, and post-adjustment deviation improvement rate; S63. Perform model accuracy evaluation: Compare the model's historical prediction values ​​with actual execution data, calculate the prediction deviation rate and analyze its distribution characteristics, verify the model's prediction stability in different scenarios, and identify model defects; 64. Evaluate the effectiveness of the adjustment mechanism: Analyze the reasons for non-adoption by statistically analyzing the number of adjustment proposals and the adoption rate, and calculate the average adjustment response time. Compare the changes in deviation before and after the adjustment to evaluate the effectiveness of the adjustment mechanism. S65. Identify model optimization directions: Determine the optimization focus based on the evaluation results. If the prediction deviation is high, optimize variable associations or parameter weights; if the adoption rate is low, simplify the implementation path; if the improvement effect is poor, enhance the model's sensitivity to external changes. S66. Automatically optimize model parameters and structure: Call the model optimization algorithm to correct variable associations, calibrate the basic budget value calculation logic, reset the adjustment mechanism parameters, and optimize the model's dimensional structure. S67. Verify optimization effects and iterative updates: Backtest the optimized model with historical data to compare evaluation indicators, select a small-scale scenario for pilot testing and collect feedback. After adjusting parameters to meet the standards, update the verified model to the production environment and record optimization logs to complete the iteration.

[0016] The technical effects and advantages of this invention are as follows: This invention establishes a real-time data link between the financial, procurement, production, and sales business segments through the ERP system interface. Data cleaning and standardization mechanisms ensure that business execution, financial, and external environment data are synchronized to the budget management module in real time. Simultaneously, relying on a pre-set monitoring indicator system, the system automatically compares budget values ​​with actual execution data in real time. When the deviation reaches a first threshold, a first-level warning is automatically triggered and pushed to the business department; when it reaches a second threshold, a second-level warning is triggered and synchronized to the finance department. This not only eliminates the problem of untimely supervision caused by information lag in traditional management through real-time data flow, but also avoids supervision becoming merely a formality by defining responsibility through tiered warnings and clearly defined push targets. It effectively realizes dynamic supervision and a closed-loop responsibility system throughout the entire budget execution process, filling the gap in supervision in traditional methods. Based on a dynamic budget model that integrates historical financial, operational, and external market data, the system sets a base budget value and a floating range to allow for adjustments. When an alert is triggered, the system automatically delves into the root causes of deviations and generates multiple budget adjustment plans, including adjustment ranges, impact scopes, and implementation paths, in conjunction with the model. It simultaneously calculates the risk coefficients and expected benefits of each plan and automatically matches them to a tiered approval process based on the plan's impact scope and risk coefficients. Once approved, the system directly updates the budget data and synchronizes it to relevant business modules. Furthermore, it periodically optimizes model parameters through full-process data analysis. This workflow, from model building to adjustment execution and continuous optimization, breaks the static and rigid nature of traditional budgets, allowing budgets to dynamically adapt to market fluctuations and business changes. Simultaneously, it reduces the blindness of adjustment decisions through risk quantification assessment, forming a complete closed loop of "anomaly identification - root cause analysis - scientific adjustment - risk control - continuous optimization." This significantly improves the dynamic adaptability and risk resilience of budget management, addressing the pain points of traditional methods in dealing with market changes and risks. Attached Figure Description

[0017] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts:

[0018] Figure 1 This is an overall flowchart of the financial budget management method based on an ERP financial system according to the present invention; Figure 2 This is a schematic diagram of the PID for the financial budget management method based on the ERP financial system of the present invention; Figure 3 This is a flowchart illustrating the steps involved in constructing a dynamic budget model for the financial budget management method based on an ERP financial system according to the present invention. Figure 4 This is a flowchart of the real-time data acquisition and synchronization steps of the financial budget management method based on an ERP financial system according to the present invention. Figure 5 This is a flowchart illustrating the intelligent monitoring and early warning steps of the financial budget management method based on an ERP financial system according to the present invention. Figure 6 This is a flowchart illustrating the automated analysis and adjustment suggestion steps of the financial budget management method based on an ERP financial system according to the present invention. Figure 7 This is a flowchart illustrating the hierarchical approval and execution steps of the financial budget management method based on an ERP financial system according to the present invention. Figure 8 This is a flowchart illustrating the closed-loop feedback and model optimization steps of the financial budget management method based on the ERP financial system of the present invention. Detailed Implementation

[0019] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0020] Reference Figures 1-2 As shown, this invention provides a technical solution: a financial budget management method based on an ERP financial system, comprising the following steps: Step 1, constructing a dynamic budget model: real-time access to business system data including finance, procurement, production, and sales through the ERP system interface to construct a unified data warehouse, and establishing a multi-dimensional dynamic budget model based on historical financial data, business data, and external market data in the ERP system. The dynamic budget model sets a basic budget value and a floating range parameter; the dynamic budget model refers to a budget calculation framework with adaptive capabilities established through machine learning algorithms. Specifically, it can be implemented by using the random forest algorithm to train the correlation between historical business data and financial indicators, and by setting the basic budget value and the floating range parameter, the model has a flexible adjustment space.

[0021] Step 2: Real-time data acquisition and synchronization: Real-time acquisition of business execution data, financial data, and external environment data through the ERP system interface; establishment of a data cleaning and standardization processing mechanism to ensure real-time data synchronization to the budget management module; real-time data acquisition and synchronization refers to the real-time transmission of cross-system data through API interfaces, specifically using Kafka message queues to achieve high-concurrency data processing, and using a data cleaning rule base to ensure consistency of multi-source data.

[0022] Step 3, Intelligent Monitoring and Early Warning: Based on a preset monitoring indicator system, the ERP system compares the budget value with the actual execution data in real time. When a deviation reaches the first threshold, a first-level early warning is automatically triggered and pushed to the corresponding business department. When the deviation reaches the second threshold, a second-level early warning is automatically triggered and synchronized to the financial management department. Intelligent monitoring and early warning refers to an automated deviation detection mechanism based on a preset rule engine. Specifically, a streaming computing engine can be used to calculate the budget execution deviation rate in real time, and a tiered early warning can be achieved through a threshold triggering mechanism.

[0023] Step 4: Automated Analysis and Adjustment Recommendations: The ERP system performs automated root cause analysis on warning items and generates multiple budget adjustment plans based on the dynamic budget model. These plans include the adjustment range, scope of impact, and implementation path, and calculate the risk coefficient and expected benefits of each plan. Automated root cause analysis refers to identifying the main causes of deviations through data mining techniques. Specifically, it can use association rule algorithms to analyze the potential relationships between business data and combine expert system rule bases to locate the root cause of the problem.

[0024] Step 5, Hierarchical Approval and Execution: Based on the scope of impact and risk coefficient of the adjustment plan, the ERP system automatically matches the corresponding approval process. After approval, the system automatically updates the budget data and synchronizes it to the relevant business modules to achieve seamless execution of the budget adjustment. The hierarchical approval process refers to the mechanism of automatically matching the approval level according to the scope of impact of the adjustment plan. Specifically, the RBAC permission model can be used to configure multi-level approval nodes, and the approval process can be driven by the workflow engine.

[0025] Step Six: Closed-Loop Feedback and Model Optimization: The ERP system regularly analyzes data from the entire budget execution process, evaluates the accuracy of the budget model and the effectiveness of the adjustment mechanism, and automatically optimizes the parameter settings of the dynamic budget model, forming a closed-loop management mechanism for continuous improvement. The closed-loop feedback mechanism refers to a self-improving system that continuously optimizes the model based on execution data. Specifically, A / B testing can be used to verify the model optimization effect, and version control can be used to manage the model iteration process.

[0026] Specifically, in scenarios involving procurement cost overruns, the ERP system collects supplier price fluctuation data in real time, cleans it, and synchronizes it to the budget module. The dynamic model recalculates the material procurement budget range based on the latest data, triggering a level-one alert when the actual purchase price exceeds the fluctuation range. The system automatically analyzes and identifies that price fluctuations stem from exchange rate changes, generating three adjustment plans: changing suppliers, adjusting purchase quantities, etc. The plan with the lowest risk factor, after approval by the department head, automatically updates the procurement budget and synchronizes it to the procurement system. Quarter-end analysis shows improved model prediction accuracy, with the fluctuation range parameter automatically narrowing based on actual fluctuations. The entire process achieves closed-loop management from data collection to execution feedback, ensuring the timeliness and effectiveness of budget adjustments. This solution effectively solves the problems of lagging budget execution supervision and slow adjustment response. The real-time data synchronization mechanism ensures dynamic consistency between the budget benchmark and the actual business status; multi-level alert triggering rules enable early detection and tiered handling of deviations; automated adjustment plan generation and approval processes significantly improve risk response efficiency; and the closed-loop feedback mechanism ensures the budget model continuously adapts to changes in the business environment. In scenarios with frequent supply chain fluctuations, this improves the speed of budget adjustment response, avoiding economic losses caused by delayed decisions.

[0027] Reference Figure 1 and Figure 3 As shown in this implementation plan: This application further proposes specific implementation steps for constructing a dynamic budget model, including determining the data collection scope and interface configuration, multi-source data cleaning and standardization processing, determining the core dimensions of the budget model, establishing variable correlation and quantification models, and setting basic budget values ​​and floating range parameters.

[0028] S11. Determine the scope of data collection and interface configuration: Based on the enterprise's business architecture, identify the core business systems and external data sources that need to be accessed. Configure the data transmission protocol between the systems through the API interface preset by the ERP system or ETL tools. Clarify the data collection fields (such as sales order volume, purchase arrival volume, production completion volume, expense amount, revenue / cost / cash flow and other financial data, industry price index, raw material futures price, competitor market share and other external market data) and collection frequency (end of day, weekly or real-time).

[0029] S12. Multi-source data cleaning and standardization: The collected raw data undergoes quality checks. Data cleaning tools are used to remove duplicate records, correct format errors (e.g., unify date format to YYYY-MM-DD), and fill missing values ​​(using mean filling, interpolation, or default values ​​based on business rules). Simultaneously, the data is standardized and mapped according to the company's unified data standards (e.g., accounting subject coding table, department classification directory, product SKU coding rules) to form consistent data field definitions across systems. Finally, the cleaned and standardized data is loaded into a unified data warehouse based on cloud architecture or local deployment, and stored by theme (e.g., business operation theme library, financial accounting theme library, market environment theme library).

[0030] S13. Determine the core dimensions of the budget model: Combine the company's strategic goals and business characteristics to define the analytical dimensions of the dynamic budget model, specifically including the time dimension (covering monthly, quarterly, and annual budget cycles), the organizational dimension (a three-tier organizational structure of group-business unit-subsidiary), the business dimension (business units divided by product line, customer group, and regional market), and the operational dimension (key business process nodes, such as R&D, procurement, production, and sales). Establish the hierarchical relationships and constraints between the dimensions through dimension modeling tools.

[0031] S14. Establish variable correlation and quantification models: Based on historical financial data (selecting execution data from the past 3-5 years of complete budget cycles), conduct statistical analysis to identify the seasonal fluctuation patterns and long-term growth trends of key financial indicators such as revenue, cost, and expenses; at the same time, establish regression relationships between business drivers such as capacity utilization and inventory turnover days in business data and financial indicators; incorporate variables such as raw material price changes and exchange rate fluctuations in external market data into the model variable pool; and train the quantitative relationship between variables and budget targets through machine learning algorithms to form a dynamically adjustable multi-dimensional budget model framework.

[0032] S15. Set basic budget values ​​and fluctuation range parameters: Based on the annual operating targets set by the company's management (such as revenue growth rate and net profit margin), combined with the historical budget execution deviation rate and business volatility characteristics, determine the basic budget values ​​for each dimension (such as the basic annual revenue budget value for a certain product line); at the same time, based on the forecast range of external market variables, business elasticity coefficient and risk management requirements, set fluctuation range parameters for each basic budget value (such as the revenue budget fluctuation range being a certain numerical range) to ensure that the model has both goal orientation and market responsiveness.

[0033] Specifically, this approach addresses the technical challenge of integrating multi-source data by establishing standardized data acquisition channels through interface configuration. Data cleaning and standardization eliminate data silos, providing high-quality input for model building. Dimensional modeling tools decompose business characteristics to form a three-dimensional analysis framework, enabling refined control of budget management. Machine learning algorithms deeply mine historical patterns to establish dynamic correlations, improving model prediction accuracy. Floating range parameters quantify risk management requirements into actionable thresholds, giving the budget model dynamic responsiveness. These steps work synergistically, constructing a dynamic budget system that adapts to market changes through systematic data integration and intelligent modeling. This solution solves the problem of integrating multi-source data, enabling real-time data exchange and standardized processing across systems. Deeply mining historical patterns through machine learning algorithms improves the accuracy of the budget model in identifying business drivers. Introducing external market variables and floating range parameters enhances the budget model's responsiveness to market fluctuations. A multi-dimensional analysis framework is built to achieve precise matching between budget indicators and corporate strategy. Ultimately, this results in a budget modeling method that combines data integration, intelligent analysis, and dynamic adjustment capabilities, effectively improving the scientific rigor and operability of corporate budget management.

[0034] This application further proposes the following calculation method for the dynamic budget model: Let the adjusted budget value be Y, the basic budget value be B, and the sum of the absolute values ​​of the historical execution deviations over the past three periods be... If the average volatility of current business drivers is F, the average change of external market variables is M, and the average deviation improvement rate after budget adjustments in the previous two periods is G, then: Here, the base budget value B refers to the initial budget benchmark value set based on the company's annual operating targets. Specifically, it can be calculated by weighting historical budget execution data with business targets to ensure the stability of budget adjustments. The absolute values ​​of historical execution deviations over the past three periods are also included. This refers to the sum of the absolute values ​​of the differences between the actual execution value and the budgeted value over the most recent three budget periods. Specifically, it can be calculated using rolling execution records stored in the data warehouse, reflecting the need to correct historical execution errors for current adjustments. The average fluctuation range F driven by current business refers to the average fluctuation of driving factors such as business volume and production efficiency within the current period. This can be achieved by comparing real-time data from the business system with historical data from the same period, quantifying dynamic changes at the business execution level. The average change value M of external market variables refers to the average change of external environmental indicators such as raw material prices and market demand indices. This can be achieved by obtaining market data through third-party data interfaces and calculating the rate of change, incorporating the impact of the external environment into the budget adjustment model. The average deviation improvement rate G after the previous two budget adjustments refers to the average improvement in the actual deviation rate after the two most recent budget adjustments. This can be calculated using regression analysis of adjustment records and execution data, evaluating the feedback of historical adjustment effects on current adjustments.

[0035] Specifically, this computational model achieves dynamic budget adjustment by constructing a multi-variable interconnected mathematical relationship. A baseline budget value serves as a benchmark parameter, ensuring that budget adjustments do not deviate from the company's strategic goals. The cumulative calculation of historical execution deviations over the past three periods allows the model to automatically correct the impact of historical errors on the current budget. The product relationship between business-driven fluctuations and external market variables couples the dynamics of business execution with changes in the external environment, enhancing the real-time responsiveness of budget adjustments. The superimposed calculation of the improvement rates from the previous two periods optimizes the current adjustment range through feedback from historical adjustment effects, forming a self-learning mechanism. Each parameter works synergistically through preset weighting coefficients, dynamically generating budget values ​​adapted to the latest business status based on real-time updates to the data warehouse. Through this approach, automatic correction of historical execution deviations, quantitative analysis of business fluctuation characteristics, and dynamic response to external market changes can be achieved during budget adjustments. Specifically, the cumulative calculation of historical deviations effectively avoids the impact of error accumulation on subsequent budgets, the coupled analysis of business and market variables enhances the sensitivity of budget adjustments to real-time changes, and the feedback mechanism of historical adjustment effects strengthens the model's self-optimization capabilities. This solves the problems of lagging adjustments and fixed parameters in traditional budget models, enabling budget adjustments to have a scientific basis and real-time response capability.

[0036] Reference Figure 1 and Figure 4As shown in this implementation plan: This application further proposes specific implementation steps for real-time data acquisition and synchronization: S21, Determine the data types and interface requirements for real-time acquisition: Based on the functional requirements of the budget management module, identify three types of data that need to be acquired in real time: business execution data (such as sales order status changes, production equipment operating parameters, and procurement arrival and acceptance time), financial data (such as expense reimbursement approval progress, accounts receivable receipt status, and accounts payable payment notifications), and external environment data (such as supplier delivery delay warnings, raw material market price fluctuations, and competitor promotional activity notifications); at the same time, clarify the ERP system interface type (such as the RESTAPI of the sales system, the OPCUA protocol of the production system, and the message queue of the third-party data service provider) and transmission protocol (such as HTTPS, AMQP, and WebSocket) corresponding to each data.

[0037] S22. Configure Interface Connection and Real-time Transmission Parameters: Through the interface management module of the ERP system, configure interface connection parameters for each data source (such as the sales management system, production execution system, and third-party data service provider platform), including API key, OAuth2.0 authentication token, and IP whitelist; set the data push frequency (such as business execution data being pushed every 3 minutes, financial data being pushed in real time, and external environment data being pushed immediately upon event occurrence) or subscription trigger conditions (such as pushing in real time when the sales order status changes to "shipped"); after completing the configuration, perform an interface connectivity test to verify whether the data can be transmitted from the source system to the middleware cache of the ERP system in real time.

[0038] S23. Establish a data cleaning rule base: Define cleaning rules for the three types of real-time collected data according to their characteristics: Remove duplicate order records from business execution data (such as multiple pushes of the same order number) and correct incorrect customer codes (verified through the master data management system); Verify the legality of amount fields in financial data (such as negative numbers need to be associated with approval records and retained to two decimal places) and filter out ineffective vouchers (such as vouchers with a status of "draft"); Filter out expired market information from external environment data (such as supplier quotations older than 24 hours) and remove abnormal price fluctuation values ​​(such as unreasonable changes with a single-day increase exceeding 3 times the industry average).

[0039] S24. Standardize execution data: In accordance with the company's unified data standards (such as accounting subject coding table, product classification catalog, timestamp format), convert the cleaned data into a standardized format: In business execution data, "Customer Name" is standardized to "Full Customer Name + Unified Social Credit Code", and "Production Equipment Number" is standardized to "Plant Area Code - Equipment Type - Serial Number"; In financial data, the "Date" field is standardized to "YYYY-MM-DD" format, and the "Amount" field is standardized to "RMB Yuan" as the unit; In external environment data, the "Market Price" field is labeled with "Data Source" (such as "Shanghai Futures Exchange" or "Industry Website") to ensure the consistency and comparability of data from various systems in the budget management module.

[0040] S25. Real-time synchronization to the budget management module and monitoring of transmission status: Standardized data is pushed to the database of the budget management module in real time via the ERP system's data bus (such as ESB Enterprise Service Bus) or message middleware (such as Kafka); at the same time, a transmission monitoring dashboard is configured in the ERP system to display indicators such as transmission delay (e.g., business data delay ≤ 5 minutes), data volume (e.g., receiving 100,000 records per hour), and success rate (e.g., ≥ 99.9%) for each data source in real time; when transmission is interrupted (e.g., no data push for 10 consecutive minutes) or data is lost (e.g., the daily data volume decreases by 50% compared to the average of the previous 3 days), the system automatically triggers a retry mechanism (e.g., after 3 failed retries) and sends an alarm notification to the budget management department (including information such as data source name, abnormal time, and affected data volume) to ensure the timeliness and integrity of data synchronization.

[0041] Specifically, by pre-defining the interface types and transmission protocols for three types of data sources, data collection conflicts caused by chaotic interface configurations can be avoided. When configuring interface parameters, the push frequency or subscription trigger conditions can be set to adapt to the real-time requirements of different business systems. For example, the data push frequency of the production system can be set to once per minute, while external market data can be updated using an event-triggered method. The connectivity testing mechanism of the middleware cache can verify the availability of the data transmission channel, for example, by sending test messages to check the interface response status. In the data cleaning rule base, duplicate order filtering rules are set for business execution data. For example, if the same order number appears repeatedly within 10 seconds, it is judged as redundant data. Amount verification rules are set for financial data. For example, the payment amount cannot exceed 120% of the contract amount. Validity filtering rules are set for external data. For example, market quotations older than 24 hours are automatically marked as expired data. During the standardization process, customer names are uniformly converted to the full name registered with the business registration authority, production equipment numbers are reorganized into the structure of factory code + production line number + equipment serial number, dates are uniformly converted to YYYY-MM-DD format, and amounts are retained to two decimal places and a currency identifier is added. Data source labels use a three-digit coding rule; for example, 001 represents real-time stock exchange quotes, and 002 represents a commodity trading platform. When synchronizing data via the enterprise service bus, an asynchronous transmission mechanism is used to ensure data processing efficiency in high-concurrency scenarios. The transmission monitoring dashboard displays data throughput, latency, and error rate metrics in real time, and automatically triggers an alarm notification when three consecutive transmission failures occur.

[0042] The above solutions resolved data acquisition conflicts caused by chaotic interface configurations, ensuring efficient access to multi-source data by clearly defining interface types and transmission protocols; eliminated data redundancy and errors caused by missing cleaning rules, improving data quality through categorized cleaning rule definitions; overcame cross-system data inconsistencies due to insufficient standardization by enhancing data availability through unified format conversion and adding source tags; and prevented the inability to handle synchronization anomalies in a timely manner by ensuring data transmission reliability through real-time monitoring and automatic retry mechanisms. The rate of duplicate orders in business execution data decreased, the accuracy of financial data amount verification improved, the rate of expired external environment data decreased, and the data transmission interruption recovery time was shortened to within 30 seconds.

[0043] Reference Figure 1 and Figure 5As shown in this implementation plan: This application further proposes specific implementation steps for intelligent monitoring and early warning: S31, Construct a monitoring indicator system: Based on the enterprise's strategic goals and business pain points, compile a list of core indicators that need to be monitored, covering three major categories of indicators: financial (such as sales revenue, sales cost, period expenses, net profit), business (such as sales order volume, production completion rate, and on-time delivery rate of procurement), and external environment (such as raw material price volatility and competitor market share change rate); for each indicator, clearly define (such as "sales revenue" = actual sales volume × actual unit price), calculation method (such as statistics by natural month / quarter), monitoring frequency (such as daily monitoring of revenue indicators and weekly monitoring of cost indicators), and responsibility attribution (such as sales revenue being the responsibility of the sales department and production completion rate being the responsibility of the production department).

[0044] S32. Configure Tiered Early Warning Thresholds: For each monitoring indicator, based on the historical data fluctuation range (e.g., the average monthly deviation of sales revenue over the past year is ±4%), the company's management tolerance (e.g., management requires net profit deviation to not exceed ±3%), and business characteristics (e.g., higher deviations are allowed during peak sales seasons), set two levels of early warning thresholds: the first threshold is for minor deviations (e.g., sales revenue deviation ±5%, production completion rate deviation ±8%), and the second threshold is for major deviations (e.g., sales revenue deviation ±8%, production completion rate deviation ±12%). The thresholds are entered and stored through the ERP system parameter configuration module and can be dynamically adjusted by quarter or business scenario (e.g., relaxing the revenue deviation threshold during promotional seasons).

[0045] S33. Real-time acquisition of actual execution data: Extract actual execution data related to monitoring indicators (such as daily sales order amount, monthly production completion quantity, and current period expense amount) from the unified data warehouse through the ERP system data interface, and synchronously obtain the budget values ​​of corresponding indicators in the budget management module (such as monthly sales budget amount, quarterly production budget quantity, and annual expense budget total); for external environmental indicators (such as raw material prices), pull the latest market data (such as daily copper price and weekly crude oil futures price) in real time through third-party data interfaces.

[0046] S34. Deviation Calculation and Verification: The ERP system automatically triggers deviation calculation logic according to the monitoring frequency (e.g., real-time, hourly, daily), with the formula "deviation rate = (actual value - budgeted value) / budgeted value × 100%"; synchronously verifies data validity (e.g., removing abnormal values ​​caused by system failures, excluding ineffective business documents) to ensure the accuracy of deviation calculation; if data is missing or abnormal (e.g., sales data for the day is not synchronized), the system automatically marks "data pending confirmation" and suspends warning triggering, recalculating after the data is repaired.

[0047] S35. Trigger and push tiered warnings: When the deviation rate reaches the first threshold, the system automatically marks the indicator as a "Level 1 Warning", associates it with the responsible department (such as the sales department's corresponding revenue indicator), and generates a warning notification (including indicator name, current actual value, budget value, deviation rate, abnormal time period, and responsible department); when the deviation rate exceeds the second threshold, it is marked as a "Level 2 Warning", and pushed to the financial management department in addition to the responsible department. The notification also includes a preliminary speculation on the cause of the deviation (such as "the rise in raw material prices has led to cost overruns") and an impact assessment (such as "net profit is expected to decrease by 5% this month"). The warning is pushed in real time through the notification module built into the ERP system (such as WeChat, email, and DingTalk), and the abnormal indicator is highlighted in the visualization dashboard of the budget management module.

[0048] S36. Early Warning Handling Tracking and Closed Loop: Upon receiving a Level 1 early warning, the business department must log in to the system within 24 hours to view the details and submit a preliminary explanation (e.g., "Revenue deviation caused by customer's temporary order cancellation") and a handling plan (e.g., "Conduct customer recovery activities next week"). Upon receiving a Level 2 early warning, the financial management department must intervene and analyze within 48 hours (e.g., verify the specific aspects of cost overruns) and provide feedback on rectification suggestions (e.g., "Adjust procurement strategy to reduce raw material costs"). The system automatically records the early warning handling progress (e.g., "Pending," "In Process," "Closed") until the early warning status is updated to "Resolved," forming a closed loop record of early warning handling.

[0049] Specifically, when the monitoring indicator system is constructed, financial indicators may include cost-to-revenue ratio and cash flow deviation; business indicators may include order fulfillment rate and inventory turnover days; and external environment indicators may include raw material price volatility and market demand change index. During the configuration of tiered early warning thresholds, the first threshold can be set to a budget deviation rate of 5%, and the second threshold can be set to a deviation rate of 10%. These threshold parameters are dynamically maintained through the system configuration interface. For real-time data acquisition, business execution data is extracted from the production system via the ERP interface, and external environment data is accessed from a third-party data platform via an API interface. During deviation calculation, the system automatically calculates the deviation rate between the actual value and the budgeted value every hour. If missing data fields or logical contradictions are found, the early warning process is suspended. After an early warning is triggered, a first-level warning is pushed to the business department head through the message center, and a second-level warning simultaneously generates a pending task for the financial analysis position. In the closed-loop processing phase, the business department must submit an adjustment plan within a specified time. The processing progress is recorded in the early warning work order until the status changes to closed.

[0050] The above solution enables real-time capture and accurate identification of anomalies during budget execution, effectively covering key influencing factors at each stage of the business chain through multi-dimensional indicator monitoring. A tiered early warning mechanism differentiates handling procedures based on varying degrees of deviation, avoiding resource waste and improving response efficiency. Data verification eliminates the risk of misjudgments caused by erroneous data, ensuring the reliability of early warning results. A closed-loop processing mechanism, through time constraints and status tracking, ensures that early warning issues are substantially resolved, eliminating the "alarm-only, no-action" deficiency of traditional early warning systems. The highlighting function of the visual dashboard accelerates anomaly localization, providing managers with intuitive decision support.

[0051] Reference Figure 1 and Figure 6 As shown in this implementation plan: This application further proposes steps for automated analysis and adjustment suggestions, including extracting data related to early warning items, performing automated root cause analysis, linking dynamic budget model parameters, generating multiple versions of adjustment plans, and calculating the risks and benefits of the plans.

[0052] S41. Extract relevant data for early warning items: Based on the type of early warning indicator (such as financial or business), extract relevant historical data (budget values ​​and actual execution values ​​for the same period in the past 3-5 years), actual execution data for the current period (such as monthly sales order volume and production completion), business-driven data (such as capacity utilization rate and raw material procurement price), and external environment data (such as industry price index and competitor dynamics) from the unified data warehouse to form a special dataset for early warning items.

[0053] S42. Automated Root Cause Analysis Execution: Call the root cause analysis module built into the ERP system (such as a decision tree-based association analysis model and causal inference algorithm) to perform in-depth mining of the specific dataset; first, identify the deviation correlation factors (such as sales revenue deviation related to a decrease in sales volume or a decrease in unit price), then determine the main influencing factors (such as a 70% decrease in sales volume) through correlation analysis (such as calculating the Pearson correlation coefficient), and finally locate the root cause (such as "an increase in raw material prices of more than 5% will lead to an increase in production costs") by combining business rules (such as "an 8% increase in the price of a key raw material will lead to an overspending of total production costs).

[0054] S43. Dynamic Budget Model Parameter Linkage: Input the root cause analysis results into the dynamic budget model to trigger the model parameter update mechanism; based on the multi-dimensional variable correlation of the model (such as the "raw material price - total production cost - sales gross profit margin" link), simulate the potential impact of different adjustment directions (such as increasing the procurement budget, reducing the sales target, and optimizing production efficiency) on the budget target, and generate an initial adjustment direction list (such as "adjusting the procurement budget", "adjusting the sales plan", "adjusting the production schedule").

[0055] S44. Generate multiple versions of adjustment plans: For each initial adjustment direction, generate a specific adjustment plan through model parameter optimization algorithms; the adjustment range is set according to the degree of impact of the root cause (e.g., an 8% increase in raw material prices corresponds to an 8%-10% increase in the procurement budget) and the company's management tolerance (e.g., sales targets can be reduced by up to 5%); the scope of impact clearly defines the organizational levels involved (e.g., subsidiaries, business units), business units (e.g., a product line, regional market) and business processes (e.g., procurement payments, sales collection); the implementation path defines specific action steps (e.g., "negotiate with suppliers to extend payment terms", "adjust next month's production schedule to reduce overtime", "launch promotional activities to reduce inventory") and responsible departments (e.g., procurement department, production department, sales department).

[0056] S45. Calculate the risks and benefits of the proposed solutions: Based on the risk prediction module of the dynamic budget model, simulate the impact of each adjustment plan on other related indicators (such as increasing the procurement budget may lead to a 2% increase in inventory costs, and adjusting the sales target may lead to a 1% decrease in market share), calculate the risk coefficient (combining the probability of risk occurrence and the degree of impact, such as 0-1 points, with 1 point being high risk); at the same time, based on historical data and market forecasts (such as an expected increase in sales volume of 10% and a decrease in costs of 5% after adjustment), calculate the expected benefits (such as an increase in net profit of 3 million yuan / month); finally, generate an evaluation report for each plan that includes the risk coefficient, expected benefits, and implementation difficulty.

[0057] Specifically, when budget execution deviates, the system first extracts historical, actual execution, and external environment data related to the warning indicators from the unified data warehouse to form a special dataset. Then, it calls the root cause analysis module to identify key influencing factors through correlation analysis. For example, it might find a strong correlation between declining sales of a product line and supply chain delays, while simultaneously verifying whether this conclusion meets the preset inventory turnover rate threshold based on business rules. After the analysis results are input into the dynamic budget model, parameters are updated, and adjustments are simulated, such as reducing planned production volume or increasing procurement lead time. Based on the degree of root cause impact, the system sets the adjustment range according to management tolerance. For example, it generates two solutions for shortening the procurement cycle by 5% or 10% to address supply chain issues, and clarifies the scope of production scheduling adjustments and supplier coordination paths. Finally, the risk prediction module calculates the risk coefficient for each solution. For example, the 10% adjustment solution has a higher risk coefficient due to the higher probability of supplier default. Simultaneously, it assesses expected benefits based on market forecast data, generating a multi-dimensional report including implementation difficulty, risk level, and benefit assessment.

[0058] The above solution enables automated diagnosis and scientific adjustment of budget deviations. The system uses data-driven methods to quickly pinpoint the root causes of deviations, avoiding the efficiency bottleneck of manual investigation; the parameter update mechanism linked to the dynamic budget model ensures real-time adaptation of adjustment plans to actual business changes; and the generation of multiple versions of plans and risk assessments provide decision-makers with quantitative decision-making basis, solving the problems of traditional adjustment plans being singular and lacking risk prediction.

[0059] This application further proposes the following formula for calculating the risk of the proposed solution: In the formula: R represents the risk coefficient; x represents the standard deviation of external market volatility; y represents the absolute value of business execution deviation; z represents the scope of adjustment impact; and t represents the historical risk frequency. Specifically, the standard deviation of external market volatility is calculated by statistically analyzing the fluctuations of external market indicators over a specific period. This can be achieved using time series analysis of historical market data, and is used to measure the potential impact of external environmental changes on budget adjustment plans. The absolute value of business execution deviation refers to the absolute difference between actual business execution data and the budget baseline value. This can be calculated by comparing the difference between real-time business execution data collected by the ERP system and the baseline data stored in the budget module, reflecting the degree to which the stability of internal execution affects the risk of the plan. The scope of adjustment impact refers to the number and hierarchical depth of business units involved in the budget adjustment plan. This can be quantitatively assessed using an organizational structure tree traversal algorithm combined with adjustment parameter mapping relationships, and is used to characterize the scope of chain reactions that may be triggered by the implementation of the plan. The historical risk frequency refers to the percentage of times that similar budget adjustments trigger risk events within a historical period. This can be achieved through correlation analysis between historical adjustment records stored in the data warehouse and risk event logs, reflecting the risk tendency of the plan based on historical experience.

[0060] Specifically, this formula calculates the risk coefficient by integrating financial, operational, and external market data from the ERP system, taking into account four key risk factors: standard deviation of external market volatility, absolute value of operational execution deviation, scope of adjustment impact, and historical risk frequency. It employs a non-linear function combination approach: firstly, it uses the square root function... To mitigate the impact of extreme values ​​in external market fluctuations, and to compress the numerical range of business deviations using ln(y+1) to avoid individual outliers dominating the results, the cube root is then used. To characterize the diminishing marginal effect of adjustment, an exponential function e is finally introduced. t / 5This model amplifies the sustained impact of historical risk frequencies, highlighting the cumulative effect of risks over time. All terms are linearly combined and then standardized by dividing by 100, ensuring the output falls within the (0,1) interval. This achieves multi-level, collaborative integrated modeling of risk factors across different dimensions. The model not only sensitively reflects the dynamic changes of various risk parameters but also excels at capturing the complex interactions between market fluctuations, execution deviations, and historical behavior. Ultimately, it outputs a highly comparable and interpretable comprehensive risk indicator, providing a quantitative basis for budget adjustment decisions. It accurately quantifies the risk level of scenarios such as budget execution deviations and adjustment plans, offering an intuitive and scientific basis for risk assessment in tiered early warning and dynamic budget adjustments. This significantly improves the accuracy of risk response and the efficiency of execution control in financial budget management.

[0061] Reference Figure 1 and Figure 7 As shown in this implementation plan: This application further proposes specific implementation steps for hierarchical approval and execution: S51, Matching approval process rules: Based on the scope of impact of the adjustment plan (such as the number of organizational levels involved: across three levels of group-business unit-subsidiary, or within a single business unit; the number of business units involved: a single product line, or multiple regional markets) and risk coefficients (such as high, medium, and low risk levels automatically assessed by the system), the corresponding approval process template is matched from the approval rule library preset by the ERP system; the rule library is pre-configured according to the "Enterprise Budget Management Measures". For example, a high-risk adjustment plan that crosses three levels of organizations triggers the "Group CEO + Board of Directors" approval process, a medium-risk adjustment plan that is within a single business unit triggers the "Business Unit General Manager + Chief Financial Officer" approval process, and a low-risk adjustment plan that involves only a part of the business triggers the "Department Head" approval process.

[0062] S52. Generate and push approval requests: Based on the matching approval process template, the system automatically generates an electronic approval request containing detailed information about the adjustment plan (including the adjustment range, scope of impact, risk assessment report, root cause analysis conclusion, and expected benefit calculation table); through the ERP system's approval flow engine, the request is pushed to the first approval node (such as the department head) according to role permissions, and the system records the push time, recipient, and current approval status (pending processing).

[0063] S53. Multi-level approval node processing: Each approval node reviews the request according to its responsibilities and authority: Business department heads focus on verifying the matching of adjustments with actual business (e.g., "Does the production schedule adjustment cover order demand?"); Financial management department heads focus on verifying the rationality of financial logic (e.g., "Does the cost increase align with the increase in raw material prices?"); Senior management (e.g., CEO) comprehensively assesses the impact on the overall corporate goals (e.g., "Does the net profit adjustment affect annual performance indicators?"); Approval nodes can fill in their approval opinions in the system (agree / reject / requires supplementary materials). If rejected, the specific reasons must be noted (e.g., "The risk coefficient calculation did not consider the impact of exchange rate fluctuations"). The system will automatically return the request to the initiator and update the approval status (rejected).

[0064] S54. Confirmation and Archiving of Approval Results: When all approval nodes are approved (approval status shows "Agreed"), the system automatically generates an electronic approval form (including electronic signatures and timestamps of each node) and marks the adjustment plan as "Approved". If supplementary materials are required, the initiator must complete the information and resubmit it within a specified period (e.g., 2 working days) until approval is granted. The final approval result (including process log, approval comments, and attachments) is permanently archived by the ERP system's document management module to meet audit traceability requirements.

[0065] S55. Automatic Budget Data Update: After approval, the system calls the parameter update interface of the budget management module to write parameters such as the adjustment range (e.g., adjusting the revenue budget of a product line from 50 million yuan to 55 million yuan) and the scope of impact (e.g., covering Q4 quarter and the East China regional market) in the adjustment plan into the budget master table of the unified data warehouse; at the same time, it triggers the data verification mechanism (e.g., checking the alignment of the adjusted budget with the company's strategic goals and the correlation with other related budgets) to ensure data consistency. After the verification is passed, the budget data update is completed.

[0066] S56. Synchronization to Business Modules: After the budget data is updated, the system pushes the adjusted budget value to relevant business systems in real time (such as the procurement budget limit of the procurement system, the capacity allocation plan of the production system, and the performance evaluation indicators of the sales system) through the Enterprise Service Bus (ESB) or API interface. During the push process, the system records the synchronization time, receives system feedback (such as "the procurement system has received the new budget limit"), and displays the "in execution" status in the visualization dashboard of the budget management module. After logging into the system, business departments can view the latest budget data and carry out subsequent business operations based on the adjusted indicators (such as adjusting purchase orders and optimizing production schedules).

[0067] Specifically, once a budget adjustment plan is generated, the system automatically matches it to an approval process template in a pre-defined rule base based on its risk factor and scope of impact. For example, high-risk, cross-organizational adjustments trigger multi-level joint approvals, while low-risk, localized adjustments only require departmental review. Electronic approval requests encapsulate adjustment parameters, impact analysis, and implementation paths in a standardized format and are pushed to the first approval node according to role permissions. Business leaders verify the alignment of the adjustment plan with the current business plan, finance leaders verify whether the budget logic complies with accounting standards, and senior management assesses the impact on strategic objectives. If any node rejects the plan, the system records the reason and returns it for modification; if the entire process is successful, the electronic approval form is automatically archived, forming an audit trail. After approval, the system calls the budget management interface to write the adjustment parameters, verifying data integrity through a transaction mechanism to avoid data conflicts between the master table and business systems. Finally, the adjustment value is pushed to business systems such as procurement and production via the enterprise service bus, updating the execution status in a visual dashboard, enabling business departments to obtain the latest budget guidance in real time.

[0068] The above solution enables intelligent and dynamic adaptation of the budget adjustment approval process, resolving the inefficiency caused by rigid traditional processes. Rule matching reduces redundant approval steps, ensuring high-level attention for high-risk matters and rapid execution for low-risk matters. Electronic processes eliminate errors from manual transmission, ensuring the integrity and traceability of approval information. A multi-dimensional review mechanism prevents biased decision-making from a single perspective, guaranteeing the business feasibility and financial compliance of the adjustment plan. Data verification and system interface collaboration ensure real-time consistency between the master budget table and business modules, avoiding execution errors caused by data asynchrony. A visual dashboard provides transparent monitoring of the execution status, helping business departments respond quickly to budget adjustment requests.

[0069] Reference Figure 1 and Figure 8 As shown in this implementation plan: This application further proposes specific implementation steps for closed-loop feedback and model optimization: S61, Determine the analysis cycle and data extraction scope: Based on the enterprise's budget management cycle (such as monthly, quarterly, annual) and business characteristics, set the analysis cycle for closed-loop feedback (usually a calendar month or quarter); extract budget execution data (such as actual revenue, cost, and expense), budget adjustment records (such as adjustment range, adjustment reasons, and approval logs), external environment data (such as raw material price fluctuations and market demand changes), and historical data of model prediction (such as initial model predictions and dynamically adjusted predictions) within the entire cycle from the unified data warehouse to form an analysis dataset.

[0070] S62. Establish an evaluation indicator system: Based on budget management objectives and business needs, define evaluation indicators for model accuracy and the effectiveness of adjustment mechanisms; model accuracy indicators include the prediction deviation rate of key indicators (such as the deviation rate between predicted and actual sales revenue) and budget execution consistency (such as the matching degree between actual execution and adjusted budget); the effectiveness indicators of adjustment mechanisms include the adoption rate of adjustment plans (such as the proportion of system-generated plans that are approved), adjustment response timeliness (such as the time from early warning to adjustment execution), and deviation improvement rate after adjustment (such as the decrease in actual deviation after adjustment compared to before adjustment).

[0071] S63. Execution Model Accuracy Assessment: By comparing the model's historical predictions with actual execution data, calculate the prediction deviation rate of key indicators in each dimension (time, organization, business, operation); analyze the characteristics of deviation distribution (such as whether there is systematic overestimation / underestimation, or concentrated deviation in specific business links); verify the model's prediction stability in different scenarios by combining business scenarios (such as promotional seasons, raw material price increase periods), and identify the model's defects in variable correlation and parameter settings (such as not considering the impact of a certain type of external variable on costs).

[0072] S64. Evaluate the effectiveness of the adjustment mechanism: Statistically analyze the number of adjustment proposals generated by the system within the analysis period, the proportion of approved proposals (adoption rate), and analyze the main reasons for non-adoption proposals (such as the adjustment range exceeding management's tolerance or the implementation path being infeasible); calculate the average adjustment response time (such as the time taken from warning triggering to adjustment execution) to assess whether it meets the business's rapid response requirements; compare the actual deviation changes of key indicators before and after the adjustment (such as the deviation rate being +10% before adjustment and decreasing to +3% after adjustment) to verify the adjustment mechanism's effect on improving deviations.

[0073] S65. Identify Model Optimization Directions: Based on the evaluation results, identify key areas for model optimization: If the prediction deviation rate is too high (e.g., the revenue prediction deviation rate for a certain product line exceeds 15%), then it is necessary to optimize the correlation between variables (e.g., supplement channel sales data not included in the model) or adjust parameter weights (e.g., increase the contribution coefficient of the market demand index to revenue); if the adoption rate of the adjustment mechanism is low (e.g., only 60%), then it is necessary to simplify the implementation path of the adjustment plan (e.g., shorten the approval time) or increase the flexibility of the adjustment range (e.g., widen the fluctuation range of non-core indicators); if the deviation improvement after adjustment is not significant (e.g., only a 2% decrease), then it is necessary to enhance the model's sensitivity to changes in the external environment (e.g., increase the real-time update frequency of exchange rate fluctuation variables).

[0074] S66. Automatically optimize model parameters and structure: Invoke model optimization algorithms (such as gradient descent and genetic algorithms) to adjust model parameters for identified problems: correct variable correlations (e.g., add "supplier delivery delay rate" as a correlation variable for production costs); recalibrate the calculation logic of the basic budget value for dimensions with high prediction deviation rates (e.g., regional market revenue) (e.g., introduce market share growth rate as a dynamic correction factor); reset parameters related to the adjustment mechanism (e.g., adjust the first-level early warning threshold from ±5% to ±6%) to improve the model's adaptability to external changes; and optimize the dimensional structure of the dynamic budget model (e.g., add a "customer segmentation" dimension to refine revenue forecasts).

[0075] S67. Verify Optimization Effects and Iterative Updates: Backtest the optimized model using historical data (e.g., using simulated data from the three months prior to optimization to verify prediction accuracy), comparing evaluation metrics before and after optimization (e.g., prediction deviation rate decreasing from 15% to 8%); conduct pilot runs in small-scale business scenarios (e.g., a specific regional market or product line), collecting budget execution data and adjustment feedback during the pilot period; further adjust model parameters based on pilot results until the optimized model reaches preset standards in accuracy, responsiveness, and adaptability (e.g., prediction deviation rate ≤ 5%, adjustment solution adoption rate ≥ 85%); finally, update the validated model to the production environment, replacing the old version model, and record optimization logs (including optimization reasons, adjusted parameters, and effect comparisons) in the budget management module, completing the closed-loop optimization iterative process.

[0076] Specifically, the analysis cycle is set in line with the company's budget management rhythm, and the extraction of full-cycle data avoids evaluation bias caused by data fragmentation. The calculation of the prediction deviation rate in the evaluation indicator system compares the model output values ​​with actual business data item by item, identifying model defects such as insufficient sensitivity to external market variables. During the effectiveness evaluation of the adjustment mechanism, the analysis of the reasons for not adopting solutions can reveal obstacles such as complex implementation paths or excessively high risk coefficients, providing a basis for subsequent optimization. In the model optimization direction identification stage, for problems with high prediction deviation, priority is given to adjusting variable correlation weights rather than the overall model structure to avoid system risks caused by over-adjustment. During automatic optimization, the algorithm is invoked to synchronously correct the basic budget value calculation logic, ensuring that parameter adjustments match the actual fluctuation patterns of the business. The verification stage adopts a combination of historical data backtesting and small-scale pilot testing, both utilizing historical experience to verify the optimization direction and preventing model overfitting through real-world scenario testing.

[0077] The above-mentioned solutions address the issues of large prediction bias and delayed adjustment response in budget models. Continuous monitoring of model performance through a closed-loop feedback mechanism allows for timely identification and targeted optimization of problematic nodes with excessive prediction bias. Quantitative evaluation of the adjustment mechanism's effectiveness quickly identifies response delays, shortening the adjustment decision-making cycle after optimizing the implementation path. The verification method combining historical data backtesting and scenario testing enhances the model's adaptability to market fluctuations while ensuring model stability. Automated parameter optimization processes reduce manual intervention, ensuring the model updates in real-time to reflect business changes. Optimization log recording provides traceable data support for model iteration, forming a knowledge accumulation system for continuous improvement.

[0078] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A financial budget management method based on an ERP financial system, characterized by, Comprise the following steps: S1, constructing a dynamic budget model: through the ERP system interface real-time access to financial, procurement, production, sales in the business system data to build a unified data warehouse, and based on the historical financial data, business data and external market data in the ERP system to establish a multi-dimensional dynamic budget model, the dynamic budget model set the basic budget value and floating interval parameters; S2, real-time data acquisition synchronization: through the ERP system interface real-time acquisition of business execution data, financial data and external environment data, to establish a data cleaning and standardization processing mechanism to ensure real-time synchronization of data to the budget management module; S3, intelligent monitoring and early warning: based on the preset monitoring index system, the ERP system real-time comparison of budget value and actual execution data, when the deviation reaches the first threshold, automatically trigger a first level warning and push to the corresponding business department, when the deviation reaches the second threshold, automatically trigger a second level warning and synchronously to the financial management department; S4, automatic analysis and adjustment suggestion: the ERP system performs automatic root cause analysis on the early warning matters, generates multiple budget adjustment schemes in combination with the dynamic budget model, the adjustment schemes include adjustment amplitude, influence range and implementation path, and calculates risk coefficient and expected benefit of each scheme; S5, hierarchical approval and execution: according to the influence range and risk coefficient of the adjustment scheme, the ERP system automatically matches the corresponding approval process, and after the approval is passed, the system automatically updates the budget data and synchronizes to the related business module, realizing seamless execution of budget adjustment; S6, closed-loop feedback and model optimization: the ERP system regularly analyzes the whole process data of budget execution, evaluates the accuracy of the budget model and the effectiveness of the adjustment mechanism, automatically optimizes the parameter setting of the dynamic budget model, and forms a closed-loop management mechanism for continuous improvement; the specific implementation steps of constructing the dynamic budget model in S1 include: determining the data acquisition range and interface configuration, multi-source data cleaning and standardization processing, determining the core dimension of the budget model, establishing variable correlation and quantitative model, setting the basic budget value and floating interval parameter; the calculation method of the dynamic budget model in S1 is as follows: let the adjusted budget value be Y, the basic budget value be B, the sum of absolute values of the historical execution deviation of the last 3 periods be , the average fluctuation amplitude of the current business driving is F, the average change value of the external market variable is M, and the average improvement rate of the budget adjustment after the last 2 periods is G, then: .

2. The ERP financial system based financial budget management method as claimed in claim 1, wherein: The specific implementation steps of constructing a dynamic budget model in S1 are as follows: S11, determine the data acquisition range and interface configuration: according to the enterprise business architecture, clear the core business system and external data source need to be accessed, through the ERP system preset API interface or ETL tool configuration data transmission protocol, determine the collection field and frequency; S12, multi-source data cleaning and standardization processing: quality inspection is made to the original data, and tools are used to eliminate duplicate records, correct format errors, and fill in missing values, according to the enterprise unified standard to form consistent field definition by standardization mapping, load the processed data to the cloud architecture or the unified data warehouse deployed locally and store by theme; S13, determine the core dimensions of the budget model: combine enterprise strategic objectives and business characteristics, define time, organization, business, and operation dimensions as model analysis dimensions, use dimension modeling tools to establish hierarchical relationship and association constraints between dimensions; S14, establish variable correlation and quantitative model: analyze historical financial data to identify key financial indicator fluctuation law and growth trend, establish regression relationship between business driving factors and financial indicators, and include external market variables into the model variable pool, train the quantitative relationship between variables and budget target through machine learning algorithm, and form a multi-dimensional dynamic budget model framework; S15, set the basic budget value and floating interval parameters: combine enterprise annual operating targets, historical budget execution deviation rate and business fluctuation characteristics to determine the basic budget value of each dimension, set the floating interval parameters for the basic budget value according to the external market variable prediction interval, business elasticity coefficient and risk management requirements.

3. The ERP financial system based financial budget management method as claimed in claim 1, wherein: The specific implementation steps of the real-time data collection synchronization in S2 are as follows: S21, determining the type of real-time collected data and interface requirements: according to the functional requirements of the budget management module, combing the three types of data that need to be collected in real time, i.e. business execution, finance and external environment, and clearly defining the corresponding interface type and transmission protocol of the ERP system; S22, configuring interface connection and real-time transmission parameters: configuring the interface connection parameters of each data source through the ERP interface management module, setting the data push frequency or subscription trigger condition, testing the interface connectivity to verify whether the data can be transmitted to the ERP system middleware cache area in real time; S23, establishing a data cleaning rule library: defining cleaning rules for the three types of real-time data characteristics respectively, eliminating duplicate order records, checking the legality of the amount and filtering expired market information; S24, performing data standardization processing: converting the cleaned data into a standardized format according to the unified standard of the enterprise, unifying the customer name, production equipment number, date and amount format, and adding a data source label to the market price field; S25, real-time synchronization to the budget management module and monitoring the transmission status: pushing the standardized data to the budget management module database through the ERP system data bus or message middleware in real time, configuring the transmission monitoring board to display the transmission indicators, and automatically retrying and alarming when an exception occurs.

4. The ERP financial system based financial budget management method as claimed in claim 1, wherein: The specific implementation steps of the intelligent monitoring and early warning in S3 are as follows: S31, constructing a monitoring index system: combining the enterprise strategic objectives and business pain points, combing the three types of core monitoring indicators of finance, business and external environment, and clearly defining the definition, calculation range, monitoring frequency and responsibility attribution of each indicator; S32, configuring hierarchical early warning thresholds: setting light and heavy two-level early warning thresholds for each monitoring indicator according to the historical data fluctuation range, enterprise management tolerance and business characteristics, and entering and supporting dynamic adjustment through the ERP system parameter configuration module; S33, real-time acquisition of actual execution data: extracting the actual execution data and corresponding budget values related to the monitoring indicators from the unified data warehouse, and obtaining the latest data of external environment indicators through a third-party interface; S34, performing deviation calculation and verification: the ERP system automatically calculates the deviation rate according to the monitoring frequency, verifies the data validity, and marks and suspends the early warning when the data is abnormal; S35, triggering hierarchical early warning and pushing: triggering level one early warning and pushing to the responsible department when the deviation rate reaches the first threshold, triggering level two early warning and pushing to the finance department when the deviation rate exceeds the second threshold, the early warning notice containing the deviation reason speculation and impact assessment, and highlighting the abnormal indicators on the visual board; S36, early warning processing tracking and closed loop: the business department feeds back the level one early warning processing plan within 24 hours, the finance department intervenes in the level two early warning within 48 hours and puts forward rectification suggestions, and the system records the processing progress until the early warning is solved to form a closed loop.

5. The ERP financial system based financial budget management method as claimed in claim 1, wherein: The specific implementation steps of the automatic analysis and adjustment suggestion in S4 are as follows: S41, extracting early warning matter related data: extracting historical, actual execution, business driving and external environment data related to early warning indicators from the unified data warehouse to form a special data set; S42, automatic root cause analysis execution: calling the root cause analysis module of the ERP system to identify deviation related factors, determining the main influencing factors through correlation analysis, and positioning the root cause combining with the business rules; S43, dynamic budget model parameter linkage: inputting the root cause analysis result into the dynamic budget model to trigger parameter update, simulating the influence of different adjustment directions on the budget target, and generating an initial adjustment direction list; S44, generating multiple version adjustment schemes: setting adjustment amplitude according to root cause influence degree and management tolerance for the initial adjustment direction, clarifying the influence range and implementation path, and generating multiple version adjustment schemes; S45, calculating scheme risk and benefit: calculating the risk coefficient of each scheme through the model risk prediction module, calculating the expected benefit combining with historical data and market prediction, and generating an evaluation report containing risk, benefit and implementation difficulty.

6. The ERP financial system based financial budget management method as claimed in claim 5, wherein: The formula for calculating the risk of the S45 is as follows: In the formula, R represents the risk coefficient; x represents the external market fluctuation standard deviation; y represents the absolute value of business execution deviation; z represents the adjustment influence range; and t represents the historical risk frequency.

7. The ERP financial system based financial budget management method as claimed in claim 1, wherein: The specific implementation steps of the hierarchical approval and execution in S5 are as follows: S51, matching approval process rules: according to the adjustment scheme influence range and risk coefficient, matching the corresponding approval process template from the preset approval rule library of the ERP system; S52, generating and pushing approval request: the system automatically generates an electronic approval request containing adjustment scheme details based on the template, pushes it to the first approval node according to the role permission, and records the relevant information; S53, multi-level approval node processing: each approval node is audited according to the permission, the business manager verifies the business matching, the financial manager verifies the financial logic, the senior management evaluates the impact on the overall target, and the rejected needs to be returned with reasons; S54, approval result confirmation and archiving: after all nodes pass, an electronic approval sheet is generated and the scheme is marked as "approved"; if additional materials are needed, they will be improved and re-submitted, and the approval result is archived by the document management module; S55, automatically updating budget data: after the approval, the system calls the budget management module interface to write the adjustment parameters into the budget master table, triggers data verification to ensure consistency, and completes the budget data update; S56, synchronizing to business module for execution: pushing the adjusted budget value to the related business system through enterprise service bus or API interface, recording the synchronization information, and displaying "executing" on the visual board, and the business department carries out business accordingly.

8. The ERP financial system based financial budget management method as claimed in claim 7, wherein: The rule library is pre-configured according to the "Enterprise Budget Management Method": high-risk and cross-three-level organization adjustment schemes trigger the "group CEO + board of directors" approval process, medium-risk and single business unit adjustment schemes trigger the "business unit general manager + financial director" approval process, and low-risk and local business adjustment schemes trigger the "department head" approval process.

9. The ERP financial system based financial budget management method as claimed in claim 1, wherein: The specific implementation steps of the closed-loop feedback and model optimization in S6 are as follows: S61, determining the analysis period and data extraction range: setting the analysis period according to the enterprise budget period and business characteristics, extracting full-cycle budget execution, adjustment record, external environment and model prediction historical data from the unified data warehouse to form an analysis data set; S62, set evaluation index system: based on budget management objectives and business needs, define model accuracy and adjustment mechanism effectiveness evaluation index; model accuracy indicators include key indicators prediction bias rate, budget implementation goodness of fit; adjustment mechanism effectiveness indicators include adjustment scheme adoption rate, adjustment response timeliness, adjustment after deviation improvement rate; S63, execute model accuracy evaluation: compare model historical prediction value and actual execution data, calculate prediction bias rate and analyze distribution characteristics, verify model prediction stability in different scenarios and identify model defects; S64, evaluate adjustment mechanism effectiveness: statistics adjustment scheme quantity and adoption rate to analyze the reasons for not adopting, and calculate the average value of adjustment response timeliness, compare the deviation change before and after adjustment to evaluate the adjustment mechanism effect; S65, identify model optimization direction: determine optimization focus according to evaluation results, if prediction deviation is high, optimize variable correlation or parameter weight, if adoption rate is low, simplify implementation path, if improvement effect is poor, enhance model sensitivity to external changes; S66, automatically optimize model parameters and structure: call model optimization algorithm to correct variable correlation, calibrate basic budget value calculation logic, reset adjustment mechanism parameters, optimize model dimension structure; S67, verify optimization effect and iterative update: backtest the optimized model with historical data and compare evaluation indicators, select small range scene pilot and collect feedback, adjust parameters to reach the standard, update the verified model to production environment, record optimization log and complete iteration.

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