Management accounting cost control and optimization method based on machine learning

Abstract

The invention provides a management accounting cost control and optimization method based on machine learning, and belongs to the technical field of management accounting systems. The method comprises the following steps: S1, data collection and preprocessing: collecting enterprise historical cost data, and preprocessing the data; s2, feature extraction and selection: extracting features related to the cost by using a machine learning technology, and screening features having significant influence on the cost through a feature selection method; s3, constructing a machine learning model: constructing the machine learning model based on the extracted features; s4, cost prediction and optimization: performing cost prediction on new data by using the trained model, optimizing the prediction cost, and realizing the target of cost control; and S5, decision support and feedback. According to the method, the accuracy of cost prediction is improved through a machine learning technology, a flexible personalized optimization scheme is provided, the model can be continuously optimized, and enterprise managers are helped to make scientific decisions, so that risks are reduced, and profits are increased.

Description

Technical Field

[0001] The present invention provides a management accounting cost control and optimization method based on machine learning, which belongs to the technical field of management accounting systems. Background Art

[0002] Most of the enterprises that apply management accounting are only product cost accounting business and they still use traditional cost accounting methods that reflect inaccurate cost information. For example, because they don’t know the exact cost of which products and which customers are profitable, they don’t know which products and customers are loss-making. This is the root cause of decision-making errors. The implementation of management accounting projects requires the participation of all employees of the enterprise. It is a cross-departmental project. Accounting, production, marketing, procurement, warehouse, management, logistics and other departments must actively participate. Enterprise informatization is very important, but it is not enough to implement ERP. ERP is only an informatization tool. It is only useful to obtain decision-making information that is conducive to increasing corporate profits and is used by managers. Management accounting can achieve this functional demand.

[0003] Therefore, in view of this, the existing structure is studied and improved, and a management accounting cost control and optimization method based on machine learning is proposed to solve the above-mentioned problems. Summary of the invention

[0004] The present invention provides a management accounting cost control and optimization method based on machine learning, which improves the accuracy of cost forecasting through machine learning technology and provides a flexible personalized optimization solution. It can also continuously optimize the model to help business managers make scientific decisions, thereby reducing risks and increasing profits.

[0005] In order to solve the above problems, the present invention proposes a technical solution: a management accounting cost control and optimization method based on machine learning, comprising the following steps:

[0006] S1. Data collection and preprocessing: Collect the historical cost data of the enterprise and preprocess the data;

[0007] S2. Feature extraction and selection: Use machine learning technology to extract features related to cost, including product characteristics, production processes, and market demand, and use feature selection methods to screen out features that have a significant impact on cost;

[0008] S3. Build a machine learning model: Based on the extracted features, build a machine learning model, including linear regression, random forest, and neural network, and learn the relationship between the cost and each feature through training the model;

[0009] S4. Cost prediction and optimization: Use the trained model to predict the cost of new data, and optimize the predicted cost by adjusting the controllable features of the production process and procurement strategy to achieve the goal of cost control;

[0010] S5. Decision support and feedback: Provide optimized cost forecast results to corporate decision makers as a basis for formulating product pricing and marketing strategies. At the same time, collect actual cost data, compare and analyze it with the predicted cost, and continuously optimize the model to improve forecast accuracy.

[0011] Furthermore, the historical cost data of the enterprise collected in step S1 includes product line costs, customer costs, and sales channel costs, and the data preprocessing in step S1 includes cleaning, conversion, and standardization to eliminate outliers.

[0012] Furthermore, the features extracted in step S2 are the key to managing accounting costs, requiring information relevance, timeliness, and accuracy.

[0013] Furthermore, the machine learning model constructed in step S3 includes input and output records and prices, which is conducive to cost accounting.

[0014] Furthermore, the cost forecasting and optimization in step 4 also includes reporting method characteristics and management function characteristics. The reporting method characteristics are the needs of management accounting forecasting, decision-making, control and evaluation and assessment, which makes the management accounting report flexible and diverse in methods.

[0015] Furthermore, the management function characteristics mentioned above strengthen the internal management of the enterprise, and must emphasize pre- and in-process control, plan for the future by preparing comprehensive budgets and responsibility budget reports, and control and evaluate the production and operation of the enterprise.

[0016] Furthermore, the optimized cost in step S5 is based on accurate data information after a large amount of data collection and analysis, and a chart analysis method can be used for comparison and analysis with the predicted cost.

[0017] Due to the adoption of the above technical solution, the beneficial effects of the management accounting cost control and optimization method based on machine learning of the present invention are as follows:

[0018] 1. High accuracy: Deep learning and analysis of cost data through machine learning technology can more accurately reflect the relationship between cost and various factors.

[0019] 2. Strong flexibility: It can adapt to changes in different business environments and cost structures, and provide enterprises with personalized cost control and optimization solutions.

[0020] 3. Decision support: Provide scientific decision-making basis for enterprise managers, reduce decision-making risks and increase enterprise profits.

[0021] 4. Continuous optimization: By continuously collecting actual cost data and feeding it back into the model, the model can be continuously optimized and the prediction accuracy can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0023] Figure 1 This is a cost control chart of a management accounting cost control and optimization method based on machine learning in the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] The present application fully explains the problems of the prior art in the technical background, and there are no superordinate or general problems. The problems to be solved by the present application are described in detail in the following invention content and specific implementation methods, the technical problems to be solved by the technical scheme are clarified, and it is determined that the technical scheme of the present application has beneficial effects relative to the objective prior art.

[0026] See also Figure 1 As shown: The present invention provides a management accounting cost control and optimization method based on machine learning, comprising the following steps:

[0027] S1. Data collection and preprocessing: Collect the historical cost data of the enterprise and preprocess the data;

[0028] S2. Feature extraction and selection: Use machine learning technology to extract features related to cost, including product characteristics, production processes, and market demand, and use feature selection methods to screen out features that have a significant impact on cost;

[0029] S3. Build a machine learning model: Based on the extracted features, build a machine learning model, including linear regression, random forest, and neural network, and learn the relationship between the cost and each feature through training the model;

[0030] S4. Cost prediction and optimization: Use the trained model to predict the cost of new data, and optimize the predicted cost by adjusting the controllable features of the production process and procurement strategy to achieve the goal of cost control;

[0031] S5. Decision support and feedback: Provide optimized cost forecast results to corporate decision makers as a basis for formulating product pricing and marketing strategies. At the same time, collect actual cost data, compare and analyze it with the predicted cost, and continuously optimize the model to improve forecast accuracy.

[0032] The historical cost data of the enterprise collected in step S1 includes product line costs, customer costs, and sales channel costs. The data preprocessing in step S1 includes cleaning, conversion, and standardization to eliminate outliers.

[0033] The features extracted in step S2 are the key to managing accounting costs and require information relevance, timeliness, and accuracy.

[0034] The machine learning model constructed in step S3 includes input and output records and prices, which is conducive to cost accounting.

[0035] The cost forecasting and optimization in step 4 also includes reporting method characteristics and management function characteristics. The reporting method characteristics are the needs of management accounting forecasting, decision-making, control and evaluation and assessment, which makes the management accounting report flexible and diverse in methods.

[0036] Requirements for basic corporate financial work

[0037] Profit = Sales Revenue – Total Cost

[0038] Total cost = fixed cost + variable cost = fixed cost + unit variable cost × sales volume

[0039] Profit = unit price × sales volume – unit variable cost × sales volume – fixed cost

[0040] Marginal contribution = sales revenue - variable cost = unit price × sales volume - unit variable cost × sales volume = sales volume (unit price - unit variable cost)

[0041] The management function characteristics mentioned above strengthen the internal management of the enterprise. It is necessary to emphasize pre- and in-process control, plan for the future by preparing comprehensive budgets and responsibility budget reports, and control and evaluate the production and operation of the enterprise.

[0042] The optimized cost in step S5 is based on accurate data information after a large amount of data collection and analysis, and a chart analysis method can be used for comparison and analysis with the predicted cost.

[0043] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A management accounting cost control and optimization method based on machine learning, comprising the following steps: S1. Data collection and preprocessing: Collect the historical cost data of the enterprise and preprocess the data; S2. Feature extraction and selection: Use machine learning technology to extract features related to cost, including product characteristics, production processes, and market demand, and use feature selection methods to screen out features that have a significant impact on cost; S3. Build a machine learning model: Based on the extracted features, build a machine learning model, including linear regression, random forest, and neural network, and learn the relationship between the cost and each feature through training the model; S4. Cost prediction and optimization: Use the trained model to predict the cost of new data, and optimize the predicted cost by adjusting the controllable features of the production process and procurement strategy to achieve the goal of cost control; S5. Decision support and feedback: Provide optimized cost forecast results to corporate decision makers as a basis for formulating product pricing and marketing strategies. At the same time, collect actual cost data, compare and analyze it with the predicted cost, and continuously optimize the model to improve forecast accuracy.

2. The management accounting cost control and optimization method based on machine learning according to claim 1, characterized in that: The historical cost data of the enterprise collected in step S1 includes product line costs, customer costs, and sales channel costs. The data preprocessing in step S1 includes cleaning, conversion, and standardization to eliminate outliers.

3. The management accounting cost control and optimization method based on machine learning according to claim 1, characterized in that: The features extracted in step S2 are the key to managing accounting costs and require information relevance, timeliness, and accuracy.

4. The management accounting cost control and optimization method based on machine learning according to claim 1, characterized in that: The machine learning model constructed in step S3 includes input and output records and prices, which is conducive to cost accounting.

5. The management accounting cost control and optimization method based on machine learning according to claim 1, characterized in that: The cost forecasting and optimization in step 4 also includes reporting method characteristics and management function characteristics. The reporting method characteristics are the needs of management accounting forecasting, decision-making, control and evaluation and assessment, which makes the management accounting report flexible and diverse in methods.

6. The management accounting cost control and optimization method based on machine learning according to claim 1, characterized in that: The management function characteristics mentioned above strengthen the internal management of the enterprise. It is necessary to emphasize pre- and in-process control, plan for the future by preparing comprehensive budgets and responsibility budget reports, and control and evaluate the production and operation of the enterprise.

7. The management accounting cost control and optimization method based on machine learning according to claim 1, characterized in that: The optimized cost in step S5 is based on accurate data information after a large amount of data collection and analysis, and a chart analysis method can be used for comparison and analysis with the predicted cost.

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