Enterprise lean management information system

Abstract

The invention discloses an enterprise lean management information system, which comprises a data model construction module, and is characterized in that the data model construction module constructs a data model through five dimensions; the optimization module is in communication connection with the data model building module, the built data model is optimized through the optimization module, and an optimal model is obtained; the collection module is used for collecting employee related index completion data; the big data analysis module is in communication connection with the collection module; the beneficial effects of the invention are that the system achieves the multi-dimensional and all-directional objective evaluation of employees through the analysis and modeling of data, and achieves the lean management of enterprises based on the objective evaluation data. By cleaning the data, the accuracy of data modeling is ensured; and displaying the multi-dimensional omnibearing objective evaluation data of the employees through a visual display module.

Description

Technical Field

[0001] The present invention belongs to the technical field of enterprise management, and in particular relates to an enterprise lean management information system. Background Art

[0002] The importance of enterprise management is to improve the efficiency of the enterprise; clarify the development direction of the enterprise; give full play to the potential of the enterprise; make the financial statements of the enterprise true and accurate and the structure reasonable; improve product quality and create a good corporate image.

[0003] The basic activities of a company can be divided into four aspects: investment, financing, operation and distribution. For manufacturing enterprises, it is also necessary to manage and control the relevant production costs. From a management perspective, investment can be divided into long-term investment and short-term investment, and financing can also be divided into long-term financing and short-term financing. Since short-term investment, short-term financing and operating cash flow management are closely related, they are usually discussed together and called working capital management. Corporate financial management is interconnected and mutually restrictive. Financing is the foundation. Without the funds required for the production and operation of the enterprise, the enterprise cannot survive and develop. Moreover, the amount of company financing also restricts the scale of the company's investment. Only when the funds raised by the enterprise are effectively invested can the purpose of financing be achieved and the value-added and development be continuously achieved. Moreover, investment, in turn, determines the scale and time of financing required by the enterprise. ; The results of investment and financing both rely on the operation of funds to be realized. Financing and investment determine the characteristics and methods of the company's daily business activities to a certain extent; but the daily activities of the enterprise also need to reasonably manage and control the working capital, and strive to improve the efficiency and effectiveness of the use of working capital; cost management runs through the entire process of investment, financing and operating activities, and permeates every link of financial management. Income and distribution affect all aspects of financing, investment, working capital and cost management. The source of income and distribution is the result of the joint action of the above-mentioned aspects of the enterprise, and at the same time it will have a counter-effect on the above-mentioned aspects; therefore, investment management, financing management, working capital management, cost management and income and distribution management are all necessary links in the creation of corporate value, and are important contents to ensure the healthy development of the enterprise and achieve sustainable growth.

[0004] Traditional corporate management evaluates employees through setting performance goals, conducting performance communication, fact collection, observation and recording; performance appraisal and performance diagnosis; but it is unable to achieve a multi-dimensional and comprehensive objective evaluation of employees. Summary of the invention

[0005] The purpose of the present invention is to provide an enterprise lean management information system to achieve multi-dimensional and all-round objective evaluation of employees, and to achieve lean management of the enterprise based on these objective evaluation data.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: an enterprise lean management information system, comprising

[0007] A data model building module, wherein the data model building module builds a data model through five dimensions;

[0008] An optimization module, the optimization module is in communication with the data model construction module, and the constructed data model is optimized by the optimization module to obtain an optimal model;

[0009] A collection module, which is used to collect employee-related indicator completion data;

[0010] A big data analysis module, which is in communication with the collection module and performs big data analysis on the collected employee-related indicator completion data through the big data analysis module;

[0011] The evaluation module is respectively connected to the big data analysis module and the optimization module in communication. The evaluation module analyzes and models the data to achieve a multi-dimensional and all-round objective evaluation of the employees, and realizes the lean management of the enterprise based on these objective evaluation data.

[0012] As a preferred technical solution of the present invention, the five dimensions include output, quality, cost, enthusiasm and creativity.

[0013] As a preferred technical solution of the present invention, the output includes the daily contribution data of the employees; the quality includes the quality of the work completed and the performance of the quality indicators.

[0014] As a preferred technical solution of the present invention, the data model construction method is as follows: collecting employees' output, quality, cost, enthusiasm, and creativity data, processing the collected data, annotating the processed data, and obtaining a data set; obtaining a target algorithm, and training the target algorithm through the data set.

[0015] As a preferred technical solution of the present invention, the data processing includes cleaning and extraction.

[0016] As a preferred technical solution of the present invention, the cleaning method is as follows: manually clean the target data to obtain a first data cleaning result; use a data cleaning algorithm to clean the target data to obtain a second data cleaning result; train the first data cleaning result and the second data cleaning result to obtain a new data cleaning algorithm, and perform the next data cleaning according to the new data cleaning algorithm.

[0017] As a preferred technical solution of the present invention, the employee-related indicators include business support capability, management capability, employee turnover rate, employee attendance rate, cost budget achievement rate, education and training attendance rate, number of cases submitted or value growth rate, and 5S activity assessment pass rate.

[0018] As a preferred technical solution of the present invention, it also includes a visualization display module, through which the multi-dimensional and all-round objective evaluation data of employees are displayed.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] By analyzing and modeling data, we can achieve multi-dimensional and all-round objective evaluation of employees, and realize lean management of the enterprise based on these objective evaluation data;

[0021] Manually clean the target data to obtain the first data cleaning result; use the data cleaning algorithm to clean the target data to obtain the second data cleaning result; train the first data cleaning result and the second data cleaning result to obtain a new data cleaning algorithm, and perform the next data cleaning according to the new data cleaning algorithm. By cleaning the data, the accuracy of data modeling is ensured;

[0022] The multi-dimensional and comprehensive objective evaluation data of employees is displayed through the visual display module. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a system diagram of 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] Example 1

[0026] See also Figure 1 , which is the first embodiment of the present invention, and which provides an enterprise lean management information system, comprising

[0027] Data model building module: The data model building module builds data models through five dimensions: output, quality, cost, enthusiasm, and creativity;

[0028] The optimization module is connected to the data model building module through communication, and the built data model is optimized by the optimization module to obtain the optimal model;

[0029] Collection module, which is used to collect employee-related indicator completion data;

[0030] The big data analysis module is connected to the collection module in communication, and the big data analysis module performs big data analysis on the collected employee-related indicator completion data;

[0031] Evaluation module: The evaluation module communicates with the big data analysis module and the optimization module respectively. The evaluation module analyzes and models the data to achieve multi-dimensional and comprehensive objective evaluation of employees, and realizes lean management of the enterprise based on these objective evaluation data.

[0032] In this embodiment, preferably, the data model construction method is as follows: collect employees' output, quality, cost, enthusiasm, and creativity data, and process the collected data, annotate the processed data to obtain a data set; obtain a target algorithm, and train the target algorithm through the data set.

[0033] In this embodiment, preferably, data processing includes cleaning and extraction; the cleaning method is as follows: manually cleaning the target data to obtain a first data cleaning result; using a data cleaning algorithm to clean the target data to obtain a second data cleaning result; training the first data cleaning result and the second data cleaning result to obtain a new data cleaning algorithm, and performing the next data cleaning according to the new data cleaning algorithm; data cleaning is to review and verify the database data, filter non-compliant data, delete duplicate data, correct erroneous data, complete format conversion, and perform data consistency checks before and after cleaning to ensure the quality of the cleaning result set. The dirty data that needs to be processed in data cleaning mainly includes erroneous data, incomplete data, and similar duplicate data. Data, according to the different classifications of dirty data, data cleaning can also be divided into three categories: attribute error cleaning, incomplete data cleaning, and similar duplicate record cleaning; attribute error cleaning: many data in the database violate the integrity constraints originally defined, and there are a large number of inconsistent, conflicting data and noise data. We should identify these erroneous data and then perform error cleaning; attribute error detection methods include quantitative error detection methods and qualitative error detection methods; quantitative error detection: generally, statistical methods are used to identify abnormal behaviors and errors based on outlier detection. Outlier detection is to find points that deviate too much from other observations. Aggarwal divides outlier detection methods into 6 types: extreme value analysis, clustering model, distance-based model, density-based model, probability model, information theory model; Qualitative error detection: relies on descriptive methods to specify a pattern or constraint of a legal data instance, so it is determined that the data that violates these patterns or constraints is erroneous data; First, the error type refers to what to detect; Qualitative error detection technology can be classified according to the type of error captured. At present, a lot of work is using integrity constraints to capture the data quality rules that the database should comply with. Although duplicate values ​​also violate integrity constraints, the identification and cleaning of duplicate values ​​is a core of data cleaning; Second, automated detection; based on whether humans are involved and the steps involved First, qualitative error detection techniques are classified. Most of the detection processes are fully automated, and some technologies involve human participation. Finally, the business intelligence layer refers to where the detection is performed. Errors can occur at any stage of data governance. Most detections are for the original database, but some errors can only be detected after data governance with more semantics and business logic. Attribute error cleaning can be divided into noise data cleaning and inconsistent data cleaning: Noise data cleaning is also called smooth noise technology. The main methods include binning and regression methods: The binning method is to smooth the ordered data values ​​through the surrounding neighboring values, but it is only locally smooth. The regression method is to use regression functions to fit the data to smooth the noise.In some cases, inconsistent data cleaning can be modified manually by referring to other materials. Knowledge engineering tools can be used to find data that violates restrictions. For example, if the functional dependency of the data is known, the attribute value can be modified through the functional relationship. However, most inconsistencies require data transformation, that is, a series of transformations are defined to correct the data. There are also many commercial tools that provide data transformation functions, such as data migration tools and ETL tools, but these functions are limited. ;

[0034] Example 2

[0035] See also Figure 1 , which is a second embodiment of the present invention, and which provides an enterprise lean management information system, comprising

[0036] Data model building module: The data model building module builds data models through five dimensions: output, quality, cost, enthusiasm, and creativity;

[0037] The optimization module is connected to the data model building module through communication, and the built data model is optimized by the optimization module to obtain the optimal model;

[0038] Collection module, which is used to collect employee-related indicator completion data;

[0039] The big data analysis module is connected to the collection module in communication, and the big data analysis module performs big data analysis on the collected employee-related indicator completion data;

[0040] Evaluation module: The evaluation module communicates with the big data analysis module and the optimization module respectively. The evaluation module analyzes and models the data to achieve multi-dimensional and comprehensive objective evaluation of employees, and realizes lean management of the enterprise based on these objective evaluation data.

[0041] In this embodiment, preferably, the output includes the daily contribution data of the employees, and takes into account the influence of uncontrollable factors; the quality includes the quality of work completed and the performance of quality indicators.

[0042] In this embodiment, preferably, the data model construction method is as follows: collect employees' output, quality, cost, enthusiasm, and creativity data, and process the collected data, annotate the processed data to obtain a data set; obtain a target algorithm, and train the target algorithm through the data set.

[0043] In this embodiment, preferably, data processing includes cleaning and extraction; the cleaning method is as follows: manually cleaning the target data to obtain a first data cleaning result; using a data cleaning algorithm to clean the target data to obtain a second data cleaning result; training the first data cleaning result and the second data cleaning result to obtain a new data cleaning algorithm, and performing the next data cleaning according to the new data cleaning algorithm; data cleaning is to review and verify the database data, filter non-compliant data, delete duplicate data, correct erroneous data, complete format conversion, and perform data consistency checks before and after cleaning to ensure the quality of the cleaning result set. The dirty data that needs to be processed in data cleaning mainly includes erroneous data, incomplete data, and similar duplicate data. Data, according to the different classifications of dirty data, data cleaning can also be divided into three categories: attribute error cleaning, incomplete data cleaning, and similar duplicate record cleaning; attribute error cleaning: many data in the database violate the integrity constraints originally defined, and there are a large number of inconsistent, conflicting data and noise data. We should identify these erroneous data and then perform error cleaning; attribute error detection methods include quantitative error detection methods and qualitative error detection methods; quantitative error detection: generally, statistical methods are used to identify abnormal behaviors and errors based on outlier detection. Outlier detection is to find points that deviate too much from other observations. Aggarwal divides outlier detection methods into 6 types: extreme value analysis, clustering model, distance-based model, density-based model, probability model, information theory model; Qualitative error detection: relies on descriptive methods to specify a pattern or constraint of a legal data instance, so it is determined that the data that violates these patterns or constraints is erroneous data; First, the error type refers to what to detect; Qualitative error detection technology can be classified according to the type of error captured. At present, a lot of work is using integrity constraints to capture the data quality rules that the database should comply with. Although duplicate values ​​also violate integrity constraints, the identification and cleaning of duplicate values ​​is a core of data cleaning; Second, automated detection; based on whether humans are involved and the steps involved First, qualitative error detection techniques are classified. Most of the detection processes are fully automated, and some technologies involve human participation. Finally, the business intelligence layer refers to where the detection is performed. Errors can occur at any stage of data governance. Most detections are for the original database, but some errors can only be detected after data governance with more semantics and business logic. Attribute error cleaning can be divided into noise data cleaning and inconsistent data cleaning: Noise data cleaning is also called smooth noise technology. The main methods include binning and regression methods: The binning method is to smooth the ordered data values ​​through the surrounding neighboring values, but it is only locally smooth. The regression method is to use regression functions to fit the data to smooth the noise.In some cases, inconsistent data cleaning can be modified manually by referring to other materials. Knowledge engineering tools can be used to find data that violates restrictions. For example, if the functional dependency of the data is known, the attribute value can be modified through the functional relationship. However, most inconsistencies require data transformation, that is, a series of transformations are defined to correct the data. There are also many commercial tools that provide data transformation functions, such as data migration tools and ETL tools, but these functions are limited. ;

[0044] In this embodiment, preferably, the employee-related indicators include business support capability, management capability, employee turnover rate, employee attendance rate, cost budget achievement rate, education and training attendance rate, number of cases submitted or value growth rate, and 5S activity assessment pass rate.

[0045] In this embodiment, preferably, a visualization display module is also included, through which the multi-dimensional and all-round objective evaluation data of employees are displayed. Employees can query their own data. Employees only need to enter the corresponding name and work number to accurately find their own data; when employees search for their own data, they need to register in advance.

[0046] Although the embodiments of the present invention have been shown and described, as detailed above, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An enterprise lean management information system, characterized by: include A data model building module, wherein the data model building module builds a data model through five dimensions; An optimization module, the optimization module is in communication with the data model construction module, and the constructed data model is optimized by the optimization module to obtain an optimal model; A collection module, which is used to collect employee-related indicator completion data; A big data analysis module, which is in communication with the collection module and performs big data analysis on the collected employee-related indicator completion data through the big data analysis module; The evaluation module is respectively connected to the big data analysis module and the optimization module in communication. The evaluation module analyzes and models the data to achieve a multi-dimensional and all-round objective evaluation of the employees, and realizes the lean management of the enterprise based on these objective evaluation data.

2. The enterprise lean management information system according to claim 1, characterized in that: The five dimensions include output, quality, cost, enthusiasm and creativity.

3. The enterprise lean management information system according to claim 2, characterized in that: The output includes the daily contribution data of employees; the quality includes the quality of work completed and the performance of quality indicators.

4. The enterprise lean management information system according to claim 1, characterized in that: The data model construction method is as follows: collecting employees' output, quality, cost, enthusiasm, and creativity data, processing the collected data, annotating the processed data, and obtaining a data set; obtaining a target algorithm, and training the target algorithm through the data set.

5. The enterprise lean management information system according to claim 4, characterized in that: The data processing includes cleaning and extraction.

6. The enterprise lean management information system according to claim 5, characterized in that: The cleaning method is as follows: manually clean the target data to obtain a first data cleaning result; use a data cleaning algorithm to clean the target data to obtain a second data cleaning result; train the first data cleaning result and the second data cleaning result to obtain a new data cleaning algorithm, and perform the next data cleaning according to the new data cleaning algorithm.

7. The enterprise lean management information system according to claim 1, characterized in that: The employee-related indicators include business support capability, management capability, employee turnover rate, employee attendance rate, cost budget achievement rate, education and training attendance rate, number of cases submitted or value growth rate, and 5S activity assessment pass rate.

8. The enterprise lean management information system according to claim 1, characterized in that: It also includes a visual display module, which displays the multi-dimensional and comprehensive objective evaluation data of employees.

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