CN115471060B - Digital value monitoring system and digital value evaluation method - Google Patents
Digital value monitoring system and digital value evaluation method Download PDFInfo
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Abstract
The invention discloses a digital value monitoring system and a digital value evaluation method, wherein the digital value monitoring system comprises a data acquisition unit, an ERP system monitoring unit and a digital value calculation unit; the data acquisition unit provides data for the ERP system monitoring unit and the digital value calculation unit through software and hardware interfaces; the ERP system monitoring unit quantifies the operation effect of the ERP system, continuously corrects the monitoring weights of each level by using machine learning, and provides data support for the data value computing unit; the digital value calculation unit is a method for obtaining digital value by utilizing the method for preparing the digital value table and the machine learning algorithm. The invention solves the problems of ERP system effect monitoring and digital value calculation in the digital transformation upgrading environment, and provides direction guidance for digitalization.
Description
Technical Field
The application relates to the field of informatization and digitalization, in particular to a digital value monitoring system and a digital value evaluation method.
Background
In general, informatization is the development of business in the physical world, with information systems providing business support. Digitization is the development of business in the digital space, the physical world responds, and digital value is generated or reconstructed.
Enterprise digitization places higher demands on informatization, requiring more efficient, stable, and comprehensive coverage of informatization. The main system of enterprise informatization is an ERP system, ERP is short for enterprise resource planning, and is an information technology for optimally managing all resources such as people, wealth, things, information and the like of an enterprise. The ERP system plays a very important role in supporting enterprise business, improving enterprise management efficiency and reducing cost. However, the failure rate of the ERP system implementation is very high, and there are all the time deletions in terms of project acceptance, effect monitoring and the like of the ERP system implementation. APICS (the original american society for production and inventory management) also provides an assessment level reference for the effect of MRPII/ERP applications, but where few assessment indicators are not quantified and where enterprise specific business conditions are not considered, thus affecting operability. In the large environment of digital transformation and upgrading, a large part of data value is discovered from the ERP system, so that the implementation effect of monitoring the ERP system is more important.
The digitized transformation and upgrading of enterprises are the necessary way for the contemporary enterprises. Digitization is not only the conversion of analog information to digital information, but is also not equivalent to intelligent manufacturing. Digitization is the process of finding digits, obtaining digits, sorting digits, obtaining digital values, that is, digits can create digital economic values for an enterprise, such as process digital values, intelligent manufacturing digital values, process digital values, product digital values, and digital trading values, etc. It can be said that the digitization reconstructs the business model of the enterprise.
While many businesses have been exposed to the wave of digital upgrades, and more businesses are planning digital upgrades, many administrators do not know where the true distinction between informatization and digitalization is, where the distinction between digitalization and intelligent manufacturing is, where the direction of digitalization is, and how to find the commercial value of the digits, which severely hampers the widespread use of digital upgrades and severely affects the effectiveness of digital upgrades.
Disclosure of Invention
The invention aims to solve the defects of project acceptance and effect monitoring implemented by an ERP system in the prior art and the defects of a method for searching digital value in the ERP system and the operation process, and provides a digital value monitoring system and a digital value evaluation method.
The aim of the invention is achieved by the following technical scheme:
in a first aspect, there is provided a digital value monitoring system comprising: the system comprises a data acquisition unit, an ERP system monitoring unit and a digital value calculation unit; the data acquisition unit acquires related data from a preset information system through a software interface on one hand; on one hand, through a preset hardware interface, data of equipment or a measuring instrument are collected by utilizing a preset communication protocol; providing data for an ERP system monitoring unit and a digital value calculating unit; the ERP system monitoring unit presets the N-level monitoring index table and the weight of each monitoring index, acquires the value of a preset field from the data acquisition unit, further judges whether to enter a machine learning process to correct the ERP monitoring weight according to the acquired monitoring data quantity, finally acquires the total effect value of ERP system operation, and outputs a preset monitoring index list; the digital value calculation unit presets a digital value table to be calculated, and the value of each preset field in the digital value table is obtained by utilizing the data acquisition unit and the ERP system monitoring unit; further, acquiring a value of a preset financial key index; and further obtaining the value of the digital value field in the digital value table by using the machine learning algorithm provided by the application.
The ERP system monitoring unit comprises:
step S201: presetting ERP monitoring index tables classified by 1 to N levels, wherein the fields of the ERP monitoring index tables of each level comprise, but are not limited to, N-level monitoring indexes, father-level monitoring indexes, time periods, N-level monitoring weights, N-level scores, N-level digital values, product labels, transaction labels, post labels, I labels, P labels and O labels, and presetting the values of the monitoring indexes, father-level monitoring indexes and monitoring weights of each level; wherein the parent level monitoring index of the nth level monitoring index is the nth-1 level monitoring index.
Step S202: obtaining all N-th fraction values in a preset period of an N-th ERP monitoring index table by utilizing the data acquired by the data acquisition unit and a preset calculation mode; further, according to the classification weighting summation of the father-level monitoring indexes of each N-level monitoring index, obtaining an N-1-level score of the N-1-level monitoring index; and by analogy, calculating the grade 1 value corresponding to the grade 1 monitoring index all the time.
Step S203: judging whether the data volume of the ERP monitoring index table is larger than a preset value; if yes, step S204 is entered, otherwise step S205 is entered.
Step S204: reading all the records of the N-th monitoring indexes, reading a record set of preset key financial indexes, entering a machine learning process from the record set of the digital value table in a preset format, and finally obtaining the values of the N-th digital values corresponding to all the N-th monitoring indexes in a preset period;
further, calculating the proportion of the value of each N-th data in the preset time period to the sum of all N-th data values of the corresponding father-level monitoring index classification in the preset time period, wherein the proportion is the N-th monitoring weight corrected in the preset time period;
further, according to the father-level monitoring index of each N-level monitoring index in the preset time period, classifying, weighting and summing the N-level data value to obtain the N-1-level data value of the N-1-level monitoring index in the preset time period; further, calculating the proportion of the value of each N-1-th stage data in the preset time period to the total value of the value of all N-1-th stage data in the corresponding father-stage monitoring index classification of the preset time period, wherein the proportion is the N-1-th stage monitoring weight after the correction of the preset time period;
and by analogy, calculating the level 1 data value corresponding to the level 1 monitoring index in the preset time period and the modified level 1 monitoring weight.
Step S205: according to the 1 st grade value of all the 1 st grade monitoring indexes and the 1 st grade monitoring weight value, weighting and summing, and calculating the total operation effect value of the ERP system; the total running effect value is compared with the related evaluation level of APICS, and the ERP implementation level of the reference APICS standard can be obtained.
Step S206: and (5) adding up the preset tag values of all the N-th monitoring indexes.
Step S207: a list of monitoring indicators classified by the aggregated tag values is output and used for effect analysis.
Step S208: and entering ERP effect monitoring of the next period, and keeping a continuous monitoring state.
In a second aspect, a digital value assessment method includes:
step S301: presetting a key financial index table and a digital value table.
Step S302: and acquiring values of preset fields in the digital value table from the data acquisition unit and the ERP system monitoring unit.
Step S303: step S303: and entering a machine learning process to obtain financial weights in the digital value table, and further calculating the value of the digital value.
Step S304: and outputting a preset digital value analysis table.
The machine learning process includes:
step 401: when the request from step S204 is received, the digital value table only needs to acquire all the data from the preset ERP monitoring standard index table; further reading all data of the key financial index table to obtain a large data set;
when requested from step S303, all data of the digital value table are acquired; further, all data of the key financial indexes are acquired, and a large data set is obtained.
Step 403: the data set is divided into a test set and a training set.
Step 404: and (5) using a training set to train the model, and optimizing the model parameters.
At step 406, model performance is evaluated using the test set.
And step 408, if the data fields reach the standard, obtaining the financial weight corresponding to each data field in the digital value table.
Step 409: further, a value of the digital value corresponding to each data field is calculated.
Compared with the prior art, the method has the following advantages:
the ERP system monitoring unit provided by the application is used for monitoring the ERP operation effect, has operability more than the MRPII/ERP evaluation level provided by APICS, is more beneficial to finding the digital value, and assists in digital transformation; the method also makes up the defect of automatic monitoring of the ERP operation effect technically, and simultaneously provides guidance for secondary development of ERP; in addition, the method overcomes the defects of unclear digital sources and digital value calculation of digital transformation and upgrading, and provides a direct data value basis for work performance assessment of personnel in a digital environment.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
fig. 1 is a schematic structural diagram of a digital value monitoring system according to an embodiment of the present application.
Fig. 2 is a flowchart of a method for an ERP system monitoring unit according to an embodiment of the present application.
Fig. 3 is a flowchart of a digital value evaluation method according to an embodiment of the present application.
Fig. 4 is a schematic diagram of a machine learning process for calculating digital value according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the accompanying drawings and examples. It will be apparent that the embodiments described are some, but not all, of the embodiments of the present application. All other embodiments, which are based on embodiments of the present application and which are not obtained without inventive effort, are within the scope of the present application.
In a first aspect, referring to fig. 1, a schematic diagram of a digital value monitoring system and a digital value evaluation method according to an embodiment of the present application includes a data acquisition unit, an ERP system monitoring unit, and a digital value calculation unit.
The data acquisition unit acquires data values of preset data fields from preset systems, including but not limited to an ERP system, a CRM system and an online mall platform; on the one hand, the data values of the equipment and the test measuring instrument are read from preset hardware interfaces including but not limited to serial interfaces using RS232 and RS485 standards and through preset communication protocols including but not limited to MODBUS communication protocols and TCP communication protocols; and providing data for the ERP system monitoring unit and the digital value calculating unit.
The specific steps of the embodiment of the ERP system monitoring unit, see fig. 2, include:
step S201: presetting ERP monitoring index tables classified in 1 to 3 layers, namely a 1 st level monitoring index table, a 2 nd level monitoring index table and a 3 rd level monitoring index table;
wherein the parent level monitoring index of the 3 rd level monitoring index is the 2 nd level monitoring index; the father level monitoring index of the 2 nd level monitoring index is the 1 st level monitoring index;
the monitoring indexes of each level in this embodiment are exemplary, and the preparation of the monitoring indexes of each level preferably refers to the related information of APICS and is prepared by combining with the self-management requirement of the enterprise;
in the embodiment, table 1 is a level 1 monitoring index table for monitoring the ERP operation effect; it should be noted that, the form data provided in this embodiment are all samples, and are preset according to the self-situation of the enterprise during actual application;
TABLE 1
The "period" in table 1 refers to a time segment of data calculation including, but not limited to, month, week, or day;
the "level 1 score" in table 1 is the weighted sum of the corresponding sub-level monitor indicators, i.e., "level 2 scores" in tables 2 and 3; the "1 st level digital value" is the weighted sum of the corresponding sub-level monitoring indexes, namely the "2 nd level digital value" of tables 2 and 3; the values of the level 1 monitoring index and the level 1 monitoring weight are preset values; the weight range is 0 to 1;
in the embodiment, table 2 and table 3 are 2 nd level monitoring index tables for monitoring ERP operation effect;
TABLE 2
TABLE 3 Table 3
In tables 2 and 3, the "parent level monitor index" is derived from the "level 1 monitor index" of table 1; the "level 2 score" in tables 2 and 3 is a weighted sum of the "level 3 scores" in tables 4 and 5, which are the corresponding sub-level monitor indicators; "level 2 digital value" is the weighted aggregate of the corresponding sub-level monitor indicators, i.e., "level 3 digital value" of tables 4, 5; the values of the level 2 monitoring index and the level 2 monitoring weight are preset values; the weight range is 0 to 1;
in the embodiment, table 4 and table 5 are level 3 monitoring index tables for monitoring ERP operation effect;
TABLE 4 Table 4
TABLE 5
In the examples, the "parent level monitor indicators" in tables 4 and 5 are derived from the "level 2 monitor indicator" in table 2 and the "level 2 monitor indicator" in table 3, respectively; the values of the 3 rd monitoring indexes and the 3 rd monitoring weights in the tables 4 and 5 are preset values; the weight range is 0 to 1;
in the examples, "I-tag" in tables 4 and 5 is an abbreviation of "input tag", and is given a value of 0 when the data source is manual input, otherwise, is 1; the I label is an important label for distinguishing informatization from digitalization; the P label is an abbreviation of the processing label and corresponds to the intermediate calculation and processing process of the monitoring index; when the processing is realized manually, the value is 0, otherwise, the processing is 1; the 'O label' is the abbreviation of the 'output label', the value of the output result is 0 when the output result needs to be written manually, and the value is 1 otherwise;
in the embodiment, the product label and the transaction label in table 4 and table 5 can only be selected from one, and the null value is 0; the post label has no null value; the product, the transaction and the monitoring index are in one-to-one relation, and the post and the monitoring index are in a many-to-one relation, namely, a plurality of posts correspond to one monitoring index; referring to table 6, the monitor index is "sales contract a" with a post label of "MK", which indicates that this monitor index corresponds to 2 posts: post M and post K.
Step S202: obtaining all 3 rd level scores in a preset period of a 3 rd level ERP monitoring index table by using the data acquired by the data acquisition unit and a preset calculation mode; the value range of the 3 rd grade value is 0 to 100; the calculation mode of the 3 rd fraction value can be realized by a person skilled in the art; further, according to the classification weighting summation of the father-level monitoring indexes of each level 3 monitoring index, obtaining a level 2 value of the level 2 monitoring index; and by analogy, calculating all the time to obtain a grade 1 value corresponding to the grade 1 monitoring index;
in an embodiment, table 6 is sample data of level 3 scores of level 3 monitor indicators and level 3 ERP monitor weights;
TABLE 6
Calculating a 2 nd fraction value to a 2 nd monitoring index according to table 6, see table 7;
TABLE 7
In table 7, the time period is 15 weeks, and the calculation process of the "level 2 score" of the level 2 monitoring index "service coverage-sales contract" is: 96×0.50+96×0.50=96; the calculation process of the 2 nd grade score of the 2 nd grade monitoring index of data accuracy rate-BOM accuracy rate is as follows: 85×0.55+90×0.45=87.25; in this embodiment, the tag values in table 7 are all null values;
similarly, calculating the 2 nd grade scores of all the corresponding 2 nd grade monitoring indexes according to the 3 rd grade scores and the 3 rd grade monitoring weights of all the 3 rd grade monitoring indexes; further, calculating the 1 st grade scores of all the 1 st grade monitoring indexes according to the 2 nd grade scores of all the 2 nd monitoring indexes and the 2 nd grade monitoring weights;
TABLE 8
Example table 8 is an example of the data of the finally obtained level 1 monitoring index.
Step S203: judging whether the record number of the 3 rd ERP monitoring index table is larger than a preset value or not; preferably, the preset value is larger than 1000, and the value is taken according to specific conditions in actual application, so that the machine learning modeling analysis can be satisfied; if yes, step S204 is entered, otherwise step S205 is entered.
Step S204: reading all the record sets of the 3 rd-level monitoring indexes, and reading the record sets of the preset key financial indexes to obtain a record set of a digital value table in a preset format, entering a machine learning process from the step S401 to the step S409, and finally obtaining the 3 rd-level digital value values corresponding to all the 3 rd-level monitoring indexes in the 15 th week of the present period in the table 6;
further, calculating the proportion of the value of each 3 rd level data in 15 weeks to the sum of all 3 rd level data values of the 15 weeks corresponding to the classification of the parent level monitoring indexes, wherein the proportion is the 3 rd level monitoring weight after 15 weeks of the period of correction, namely the value of the 3 rd level monitoring weight in the table 6;
further, according to the father-level monitoring index of each 3 rd-level monitoring index in 15 weeks of the current period, classifying, weighting and summing are carried out to obtain the 2 nd-level data value of the 2 nd-level monitoring index in 15 weeks of the current period; further, calculating the proportion of the value of each level 2 data in 15 weeks of the current period to the sum of the values of all level 2 data in the corresponding father level monitoring index classification of the current period, wherein the proportion is the level 2 monitoring weight after 15 weeks of the current period are corrected;
and by analogy, calculating the data value of the level 1 corresponding to the level 1 monitoring index and the modified level 1 monitoring weight until 15 weeks in the present period;
the machine learning process is described later with reference to this embodiment.
Step S205: according to the level 1 value of the level 1 monitoring index and the level 1 monitoring weight, weighting and summing, and calculating the total value of the running effect of the ERP system;
according to the embodiment, according to table 8, the total value of the ERP effect is calculated as follows:
92.06×0.30+80.58×0.70=84.02
in this embodiment, the total ERP effect value for 15 weeks is 84.02; the total running effect value is compared with the related evaluation level of APICS, and the ERP implementation level of the APICS standard can be obtained.
Step S206: adding up IPO label values of all the level 3 monitoring indexes; see table 9 for examples.
Step S207: outputting a monitoring index list classified by the aggregated tag values, and using the monitoring index list for effect analysis;
TABLE 9
Table 9 is an example of effect analysis from which more information can be analyzed in practical applications, e.g., improving human behavior criteria, requiring secondary development of ERP systems, requiring digitization, etc.
Step S208: and entering ERP effect monitoring of the next period, and keeping a continuous monitoring state.
On the other hand, referring to fig. 3, a flowchart of a digital value evaluation method is provided for an embodiment of the present application, including:
step S301: presetting a key financial index and a digital value table;
table 10
TABLE 11
In an embodiment, table 10 is a key financial index style; table 11 shows the patterns of the digital value sheets.
Step S302: acquiring values of relevant fields in the key financial index and the digital value table from a data acquisition unit or an ERP system monitoring unit; example table 12 is a data sample of key financial indicators for a period of time equal to 15 weeks, table 13 is a data sample of digital value tables for a period of time equal to 15 weeks;
table 12
TABLE 13
In Table 13, the "data field" is the data that is digitized to be monitored, and this embodiment provides but is not limited to 2 sources: (1) All the level 3 monitoring indicators from step S201, the "data field" is the "level 3 monitoring indicator" having a value in table 4 and table 5, the "data score" is the "level 3 score" having a value in table 4 and table 5, and the value of the "ERP monitoring tag" is equal to 1; (2) Other sources, including but not limited to online mall sales platforms, OA systems, etc., where the value of "ERP monitor tag" is equal to 0; the data fields of digital monitoring are increased and reduced according to the situation in the practical application;
the meaning of "product label, transaction label, post label, I label, P label, O label" in table 13 is referred to in the ERP system monitoring unit section of the present application;
the values of "financial weight-gross rate, financial weight-net rate" in example table 13 are from the financial weights of table 12; the data score weight-gross interest rate, data score weight-net interest rate and digital value are the data calculated in the step S303;
in addition, the embodiment tables 12 and 13 are part of data samples, and the data value table is a large data record set in practical application.
Step S303: a machine learning process is entered and a value of the digital value is calculated.
The machine learning process uses key financial indexes of table 12, namely, the gross rate and the net rate, as dependent variables, and the data score of table 13 as independent variables, and obtains the relation between the key financial indexes and the data score by using multiple linear regression analysis, namely, the data score weight-gross rate and the data score weight-net rate in table 13; modeling by using a multi-element linear prediction function, and estimating model parameters by minimizing a least square loss function; referring to fig. 4, the specific steps include:
step 401: when the request from step S204 is received, the digital value table reads the ERP monitoring index table, that is, the record set in which "ERP monitoring signature" in embodiment table 13 is equal to 1; further reading all historical data (all data of table 12) of the key financial indexes, and finally obtaining a large data set;
when the request from the step S303 is received, acquiring the required data in the digital value table from the data acquisition unit and the ERP system monitoring unit; further, all data of the key financial indicators (all data of table 12) are acquired, resulting in a large dataset.
Step 403: the data set is divided into a test set and a training set, preferably with a ratio of 70% to 30% of the test set to the record set.
Step 404: and (5) using a training set to train the model, and optimizing the model parameters.
At step 406, model performance is evaluated using the test set.
the calculation formula is as follows:
wherein:
(1) f (c) is a value of a financial index, and examples are a financial index value corresponding to the gross rate or a financial index value corresponding to the net rate in table 12;
(2) n is the record number of the present digital value table, example table 13, n=4;
(3) w is the weight sought by the multiple linear regression model, examples, namely "data score weight-gross interest rate" or "data score weight-net interest rate" of table 13;
(4) c is a data score, an example, a value of "data score" of Table 13;
step 409: calculating the value of the digital value corresponding to each data field;
the numerical value is calculated by the following formula:
wherein:
(1) v is the data value, example, the digital value column of Table 13;
(2) c is the value of the data score, in the example, the data score column of Table 13
(3) m is the number of financial indicators, example table 12, m=2;
(4) w is a data score weight corresponding to the data field, and in the embodiment, table 13 takes a value of "data score weight-gross interest rate" when "i=1", and takes a value of "data score weight-net interest rate" when "i=2";
(5) a is a financial weight, example, table 12, when "i=1", the financial weight of the gross interest rate is equal to 0.4; when "i=2", the financial weight of the net interest rate is equal to 0.6;
example, table 14 is the final data;
TABLE 14
\
Upon request from S204, the output digital value is used to correct the ERP monitoring weights for each level, see description of embodiment step S204.
Step S304: outputting the digital value table, one skilled in the art can further derive further extended analysis from table 14.
In summary, by using the digital value monitoring system, the whole application effect of the ERP can be monitored, the success rate of the ERP project is improved, and the improvement direction of the ERP system can be obtained; by utilizing the digital value evaluation method, visual direction guidance is provided for enterprise digital transformation, and a direct basis is provided for digital commercial value design; the application has the following beneficial effects and advantages:
(1) The application effect of the ERP can be monitored in real time through the digital value monitoring system, the improvement direction of informatization is provided, and powerful technical support is provided for converting informatization into digital transformation;
(2) The ERP system monitoring unit combines the key financial indexes of the enterprise, so that the ERP/MRPII grade evaluation provided by APICS is more in line with the specific situation of the enterprise, and the quantized indexes are more operable;
(3) Through the label and the digital value analysis in the ERP system monitoring unit, the digital loss in informatization can be found, and direct guidance is provided for converting informatization into digital transformation;
(4) By the digital value evaluation method provided by the application, the composition of the digital value can be quickly found by utilizing the digital value directly provided by each data field, and direct guidance is provided for the reconstruction of the digitized commercial value;
(5) By the digital value evaluation method, the label analysis of the output digital value table can conveniently find the data, the positions and the processes which need to be digitalized, and can find the optimization direction of process improvement and intelligent manufacturing;
the foregoing detailed description is of the preferred embodiment of the present application and is not intended to limit the invention thereto, but rather to cover any and all modifications, equivalents, and alternatives falling within the scope of the present application.
Claims (5)
1. A digital value monitoring system, comprising: the system comprises a data acquisition unit, an ERP system monitoring unit and a digital value calculation unit;
the data acquisition unit acquires related data from a preset information system through a software interface on one hand; on one hand, through a preset hardware interface, data of equipment or a measuring instrument are collected by utilizing a preset communication protocol; providing data for an ERP system monitoring unit and a digital value calculating unit;
the ERP system monitoring unit presets the N-level monitoring index table and the weight of each monitoring index, acquires the value of a preset field from the data acquisition unit, and further judges whether to enter the utilization formula according to the acquired monitoring data quantity
And formula->
Correcting the ERP monitoring weight in the machine learning process, finally obtaining the total value of the running effect of the ERP system, and outputting a preset monitoring index list; wherein f (c) is the value of the financial index, n is the record number of the digital value table in the current period, w is the weight of the data score, and c is the data score; v is the data value, m is the number of financial indexes, and a is the financial weight;
the digital value calculation unit is used for presetting a digital value table to be calculated, and acquiring the value of each preset field in the digital value table by utilizing the data acquisition unit and the ERP system monitoring unit; further, acquiring a value of a preset financial key index; still further utilize the formula
And formula->
And (3) obtaining the value of the digital value field in the digital value table.
2. The digital value monitoring system of claim 1, wherein the ERP system monitoring unit comprises:
step S201: presetting ERP monitoring index tables classified by 1 to N levels, wherein the fields of the ERP monitoring index tables of each level comprise N-th monitoring indexes, father-level monitoring indexes, time periods, N-th monitoring weights, N-th scores, N-th digital values and preset analysis labels, and presetting the values of the monitoring indexes, father-level monitoring indexes and monitoring weights of each level; the father level monitoring index of the N level monitoring index is the N-1 level monitoring index;
step S202: obtaining all N-th fraction values in a preset period of an N-th ERP monitoring index table by utilizing the data acquired by the data acquisition unit and a preset calculation mode; further, according to the classification weighting summation of the father-level monitoring indexes of each N-level monitoring index, obtaining an N-1-level score of the N-1-level monitoring index; and by analogy, calculating all the time to obtain a grade 1 value corresponding to the grade 1 monitoring index;
step S203: judging whether the data volume of the ERP monitoring index table is larger than a preset value; if yes, go to step S204, otherwise go to step S205;
step S204: reading all records of N-th monitoring indexes, reading a record set of preset key financial indexes, entering a digital value table record set of a preset format, and entering a utilization formula
And formula->
Finally, the value of the Nth digital value corresponding to all the N-th monitoring indexes in the current period can be obtained; wherein f (c) is the value of the financial index, n is the record number of the digital value table in the current period, w is the weight of the data score, and c is the data score; v is the data value, m is the number of financial indexes, and a is the financial weight;
further, calculating the proportion of the value of each N-th data in the preset time period to the sum of all N-th data values of the corresponding father-level monitoring index classification in the preset time period, wherein the proportion is the N-th monitoring weight after the current period correction;
further, according to the father-level monitoring index of each N-level monitoring index in the preset time period, classifying, weighting and summing the N-level data value to obtain the N-1-level data value of the N-1-level monitoring index in the preset time period; further, calculating the proportion of the value of each N-1-th stage data in the preset time period to the total value of the value of all N-1-th stage data in the corresponding father-stage monitoring index classification of the preset time period, wherein the proportion is the N-1-th stage monitoring weight after the correction of the preset time period;
and the same is said to be true, the data value of the 1 st stage corresponding to the 1 st stage monitoring index in the preset time period and the modified 1 st stage monitoring weight are all calculated;
step S205: according to the 1 st grade value of all the 1 st grade monitoring indexes and the 1 st grade monitoring weight value, weighting and summing, and calculating the total operation effect value of the ERP system; comparing the total running effect value with the related evaluation level of APICS to obtain an ERP implementation level of the reference APICS standard;
step S206: adding up preset label values of all N-th monitoring indexes;
step S207: outputting a monitoring index list classified by the aggregated tag values, and using the monitoring index list for effect analysis;
step S208: and entering ERP effect monitoring of the next period, and keeping a continuous monitoring state.
3. The digital value monitoring system of claim 2, wherein the machine learning process comprises:
step 401: when the request from step S204 is received, the digital value table only needs to acquire all the data from the preset ERP monitoring standard index table; further reading all data of the key financial index table to obtain a large data set;
step 402, preprocessing a data set;
step 403: dividing the data set into a test set and a training set;
step 404: optimizing model parameters by using a training set training model;
step 405, obtaining an optimized model;
step 406, evaluating model performance using the test set;
step 407, judging whether the performance meets the standard, if not, returning to step 404;
step 408, if the standard is reached, obtaining the financial weight corresponding to each data field in the digital value table; the calculation formula is as follows:
wherein f (c) is the value of the financial index, n is the record number of the digital value table in the current period, w is the weight of the data score, and c is the data score;
step 409: further, calculating a value of the digital value corresponding to each data field; the numerical value is calculated by the following formula:
wherein: wherein w is a data score weight and c is a data score; v is the data value, m is the number of financial indicators, and a is the financial weight.
4. A method of digital value assessment, comprising:
step S301: presetting a key financial index table and a digital value table;
step S302: acquiring values of preset fields in a digital value table from a data acquisition unit and an ERP system monitoring unit;
step S303: enter into the utilization formula
And formula->
Obtaining financial weights in the digital value table, and further calculating the value of the digital value; wherein f (c) is the value of the financial index, n is the record number of the digital value table in the current period, w is the weight of the data score, and c is the data score; v is the data value, m is the number of financial indexes, and a is the financial weight;
step S304: and outputting a preset digital value analysis table.
5. The digitized value assessment method of claim 4 wherein said machine learning process comprises:
step 401: when requested from step S303, all data of the digital value table are acquired; further, acquiring all data of the key financial indexes to obtain a large data set;
step 402, preprocessing a data set;
step 403: dividing the data set into a test set and a training set;
step 404: optimizing model parameters by using a training set training model;
step 405, obtaining an optimized model;
step 406, evaluating model performance using the test set;
step 407, judging whether the performance meets the standard, if not, returning to step 404;
step 408, if the standard is reached, obtaining the financial weight corresponding to each data field in the digital value table; the calculation formula is as follows:
wherein f (c) is the value of the financial index, n is the record number of the digital value table in the current period, w is the weight of the data score, and c is the data score;
step 409: further, calculating a value of the digital value corresponding to each data field; the numerical value is calculated by the following formula:
wherein: wherein w is a data score weight and c is a data score; v is the data value, m is the number of financial indicators, and a is the financial weight.
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