CN112183920A - Industrial product optimal cost method and device based on analytic hierarchy process - Google Patents
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Abstract
The invention discloses an industrial product optimal cost method and device based on an analytic hierarchy process, wherein the method comprises the following steps: constructing an analysis index database which comprises a plurality of analysis index levels and corresponding analysis indexes; acquiring a demand construction evaluation model of a demand side, wherein the evaluation model comprises a plurality of analysis index levels and analysis indexes; comparing the importance of the analysis indexes step by step; step-by-step constructing an analysis index judgment matrix for the analysis index stages, judging whether the analysis index stages meet the condition of acceptable consistency, acquiring scoring rules and scores of all analysis indexes, and weighting to obtain effect scores; adding the effect scores of the analysis indexes to obtain the final evaluation score of each supplier; and sorting the final evaluation scores of all the suppliers according to a sorting rule. According to the invention, the analysis index database is constructed in advance, and the corresponding evaluation model is selected according to the specific situation of the demand side for rapid and accurate judgment, so that the accuracy and the speed of evaluation of suppliers are improved.
Description
Technical Field
The application relates to the technical field of computer analysis, in particular to an industrial product optimal cost analysis device based on an analytic hierarchy process.
Background
In the field of supply chains, after a demand side puts forward a part processing demand, the cost of an industrial product of a part processing manufacturer is often evaluated, and a common method is to quantitatively evaluate the cost of the industrial product of the part processing manufacturer from a single dimension; or simply to evaluate its value from a combination of several dimensions. For example, the cost of the industrial product of each processing factory is evaluated for a certain non-standard component.
The existing scheme comprises a splitting method and a simple comprehensive weighting method. The splitting method separates out a plurality of analysis indexes for evaluation and comparison. For example, the processing capacity and price are evaluated and several results are ranked against their merits. Carrying out a series of decomposition scoring on the analysis indexes by a simple comprehensive weighting method, and then obtaining a comprehensive score of the industrial product cost influence factor by simple weighted synthesis; by analogy, other analysis indexes also obtain final scores, and then the final scores are ranked and compared to evaluate the optimal cost influence factors of the promoted industrial products.
However, both of these solutions have the following disadvantages: 1) dimensionless: the scores of the existing industrial product cost influence factors are calculated based on absolute quantities or relative quantities to obtain quantity values, and the quantity and unit quantification of different industrial product cost influence factors are differentiated (namely, the quantity values are different, comparability does not exist in different data levels, and the calculated scales are inconsistent). 2) Not accurate enough: different industrial product cost influence factors show different effects in each index, if each index is simply given a weighted value to be graded or a certain index is taken for comparison, the accuracy is not enough, and the optimal industrial product cost influence factor cannot be effectively evaluated and popularized. 3) The repeated utilization rate is low, the evaluation structure is strong in arbitrariness, independent evaluation is carried out on different demanders, and the visual utilization rate of the evaluation data is low.
Disclosure of Invention
The application aims to provide a more scientific and accurate industrial product optimal cost analysis device based on an analytic hierarchy process.
One of the objectives of the present invention is to provide an analytic hierarchy process based method for optimizing cost of industrial products, which comprises:
constructing an analysis index database for evaluating the cost of industrial products, wherein analysis indexes in the analysis index database are divided into a plurality of analysis index levels, and each analysis index level comprises a plurality of analysis indexes;
acquiring the demand of a demand party on the cost of industrial products to construct an evaluation model, matching the demand with analysis indexes in an analysis index database step by step, screening or reconstructing the analysis index level of the demand party and the analysis index of each analysis index level, and storing the newly added analysis index in the analysis index database;
comparing the importance of the analysis indexes step by step;
constructing an analysis index judgment matrix for evaluating the cost of the industrial product step by step for the analysis index stages, and judging that the analysis index judgment matrix meets the condition of acceptable consistency;
obtaining the grading detailed rule and the grading of each analysis index and weighting to obtain an effect score;
adding the effect scores of the analysis indexes to obtain the final evaluation score of each supplier;
and sorting the final evaluation scores of all the suppliers according to a sorting rule.
Further, in the method for analyzing the optimal cost of the industrial product based on the analytic hierarchy process, the importance measure of the analysis index is represented by natural numbers 1 to 9, 1 represents that one of the two analysis indexes compared with each other has the same relative importance degree with respect to the other analysis index, 9 represents that one of the two factors compared with each other has the largest relative importance degree with respect to the other analysis index, and vice versa.
Further, in the method for analyzing the optimal cost of the industrial product based on the analytic hierarchy process, the method for judging whether the judgment matrix meets the condition of acceptable consistency is as follows:
a, carrying out judgment matrix normalization processing on the analysis indexes step by step, wherein the normalization method is to sum each row of the judgment matrix and calculate the proportion of each row of data in the sum;
b, calculating the average of each row in the normalized judgment matrix, namely the weight of each analysis index of the supplier;
c, calculating an approximate solution of the feature vector;
d, calculating the maximum characteristic root of the judgment matrix;
calculating the consistency index value of the judgment matrix;
f, confirming an average random consistency index RI;
g, calculating the random consistency proportion of the judgment matrix;
h, judging that the judgment matrix meets the condition of acceptable consistency;
and I, calculating the weight coefficient of the analysis indexes to the total indexes in each analysis index stage step by step according to the analysis index stages so as to obtain a weight table of the evaluation indexes selected by the supplier, wherein the sum of the weight coefficients of all the analysis indexes to the total target in each analysis index stage is 1.
Further, in the method for analyzing the optimal cost of the industrial product based on the analytic hierarchy process, the weight coefficient corresponding to the analytic index in each analytic index level and the weight coefficient corresponding to the total target of each analytic index in each lowest analytic index level are set up a fine scoring rule and a score for each lowest analytic index at least, each fine scoring rule is subdivided into a plurality of check items, corresponding scoring is obtained when the scoring is achieved, poor or no relevant record is achieved, the score is 0, the effect scores of all the lowest analytic indexes of the supplier are obtained, and the effect scores are summed and sorted to obtain the cost ranking of the industrial product.
Another object of the present invention is to provide an apparatus for analyzing an optimal cost of an industrial product based on an analytic hierarchy process, the apparatus comprising:
the analysis index database module is used for acquiring analysis indexes and storing the analysis index levels and the analysis indexes of each analysis index level;
the evaluation model building module is used for building an evaluation model according to the demand of a demand side, the evaluation model comprises a preset evaluation model and an individualized evaluation model, and the preset evaluation model and the individualized evaluation model comprise a plurality of analysis index levels and analysis indexes of each analysis index level;
the weight coefficient generation module is used for constructing an importance metric pairwise comparison matrix table of the analysis indexes in each analysis index level, calculating the weight coefficient corresponding to each analysis index in each analysis index level to a higher-level analysis index according to a normalization algorithm, and at least comprising the weight coefficient corresponding to each analysis index in the lowest-level analysis index level to a total index;
the effect score generation module is used for acquiring the weight coefficient of each analysis index in the lowest analysis index level and the score of each analysis index in the lowest analysis index level corresponding to each supplier, and calculating the effect score of each analysis index in the lowest analysis index level;
and the evaluation result acquisition module is used for adding the effect scores of all the analysis indexes in the lowest analysis index level to obtain the final evaluation score of each supplier.
Further, in the device for analyzing optimal cost of industrial products based on an analytic hierarchy process of the present invention, the weight coefficient generating module includes:
and the pairwise comparison matrix table construction module is used for constructing two pairs of comparison matrix tables of analysis indexes step by step according to the analysis index level, wherein the matrix elements are relative importance coefficients obtained after the pairwise comparison of the two analysis indexes corresponding to the matrix elements in the matrix tables is relatively important.
Further, in the device for analyzing optimal cost of industrial products based on an analytic hierarchy process of the present invention, the weight coefficient generation module further includes:
the normalization module is used for normalizing the relative importance coefficients in the sub-target layer pair comparison matrix table and the index layer pair comparison matrix table according to columns;
the summing module is used for summing the normalized analysis index level pairwise comparison matrix table and the index layer pairwise comparison matrix table according to rows to obtain a characteristic vector;
and the weight calculation module is used for dividing the feature vector by the total number of the analysis indexes of the above levels to obtain a weight coefficient corresponding to the analysis index of each analysis index level in each analysis index level and each weight coefficient.
Further, in the device for analyzing the optimal cost of industrial products based on the analytic hierarchy process of the present invention, the evaluation result obtaining module includes:
and the sequencing module is used for sequencing the final evaluation scores of the analysis indexes of the suppliers to obtain the industrial product cost sequencing.
Furthermore, in the industrial product optimal cost analysis device based on the analytic hierarchy process, the evaluation model construction module comprises the analysis index levels and the analysis indexes of each analysis index level constructed according to the overall requirements of the purchasing industry, so that a demand party can carry out fuzzy evaluation and judgment, wherein the analysis indexes comprise one or a combination of several of material quality level, processing technology, delivery requirements, risks and design requirements; the personalized customized evaluation model constructs analysis index levels and analysis indexes of each analysis index level according to specific requirement parameters of a demand side.
Further, in the device for analyzing the optimal cost of the industrial product based on the analytic hierarchy process, the supplier can be among a plurality of new suppliers of a demand party to find a supplier meeting the requirement; the system can also be a plurality of new suppliers and a plurality of existing suppliers of the demand side so as to improve the list of the suppliers and improve the management efficiency of the supply chain of the demand side.
Another object of the present invention is to provide an apparatus for analyzing an optimal cost of an industrial product based on an analytic hierarchy process, the apparatus comprising:
the analysis index database module is used for acquiring analysis indexes and storing the analysis index levels and the analysis indexes of each analysis index level;
the evaluation model building module is used for building an evaluation model according to the demand of a demand side, the evaluation model comprises a preset evaluation model and an individualized evaluation model, and the preset evaluation model and the individualized evaluation model comprise a plurality of analysis index levels and analysis indexes of each analysis index level;
the weight coefficient generation module is used for constructing an importance metric pairwise comparison matrix table of the analysis indexes in each analysis index level, calculating the weight coefficient corresponding to each analysis index in each analysis index level to a higher-level analysis index according to a normalization algorithm, and at least comprising the weight coefficient corresponding to each analysis index in the lowest-level analysis index level to a total index;
the effect score generation module is used for acquiring the weight coefficient of each analysis index in the lowest analysis index level and the score of each analysis index in the lowest analysis index level corresponding to each supplier, and calculating the effect score of each analysis index in the lowest analysis index level;
and the evaluation result acquisition module is used for adding the effect scores of all the analysis indexes in the lowest analysis index level to obtain the final evaluation score of each supplier.
Further, in the device for analyzing optimal cost of industrial products based on an analytic hierarchy process of the present invention, the weight coefficient generating module includes:
and the pairwise comparison matrix table construction module is used for constructing two pairs of comparison matrix tables of analysis indexes step by step according to the analysis index level, wherein the matrix elements are relative importance coefficients obtained after the pairwise comparison of the two analysis indexes corresponding to the matrix elements in the matrix tables is relatively important.
Further, in the device for analyzing optimal cost of industrial products based on an analytic hierarchy process of the present invention, the weight coefficient generation module further includes:
the normalization module is used for normalizing the relative importance coefficients in the sub-target layer pair comparison matrix table and the index layer pair comparison matrix table according to columns;
the summing module is used for summing the normalized analysis index level pairwise comparison matrix table and the index layer pairwise comparison matrix table according to rows to obtain a characteristic vector;
and the weight calculation module is used for dividing the feature vector by the total number of the analysis indexes of the above levels to obtain a weight coefficient corresponding to the analysis index of each analysis index level in each analysis index level and each weight coefficient.
Further, in the device for analyzing the optimal cost of industrial products based on the analytic hierarchy process of the present invention, the evaluation result obtaining module includes:
and the sequencing module is used for sequencing the final evaluation scores of the analysis indexes of the suppliers to obtain the industrial product cost sequencing.
Furthermore, in the industrial product optimal cost analysis device based on the analytic hierarchy process, the evaluation model construction module comprises the analysis index levels and the analysis indexes of each analysis index level constructed according to the overall requirements of the purchasing industry, so that a demand party can carry out fuzzy evaluation and judgment, wherein the analysis indexes comprise one or a combination of several of material quality level, processing technology, delivery requirements, risks and design requirements; the personalized customized evaluation model constructs analysis index levels and analysis indexes of each analysis index level according to specific requirement parameters of a demand side.
Further, in the device for analyzing the optimal cost of the industrial product based on the analytic hierarchy process, the supplier can be among a plurality of new suppliers of a demand party to find a supplier meeting the requirement; the system can also be a plurality of new suppliers and a plurality of existing suppliers of the demand side so as to improve the list of the suppliers and improve the management efficiency of the supply chain of the demand side.
Compared with the prior art, the method and the system have the advantages that the analysis index database is constructed in advance, various evaluation models are adopted to evaluate suppliers, the corresponding evaluation models are selected according to the specific conditions of the demand side to judge quickly and accurately, and the accuracy and the speed of evaluation of the suppliers are improved. The method comprises the steps of decomposing a total index of influence factors influencing the cost of the industrial product into a plurality of analysis indexes (establishing analysis index levels), further decomposing each lower analysis index level into a next lower analysis index level, constructing a pairwise comparison matrix table of each analysis index level to obtain a weight coefficient of each analysis index, then obtaining effect scores of all the lowest analysis indexes according to the effect scores and corresponding weight coefficients of the lowest analysis indexes of all suppliers, and finally adding the effect scores of all the lowest analysis indexes of all the suppliers to obtain a final evaluation score of each supplier. The method is based on an analytic hierarchy process on the whole, so that the evaluation result can be more scientific and accurate.
Drawings
FIG. 1 is a schematic flow chart of the analytic hierarchy process based industrial product optimal cost method of the present invention.
FIG. 2 is a schematic structural diagram of an industrial product optimal cost analysis device based on an analytic hierarchy process according to the present invention.
FIG. 3 is a weight table of evaluation indexes selected by new suppliers of the optimum cost analysis device for industrial products based on the analytic hierarchy process.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description is provided in conjunction with fig. 1 to 3.
An analytic hierarchy process based industrial product optimal cost method comprises the following steps:
constructing an analysis index database for evaluating the cost of industrial products, wherein analysis indexes in the analysis index database are divided into a plurality of analysis index levels, and each analysis index level comprises a plurality of analysis indexes;
acquiring the demand of a demand party on the cost of industrial products to construct an evaluation model, matching the demand with analysis indexes in an analysis index database step by step, screening or reconstructing the analysis index level of the demand party and the analysis index of each analysis index level, and storing the newly added analysis index in the analysis index database;
comparing the importance of the analysis indexes step by step;
constructing an analysis index judgment matrix for evaluating the cost of the industrial product step by step for the analysis index stages, and judging that the analysis index judgment matrix meets the condition of acceptable consistency;
obtaining the grading detailed rule and the grading of each analysis index and weighting to obtain an effect score;
adding the effect scores of the analysis indexes to obtain the final evaluation score of each supplier;
and sorting the final evaluation scores of all the suppliers according to a sorting rule.
Further, in the method for analyzing the optimal cost of the industrial product based on the analytic hierarchy process, the importance measure of the analysis index is represented by natural numbers 1 to 9, 1 represents that one of the two analysis indexes compared with each other has the same relative importance degree with respect to the other analysis index, 9 represents that one of the two factors compared with each other has the largest relative importance degree with respect to the other analysis index, and vice versa.
Further, in the method for analyzing the optimal cost of the industrial product based on the analytic hierarchy process, the method for judging whether the judgment matrix meets the condition of acceptable consistency is as follows:
a, carrying out judgment matrix normalization processing on the analysis indexes step by step, wherein the normalization method is to sum each row of the judgment matrix and calculate the proportion of each row of data in the sum;
b, calculating the average of each row in the normalized judgment matrix, namely the weight of each analysis index of the supplier;
c, calculating an approximate solution of the feature vector;
d, calculating the maximum characteristic root of the judgment matrix;
calculating the consistency index value of the judgment matrix;
f, confirming an average random consistency index RI;
g, calculating the random consistency proportion of the judgment matrix;
h, judging that the judgment matrix meets the condition of acceptable consistency;
and I, calculating the weight coefficient of the analysis indexes to the total indexes in each analysis index stage step by step according to the analysis index stages so as to obtain a weight table of the evaluation indexes selected by the supplier, wherein the sum of the weight coefficients of all the analysis indexes to the total target in each analysis index stage is 1.
Further, in the method for analyzing the optimal cost of the industrial product based on the analytic hierarchy process, the weight coefficient corresponding to the analytic index in each analytic index level and the weight coefficient corresponding to the total target of each analytic index in each lowest analytic index level are set up a fine scoring rule and a score for each lowest analytic index at least, each fine scoring rule is subdivided into a plurality of check items, corresponding scoring is obtained when the scoring is achieved, poor or no relevant record is achieved, the score is 0, the effect scores of all the lowest analytic indexes of the supplier are obtained, and the effect scores are summed and sorted to obtain the cost ranking of the industrial product.
Another object of the present invention is to provide an apparatus for analyzing an optimal cost of an industrial product based on an analytic hierarchy process, the apparatus comprising:
the analysis index database module is used for acquiring analysis indexes and storing the analysis index levels and the analysis indexes of each analysis index level;
the evaluation model building module is used for building an evaluation model according to the demand of a demand side, the evaluation model comprises a preset evaluation model and an individualized evaluation model, and the preset evaluation model and the individualized evaluation model comprise a plurality of analysis index levels and analysis indexes of each analysis index level;
the weight coefficient generation module is used for constructing an importance metric pairwise comparison matrix table of the analysis indexes in each analysis index level, calculating the weight coefficient corresponding to each analysis index in each analysis index level to a higher-level analysis index according to a normalization algorithm, and at least comprising the weight coefficient corresponding to each analysis index in the lowest-level analysis index level to a total index;
the effect score generation module is used for acquiring the weight coefficient of each analysis index in the lowest analysis index level and the score of each analysis index in the lowest analysis index level corresponding to each supplier, and calculating the effect score of each analysis index in the lowest analysis index level;
and the evaluation result acquisition module is used for adding the effect scores of all the analysis indexes in the lowest analysis index level to obtain the final evaluation score of each supplier.
Further, in the device for analyzing optimal cost of industrial products based on an analytic hierarchy process of the present invention, the weight coefficient generating module includes:
and the pairwise comparison matrix table construction module is used for constructing two pairs of comparison matrix tables of analysis indexes step by step according to the analysis index level, wherein the matrix elements are relative importance coefficients obtained after the pairwise comparison of the two analysis indexes corresponding to the matrix elements in the matrix tables is relatively important.
Further, in the device for analyzing optimal cost of industrial products based on an analytic hierarchy process of the present invention, the weight coefficient generation module further includes:
the normalization module is used for normalizing the relative importance coefficients in the sub-target layer pair comparison matrix table and the index layer pair comparison matrix table according to columns;
the summing module is used for summing the normalized analysis index level pairwise comparison matrix table and the index layer pairwise comparison matrix table according to rows to obtain a characteristic vector;
and the weight calculation module is used for dividing the feature vector by the total number of the analysis indexes of the above levels to obtain a weight coefficient corresponding to the analysis index of each analysis index level in each analysis index level and each weight coefficient.
Further, in the device for analyzing the optimal cost of industrial products based on the analytic hierarchy process of the present invention, the evaluation result obtaining module includes:
and the sequencing module is used for sequencing the final evaluation scores of the analysis indexes of the suppliers to obtain the industrial product cost sequencing.
Furthermore, in the industrial product optimal cost analysis device based on the analytic hierarchy process, the evaluation model construction module comprises the analysis index levels and the analysis indexes of each analysis index level constructed according to the overall requirements of the purchasing industry, so that a demand party can carry out fuzzy evaluation and judgment, wherein the analysis indexes comprise one or a combination of several of material quality level, processing technology, delivery requirements, risks and design requirements; the personalized customized evaluation model constructs analysis index levels and analysis indexes of each analysis index level according to specific requirement parameters of a demand side.
Further, in the device for analyzing the optimal cost of the industrial product based on the analytic hierarchy process, the supplier can be among a plurality of new suppliers of a demand party to find a supplier meeting the requirement; the system can also be a plurality of new suppliers and a plurality of existing suppliers of the demand side so as to improve the list of the suppliers and improve the management efficiency of the supply chain of the demand side.
The invention takes the alternative suppliers which seek strategic materials by the demand party as the specific implementation case to explain the evaluation system module and the evaluation method of the suppliers in detail as follows: the demand side needs two key parts of the operating table, mainly supports the main body structure of the operating table, has very high safety level requirements, belongs to typical strategic materials, and is mainly cooperated by two suppliers, namely a supplier A and a supplier B. In the process of cooperation with the supplier B, the quality part and the material planning part continuously feed back the quality and delivery problems, particularly, the two times of line stop caused by poor quality exists, so that the demander has to change the originally shipped goods into emergency air transportation, and the air transportation cost is about 20 ten thousand more. The demander therefore also intends to find a new supplier to replace the B supplier.
The method comprises the following steps that firstly, a demander panel screens a material quality level, a processing technology, a delivery requirement, a risk and design requirement primary analysis index and a related secondary index from an analysis index database according to the requirement of a demander as analysis indexes of the demander, and compares the material quality level, the processing technology, the delivery requirement, the risk and the design requirement in pairs by using a nine-part method measurement to construct an analysis index judgment matrix table suitable for the actual situation of the Micheley company: table 1 analysis index metrics are as follows:
with the participation of the expert team, the judgment matrix of the 5 indexes evaluated by the new supplier is as follows, and the details are shown in the table 2:
and (3) carrying out normalization processing on the new supplier evaluation primary index judgment matrix table in the table 2. The normalization method is to sum up each column of the decision matrix and calculate the ratio of each column of data in the summation. The formula is as followsij=bij/bij(i, j =1, 2, 3.. n), the normalized calculation according to the formula is as follows: the normalized calculation according to the formula is as follows:
and calculating the average of each row in the normalized judgment matrix, namely the weight of the new supplier evaluation primary index. The calculation formula is as follows:
，the index weight matrix calculation result is:
computing an approximate solution to the feature vector:
calculating the maximum characteristic root of the judgment matrix:
。
calculating a consistency index value of the judgment matrix:
the average random consistency index is found in table 3 below, where RI =1.12,
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| | 0 | 0 | 0.58 | 0.9 | 1.12 | 1.24 | 1.32 | 1.41 | 1.46 |
calculating the random consistency proportion of the judgment matrix:
， 
，indicating that the decision matrix meets the acceptable consistency condition. The weights of the corresponding 5 items of first-level evaluation indexes are shown in the table 4 below as the weights of the selected evaluation first-level indexes of new suppliers of the demand parties:
the second step is that: and respectively judging the judgment matrixes of the secondary indexes under 5 primary indexes, judging that the judgment matrixes meet the condition of acceptable consistency, and obtaining a judgment result of the weight of the secondary indexes under quality control by referring to the judgment matrixes of the primary indexes, the weight calculation method and the primary index consistency check process. The first-level indexes comprise five aspects of material quality level, processing technology, delivery requirements, risks and design requirements. Wherein the secondary indexes of the material quality level are a quality control system, incoming material quality control, production process quality control and shipment quality control; the secondary indexes of the processing technology are a manufacturing information system, a production technical level and a personnel technical level; the secondary indexes of delivery requirements are on-time delivery level, delivery accuracy, delivery elasticity and the secondary indexes of risks are price competitiveness, cost analysis capability, value engineering value analysis and company financial condition; the secondary indexes of the design requirement are the productivity utilization rate, equipment diversity, engineering change control level and management team cooperative willingness.
The third step: and calculating the weight of the secondary index to the total target according to the primary index weight and the secondary index weight. Thus, a weight table of the new supplier selection evaluation index of the michomol company is shown in fig. 3 (table 5. the new supplier selection evaluation index weight table of the demand side).
And (3) formulating a scoring rule of the new supplier selection evaluation index: in order to make the evaluation indexes selected by new suppliers more operable and integrate the industry characteristics and the requirements of demanders, an expert team sets up scoring rules for 19 secondary indexes. Each secondary index is set to 10 points and subdivided into several examination terms and scoring criteria. And if the project is achieved, obtaining a corresponding score, and if the project is achieved poorly or no relevant records exist, obtaining an evaluation result, wherein the score is 0.
According to the evaluation results, the A, C, F supplier scores were all greater than the B supplier score of 5.73. The panel therefore recommends eliminating the B supplier and selecting the C supplier as the replacement supplier for cultivation. This result is in agreement with the demander demand expectation.
Compared with the prior art, the method and the system have the advantages that the analysis index database is constructed in advance, various evaluation models are adopted to evaluate suppliers, the corresponding evaluation models are selected according to the specific conditions of the demand side to judge quickly and accurately, and the accuracy and the speed of evaluation of the suppliers are improved. The method comprises the steps of decomposing a total index of influence factors influencing the cost of the industrial product into a plurality of analysis indexes (establishing analysis index levels), further decomposing each lower analysis index level into a next lower analysis index level, constructing a pairwise comparison matrix table of each analysis index level to obtain a weight coefficient of each analysis index, then obtaining effect scores of all the lowest analysis indexes according to the effect scores and corresponding weight coefficients of the lowest analysis indexes of all suppliers, and finally adding the effect scores of all the lowest analysis indexes of all the suppliers to obtain a final evaluation score of each supplier. The method is based on an analytic hierarchy process on the whole, so that the evaluation result can be more scientific and accurate.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
Although the present application has been described with reference to a preferred embodiment, it should be understood that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the application, and all changes, substitutions and alterations that fall within the spirit and scope of the application are to be understood as being included within the following description of the preferred embodiment.
Claims (10)
1. An analytic hierarchy process based industrial product optimal cost method is characterized in that the method comprises the following steps:
constructing an analysis index database for evaluating the cost of industrial products, wherein analysis indexes in the analysis index database are divided into a plurality of analysis index levels, and each analysis index level comprises a plurality of analysis indexes;
acquiring the demand of a demand party on the cost of industrial products to construct an evaluation model, matching the demand with analysis indexes in an analysis index database step by step, screening or reconstructing the analysis index level of the demand party and the analysis index of each analysis index level, and storing the newly added analysis index in the analysis index database;
comparing the importance of the analysis indexes step by step;
constructing an analysis index judgment matrix for evaluating the cost of the industrial product step by step for the analysis index stages, and judging that the analysis index judgment matrix meets the condition of acceptable consistency;
obtaining the grading detailed rule and the grading of each analysis index and weighting to obtain an effect score;
adding the effect scores of the analysis indexes to obtain the final evaluation score of each supplier;
and sequencing the final evaluation scores of the cost of each industrial product according to a sequencing rule.
2. The analytic hierarchy process-based industrial optimum cost method of claim 1, wherein: the importance measure of the analysis indexes is represented by natural numbers 1 to 9, wherein 1 represents that one of the two analysis indexes compared with each other has the same relative importance degree relative to the other analysis index, 9 represents that one of the two factors compared with each other has the maximum relative importance degree relative to the other analysis index, and the other factor is represented by reciprocal.
3. The analytic hierarchy process-based industrial product optimal cost method of claim 2, wherein the method of judging whether the judgment matrix satisfies the condition of acceptable consistency is as follows:
a, carrying out judgment matrix normalization processing on the analysis indexes step by step, wherein the normalization method is to sum each row of the judgment matrix and calculate the proportion of each row of data in the sum;
b, calculating the average of each row in the normalized judgment matrix, namely the weight of each analysis index of the supplier;
c, calculating an approximate solution of the feature vector;
d, calculating the maximum characteristic root of the judgment matrix;
calculating the consistency index value of the judgment matrix;
f, confirming an average random consistency index RI;
g, calculating the random consistency proportion of the judgment matrix;
h, judging that the judgment matrix meets the condition of acceptable consistency;
and I, calculating the weight coefficient of the analysis indexes to the total indexes in each analysis index stage step by step according to the analysis index stages so as to obtain a weight table of the evaluation indexes selected by the supplier, wherein the sum of the weight coefficients of all the analysis indexes to the total target in each analysis index stage is 1.
4. The analytic hierarchy process-based industrial optimum cost method of claim 3, wherein: and setting a scoring rule and a score for each lowest analysis index level, and subdividing a plurality of inspection items for each scoring rule to obtain corresponding scores if the total target weight is larger than or equal to a preset total target weight, and obtaining effect scores of all the lowest analysis indexes of the industrial product cost, summing and sorting the effect scores to obtain the industrial product cost sorting.
5. An industrial product optimal cost analysis device based on an analytic hierarchy process, the device comprising:
the analysis index database module is used for acquiring analysis indexes and storing the analysis index levels and the analysis indexes of each analysis index level;
the evaluation model building module is used for building an evaluation model according to the demand of a demand side, the evaluation model comprises a preset evaluation model and an individualized evaluation model, and the preset evaluation model and the individualized evaluation model comprise a plurality of analysis index levels and analysis indexes of each analysis index level;
the weight coefficient generation module is used for constructing an importance metric pairwise comparison matrix table of the analysis indexes in each analysis index level, calculating the weight coefficient corresponding to each analysis index in each analysis index level to a higher-level analysis index according to a normalization algorithm, and at least comprising the weight coefficient corresponding to each analysis index in the lowest-level analysis index level to a total index;
the effect score generation module is used for acquiring the weight coefficient of each analysis index in the lowest analysis index level and the score of each analysis index in the lowest analysis index level corresponding to each supplier, and calculating the effect score of each analysis index in the lowest analysis index level;
and the evaluation result acquisition module is used for adding the effect scores of all the analysis indexes in the lowest analysis index level to obtain the final evaluation score of the cost of each industrial product.
6. The analytic hierarchy process-based industrial product optimal cost analysis apparatus of claim 5, wherein: the weight coefficient generation module includes:
and the pairwise comparison matrix table construction module is used for constructing two pairs of comparison matrix tables of analysis indexes step by step according to the analysis index level, wherein the matrix elements are relative importance coefficients obtained after the pairwise comparison of the two analysis indexes corresponding to the matrix elements in the matrix tables is relatively important.
7. The analytic hierarchy process-based industrial product optimal cost analysis apparatus of claim 6, wherein: the weight coefficient generation module further includes:
the normalization module is used for normalizing the relative importance coefficients in the sub-target layer pair comparison matrix table and the index layer pair comparison matrix table according to columns;
the summing module is used for summing the normalized analysis index level pairwise comparison matrix table and the index layer pairwise comparison matrix table according to rows to obtain a characteristic vector;
and the weight calculation module is used for dividing the feature vector by the total number of the analysis indexes of the above levels to obtain a weight coefficient corresponding to the analysis index of each analysis index level in each analysis index level and each weight coefficient.
8. The analytic hierarchy process-based industrial product optimal cost analysis apparatus of claim 7, wherein: the evaluation result acquisition module includes:
and the sequencing module is used for sequencing the final evaluation scores of the cost analysis indexes of the industrial products of the plurality of suppliers to obtain the cost sequencing of the industrial products.
9. The analytic hierarchy process-based industrial product optimal cost analysis apparatus of claim 8, wherein: the evaluation model building module comprises analysis index levels and analysis indexes of each analysis index level which are built according to the overall requirements of the purchasing industry and are used for a demand party to carry out fuzzy evaluation and judgment, and the analysis indexes comprise one or a combination of more of material quality level, processing technology, delivery requirements, risks and design requirements; the personalized customized evaluation model constructs analysis index levels and analysis indexes of each analysis index level according to specific requirement parameters of a demand side.
10. The analytic hierarchy process-based industrial product optimal cost analysis apparatus of claim 9, wherein: the supplier can be a plurality of new suppliers of the demand side to find new suppliers meeting the requirements; the system can also be a plurality of new suppliers and a plurality of existing suppliers of the demand side so as to improve the list of the suppliers and improve the management efficiency of the supply chain of the demand side.
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| CN113762795A (en) * | 2021-09-13 | 2021-12-07 | 浙江万维空间信息技术有限公司 | Industrial chain diagnosis method and system based on hierarchical analysis |
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| CN113689226A (en) * | 2021-07-08 | 2021-11-23 | 深圳市维度数据科技股份有限公司 | Method and device for selecting address of commercial complex, electronic equipment and storage medium |
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| CN113887914A (en) * | 2021-09-26 | 2022-01-04 | 广东闯越企业集团有限公司 | Competition scoring processing method and device and computer readable storage medium |
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