CN112613696A - Supplier processing method, supplier processing device, storage medium and processor - Google Patents
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Abstract
The invention discloses a supplier processing method, a supplier processing device, a storage medium and a processor. Wherein, the method comprises the following steps: determining a plurality of indexes; acquiring index data of a plurality of suppliers aiming at a plurality of indexes respectively; processing the index data of the indexes by adopting a factor analysis method to obtain a plurality of common factors corresponding to the indexes; and a plurality of factor scores for the suppliers on each of the plurality of common factors, respectively; determining a composite factor score for the plurality of suppliers based on the factor scores for the plurality of suppliers on each of the plurality of common factors, respectively; and according to the comprehensive factor scores of the multiple suppliers, carrying out clustering analysis on the multiple suppliers to obtain a clustering result of each supplier in the multiple suppliers. The invention solves the technical problem of inaccurate evaluation result caused by single evaluation of a plurality of suppliers.
Description
Technical Field
The invention relates to the field of suppliers, in particular to a supplier processing method, a supplier processing device, a storage medium and a processor.
Background
The evaluation of the performance of the supplier is the key for managing and selecting the supplier, and a set of complete, scientific and comprehensive evaluation index system is required for comprehensively evaluating the system of the supplier. The influence of the supplier selection standard on the participation of the supplier on the supplier selection standard and the evaluation system are not only important bases for selecting the supplier by long-term cooperation of the enterprise, but also important determinants for the performance management of the enterprise purchasing cost.
At present, the existing equipment supplier performance evaluation system has the following problems: the difficulty of combing the basic information of suppliers in a data source (PMS machine account and defects) is high; the evaluation index of the existing evaluation system is single, so that the performance evaluation result of a supplier is inaccurate; the current mainstream supplier performance evaluation system in the market is too single, and accurate supplier performance evaluation results can not be obtained through fewer supplier samples.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a supplier processing method, a supplier processing device, a storage medium and a processor, which are used for at least solving the technical problem that evaluation results are inaccurate due to the fact that a plurality of suppliers are evaluated too singly.
According to an aspect of an embodiment of the present invention, there is provided a supplier processing method including: determining a plurality of indexes; acquiring index data of a plurality of suppliers aiming at the plurality of indexes respectively; processing the index data of the plurality of indexes by adopting a factor analysis method to obtain a plurality of common factors corresponding to the plurality of indexes and a factor score of each common factor of the plurality of suppliers in the plurality of common factors; determining a composite factor score for the plurality of suppliers based on the factor scores for the plurality of suppliers on each of the plurality of common factors, respectively; and according to the comprehensive factor scores of the suppliers, carrying out cluster analysis on the suppliers to obtain a cluster result of each supplier in the suppliers.
Optionally, determining a composite factor score for each of the plurality of suppliers based on the factor scores for each of the plurality of common factors for each of the plurality of suppliers, respectively, comprises: determining a weight for each common factor of the plurality of common factors, respectively; and respectively determining the comprehensive factor scores of the plurality of suppliers according to the factor scores of the plurality of suppliers on each common factor in the plurality of common factors and the weight of each common factor.
Optionally, performing cluster analysis on the multiple suppliers according to the composite factor scores of the multiple suppliers to obtain a cluster result to which each supplier in the multiple suppliers belongs, including: clustering the suppliers by using a clustering analysis method according to the comprehensive factor scores of the suppliers to obtain a plurality of clustering results; selecting one or more clustering results from the plurality of clustering results as a clustering result to which each of the plurality of suppliers belongs.
Optionally, selecting one or more clustering results from the plurality of clustering results as a clustering result to which each of the plurality of suppliers belongs includes: determining the accuracy of the plurality of clustering results according to a discriminant function; and comparing the accuracy of the plurality of clustering results, and selecting one or more clustering results with the highest accuracy as the clustering result of each supplier in the plurality of suppliers.
Optionally, the method further comprises: subjective weights of the indexes are respectively determined through a hierarchical analysis method, and objective weights of the indexes are respectively determined through a factor analysis method; respectively determining the comprehensive weight of the indexes according to the subjective weight and the objective weight; processing the index data of the plurality of suppliers aiming at the plurality of indexes respectively to obtain index scores of the plurality of suppliers aiming at the plurality of indexes respectively; obtaining the comprehensive index scores of the suppliers according to the index scores of the suppliers aiming at the indexes and the comprehensive weights corresponding to the indexes; and obtaining a category result of each supplier in the plurality of suppliers according to the comprehensive index score.
Optionally, the method further comprises: determining a first rank of each of the plurality of suppliers according to the clustering result; determining a second rank for each of the plurality of suppliers based on the category results; determining the same grade as a grade of a provider in case that the first grade is the same as the second grade; alternatively, in the case where the first rank is different from the second rank, the first rank is determined as a rank of a supplier.
Optionally, the plurality of metrics are equipment metrics of a supplier-provided equipment, and the plurality of metrics includes at least two of: the number of voltage grades, the number of types of equipment, the quantity of owned equipment, the change condition of the equipment, the defects directly influencing the operation of the equipment, the defects not directly influencing the operation of the equipment, the number of abnormal phenomena, quality problems, quality accidents, familial defects, the total running time, the average running time and the loading and using range.
According to another aspect of the embodiments of the present invention, there is also provided a vendor processing apparatus, including: a first determination module to determine a plurality of metrics; the acquisition module is used for acquiring index data of a plurality of suppliers aiming at the plurality of indexes respectively; the processing module is used for processing the index data of the indexes by adopting a factor analysis method to obtain a plurality of public factors corresponding to the indexes and factor scores of the suppliers on each public factor in the public factors; a second determining module for determining a composite factor score for the plurality of suppliers based on the factor scores for the plurality of suppliers on each of the plurality of common factors, respectively; and the clustering module is used for carrying out clustering analysis on the plurality of suppliers according to the comprehensive factor scores of the plurality of suppliers to obtain a clustering result of each supplier in the plurality of suppliers.
According to another aspect of the embodiments of the present invention, there is also provided a storage medium including a stored program, wherein when the program runs, a device on which the storage medium is located is controlled to execute the vendor processing method according to any one of the above claims.
According to another aspect of the embodiments of the present invention, there is also provided a processor configured to execute a program, where the program executes to perform the supplier processing method of any one of the above claims.
In the embodiment of the invention, a mode of determining a plurality of indexes and acquiring index data of a plurality of suppliers aiming at the plurality of indexes is adopted, the index data is processed by adopting a factor analysis method to obtain the respective comprehensive factor scores of the plurality of suppliers, and the plurality of suppliers are subjected to cluster analysis to obtain the clustering result, so that the purpose of clustering and dividing the plurality of suppliers according to the index data is achieved, the technical effect of clustering the plurality of suppliers according to the plurality of index data is realized, and the technical problem of inaccurate evaluation result caused by single evaluation of the plurality of suppliers is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
FIG. 1 is a flow chart of a supplier processing method provided in accordance with an embodiment of the present invention;
FIG. 2 is a schematic flow diagram of a supplier processing method provided in accordance with an alternative embodiment of the present invention;
FIG. 3 is a schematic flow diagram providing for processing vendor grades based on indexing weight according to an alternative embodiment of the present invention;
fig. 4 is a block diagram of a vendor processing apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present invention, there is provided a vendor process method embodiment, it should be noted that the steps illustrated in the flowchart of the figure may be performed in a computer system, such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a flowchart of a supplier processing method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, determining a plurality of indexes;
step S104, acquiring index data of a plurality of suppliers aiming at a plurality of indexes respectively;
step S106, index data of a plurality of indexes are processed by adopting a factor analysis method, a plurality of common factors corresponding to the plurality of indexes are obtained, and a plurality of suppliers respectively obtain factor scores on each common factor in the plurality of common factors;
step S108, respectively determining the comprehensive factor scores of a plurality of suppliers based on the factor scores of the plurality of suppliers on each common factor in a plurality of common factors;
step S110, according to the comprehensive factor scores of the multiple suppliers, carrying out cluster analysis on the multiple suppliers to obtain a cluster result of each supplier in the multiple suppliers.
In the step, the index data of the multiple suppliers aiming at the multiple indexes is obtained according to the multiple determined indexes, the comprehensive factor score of each supplier is obtained by adopting a factor analysis method, and the comprehensive factor score of each supplier is clustered, so that the purpose of clustering the suppliers according to the multiple index data of the suppliers is achieved, the technical effect of carrying out grade division on the multiple suppliers according to the comprehensive level of the equipment provided by the suppliers is realized, and the technical problem that the evaluation result is inaccurate because the multiple suppliers are evaluated too singly is solved.
As an alternative embodiment, in order to know more comprehensively and accurately, the index is gathered as much as possible, but the information overlapping and the calculation amount are increased. And the reduction of variables inevitably leads to the problem that information is incomplete and cannot be comprehensively known. The factor analysis can solve the problems very effectively, original variables are integrated into a few common factors which can be explained through factor rotation, and the original problems can be more comprehensively explained through the common factors (the cumulative variance contribution rate is usually more than 80%), so that not only is the information loss reduced, but also the information redundancy problem is solved.
Therefore, the equipment standing account information and the quality information are comprehensively considered by the factor analysis method, and the evaluation indexes based on the equipment multi-feature attributes are provided by combining the equipment feature attributes of multiple dimensions such as the supplier sales volume, the after-sales service quality and the like, so that the more reasonable and targeted supplier evaluation method can play an important role in the performance evaluation work of the suppliers at the present stage.
As an alternative embodiment, the plurality of indicators that are sources of data for analyzing the supplier may provide equipment indicators of the equipment for the supplier, wherein the plurality of equipment indicators may include at least two of: the number of voltage grades, the number of types of equipment, the quantity of owned equipment, the change condition of the equipment, the defects directly influencing the operation of the equipment, the defects not directly influencing the operation of the equipment, the number of abnormal phenomena, quality problems, quality accidents, familial defects, the total running time, the average running time and the loading and using range. The various equipment indexes can be divided into main attributes of six equipment features, such as ledger information, defect information, quality events, familial defects, runtime, and equipment installation range, and each equipment feature is briefly described below.
(1) Standing book information: the equipment ledger is the main basis for mastering the asset condition of the enterprise equipment and reflecting the ownership, equipment distribution and change conditions of various types of equipment of the enterprise. The equipment ownership amount is the data amount corresponding to the supplier in the standing book data; the voltage class quantity and the equipment model quantity are respectively the voltage class quantity and the equipment model quantity corresponding to the isolating switch produced by the supplier in the ledger data.
(2) Defect information: the equipment defects refer to equipment conditions and abnormal phenomena influencing safe, stable and economic operation and polluting the environment in the operation or standby equipment in the production process. The equipment defects are classified into equipment defects directly affecting safe operation of the equipment, equipment defects not directly affecting safe operation, and equipment abnormal phenomena.
(3) Quality events: the equipment quality event or the equipment engineering quality accident refers to an event that the engineering quality does not meet the quality standards specified by regulations, specifications and contracts due to equipment and other reasons, the service life is influenced, and hidden dangers and damages are caused to the engineering safe operation. According to the regulation of the GB/T19000-2000 quality management system standard in China, when the engineering product does not meet the requirement of a certain regulation, the quality is called as unqualified; without meeting certain expected use requirements or reasonably expected requirements, referred to as quality defects. For example, all cases where the quality requirements are not met and the engineering quality is not acceptable must be reworked, consolidated or scrapped, thereby creating a quality problem with direct economic losses below 5000 yuan; and the quality of the engineering is unqualified, and the engineering must be repaired, reinforced or scrapped, so that the quality accident with direct economic loss of more than 5000 yuan is caused.
(4) Familial defects: the same type of defects occur in the operation of different types, specifications, series and even varieties of power equipment produced by the same manufacturer. The defects may be caused by the same process, the same material, the same design concept and idea, and the like.
(5) Operating time: the operating time of the device, or the life cycle of the device, is the time which elapses from the start of the use of the device until the use of the device is removed, which is technically or economically undesirable. The life cycle of the device is as follows: the material life of the equipment is also called the natural life. It refers to the time that the equipment has elapsed from being put into use until it is scrapped due to physical wear causing the equipment to lose its value of use altogether. ② the technical life of the equipment, also called useful life. The method is characterized in that the time for the original equipment to lose the use value and quit the use process before the service life of a substance is not reached is elapsed from the time when the equipment is put into use to the time when a novel device with better performance and higher efficiency is produced due to the technical progress. Economic life of the equipment. Means the time from the start of the equipment being put into use until the use cost is drastically increased due to the equipment aging, and the equipment is continuously used without economic justification and is withdrawn from the use process. The runtime attribute of the equipment is also one of the important indicators for equipment evaluation and vendor evaluation.
(6) Equipment installation range: the equipment installation range is the number of the provincial companies adopting the supplier equipment, and is one of the most intuitive indexes for measuring the acceptance degree of the equipment.
Table 1 is a supplier performance composite rating table in accordance with an alternative embodiment of the present invention.
TABLE 1
As an alternative embodiment, the determining the composite factor score of the plurality of suppliers respectively based on the factor score of the plurality of suppliers respectively on each common factor in the plurality of common factors may be implemented as follows: determining a weight of each common factor in the plurality of common factors respectively; and respectively determining the comprehensive factor scores of the plurality of suppliers according to the factor scores of the plurality of suppliers on each common factor in the plurality of common factors and the weight of each common factor.
As an alternative embodiment, when the common factor of the suppliers obtained by the factor analysis is 3, the calculation formula of the composite factor score of the suppliers may be: the composite score is the weight of factor 1 + the weight of factor 2 + the weight of factor 3. The weight of the factor 1 is the variance contribution ratio of the factor 1/(the variance contribution ratio of the factor 1 + the variance contribution ratio of the factor 2 + the variance contribution ratio of the factor 3), and the weight calculation methods of the factor 2 and the factor 3 are the same as those of the factor 1.
As an alternative embodiment, according to the composite factor scores of multiple suppliers, performing cluster analysis on the multiple suppliers to obtain a clustering result to which each supplier in the multiple suppliers belongs may be implemented as follows: clustering the suppliers by using a clustering analysis method according to the comprehensive factor scores of the suppliers to obtain a plurality of clustering results; and selecting one or more clustering results from the plurality of clustering results as the clustering results to which each supplier in the plurality of suppliers belongs.
After the composite factor scores of the suppliers are obtained, various methods can be selected for clustering, for example, the clustering methods which can be adopted include an interclass connection method, an intraclass connection method, a nearest neighbor element method, a farthest neighbor element method, a centroid clustering method, a median clustering method, a Ward method, a selection and optimization method and the like. Although different methods can perform clustering analysis on suppliers according to a plurality of common factor scores, the positive judgment rates of the different methods are different. As an optional implementation manner, after comparing results obtained by multiple clustering methods with real data, it is generally considered that the clustering result of the farthest neighboring element method most conforms to the theorem, so that the clustering result corresponding to one or more clustering methods most conforming to the theorem can be selected according to the clustering result.
As an optional embodiment, one or more clustering results are selected from the multiple clustering results, and the clustering result to which each supplier in the multiple suppliers belongs can be determined by a discriminant function, which can specifically be implemented by the following steps: determining the accuracy of a plurality of clustering results according to the discriminant function; and comparing the accuracy of the plurality of clustering results, and selecting one or more clustering results with the highest accuracy as the clustering result of each supplier in the plurality of suppliers. After a plurality of clustering results are obtained, a discriminant function is established, and a clustering analysis method can be reasonably selected by utilizing the positive judgment rate and the supplier classification results.
Discriminant analysis is a suitable statistical analysis method when the interpreted variables are attribute variables and the interpreted variables are metric variables. Discriminant analysis has three basic assumptions, which are: (1) no co-linearity exists between the explanatory variables; (2) the covariance matrices of each set of explanatory variables are equal; (3) each explanatory variable follows a multivariate normal distribution.
As an optional implementation manner, when performing discriminant analysis on a plurality of clustering results by using a discriminant function, two cases of the same covariance matrix and different covariance matrices may be discussed separately.
When the covariance arrays are the same, the method is providedk total G1，......，GkTheir mean values are respectively μ1，......，μkThe covariance arrays are all Σ.
Similar to the two general discussions, the discriminant function is:
the corresponding discriminant rule is:
when the covariance arrays are different, the discriminant function is:
Vij(T)＝(x-μi)′∑-1(x-μi)-(x-μj)′∑-1(x-μj)
the judgment rule is as follows:
as an alternative embodiment, the category results to which the multiple suppliers belong may also be obtained by multiple methods, for example, the following method may be used: subjective weights of the indexes are respectively determined through a hierarchical analysis method, and objective weights of the indexes are respectively determined through a factor analysis method; respectively determining the comprehensive weight of a plurality of indexes according to the subjective weight and the objective weight; processing the index data of the plurality of suppliers aiming at the plurality of indexes respectively to obtain index scores of the plurality of suppliers aiming at the plurality of indexes respectively; obtaining the comprehensive index scores of the suppliers according to the index scores of the suppliers aiming at the indexes and the comprehensive weights corresponding to the indexes; and obtaining the category result of each supplier in the plurality of suppliers according to the comprehensive index score.
As an optional implementation manner, aiming at the problem that the weight setting of the performance evaluation index of the supplier is relatively single, the subjective weight can be obtained by using an analytic hierarchy process, the objective weight can be obtained by using a factor analysis method, the combined weight can be calculated by using a game theory combined weighting method and introduced into a fuzzy comprehensive evaluation method, and the final evaluation can be completed. Obtaining the subjective weight by using the analytic hierarchy process may include constructing a judgment matrix, ranking the hierarchical lists, checking consistency, and the like. The factor analysis method can classify several characteristics with close correlation in a plurality of selected indexes into the same class. The comprehensive weights of the indexes are respectively determined according to the subjective weights and the objective weights, a game theory combined weighting method can be adopted, and the combined weighting method based on the game theory is a process of combining the weights acquired by different methods to seek the most reasonable index weights. According to the data of each supplier on the indexes and the comprehensive weight of the indexes, the comprehensive index scores of the suppliers can be obtained, and then the category result of each supplier in the suppliers can be obtained in a clustering mode.
The comprehensive weight of the supplier index obtained by the combined weighting method based on the game theory is illustrated below.
Step S1 is to obtain subjective weight and objective weight of the supplier evaluation index by using an analytic hierarchy process and a factor analysis process, and form a weight vector set W ═ ω1,ω2Where ω is1Subjective weight vector, omega, determined for analytic hierarchy process2An objective weight vector determined for the factorial analysis. The linear combination of the two weight vectors is then:
step S2, according to the thought of the game aggregation model, the two weights are optimally and linearly combined, the deviation minimization is taken as the target, and the two linear combination coefficients alpha of the formula are combined1、α2Optimizing to obtain the most reasonable weight, thereby determining the objective function as:
min||W-ωk||2，k＝1，2
step S3, equivalently transforming the above equation into a linear equation system that optimizes the first derivative condition using matrix differential properties as follows:
step S4, obtaining the optimized linear combination coefficient alpha according to the formula1、α2After normalization processing is carried out, the comprehensive weight W based on game theory combined weighting is finally obtained as follows:
wherein,
in step S5, a fuzzy comprehensive evaluation vector P is calculated. As follows:
by the maximum membership rule Max ═ pjSelect supplier rank.
As an alternative embodiment, determining the rating of the supplier may be accomplished by: determining a first grade of each supplier in the plurality of suppliers according to the clustering result; determining a second rating for each of the plurality of suppliers based on the classification result; determining the same grade as the grade of the supplier in case that the first grade is the same as the second grade; alternatively, in the case where the first rank is different from the second rank, the first rank is determined as the rank of the supplier. The clustering result or the category result of the suppliers may include grades of the suppliers, for example, 150 suppliers are classified into a grade a, B and C suppliers according to their index data. By selecting and comparing two supplier grade division modes, the scientificity and comprehensiveness of the assessment method can be increased, the factor range taking into consideration is enlarged, and the defect of only a single assessment mode is avoided.
An alternative embodiment of the present invention is described below with reference to the above-mentioned and alternative embodiments.
Fig. 2 is a schematic flow chart diagram of a supplier processing method according to an alternative embodiment of the invention. As shown in fig. 2, the method comprises the steps of:
step S1, collecting data of a plurality of suppliers; step S2, screening evaluation characteristics from the supplier data, wherein the screened evaluation characteristics comprise: 6 evaluation characteristics such as an equipment account, equipment defects, quality events, familial defects, running time and loading range, wherein each evaluation characteristic can comprise at least one set data index; step S3, performing factor analysis on the data indexes to obtain a plurality of common factors capable of explaining most data variation, and calculating factor scores of a plurality of suppliers according to the common factors; step S4, calculating the comprehensive factor score of each supplier according to the obtained factor scores of a plurality of suppliers; step S5, carrying out cluster analysis on a plurality of suppliers according to the comprehensive factor score of each supplier to obtain at least one cluster result; step S6, when more than one clustering result is obtained in step S5, the multiple clustering results are subjected to discriminant analysis to obtain a clustering result with an optimal result, and then the step S7 is carried out, and when one clustering result is obtained in step S5, the step S7 is directly carried out; and step S7, classifying the grade for multiple suppliers according to the clustering result.
FIG. 3 is a schematic flow chart of processing vendor grades according to an indexing weight according to an alternative embodiment of the present invention, as shown in FIG. 3, the method comprising the steps of:
step S1, collecting data of a plurality of suppliers; step S2, primarily processing data and screening out evaluation characteristics; step S3, obtaining objective weight of each evaluation characteristic by using a factor analysis method; step S4, acquiring subjective weight of each evaluation characteristic by using an analytic hierarchy process; step S5, integrating the subjective weight and the objective weight into a comprehensive weight of the evaluation characteristics by using a combined weighting method based on the game theory; step S6, a plurality of suppliers are graded using a fuzzy comprehensive evaluation method.
Example 2
According to the embodiment of the invention, the invention further provides a supplier processing device. Fig. 4 is a block diagram of a vendor processing apparatus according to an embodiment of the present invention, and as shown in fig. 4, the vendor processing apparatus 40 includes: a first determination module 402, an acquisition module 404, a processing module 406, a second determination module 408, and a clustering module 410. The supplier processing means will be described in detail below.
A first determining module 402 for determining a plurality of metrics;
an obtaining module 404, connected to the first determining module 402, for obtaining index data of a plurality of suppliers for a plurality of indexes respectively;
a processing module 406, connected to the obtaining module 404, configured to process the index data of the multiple indexes by using a factor analysis method, so as to obtain multiple common factors corresponding to the multiple indexes, and a factor score of each common factor in the multiple common factors for each of the multiple suppliers;
a second determining module 408, connected to the processing module 406, for determining the composite factor scores of the multiple suppliers based on the factor scores of the multiple suppliers on each common factor of the multiple common factors;
the clustering module 410 is connected to the second determining module 408, and configured to perform clustering analysis on the multiple suppliers according to the comprehensive factor scores of the multiple suppliers, so as to obtain a clustering result of each supplier in the multiple suppliers.
Example 3
The embodiment of the invention also provides a storage medium. Alternatively, in this embodiment, the storage medium may be configured to store the program code executed by the supplier processing method provided in embodiment 1.
Optionally, in this embodiment, the storage medium may be located in any one of computer terminals in a computer terminal group in a computer network, or in any one of mobile terminals in a mobile terminal group.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: determining a plurality of indexes; acquiring index data of a plurality of suppliers aiming at a plurality of indexes respectively; processing the index data of the indexes by adopting a factor analysis method to obtain a plurality of public factors corresponding to the indexes and factor scores of a plurality of suppliers on each public factor in the public factors; determining a composite factor score for the plurality of suppliers based on the factor scores for the plurality of suppliers on each of the plurality of common factors, respectively; and according to the comprehensive factor scores of the multiple suppliers, carrying out clustering analysis on the multiple suppliers to obtain a clustering result of each supplier in the multiple suppliers.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: determining a weight for each common factor of the plurality of common factors, respectively; and respectively determining the comprehensive factor scores of the plurality of suppliers according to the factor scores of the plurality of suppliers on each common factor in the plurality of common factors and the weight of each common factor.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: according to the comprehensive factor scores of the multiple suppliers, carrying out cluster analysis on the multiple suppliers to obtain a cluster result of each supplier in the multiple suppliers, wherein the cluster result comprises the following steps: clustering the suppliers by using a clustering analysis method according to the comprehensive factor scores of the suppliers to obtain a plurality of clustering results; and selecting one or more clustering results from the plurality of clustering results as the clustering results to which each supplier in the plurality of suppliers belongs.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: selecting one or more clustering results from the plurality of clustering results as a clustering result to which each supplier of the plurality of suppliers belongs, including: determining the accuracy of a plurality of clustering results according to the discriminant function; and comparing the accuracy of the plurality of clustering results, and selecting one or more clustering results with the highest accuracy as the clustering result of each supplier in the plurality of suppliers.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: the method further comprises the following steps: subjective weights of the indexes are respectively determined through a hierarchical analysis method, and objective weights of the indexes are respectively determined through a factor analysis method; respectively determining the comprehensive weight of a plurality of indexes according to the subjective weight and the objective weight; processing the index data of the plurality of suppliers aiming at the plurality of indexes respectively to obtain index scores of the plurality of suppliers aiming at the plurality of indexes respectively; obtaining the comprehensive index scores of the suppliers according to the index scores of the suppliers aiming at the indexes and the comprehensive weights corresponding to the indexes; and obtaining the category result of each supplier in the plurality of suppliers according to the comprehensive index score.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: the method further comprises the following steps: determining a first grade of each supplier in the plurality of suppliers according to the clustering result; determining a second rating for each of the plurality of suppliers based on the classification result; determining the same grade as the grade of the supplier in case that the first grade is the same as the second grade; alternatively, in the case where the first rank is different from the second rank, the first rank is determined as the rank of the supplier.
Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: the plurality of metrics provides equipment metrics for the equipment of the equipment for the supplier, the plurality of metrics including at least two of: the number of voltage grades, the number of types of equipment, the quantity of owned equipment, the change condition of the equipment, the defects directly influencing the operation of the equipment, the defects not directly influencing the operation of the equipment, the number of abnormal phenomena, quality problems, quality accidents, familial defects, the total running time, the average running time and the loading and using range.
Example 4
An embodiment of the present invention may provide a computer device, and optionally, in this embodiment, the computer device may be located in at least one network device of a plurality of network devices of a computer network. The computer device includes a memory and a processor.
The memory may be configured to store software programs and modules, such as program instructions/modules corresponding to the data processing method and apparatus in the embodiments of the present invention, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, so as to implement the data processing method. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory located remotely from the processor, and these remote memories may be connected to the computer terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: determining a plurality of indexes; acquiring index data of a plurality of suppliers aiming at a plurality of indexes respectively; processing the index data of the indexes by adopting a factor analysis method to obtain a plurality of public factors corresponding to the indexes and factor scores of a plurality of suppliers on each public factor in the public factors; determining a composite factor score for the plurality of suppliers based on the factor scores for the plurality of suppliers on each of the plurality of common factors, respectively; and according to the comprehensive factor scores of the multiple suppliers, carrying out clustering analysis on the multiple suppliers to obtain a clustering result of each supplier in the multiple suppliers.
Optionally, the processor may further execute the program code of the following steps: determining a composite factor score for the plurality of suppliers based on the factor scores for the plurality of suppliers on each of the plurality of common factors, respectively, comprising: determining a weight for each common factor of the plurality of common factors, respectively; and respectively determining the comprehensive factor scores of the plurality of suppliers according to the factor scores of the plurality of suppliers on each common factor in the plurality of common factors and the weight of each common factor.
Optionally, the processor may further execute the program code of the following steps: according to the comprehensive factor scores of the multiple suppliers, carrying out cluster analysis on the multiple suppliers to obtain a cluster result of each supplier in the multiple suppliers, wherein the cluster result comprises the following steps: clustering the suppliers by using a clustering analysis method according to the comprehensive factor scores of the suppliers to obtain a plurality of clustering results; and selecting one or more clustering results from the plurality of clustering results as the clustering results to which each supplier in the plurality of suppliers belongs.
Optionally, the processor may further execute the program code of the following steps: selecting one or more clustering results from the plurality of clustering results as a clustering result to which each supplier of the plurality of suppliers belongs, including: determining the accuracy of a plurality of clustering results according to the discriminant function; and comparing the accuracy of the plurality of clustering results, and selecting one or more clustering results with the highest accuracy as the clustering result of each supplier in the plurality of suppliers.
Optionally, the processor may further execute the program code of the following steps: the method further comprises the following steps: subjective weights of the indexes are respectively determined through a hierarchical analysis method, and objective weights of the indexes are respectively determined through a factor analysis method; respectively determining the comprehensive weight of a plurality of indexes according to the subjective weight and the objective weight; processing the index data of the plurality of suppliers aiming at the plurality of indexes respectively to obtain index scores of the plurality of suppliers aiming at the plurality of indexes respectively; obtaining the comprehensive index scores of the suppliers according to the index scores of the suppliers aiming at the indexes and the comprehensive weights corresponding to the indexes; and obtaining the category result of each supplier in the plurality of suppliers according to the comprehensive index score.
Optionally, the processor may further execute the program code of the following steps: the method further comprises the following steps: determining a first grade of each supplier in the plurality of suppliers according to the clustering result; determining a second rating for each of the plurality of suppliers based on the classification result; determining the same grade as the grade of the supplier in case that the first grade is the same as the second grade; alternatively, in the case where the first rank is different from the second rank, the first rank is determined as the rank of the supplier.
Optionally, the processor may further execute the program code of the following steps: the plurality of metrics provides equipment metrics for the equipment of the equipment for the supplier, the plurality of metrics including at least two of: the number of voltage grades, the number of types of equipment, the quantity of owned equipment, the change condition of the equipment, the defects directly influencing the operation of the equipment, the defects not directly influencing the operation of the equipment, the number of abnormal phenomena, quality problems, quality accidents, familial defects, the total running time, the average running time and the loading and using range.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A supplier processing method, comprising:
determining a plurality of indexes;
acquiring index data of a plurality of suppliers aiming at the plurality of indexes respectively;
processing the index data of the plurality of indexes by adopting a factor analysis method to obtain a plurality of common factors corresponding to the plurality of indexes and a factor score of each common factor of the plurality of suppliers in the plurality of common factors;
determining a composite factor score for the plurality of suppliers based on the factor scores for the plurality of suppliers on each of the plurality of common factors, respectively;
and according to the comprehensive factor scores of the suppliers, carrying out cluster analysis on the suppliers to obtain a cluster result of each supplier in the suppliers.
2. The method of claim 1, wherein determining a composite factor score for a plurality of suppliers based on the factor scores for the plurality of suppliers on each of the plurality of common factors, respectively, comprises:
determining a weight for each common factor of the plurality of common factors, respectively;
and respectively determining the comprehensive factor scores of the plurality of suppliers according to the factor scores of the plurality of suppliers on each common factor in the plurality of common factors and the weight of each common factor.
3. The method of claim 1, wherein performing cluster analysis on the plurality of suppliers according to the composite factor scores of the plurality of suppliers to obtain a cluster result to which each supplier in the plurality of suppliers belongs comprises:
clustering the suppliers by using a clustering analysis method according to the comprehensive factor scores of the suppliers to obtain a plurality of clustering results;
selecting one or more clustering results from the plurality of clustering results as a clustering result to which each of the plurality of suppliers belongs.
4. The method of claim 3, wherein selecting one or more clustering results from the plurality of clustering results as the clustering results to which each of the plurality of suppliers belongs comprises:
determining the accuracy of the plurality of clustering results according to a discriminant function;
and comparing the accuracy of the plurality of clustering results, and selecting one or more clustering results with the highest accuracy as the clustering result of each supplier in the plurality of suppliers.
5. The method according to any one of claims 1 to 4, further comprising:
subjective weights of the indexes are respectively determined through a hierarchical analysis method, and objective weights of the indexes are respectively determined through a factor analysis method;
respectively determining the comprehensive weight of the indexes according to the subjective weight and the objective weight;
processing the index data of the plurality of suppliers aiming at the plurality of indexes respectively to obtain index scores of the plurality of suppliers aiming at the plurality of indexes respectively;
obtaining the comprehensive index scores of the suppliers according to the index scores of the suppliers aiming at the indexes and the comprehensive weights corresponding to the indexes;
and obtaining a category result of each supplier in the plurality of suppliers according to the comprehensive index score.
6. The method of claim 5, further comprising:
determining a first rank of each of the plurality of suppliers according to the clustering result;
determining a second rank for each of the plurality of suppliers based on the category results;
determining the same grade as a grade of a provider in case that the first grade is the same as the second grade; alternatively, in the case where the first rank is different from the second rank, the first rank is determined as a rank of a supplier.
7. The method of claim 6, wherein the plurality of metrics are equipment metrics for a supplier-provided equipment, the plurality of metrics including at least two of:
the number of voltage grades, the number of types of equipment, the quantity of owned equipment, the change condition of the equipment, the defects directly influencing the operation of the equipment, the defects not directly influencing the operation of the equipment, the number of abnormal phenomena, quality problems, quality accidents, familial defects, the total running time, the average running time and the loading and using range.
8. A vendor process apparatus, comprising:
a first determination module to determine a plurality of metrics;
the acquisition module is used for acquiring index data of a plurality of suppliers aiming at the plurality of indexes respectively;
the processing module is used for processing the index data of the indexes by adopting a factor analysis method to obtain a plurality of public factors corresponding to the indexes and factor scores of the suppliers on each public factor in the public factors;
a second determining module for determining a composite factor score for the plurality of suppliers based on the factor scores for the plurality of suppliers on each of the plurality of common factors, respectively;
and the clustering module is used for carrying out clustering analysis on the plurality of suppliers according to the comprehensive factor scores of the plurality of suppliers to obtain a clustering result of each supplier in the plurality of suppliers.
9. A storage medium, characterized in that the storage medium includes a stored program, wherein a device in which the storage medium is located is controlled to execute the supplier processing method according to any one of claims 1 to 7 when the program is executed.
10. A processor, configured to run a program, wherein the program when executed performs the vendor process method of any one of claims 1 to 7.
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