CN112132514A - Material purchase assessment method - Google Patents

Material purchase assessment method Download PDF

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Publication number
CN112132514A
CN112132514A CN202011015861.9A CN202011015861A CN112132514A CN 112132514 A CN112132514 A CN 112132514A CN 202011015861 A CN202011015861 A CN 202011015861A CN 112132514 A CN112132514 A CN 112132514A
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evaluation
value
node
child node
item
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李晋
陈建江
于红艳
孙蔚
白玉
林森
刘桂镗
许文婷
丁亦凡
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Beijing Research Institute of Mechanical and Electrical Technology
Harbin Engineering University
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Beijing Research Institute of Mechanical and Electrical Technology
Harbin Engineering University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis

Abstract

A material purchasing evaluation method solves the problem that the accuracy and universality of the existing material purchasing evaluation are poor, and belongs to the technical field of material purchasing management. The invention comprises the following steps: taking each purchase scheme to be evaluated as an evaluation object, determining a problem to be evaluated according to a plurality of purchase schemes to be evaluated, and constructing a tree-structure evaluation model according to the problem to be evaluated; determining the numerical value of an evaluation item of an evaluation object in the tree structure evaluation model, and sequentially performing homotrenization processing and normalization processing; determining the outermost layer child nodes and parent nodes thereof according to the depth of the tree in the tree structure evaluation model; obtaining the evaluation value of the father node by using an approximate ideal solution sorting method and a normalized numerical value of an evaluation item serving as the outermost child node; and taking the father node of which the evaluation value is obtained as a child node, and evaluating the father node of the child node by adopting a hierarchical weighting method and the evaluation value of the child node until the evaluation value of the root node is obtained, wherein the evaluation value of the root node is the evaluation value of an evaluation object.

Description

Material purchase assessment method
Technical Field
The invention relates to a material purchasing evaluation method, and belongs to the technical field of material purchasing management.
Background
In recent years, the importance of material purchasing management is increasing, and in order to standardize material purchasing management, the traditional enterprise purchasing method cannot adapt to the development of the society. In order to meet the requirement, many enterprises implement a bidding network material purchasing system, and through effective e-commerce bidding purchasing management, the enterprises can more widely know the supply and demand of materials, market conditions of price change and credit qualification status of suppliers, so that the enterprises expand the selection range, increase the transparency of purchasing, reduce intermediate links, reduce the occurrence of illegal operations, and have great significance for controlling the enterprises, reducing purchasing cost and improving economic benefits of the enterprises.
The development direction of economic globalization makes purchasing an important strategic way in enterprise market competition. Purchasing is also changed from a functional management to a strategic attack, and the effort of finding the right strategic way is to increase the purchasing performance, which is a good thing for purchasing evaluation and a better one for enterprise development. However, in actual purchasing, the evaluation work is often not performed due to many hidden trouble problems or method problems of purchasing evaluation.
The currently commonly used comprehensive evaluation methods include an analytic hierarchy process (AHQ) method, a fuzzy comprehensive evaluation method (FCE) method, a rank and ratio method (RSR) method, a key event method (CIM) method, a near ideal solution ordering method (TOPSIS method) and the like.
Among them, the approximate ideal solution ranking method was first proposed in 1981 by c.l.hwang and k.yoon, and the TOPSIS method ranks according to the closeness of a limited number of evaluation objects to an ideal target, which is a method of evaluating relative merits in existing objects. There are two idealized targets (Ideal Solution), one is positive Ideal target or optimal target, and the other is negative Ideal target or worst target, and the object with the best evaluation should be the closest to the optimal target and the farthest to the worst target.
When the existing comprehensive evaluation methods are used for evaluation, the weight of each index mostly depends on subjective judgment, a healthy evaluation system is lacked, sufficient scientific basis is not provided, inaccurate evaluation exists, and then the result is a blind project, so that a large amount of loss is caused. When a specific problem is encountered, a specific algorithm is needed to complete the operation, and some algorithms with relatively wide application range are lacked.
Disclosure of Invention
Aiming at the problem that the accuracy and the universality of the conventional material purchasing evaluation are poor, the invention provides a comprehensive material purchasing evaluation method.
The invention discloses a material purchasing evaluation method, which comprises the following steps:
s1, taking each purchase scheme to be evaluated as an evaluation object, determining a problem to be evaluated according to the purchase schemes to be evaluated, and constructing a tree structure evaluation model according to the problem to be evaluated, wherein the tree structure evaluation model comprises a plurality of evaluation items influencing the quality of the evaluation object, and each evaluation item can be divided into a plurality of sub evaluation items according to influence factors to form a tree structure consisting of a plurality of sub nodes, a father node and a root node;
s2, determining the numerical value of the evaluation item of the evaluation object in the tree structure evaluation model;
s3, performing homotrending processing on the evaluation item values determined by the evaluation objects;
s4, carrying out normalization processing according to the evaluation item values after homotrending processing;
s5, determining the outermost child node and the father node thereof according to the depth of the tree in the tree structure evaluation model;
s6, obtaining the evaluation value of the father node by using an approximate ideal solution sorting method and the normalized numerical value of the evaluation item of the outermost child node;
and S7, taking the father node which obtains the evaluation value as a child node, and evaluating the father node by adopting a hierarchical weighting method and the evaluation value of the child node until obtaining the evaluation value of the root node, wherein the evaluation value of the root node is the evaluation value of an evaluation object.
Preferably, the S3 includes:
the evaluation indexes of the evaluation items are high-quality indexes, low-quality indexes or moderate indexes, the values of the evaluation items are subjected to homotrending treatment, and the values of the evaluation items of the low-quality indexes and the moderate indexes are converted into the values of the evaluation items of the high-quality indexes:
Figure BDA0002699036330000021
Xmndenotes the value, X'mnThe evaluation item number after the homotrending process is indicated, W is an optimum value of the evaluation item, M is 1,2 … M, N is 1,2 … N, M indicates the number of evaluation objects, and N indicates the number of evaluation items.
Preferably, the normalized values of the evaluation items in S4 are:
Figure BDA0002699036330000022
wherein W represents the optimal value of the corresponding evaluation item, and a represents the worst value of the corresponding evaluation item.
Preferably, the S6 includes:
s61, constructing a decision matrix V according to the normalized numerical value of the evaluation item r as the outermost layer child node:
Figure BDA0002699036330000031
wherein, X ″)mrA normalized numerical value of an evaluation term R representing an m-th evaluation object as an outermost-layer child node, where R is 1,2 … R, and R represents the number of evaluation terms as the outermost-layer child node;
s62, acquiring an information weight matrix B of the expert on the attribute of the evaluation item according to the DELPHI method:
Figure BDA0002699036330000032
wherein B ismrThe weight corresponding to the evaluation term r as the outermost layer child node is normalized;
and S63, obtaining a weighting judgment matrix Z according to the condition that Z is VB:
Figure BDA0002699036330000033
s64, determining the solution of the positive ideal point and the solution of the negative ideal point of the evaluation item of the child node at the outermost layer, wherein the solution of the positive ideal point is
Figure BDA0002699036330000034
Solution of negative ideal points to
Figure BDA0002699036330000035
y+Is the optimum value of R evaluation terms, y-Is the worst value of the R evaluation terms;
s65, calculating the Euclidean distance between each evaluation object and the positive ideal point and the negative ideal point, acquiring the relative proximity of each evaluation object and the ideal point according to the Euclidean distance, and acquiring the relative proximity c between each evaluation object and the ideal point according to the Euclidean distancem；
Figure BDA0002699036330000036
Is the euclidean distance between the mth evaluation object and the positive ideal point:
Figure BDA0002699036330000041
Figure BDA0002699036330000042
is the euclidean distance between the mth evaluation object and the positive ideal point:
Figure BDA0002699036330000043
Zmrrepresenting elements in the weighted decision matrix Z;
Figure BDA0002699036330000044
cmis the evaluation value of the parent node of the outermost child node.
Preferably, in S7, the parent node of the outermost child node is taken as the child node;
the hierarchical weighting method comprises the following steps:
Figure BDA0002699036330000045
wherein Q is the evaluation value of the father node, K is the number of child nodes of the father node, dkIs the evaluation value of the kth child node, pkThe weight is the corresponding weight of the kth child node, and the weight is normalized;
and sequentially calculating the evaluation values of the father nodes by a hierarchical weighting method until the evaluation value of the root node is obtained.
The tree structure evaluation model has the beneficial effects that the tree structure evaluation model comprises influence factors of evaluation items, so that the evaluation is more comprehensive, the accuracy is improved, meanwhile, an approximate ideal solution sorting method and a hierarchical weighting method are combined, each node of the tree structure evaluation model is calculated, the evaluation value is calculated step by step, the actual value is combined with the evaluation value, the evaluation value of an evaluation object is finally obtained, and the universality is higher.
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FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
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 embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
As shown in fig. 1, the method for evaluating material procurement of the present embodiment includes:
step one, each purchase scheme to be evaluated is taken as an evaluation object, the problem to be evaluated is determined according to a plurality of purchase schemes to be evaluated, a tree structure evaluation model is built according to the problem to be evaluated, the tree structure evaluation model comprises a plurality of evaluation items influencing the quality of the evaluation object, a plurality of sub evaluation items can be divided under each evaluation item according to influence factors, and a tree structure formed by a plurality of sub nodes, a father node and a root node is formed;
the tree structure evaluation model of the present embodiment is as follows:
Figure BDA0002699036330000051
the evaluation class problem of any object can be summarized into different tree structure evaluation models according to different emphasis of users in an abstract summarization mode. The tree structure evaluation model can be designed according to user requirements and also can be a template designed by an expert, and as the same or similar characteristics exist in the same type of products or the repeated reference of the same or similar characteristics exists in the evaluation process, the characteristics are extracted, the corresponding tree structure evaluation model is established, and the rapid design is realized by calling the tree structure evaluation model.
For example, a detailed table of a tree structure evaluation model of the drone is shown in table 1:
TABLE 1
Figure RE-GDA0002731466340000052
Figure RE-GDA0002731466340000061
Usually, the sub-evaluation item will also have sub-items, and the table in this case will be larger in scale, and for the sake of general applicability, a table with at most 5 levels of sub-items is generally used to characterize the evaluation problem.
Determining the numerical value of an evaluation item (including a sub evaluation item) of an evaluation object in the tree structure evaluation model;
step three, carrying out homotrending treatment on the evaluation item numerical values determined by each evaluation object;
in the evaluation item system, the larger the numerical value of some evaluation items, the better (called high-quality index); some evaluation term values are as small as possible (called low-quality index); some evaluation items are not the minimum or the best (called moderate indexes), and the evaluation items have no unified measurement standard and different dimensions. The evaluation assessment score may take a variety of forms: percent, precise five-point (precise to 1 bit after decimal point), fuzzy five-point (excellent, good, medium, normal and poor), two-level (pass/fail), and the like.
Performing normalization processing according to the evaluation item values subjected to homotrenization processing;
the values characterizing the evaluation term are normalized by means of scaling to the [0,1] interval by the maximum value when the evaluation term is optimized and the minimum value when the evaluation term is most degraded.
Fifthly, determining the outermost layer child nodes and parent nodes thereof according to the depth of the tree in the tree structure evaluation model;
and (3) calculating to obtain the depth H of the tree structure evaluation model, and if the depth of the evaluation object 1 in the table 1 is 3, traversing to obtain the node with the depth H in the model tree, wherein the node with the depth H is determined as the leaf node of the outermost layer because the depth of the model tree is H. The parent node geometry of the leaf nodes is searched for as Set { P1, P2 … } through an algorithm, and then child nodes of the nodes such as P1, P2 and the like are outermost leaf nodes and meet the meaning and algorithm requirements of the TOPSIS algorithm, namely evaluation values of the P1 and P2 nodes can be calculated through the TOPSIS algorithm.
For example: the evaluation items of weapon mount quantity, weapon load capacity, launching distance and weapon longitude coefficient are the outermost nodes; maneuverability, armor coefficient, minimum height of penetration and penetration speed are the outermost nodes,
step six, obtaining the evaluation value of the father node of the child node by using an approximate ideal solution sorting method and a normalized numerical value of an evaluation item serving as the outermost child node;
after the evaluation of the leaf nodes of the depth H layer of the depth model tree is completed, the corresponding depth is that all the H-1 nodes have corresponding values in the [0-1] interval, the value of the node type which is a parent node is the value of the node of the layer obtained through the evaluation of the TOPSIS algorithm, and the value of the leaf node is the value of the node which is processed through the homochemotaxis and normalization. Calculating the evaluation value of the attack capability of the evaluation item by using the evaluation items of weapon mounting quantity, weapon loading quantity, launching distance and weapon longitude coefficient; calculating the evaluation value of the penetration capacity of the evaluation item by using the evaluation items of the maneuvering capacity, the armor coefficient, the lowest penetration height and the penetration speed; calculating an evaluation value of the situation perception capability of the evaluation item by utilizing the intelligent level, the radar distance resolution, the radar azimuth resolution, the information sharing capability, the information receiving capability and the information processing capability;
and step seven, taking the father node which obtains the evaluation value as a child node, and evaluating the father node by adopting a hierarchical weighting method and the evaluation value of the child node until the evaluation value of the root node is obtained, wherein the value meets the condition that the numerical value is in the interval of [0,1], and the larger the value is, the closer the performance of each index of the evaluation object is to the ideal extreme value is. The evaluation value of the root node is an evaluation value of an evaluation object. As shown in table 1, the evaluation values of the attack ability, the penetration ability, and the situation awareness ability are used to calculate the evaluation value of the unmanned aerial vehicle, and the unmanned aerial vehicle serves as a root node and the evaluation value thereof is the final evaluation value.
The third step of the present embodiment includes:
the evaluation indexes of the evaluation items are high-quality indexes, low-quality indexes or moderate indexes, the values of the evaluation items are subjected to homotrending treatment, and the values of the evaluation items of the low-quality indexes and the moderate indexes are converted into the values of the evaluation items of the high-quality indexes:
Figure BDA0002699036330000071
Xmndenotes the value, X'mnThe evaluation item number after the homotrending process is indicated, W is an optimum value of the evaluation item, M is 1,2 … M, N is 1,2 … N, M indicates the number of evaluation objects, and N indicates the number of evaluation items.
In the embodiment, the low-quality index is converted into the high-quality index, and the conversion method commonly uses a reciprocal method, namely the low-quality index X in the original data is converted into the high-quality indexmn(m-1, 2,3 …; n-1, 2,3 …) by
Figure BDA0002699036330000072
Transforming the data into high-quality indexes, and then establishing an original data table after homotrenization; and can adjust (expand or contract a certain ratio) the converted data appropriately.
The normalized values of the evaluation items in the fourth step of the present embodiment are:
Figure BDA0002699036330000073
wherein W represents the optimal value of the corresponding evaluation item, and a represents the worst value of the corresponding evaluation item.
The sixth step of the present embodiment includes:
sixthly, constructing a decision matrix V according to the normalized numerical value of the evaluation item r serving as the outermost layer child node:
Figure BDA0002699036330000081
wherein, X ″)mrA normalized numerical value of an evaluation term R representing an m-th evaluation object as an outermost-layer child node, where R is 1,2 … R, and R represents the number of evaluation terms as the outermost-layer child node;
sixthly, acquiring an information weight matrix B of the expert on the attribute of the evaluation item according to a DELPHI method:
Figure BDA0002699036330000082
wherein B ismrThe weight corresponding to the evaluation term r as the outermost layer child node is normalized;
and sixthly, acquiring a weighting judgment matrix Z according to the condition that Z is VB:
Figure BDA0002699036330000083
sixthly, determining the solution of the positive ideal point and the solution of the negative ideal point of the evaluation item of the child nodes at the outermost layer, wherein the solution of the positive ideal point is
Figure BDA0002699036330000084
Solution of negative ideal points to
Figure BDA0002699036330000085
y+Is the optimum value of R evaluation terms, y-Is the worst value of the R evaluation terms;
sixthly, calculating the Euclidean distance between each evaluation object and the positive ideal point and the negative ideal point, acquiring the relative proximity of each evaluation object and the ideal point according to the Euclidean distance, and acquiring the relative proximity c between each evaluation object and the ideal point according to the Euclidean distancem；
Figure BDA0002699036330000086
Is the euclidean distance between the mth evaluation object and the positive ideal point:
Figure BDA0002699036330000091
Figure BDA0002699036330000092
is the euclidean distance between the mth evaluation object and the positive ideal point:
Figure BDA0002699036330000093
Zmrrepresenting elements in the weighted decision matrix Z;
Figure BDA0002699036330000094
cmis the evaluation value of the parent node of the outermost child node.
Calculating each evaluation object cmAnd ranked in order, cmThe larger the value, the closer the mth evaluation object is to the ideal point, i.e., the more excellent the mth evaluation object is. And we can know c by formulamA value of 0 to 1]Within the interval.
In the step S7, the parent node of the outermost child node is taken as the child node;
the hierarchical weighting method comprises the following steps:
Figure BDA0002699036330000095
wherein Q is the evaluation value of the father node, K is the number of child nodes of the father node, dkIs the evaluation value of the kth child node, pkThe weight is the corresponding weight of the kth child node, and the weight is normalized;
and sequentially calculating the evaluation values of the father nodes by a hierarchical weighting method until the evaluation value of the root node is obtained.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the features described in the various dependent claims and herein may be combined in a manner different from that described in the original claim. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.

Claims (5)

1. A method for assessing procurement of materials, the method comprising:
s1, taking each purchase scheme to be evaluated as an evaluation object, determining the problem to be evaluated according to the purchase schemes to be evaluated, and constructing a tree structure evaluation model according to the problem to be evaluated, wherein the tree structure evaluation model comprises a plurality of evaluation items influencing the quality of the evaluation object, and each evaluation item can be divided into a plurality of sub evaluation items according to influence factors to form a tree structure consisting of a plurality of sub nodes, a father node and a root node;
s2, determining the numerical value of the evaluation item of the evaluation object in the tree structure evaluation model;
s3, performing homotrending processing on the evaluation item values determined by the evaluation objects;
s4, carrying out normalization processing according to the evaluation item values after homotrending processing;
s5, determining the outermost child node and the father node thereof according to the depth of the tree in the tree structure evaluation model;
s6, obtaining the evaluation value of the father node by using an approximate ideal solution sorting method and the normalized numerical value of the evaluation item of the outermost child node;
and S7, taking the father node which obtains the evaluation value as a child node, and evaluating the father node by adopting a hierarchical weighting method and the evaluation value of the child node until obtaining the evaluation value of the root node, wherein the evaluation value of the root node is the evaluation value of an evaluation object.
2. The method for evaluating material procurement according to claim 1, wherein S3 comprises:
the evaluation indexes of the evaluation items are high-quality indexes, low-quality indexes or moderate indexes, the values of the evaluation items are subjected to homotrending treatment, and the values of the evaluation items of the low-quality indexes and the moderate indexes are converted into the values of the evaluation items of the high-quality indexes:
Figure FDA0002699036320000011
Xmndenotes the value, X'mnThe evaluation item values after the homotrending process are shown, W is the optimum value of the evaluation item, M is 1,2 … M, N is 1,2 … N, M is the number of evaluation objects, and N is the number of evaluation items.
3. The method for evaluating material procurement as claimed in claim 2, wherein the normalized values of the evaluation terms in S4 are:
Figure FDA0002699036320000012
wherein W represents the optimal value of the corresponding evaluation item, and a represents the worst value of the corresponding evaluation item.
4. The method for evaluating material procurement according to claim 3, wherein S6 comprises:
s61, constructing a decision matrix V according to the normalized numerical value of the evaluation item r as the outermost layer child node:
Figure FDA0002699036320000021
wherein, X ″)mrA normalized numerical value of an evaluation term R representing an m-th evaluation object as an outermost-layer child node, where R is 1,2 … R, and R represents the number of evaluation terms as the outermost-layer child node;
s62, acquiring an information weight matrix B of the expert on the attribute of the evaluation item according to the DELPHI method:
Figure FDA0002699036320000022
wherein B ismrThe weight corresponding to the evaluation term r as the outermost layer child node is normalized;
and S63, obtaining a weighting judgment matrix Z according to the condition that Z is VB:
Figure FDA0002699036320000023
s64, determining the solution of the positive ideal point and the solution of the negative ideal point of the evaluation item of the child node at the outermost layer, wherein the solution of the positive ideal point is
Figure FDA0002699036320000024
Solution of negative ideal points to
Figure FDA0002699036320000025
y+Is the optimum value of R evaluation terms, y-Is the worst value of the R evaluation terms;
s65, calculating Euclidean distance between each evaluation object and the positive ideal point and the negative ideal point, and acquiring the Euclidean distance between each evaluation object and the ideal point according to the Euclidean distanceRelative proximity and according to the Euclidean distance, relative proximity c between each evaluation object and the ideal point is obtainedm；
Figure FDA0002699036320000031
Is the euclidean distance between the mth evaluation object and the positive ideal point:
Figure FDA0002699036320000032
Figure FDA0002699036320000033
is the euclidean distance between the mth evaluation object and the positive ideal point:
Figure FDA0002699036320000034
Zmrrepresenting elements in the weighted decision matrix Z;
Figure FDA0002699036320000035
cmis the evaluation value of the parent node of the outermost child node.
5. The method for evaluating purchase of material according to claim 4, wherein in said S7, a parent node of an outermost child node is used as a child node;
the hierarchical weighting method comprises the following steps:
Figure FDA0002699036320000036
wherein Q is the evaluation value of the father node, K is the number of child nodes of the father node, dkIs the evaluation value of the kth child node, pkThe weight is the corresponding weight of the kth child node, and the weight is normalized;
and sequentially calculating the evaluation values of the father nodes by a hierarchical weighting method until the evaluation value of the root node is obtained.
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