CN111242471A - Index system for selecting selling enterprises in supply chain with farmer cooperative as leading factor - Google Patents

Index system for selecting selling enterprises in supply chain with farmer cooperative as leading factor Download PDF

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CN111242471A
CN111242471A CN202010020902.7A CN202010020902A CN111242471A CN 111242471 A CN111242471 A CN 111242471A CN 202010020902 A CN202010020902 A CN 202010020902A CN 111242471 A CN111242471 A CN 111242471A
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王佳丽
贝金娣
孟双凤
彭雪
韩一鸣
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Abstract

The invention provides an index system for a supply chain selective marketing enterprise taking a farmer cooperative as a leading factor, belonging to the field of agricultural product supply chain production and operation main body selection. The invention comprises the following steps: establishing an agricultural product supply chain, and selecting a sales enterprise index system; calculating the weight of the subjective and objective indexes by using an analytic hierarchy process; and (4) analyzing the specific cooperative case by combining the TOPSIS method, and selecting the optimal supply chain partner. The selection index system comprises 3 first-level indexes, 10 second-level indexes and 20 third-level indexes. The invention is beneficial to effectively shortening the response period of the agricultural product supply chain, is beneficial to information sharing, reduces the total cost of the supply chain and realizes the industrial fusion of the whole supply chain.

Description

Index system for selecting selling enterprises in supply chain with farmer cooperative as leading factor
Technical Field
The invention belongs to the field of agricultural product supply chain production and operation main body selection, and particularly designs an index system of agricultural product supply chain selective sales enterprises dominated by farmer cooperative.
Background
The agricultural product supply chain is a functional chain structure which takes agricultural products as circulation objects, starts from the purchase of agricultural product production data, enters a circulation link through planting and breeding, process supervision, receiving and circulation processing, and reaches a terminal consumer, integrates related information flow, product flow, service flow, fund flow and knowledge flow, and links an agricultural product production data supplier, an agricultural product production base (scattered peasant households), an agricultural product circulation processor, a wholesaler, a retailer and the terminal consumer into a whole.
Disclosure of Invention
The invention provides an index system for selecting a selling enterprise by a supply chain taking a farmer cooperative as a leading factor, which has the following specific technical scheme:
the technical problem of the invention is mainly solved by the following technical scheme:
an index system for selecting a selling enterprise in a supply chain dominated by farmer cooperative is characterized by comprising the following steps:
s1: constructing an index system of a supply chain selective sales enterprise which takes the farmer cooperative as a leading factor;
s2: calculating the weights of the subjective and objective indexes by using an analytic hierarchy process, so that all indexes have objectivity during evaluation;
s3: and (4) performing case analysis on specific cooperative agencies by combining the TOPSIS method to sort the results and select the optimal supply chain partner.
S1: constructing an index system of a supply chain selective sales enterprise which takes farmer cooperative as a leading factor:
(1) enterprise operation capability
The enterprise operation capacity index reflects financial capacity, human resources and market influence capacity; the enterprise operation capacity of the invention comprises 3 secondary indexes; 3 indexes of capital strength, financing capacity and capital turnover capacity are selected to reflect financial capacity, 4 indexes of staff cohesion, staff capacity level, staff average education level and training investment are selected to reflect human resource capacity, and market share and enterprise popularity are selected to reflect market influence capacity;
(2) enterprise management capability
The enterprise management capability embodies the service level, the management level and the sales level; the enterprise management capacity of the invention comprises 3 secondary indexes, and service level capacity is expressed by selecting service range, customer satisfaction, customer demand prediction capacity and customer demand feedback capacity; the management level is represented by operation maintenance capacity, organization structure, information sharing level and inventory management level; the distribution channel diversity, sales amount and warehousing and distribution capacity are selected to represent the sales level;
(3) enterprise development
The enterprise development reflects enterprise reputation and enterprise environment; the enterprise development of the invention comprises 2 secondary indexes; the enterprise reputation is represented by the performance ability, the social responsibility and the cooperative willingness, and the geographical environment represents the enterprise environment;
s2: calculating the weight of the subjective and objective indexes by using an analytic hierarchy process;
s3: analyzing a TOPSIS model;
the invention has the following advantages:
(1) the evaluation result of the designed index body is objective and accurate;
(2) the invention improves the competitiveness of farmers in the market and increases the income of farmers. The agricultural product supply chain dominated by the agricultural cooperative can effectively shorten the response period of the agricultural product supply chain, and the agricultural cooperative can integrate resources into the agricultural product supply chain as much as possible, provide information to other supply chain cooperative partners in time, ensure information sharing, reduce the total cost of the supply chain and increase the income of farmers;
(3) according to the agricultural product supply chain research method, the agricultural product supply chain is researched, production elements such as land, resources, technologies, labor force and the like are effectively integrated through integration and control of logistics, fund flow and information flow by a farmer cooperative, and meanwhile, downstream agricultural product distributors or agricultural product processing enterprises of the supply chain can be better contacted, so that coordinated development of all nodes of the supply chain is promoted;
(4) the invention realizes the optimization of the agricultural product supply chain joint partner and the maximization of the whole income which are dominated by the farmer cooperative, and realizes the optimization of the whole agricultural product supply chain dominated by the farmer cooperative.
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FIG. 1 is a basic block diagram of the agricultural product supply chain.
Fig. 2 is a flow chart of an embodiment.
FIG. 3 is an index system diagram of a supply chain selective sales enterprise dominated by farmer cooperative.
Detailed Description
The technical solution of the present invention is further specifically described by way of example with reference to fig. 1, fig. 2 and fig. 3.
An index system for selecting a selling enterprise in a supply chain dominated by farmer cooperative, which is characterized by comprising the following steps:
s1: constructing an index system of a supply chain selective sales enterprise which takes the farmer cooperative as a leading factor;
s2: an index system is established by an analytic hierarchy process, and subjective and objective index weights are calculated according to an index evaluation system model, so that all indexes are more objective during evaluation;
s3: and (4) performing case analysis on specific cooperative agencies by combining the TOPSIS method to sort the results and select the optimal supply chain partner.
The S1 specifically includes:
s1: designing an index system, wherein the index system comprises 3 first-level indexes, 10 second-level indexes and 20 third-level indexes; the primary indexes comprise three aspects of operation capacity, management capacity and enterprise development; the secondary indexes comprise financial capacity, human resources, market influence capacity, service level, management level, sales level, enterprise reputation and enterprise environment; the third-level indexes comprise capital strength, financing capacity, capital turnover capacity, employee cohesion, employee ability level, employee average education level, training investment, market share, enterprise popularity, service range, customer satisfaction, customer demand forecasting capacity, customer demand feedback capacity, operation maintenance capacity, organizational structure rationality, information sharing level, inventory management level, distribution channel diversity, sales storage and delivery capacity, performance capacity, social responsibility, cooperation willingness and geographic environment;
the S2 specifically includes:
s21: the method for solving the index weight comprises the following steps:
(1) structural hierarchical model
Determining the problem to be researched, establishing a hierarchical structure of the research problem, dividing a decision target, a considered factor and a decision object into a highest layer, a middle layer and a lowest layer according to the mutual relation among the decision target, the considered factor and the decision object, and constructing a hierarchical structure model;
(2) structural judgment matrix
Comparing the factors of the middle layer with each other pairwise, and adopting a relative scale at the moment to reduce the difficulty of comparing the factors with different properties with each other as much as possible so as to improve the accuracy. Define matrix a ═ (b)ij)n*n，bji＝1/bij，bii＝1(i,j＝1,2,...,n)
The decision matrix scale and meaning are shown in Table 1
TABLE 1 judge matrix Scale and implications
Figure BDA0002360702410000021
(3) Normalizing the judgment matrix
Figure BDA0002360702410000022
(4) Matrix row-wise addition
Figure BDA0002360702410000031
(5) Weight vector normalization process
Figure BDA0002360702410000032
Obtaining a weight vector W ═ W1,w2,...,wn)T
(6) Calculating a maximum eigenvalue
Figure BDA0002360702410000033
(7) Consistency check
And (3) carrying out consistency check on the judgment matrix, wherein the check index is C.R and is calculated by the following formula:
Figure BDA0002360702410000034
Figure BDA0002360702410000035
wherein: m is the order number of the judgment matrix, and the R.I value can be obtained by looking up a table 2;
TABLE 2 R.I value Table
Figure BDA0002360702410000036
Consistency test, when C.R <0.1, the consistency requirement is considered to be met; otherwise, the judgment matrix needs to be reconstructed, and consistency check is carried out again;
s22: calculating the subjective and objective index weight according to the formula:
(1) first order index weight coefficient calculation
By processing the data in the questionnaire, the ratio of relative importance of pairwise comparison between the factors is determined layer by layer from top to bottom, and a comprehensive judgment matrix is constructed as shown in table 3:
TABLE 3 first-level index decision matrix
Figure BDA0002360702410000037
The calculation of the first-order index weight coefficients is shown in table 4:
TABLE 4 first-level index weight coefficient calculation detailed table
Figure BDA0002360702410000038
Wherein λmax＝3.038511091 CI＝0.019255545 CR＝0.033199216<0.1；
(2) Second order index weight coefficient calculation
The same method, construct the judgment matrix of the secondary index corresponding to each primary index, calculate the maximum eigenvalue lambdamaxAnd corresponding normalized feature vector (calculation steps are omitted)) Then, the weight coefficient of each second-level index relative to the first-level index can be obtained;
① the weight calculation process of the secondary index under the operation capability is shown in Table 5:
TABLE 5
Figure BDA0002360702410000041
Detailed calculation table
Figure BDA0002360702410000042
Wherein λmax＝3 C·I＝0 C·R＝0<0.1；
② the weight calculation process of the secondary index under the management ability is shown in Table 6.
TABLE 6
Figure BDA0002360702410000043
Detailed calculation table
Figure BDA0002360702410000044
Wherein λmax＝3.038511091 C·I＝0.019255545 C·R＝0.033199216<0.1；
(3) Three-level index weight coefficient calculation
In the same method, a judgment matrix of the three-level index is constructed corresponding to each two-level index, and the maximum eigenvalue lambda of the judgment matrix is calculatedmaxAnd corresponding normalized feature vectors (the calculation steps are omitted), so that the weight coefficient of each relative second-level index and each third-level index can be obtained.
① the process of calculating the weights of the three-level indicators for financial ability is shown in Table 7.
TABLE 7
Figure BDA0002360702410000045
Detailed calculation table
Figure BDA0002360702410000046
Wherein λmax＝3 C·I＝0 C·R＝0<0.1
② the weight calculation process of the three-level index under human resources is shown in Table 8.
TABLE 8
Figure BDA0002360702410000047
Detailed calculation table
Figure BDA0002360702410000048
Wherein λmax＝3.038511091 C·I＝0.019255545 C·R＝0.033199216<0.1
③ weight calculation process for the three-level index at the service level is shown in Table 9.
TABLE 9
Figure BDA0002360702410000051
Detailed calculation table
Figure BDA0002360702410000052
Wherein λmax＝4.043381401 CI＝0.014460467 CR＝0.016067186<0.1
④ weight calculation process for the three-level index at the management level is shown in Table 10.
Watch 10
Figure BDA0002360702410000053
Detailed calculation table
Figure BDA0002360702410000054
Wherein λmax＝4.043381401 C·I＝0.014460467 C·R＝0.016067186<0.1；
⑤ the weight calculation process for the three-level index for sales capability is shown in Table 11.
TABLE 11
Figure BDA0002360702410000055
Detailed calculation table
Figure BDA0002360702410000056
Wherein λmax＝3.038511091 C·I＝0.019255545 C·R＝0.033199216<0.1；
(4) The comprehensive weight calculation results are sorted and summarized to form the supply chain processing enterprise partner selection evaluation index comprehensive weight shown in the table 12.
TABLE 12 supply chain marketing business partner selection evaluation index Integrated weights
Figure BDA0002360702410000057
Figure BDA0002360702410000061
(8) Compute hierarchical Total ordering
The obtained final hierarchical total ordering takes the weight as the weight coefficient of the TOPSIS method to construct a weighted standard decision matrix;
analysis of TOPSIS model:
the main steps of the supply chain cooperative partnership selection of the TOPSIS method based on entropy weight are as follows:
(1) structural judgment matrix
Assuming that the evaluation model has n evaluation objects and m evaluation indexes, the evaluation problem decision matrix a is:
Figure BDA0002360702410000062
a in the matrixij(i 1, 2.. n; j 1, 2.. m) represents the j-th evaluation index value of the i-th supply chain partner; carrying out normalization processing on decision matrixes of related supply chain partners to obtain a normalization matrix R:
Figure BDA0002360702410000063
in the matrix rij(i 1, 2.. times.n; j 1, 2.. times.m) is aij(i 1,2, n, j 1,2, m), wherein
Figure BDA0002360702410000064
(2) Calculating the entropy value of the j index according to an entropy weight formula, namely:
Figure BDA0002360702410000071
in the formula, the first step is that,
Figure BDA0002360702410000072
according to gj＝1-ejCalculating to obtain the difference coefficient of the j index, and calculating the weight omega according to the difference coefficientj：
Figure BDA0002360702410000073
(3) By calculating the normalization matrix and the index weights, a weighted normalization matrix V can be constructed:
Figure BDA0002360702410000074
wherein v isij＝ωjrij；
(4) Computing a positive ideal solution and a negative ideal solution
To rank the candidate supply chain partners for goodness and further to select the optimal supply chain partner, a positive ideal solution V of the calculation index is required+Negative ideal solution V-：
Figure BDA0002360702410000075
Figure BDA0002360702410000076
J in formula 12 and formula 131Shown is a set of benefit indicators, J2Representing a set of cost-type indicators;
(5) calculating the distance between the supply chain partner and the positive ideal solution and the negative ideal solution:
Figure BDA0002360702410000077
Figure BDA0002360702410000078
(6) calculating the relative closeness of each supply chain partner to the ideal solution according to the distance obtained by solving the formula 14 and the formula 15, and obtaining the relative closeness C of each supply chain partner to the positive and negative ideal solutionsi：
Figure BDA0002360702410000079
(7) According to the relative closeness obtained by calculation, carrying out quality sorting
The evaluation of each supply chain partner is ranked according to the calculated relative closeness, the larger the relative closeness is, the better the evaluation result is, so that the farmer cooperative can scientifically and accurately select the supply chain partner relationship, further the whole supply chain optimization is realized, and the development of agricultural modernization is promoted.
Example (b):
taking four sales enterprises of Kunfeng soybean professional cooperative, namely Kangtai supermarket W, Huafeng supermarket X, Tianze supermarket Y and Di Tai supermarket Z as examples, according to the evaluation model established in the text, the 4 sales enterprises are comprehensively evaluated and sorted, and the optimal supply chain partner is selected;
and (3) data analysis:
the method comprises the steps that 4 alternative sales enterprises of the Qufeng soybean professional cooperative are evaluated and researched, and when the data collection aspect is involved, quantitative indexes directly obtain partial original data from the sales enterprises, subjective indexes issue 20 questionnaires to relevant leaders and partial employees of farmer cooperative, and effective questionnaires are extracted from the questionnaires to serve as a value-taking basis;
after the weights are obtained through an analytic hierarchy process, 4 candidate sales enterprise partners are ranked by using a TOPSIS method, so that a basis is provided for selection of farmer cooperative companies, and ranking and optimization of the 4 candidate sales enterprise partners by using the TOPSIS method are mainly performed through the following processes:
(1) constructing a weighted normalized matrix of 4 candidate sales enterprise partner evaluation indexes
①, carrying out vector normalization by a formula 8 to obtain a normalized decision matrix Y as follows:
Figure BDA0002360702410000081
② use v according to normalized data results of 4 candidate processing enterprise partners and the weight obtained by analytic hierarchy processij＝ωjrijA weighted normalization matrix Z can be obtained:
Figure BDA0002360702410000082
(2) determining positive ideal solution and negative ideal solution of 4 candidate sales enterprise partner evaluation indexes
Solving positive ideal solutions and negative ideal solutions of 4 alternative processing enterprises according to formulas 12 and 13:
S+＝(0.0063、0.0024、0.0014、0.0015、0.0006、0.0186、0.0931、0.3137、0.0009、0.0038、0.0003、0.0007、0.0009、0.0043、0.0035、0.0012、0.0145、0.0006、0.0004)
S-＝(0.0057、0.0020、0.0011、0.0013、0.0005、0.0167、0.0706、0.0919、0.0008、0.0036、0.0003、0.0007、0.0007、0.0024、0.0015、0.0011、0.0136、0.0006、0.0004)
(3) determining relative proximity of 4 candidate marketing enterprise partners to positive and negative ideal solutions
The relative closeness results of each sales enterprise partner to the positive ideal solution and the negative ideal solution are obtained by substituting the distances between the 4 sales enterprise partners and the positive ideal solution and the negative ideal solution obtained by the TOPSIS method into formula 2 according to formula 14 and formula 15, and are shown in table 15.
TABLE 15 closeness of selling enterprises to ideal solutions
Figure BDA0002360702410000091
The final result shows that the Di Tai supermarket is the most elegant partner of 4 sales enterprises, and the corresponding ranks are the Di Tai supermarket, the Kangtai supermarket, the Tianze supermarket and the Huafeng supermarket respectively; the evaluation results were also approved by Kunfeng Soybean professional Cooperation; therefore, in selecting a marketing partner, damasco is the most worthy marketing partner of the queenhole soybean professional cooperative.

Claims (2)

1. An index system for selecting a selling enterprise in a supply chain dominated by farmer cooperative, which is characterized by comprising the following steps:
s1: constructing an index system of a supply chain selective sales enterprise which takes the farmer cooperative as a leading factor;
s2: calculating the weights of the subjective and objective indexes by using an analytic hierarchy process, so that all indexes have objectivity during evaluation;
s3: and performing case analysis on specific cooperative agencies by combining a TOPSIS method, ranking the results, and selecting the optimal supply chain partner.
2. The index system of the supply chain selective marketing enterprise dominated by farmer cooperative according to claim 1, wherein a selective index is constructed, and the S1 specifically comprises:
s1: constructing a sales enterprise evaluation index system in a supply chain:
(1) enterprise operation capability
The enterprise operation capacity comprises 3 secondary indexes of financial capacity, human resources and market influence capacity. 3 three-level indexes of capital strength, financing capacity and capital turnover capacity are selected to reflect financial capacity; 4 three-level indexes of the cohesion of the staff, the competence level of the staff, the average education level of the staff and the training investment are selected to reflect the human resource competence; 2 three-level indexes of market share and enterprise awareness are selected to reflect market influence capacity;
(2) enterprise management capability
The enterprise management capability comprises 3 secondary indexes of service level, management level and sales level. 4 three-level indexes of a service range, customer satisfaction, customer demand forecasting capacity and customer demand feedback capacity are selected to reflect service level capacity; 4 three-level indexes of operation maintenance capacity, organization structure, information sharing level and inventory management level are selected to reflect the management level; 3 indexes of distribution channel diversity, sales and storage and delivery capacity are selected to reflect the sales level;
(3) enterprise development
The enterprise development comprises 2 secondary indexes of enterprise reputation and enterprise environment. The 2 three-level indexes of performance ability, social responsibility and cooperative willingness are selected to reflect the reputation of the enterprise, and the geographical environment reflects the environment of the enterprise.
CN202010020902.7A 2020-01-09 2020-01-09 Index system for selecting selling enterprises in supply chain with farmer cooperative as leading factor Pending CN111242471A (en)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112132514A (en) * 2020-09-24 2020-12-25 哈尔滨工程大学 Material purchase assessment method
CN113837648A (en) * 2021-10-11 2021-12-24 讯飞智元信息科技有限公司 Enterprise relevance analysis method and device and associated enterprise recommendation method and device
CN115345585A (en) * 2022-08-16 2022-11-15 清华大学苏州汽车研究院（吴江） Supply chain intelligent management system for enterprise operation
CN117593094A (en) * 2023-12-21 2024-02-23 北京美在客科技有限公司 Big data terminal sales platform system

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112132514A (en) * 2020-09-24 2020-12-25 哈尔滨工程大学 Material purchase assessment method
CN113837648A (en) * 2021-10-11 2021-12-24 讯飞智元信息科技有限公司 Enterprise relevance analysis method and device and associated enterprise recommendation method and device
CN113837648B (en) * 2021-10-11 2023-11-17 讯飞智元信息科技有限公司 Enterprise relevance analysis method, associated enterprise recommendation method and device
CN115345585A (en) * 2022-08-16 2022-11-15 清华大学苏州汽车研究院（吴江） Supply chain intelligent management system for enterprise operation
CN117593094A (en) * 2023-12-21 2024-02-23 北京美在客科技有限公司 Big data terminal sales platform system

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