CN117557073A - Full life cycle provider service management method and system - Google Patents
Full life cycle provider service management method and system Download PDFInfo
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Abstract
The invention discloses a service management method and a service management system for suppliers in a full life cycle, which relate to the technical field of data processing, and the method comprises the following steps: obtaining a service supply chain; the method comprises the steps that a provider cloud management platform is connected to identify a plurality of service nodes; obtaining a service demand change curve; historical production data acquisition is carried out on each supplier in the supplier storage block to obtain a supply variable change curve; predicting based on the service demand change and the supply quantity change curve to obtain a prediction matching degree set corresponding to each service node; and acquiring matching suppliers corresponding to the service nodes from the predicted matching degree set, outputting a plurality of matching suppliers corresponding to the service nodes respectively, carrying out long-term supply identification on the matching suppliers, and storing the long-term supply identification in a long-term supply management module. The invention solves the technical problems of hysteresis and poor management effect of the service management of the suppliers in the prior art, and achieves the technical effects of improving the service management efficiency and the management quality.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a full life cycle service management method and system for suppliers.
Background
Along with the increase of market competition, ensuring the development of high quality business has very important significance for the development of enterprises. In the process of business development, the advantages and disadvantages of the suppliers have great influence on the overall completion degree of the business, however, since the problem of supply occurs after the suppliers are determined, the management of the suppliers has hysteresis, and the enterprises cannot conduct reliable business management of the suppliers. In the prior art, the technical problems of hysteresis and poor management effect exist in the management of the suppliers.
Disclosure of Invention
The application provides a full life cycle service management method and system for a provider, which are used for solving the technical problems of hysteresis and poor management effect of service management in the prior art.
In view of the above, the present application provides a full life cycle service management method and system for a provider.
In a first aspect of the present application, there is provided a full life cycle vendor business management method, the method comprising:
identifying a business process of a target enterprise to obtain a business supply chain, wherein the business supply chain comprises a plurality of business nodes;
the connection provider cloud management platform identifies the service nodes, and the service nodes are taken as blocks to correspondingly acquire provider storage blocks corresponding to each service node;
historical demand collection is carried out on the plurality of corresponding service nodes in the target enterprise, so that a service demand change curve is obtained;
historical production data acquisition is carried out on each supplier in the supplier storage block to obtain a supply variable change curve;
predicting based on the service demand change curve and the supply quantity change curve to obtain a prediction matching degree set corresponding to each service node;
and acquiring matching suppliers corresponding to the service nodes from the predicted matching degree set, outputting a plurality of matching suppliers corresponding to the service nodes respectively, carrying out long-term supply identification on the matching suppliers, and storing the long-term supply identification in a long-term supply management module.
In a second aspect of the present application, there is provided a full life cycle vendor business management system, the system comprising:
the system comprises a supply chain acquisition module, a service supply chain generation module and a service management module, wherein the supply chain acquisition module is used for identifying a service flow of a target enterprise to obtain a service supply chain, and the service supply chain comprises a plurality of service nodes;
the storage block obtaining module is used for connecting with the provider cloud management platform to identify the plurality of service nodes, and correspondingly obtaining the provider storage block corresponding to each service node by taking the plurality of service nodes as blocks;
the change curve acquisition module is used for acquiring historical requirements of the corresponding service nodes in the target enterprise to obtain a service requirement change curve;
the supply quantity change curve obtaining module is used for acquiring historical production data of each supplier in the supplier storage block to obtain a supply quantity change curve;
the matching degree set obtaining module is used for predicting based on the service demand change curve and the supply quantity change curve to obtain a prediction matching degree set corresponding to each service node;
and the matching provider storage module is used for acquiring matching providers of corresponding service nodes from the prediction matching degree set, outputting a plurality of matching providers respectively corresponding to the service nodes, carrying out long-term supply identification on the matching providers, and storing the long-term supply identification into the long-term provider management module.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method comprises the steps of identifying a business process of a target enterprise to obtain a business supply chain, wherein the business supply chain comprises a plurality of business nodes; the method comprises the steps of identifying a plurality of service nodes by a connection provider cloud management platform, correspondingly acquiring provider storage blocks corresponding to each service node by taking the plurality of service nodes as blocks, then carrying out historical demand acquisition on the corresponding plurality of service nodes in a target enterprise to obtain a service demand change curve, further carrying out historical production data acquisition on each provider in the provider storage blocks to obtain a supply change curve, predicting the supply change curve based on the service demand change and the supply change curve to obtain a prediction matching degree set corresponding to each service node, then obtaining matching providers corresponding to the service nodes from the prediction matching degree set, outputting a plurality of matching providers corresponding to the service nodes respectively, carrying out long-term supply identification on the matching providers, and storing the long-term supply identification in a long-term provider management module. The technical effects of improving service management efficiency and management quality are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a full life cycle vendor business management method according to an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of long-term supply identification for multiple matched suppliers in a full life cycle service management method for suppliers according to an embodiment of the present application;
fig. 3 is a schematic flow chart of updating a corresponding identifier provider in a full life cycle provider service management method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a full life cycle service management system of a provider according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a supply chain obtaining module 11, a storage block obtaining module 12, a change curve obtaining module 13, a supply quantity change curve obtaining module 14, a matching degree set obtaining module 15 and a matching supplier storage module 16.
Detailed Description
The application provides a full life cycle service management method and system for a provider, which are used for solving the technical problems of hysteresis and poor management effect of service management of the provider in the prior art.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein.
It should be noted that the terms "comprises" and "comprising," along with any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides a full life cycle service management method for a provider, where the method includes:
s100: identifying a business process of a target enterprise to obtain a business supply chain, wherein the business supply chain comprises a plurality of business nodes;
in one possible embodiment, the target enterprise is any enterprise that needs business process identification. And determining a corresponding business process according to the business type to be completed by the target enterprise, and further determining business nodes to be managed according to different stages of the business process. Preferably, the service nodes include material procurement nodes, shipping nodes, distribution nodes, after-market nodes, and the like. By acquiring the service supply chain, a basis is provided for subsequent supply service management.
S200: the connection provider cloud management platform identifies the service nodes, and the service nodes are taken as blocks to correspondingly acquire provider storage blocks corresponding to each service node;
in one possible embodiment, the provider cloud management platform is utilized to identify the service nodes, determine the service nodes to be managed, then use the service nodes as blocks in a service supply chain, and match the plurality of provider storage blocks stored in the provider cloud management platform, so as to obtain a provider storage block corresponding to each service node. The provider cloud management platform is a platform for comprehensively managing a plurality of providers in a service supply chain. The provider storage block is used for storing provider information which can meet the requirement of each service node. Illustratively, the supplier storage blocks include a raw material procurement supplier storage block, a material carrier storage block, a business outer contractor storage block, and the like. By acquiring the provider storage block corresponding to each service node, a management object is provided for subsequent provider service management, and the technical effects of improving the timeliness and the management accuracy of provider service management are achieved.
S300: historical demand collection is carried out on the plurality of corresponding service nodes in the target enterprise, so that a service demand change curve is obtained;
in one embodiment, the long-term collection of demand data over a historical period of time is performed by a corresponding plurality of business nodes in the target enterprise. And then taking time as an abscissa axis and the requirement of the service node as an ordinate axis, taking the history data acquired for a long time as input data, finding corresponding coordinate points on the coordinate axes, and further sequentially connecting a plurality of coordinate point sets corresponding to a plurality of service nodes according to a time sequence to generate a service requirement change curve. Preferably, the colors of curves corresponding to different service nodes in the service demand change curve are different. The business demand change curve reflects the demand change conditions of a plurality of business nodes of the target enterprise in the historical time, and provides basis for subsequent management.
S400: historical production data acquisition is carried out on each supplier in the supplier storage block to obtain a supply variable change curve;
in one possible embodiment, the supply amount of each supplier in the supplier storage block in the historical time is collected one by one, then the time is taken as an abscissa axis, the supply amount is taken as an ordinate axis, the collected historical supply amount of the supplier is taken as input data, corresponding coordinate points are found on the coordinate axis, and a plurality of corresponding coordinate point sets are sequentially connected according to time sequence, so that a supply amount change curve corresponding to each supplier is generated. Wherein the supply volume change curve reflects supply volume change conditions of each supplier to a target enterprise in a historical time.
S500: predicting based on the service demand change curve and the supply quantity change curve to obtain a prediction matching degree set corresponding to each service node;
further, based on the service demand change curve and the supply quantity change curve, prediction is performed to obtain a prediction matching degree set corresponding to each service node, and step S500 in the embodiment of the present application further includes:
acquiring a supply period of the service supply chain, taking a time node corresponding to each supply period as a prediction node, and establishing a plurality of prediction nodes;
carrying out Markov chain prediction on the service demand change curve corresponding to each service node according to the plurality of prediction nodes, and outputting demand prediction indexes corresponding to the plurality of prediction nodes;
carrying out Markov chain prediction on the supply quantity change curve corresponding to each supplier according to the plurality of prediction nodes, and outputting supply prediction indexes corresponding to the plurality of prediction nodes;
and matching the demand prediction indexes corresponding to the plurality of prediction nodes with the supply prediction indexes corresponding to the plurality of prediction nodes to obtain the prediction matching degree of each provider under the service node, and the like, and outputting a prediction matching degree set corresponding to each service node.
Further, as shown in fig. 2, the identifying the business process of the target enterprise to obtain the business supply chain, step S500 in the embodiment of the present application further includes:
acquiring a plurality of importance indexes by identifying the service importance of each service node in the service supply chain, and acquiring important service nodes with the importance indexes larger than a preset importance index according to the plurality of importance indexes;
continuously predicting the important service nodes to obtain a secondary prediction matching degree set;
and carrying out long-term supply identification on the plurality of matched suppliers according to the secondary prediction matching degree set and the prediction matching degree set.
Further, step S500 in the embodiment of the present application further includes:
acquiring the supply fault frequency of each supplier;
and setting state transfer factors of all nodes in the plurality of prediction nodes based on the supply fault frequency, and optimizing a supply quantity change curve corresponding to each supplier according to the state transfer factors, wherein the state transfer factors are fault disturbance factors.
In the embodiment of the application, the service demand and the supply quantity of each service node are subjected to matching degree prediction according to the obtained service demand change curve and the supply quantity change curve, and a prediction matching degree set corresponding to each node is obtained. The prediction matching degree set corresponding to each service node is a matching degree set obtained after predicting the current service requirement and the supply amount matching condition according to the service requirement in each service node and the historical conditions of the supply amounts provided by a plurality of suppliers corresponding to each service node. The higher the predictive match, the more suitable the provider for the corresponding service node.
In one embodiment, a supply period of the service supply chain is determined, wherein the supply period may be a half year, a year, etc. of a time period in which the target enterprise performs an interval between two adjacent service development. And determining a plurality of prediction nodes in a future time period by taking the time node corresponding to each supply period as one prediction node. The time between every two predicted nodes is one supply cycle. Preferably, the markov chain is used for predicting the service demand change curve corresponding to each service node in the plurality of prediction nodes, that is, when each prediction node is used, the state possibly possessed by each service node of the current prediction node is predicted by using the markov chain based on the service demand state reflected in the service demand change curve of each service node in the history time, so as to obtain the demand prediction index corresponding to the plurality of prediction nodes. Wherein the markov chain describes a sequence of states, each state value of which depends on a finite number of states above. Whereas a markov chain is a sequence of random variables with markov properties. The demand prediction index is used for describing the quantity of the service demand prediction quantity of each service node corresponding to each prediction node. Further, according to the same method, the supply prediction index corresponding to the plurality of prediction nodes is outputted by using markov chain prediction for each supply amount change curve corresponding to each supplier at the plurality of prediction nodes. The supply prediction index is used for describing the quantity of the predicted supply quantity of each supplier in each prediction node.
Furthermore, by matching the demand predictors corresponding to the plurality of service nodes of the plurality of prediction nodes with the supply predictors corresponding to the plurality of suppliers corresponding to each service node, respectively, preferably, by using a cosine similarity formula, similarity calculation is performed on the demand predictors corresponding to the plurality of service nodes of the plurality of prediction nodes and the supply predictors corresponding to the plurality of suppliers corresponding to each service node, respectively, and the calculated similarity is used as the prediction matching degree of each supplier under the service node. And further, using the service nodes as indexes, performing cluster analysis on the obtained predicted matching degrees of a plurality of suppliers under the service nodes, and aggregating the predicted matching degrees of the suppliers belonging to the same service node into one set to generate one predicted matching degree set of each service node pair. Therefore, the aim of predicting the matching degree of a plurality of suppliers corresponding to each service node is fulfilled, and the technical effect of providing reliable basis for the management of the suppliers is achieved.
Preferably, the service importance of each service node in the service supply chain is identified, and a plurality of importance indexes are obtained. The importance indexes describe the business importance degree of each business node to the target enterprise. Acquiring the duration and the node cost of each service node, then respectively comparing the duration of each service node with the sum of the durations of all service nodes to obtain a plurality of first importance factors, further comparing the node cost of each service node with the sum of the node costs of all service nodes to obtain a plurality of second importance factors, and carrying out weighted calculation on the plurality of first importance factors and the plurality of second importance factors to obtain the plurality of importance indexes. The preset importance index is the minimum value that the importance index corresponding to the service node needs to meet when the service node which is set by the person skilled in the art can be used as the important service node which needs to be continuously predicted. Screening the importance indexes based on the preset importance indexes, and taking a plurality of service nodes with the importance indexes larger than the preset importance indexes as important service nodes. The method and the device achieve the aim of screening a plurality of service nodes, and achieve the technical effects of reducing the number of the service nodes needing to be continuously predicted and improving the service management efficiency. And further, continuously predicting the important service nodes and the corresponding multiple suppliers based on the Markov chain, and obtaining a secondary prediction matching degree set based on the same prediction method. And further combining the secondary predicted matching degree and the predicted matching degree set, and carrying out long-term supply identification by a plurality of matching suppliers which meet the minimum predicted matching degree set by a person skilled in the art.
In one embodiment, the number of supply faults of each supplier in the historical time is collected, and then the collection result is respectively compared with the historical total supply times of each supplier, so that the supply fault frequency of each supplier is obtained. Preferably, a state transfer factor of each node is set in the plurality of prediction nodes based on the supply fault frequency, and a supply quantity change curve corresponding to each supplier is optimized according to the state transfer factor, wherein the state transfer factor is a fault disturbance factor. That is, the supply amount change curve is adjusted according to the state transition factors of the respective nodes.
S600: and acquiring matching suppliers corresponding to the service nodes from the predicted matching degree set, outputting a plurality of matching suppliers corresponding to the service nodes respectively, carrying out long-term supply identification on the matching suppliers, and storing the long-term supply identification in a long-term supply management module.
Further, as shown in fig. 3, the provider cloud management platform further includes a risk identification module, where the risk identification module is connected to the long-term provider management module, and step S600 in this embodiment of the present application further includes:
performing fusion training according to a plurality of risk indexes, and establishing a risk identification module, wherein the plurality of risk indexes comprise delivery cycle accuracy, product supply defect degree and emergency delivery feedback degree;
performing risk identification on each identification provider stored in the long-term provider management module according to the risk identification module to obtain a first risk assessment index;
performing risk identification on each identification provider when the identification provider is not stored in the long-term provider management module according to the risk identification module, and acquiring a second risk assessment index;
and if the first risk assessment index is larger than the second risk assessment index, updating the corresponding identification provider.
Further, if the first risk assessment index is greater than the second risk assessment index, the corresponding identification provider is updated, and step S600 in the embodiment of the present application further includes:
if the first risk assessment index is larger than the second risk assessment index, screening and updating suppliers from the supplier storage blocks corresponding to the service nodes by using a digital twin technology;
and updating and replacing the identification provider by the updating provider, wherein the updating provider is a provider with a risk index larger than the first risk assessment index in the provider storage block.
Further, step S600 in the embodiment of the present application further includes:
acquiring a feature set of the identification provider;
matching the provider storage blocks corresponding to the service nodes according to the characteristic set of the identified provider by establishing a digital twin network, and outputting N provider sets with similarity greater than preset similarity;
performing risk identification on the N supplier sets according to the risk identification module to obtain N risk indexes;
and selecting suppliers with risk indexes larger than the first risk assessment index according to N risk indexes corresponding to the N supplier sets as the updated suppliers.
In one embodiment, the matching providers of the corresponding service nodes are obtained from the predicted matching degree set, so that a plurality of matching providers respectively corresponding to the service nodes are output, and then the plurality of matching providers with long-term provider identification are stored in a long-term provider management module. The long-term provider management module is a functional module for intelligently managing the long-term provider.
Preferably, the risk indicator is an item for evaluating the service supply risk of the provider, including the accuracy of the lead time, the defect degree of product supply and the feedback degree of emergency delivery. And further, taking the plurality of risk indexes as indexes, extracting historical management data of the provider cloud platform to obtain a plurality of sample risk index data sets corresponding to a plurality of sample providers, wherein each sample risk index data set corresponds to one sample provider, and comprehensively analyzing the plurality of sample risk index data sets by a person skilled in the art to obtain a plurality of sample risk assessment indexes. Taking a plurality of sample risk index data sets, a plurality of sample suppliers, a plurality of sample risk assessment indexes and a plurality of risk indexes as training data, and performing supervision training on a network layer constructed based on a convolutional neural network until output reaches convergence, so as to obtain the risk identification module after training is completed. The risk identification module is used for intelligently identifying the service supply risk of the provider. And carrying out risk identification on each identification provider stored in the long-term provider management module by using the risk identification module to acquire a first risk assessment index. The first risk assessment index is used for describing the risk degree of each identification provider. And further, performing risk identification on each identification provider when the identification provider is not stored in the long-term provider management module by using the risk identification module, and acquiring a second risk assessment index. Wherein the first risk assessment indicator reflects the risk level of each identified provider after being selected into the long-term provider. The second risk assessment indicator reflects the degree of risk of each identified provider when not selected to be a long-term provider. And then if the first risk assessment index is larger than the second risk assessment index, indicating that the risk degree of each identification provider is increased after the identification provider is selected into the long-term provider, and updating the corresponding identification provider. And further analyzing the risk degree of each identification provider, analyzing the risk degree change of each identification provider before and after the identification provider is selected into the long-term provider, and determining whether the identification provider can be used as the long-term provider.
In one possible embodiment, when the first risk assessment index is greater than the second risk assessment index, the provider storage block corresponding to the service node is screened for updated providers by using a digital twin technique. Preferably, the feature set of the identification provider is obtained, wherein the feature set is used for describing the condition of the identification provider and comprises the features of scale, delivery rate and the like. And then, by establishing a digital twin network, matching is carried out from the provider storage blocks corresponding to the service nodes according to the characteristic set of the identification provider, and N provider sets with similarity greater than preset similarity are output. Wherein the digital twin network is used for mapping input data into two eigenvectors, and determining a 'distance' between the two eigenvectors, namely, output similarity to represent the difference between the inputs. And performing risk identification on the N provider sets according to the risk identification module to obtain N risk indexes. The N risk indexes are used for providing basis for screening N provider sets meeting preset similarity, and then the provider with the risk index larger than the first risk assessment index is selected as the updated provider by the N risk indexes corresponding to the N provider sets. And then updating and replacing the identification provider by the updating provider, wherein the updating provider is a provider with a risk index larger than the first risk assessment index in the provider storage block. The technical effect of continuously analyzing risks of long-term suppliers and guaranteeing the supply quality is achieved.
In summary, the embodiments of the present application have at least the following technical effects:
according to the method, a service supply chain is obtained by identifying a service flow of a target enterprise, then a provider cloud management platform is connected to identify a plurality of service nodes, a plurality of service nodes are used as blocks to correspondingly obtain provider storage blocks corresponding to each service node, further a service demand change curve is obtained, historical production data acquisition is carried out on each provider in the provider storage blocks to obtain a supply change curve, basis is provided for subsequent service management, prediction is carried out based on the service demand change and the supply change curve, a prediction matching degree set corresponding to each service node is obtained, a matching provider corresponding to the service node is obtained from the prediction matching degree set, a plurality of matching providers corresponding to the service nodes are output, long-term supply identification is carried out on the matching providers, and the long-term supply identification is stored in a long-term provider management module. The technical effects of improving the quality of service management of the suppliers and improving the management accuracy are achieved.
Example two
Based on the same inventive concept as the full life cycle vendor business management method in the foregoing embodiments, as shown in fig. 4, the present application provides a full life cycle vendor business management system, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
a supply chain obtaining module 11, configured to identify a business process of a target enterprise, to obtain a business supply chain, where the business supply chain includes a plurality of business nodes;
the storage block obtaining module 12 is configured to connect to a provider cloud management platform to identify the plurality of service nodes, and correspondingly obtain a provider storage block corresponding to each service node by using the plurality of service nodes as a block;
the change curve obtaining module 13 is configured to collect historical requirements of the plurality of service nodes corresponding to the target enterprise, so as to obtain a service requirement change curve;
a supply variable curve obtaining module 14, configured to obtain a supply variable curve by collecting historical production data of each supplier in the supplier storage block;
the matching degree set obtaining module 15 is configured to predict based on the service demand change curve and the supply quantity change curve, and obtain a predicted matching degree set corresponding to each service node;
and the matching provider storage module 16 is configured to obtain matching providers of corresponding service nodes from the predicted matching degree set, output a plurality of matching providers corresponding to the service nodes respectively, perform long-term supply identification on the plurality of matching providers, and store the long-term supply identification in the long-term provider management module.
Further, the matching degree set obtaining module 15 is configured to perform the following steps:
acquiring a supply period of the service supply chain, taking a time node corresponding to each supply period as a prediction node, and establishing a plurality of prediction nodes;
carrying out Markov chain prediction on the service demand change curve corresponding to each service node according to the plurality of prediction nodes, and outputting demand prediction indexes corresponding to the plurality of prediction nodes;
carrying out Markov chain prediction on the supply quantity change curve corresponding to each supplier according to the plurality of prediction nodes, and outputting supply prediction indexes corresponding to the plurality of prediction nodes;
and matching the demand prediction indexes corresponding to the plurality of prediction nodes with the supply prediction indexes corresponding to the plurality of prediction nodes to obtain the prediction matching degree of each provider under the service node, and the like, and outputting a prediction matching degree set corresponding to each service node.
Further, the matching degree set obtaining module 15 is configured to perform the following steps:
acquiring the supply fault frequency of each supplier;
and setting state transfer factors of all nodes in the plurality of prediction nodes based on the supply fault frequency, and optimizing a supply quantity change curve corresponding to each supplier according to the state transfer factors, wherein the state transfer factors are fault disturbance factors.
Further, the matching degree set obtaining module 15 is configured to perform the following steps:
acquiring a plurality of importance indexes by identifying the service importance of each service node in the service supply chain, and acquiring important service nodes with the importance indexes larger than a preset importance index according to the plurality of importance indexes;
continuously predicting the important service nodes to obtain a secondary prediction matching degree set;
and carrying out long-term supply identification on the plurality of matched suppliers according to the secondary prediction matching degree set and the prediction matching degree set.
Further, the matching provider storage module 16 is configured to perform the following steps:
performing fusion training according to a plurality of risk indexes, and establishing a risk identification module, wherein the plurality of risk indexes comprise delivery cycle accuracy, product supply defect degree and emergency delivery feedback degree;
performing risk identification on each identification provider stored in the long-term provider management module according to the risk identification module to obtain a first risk assessment index;
performing risk identification on each identification provider when the identification provider is not stored in the long-term provider management module according to the risk identification module, and acquiring a second risk assessment index;
and if the first risk assessment index is larger than the second risk assessment index, updating the corresponding identification provider.
Further, the matching provider storage module 16 is configured to perform the following steps:
if the first risk assessment index is larger than the second risk assessment index, screening and updating suppliers from the supplier storage blocks corresponding to the service nodes by using a digital twin technology;
and updating and replacing the identification provider by the updating provider, wherein the updating provider is a provider with a risk index larger than the first risk assessment index in the provider storage block.
Further, the matching provider storage module 16 is configured to perform the following steps:
acquiring a feature set of the identification provider;
matching the provider storage blocks corresponding to the service nodes according to the characteristic set of the identified provider by establishing a digital twin network, and outputting N provider sets with similarity greater than preset similarity;
performing risk identification on the N supplier sets according to the risk identification module to obtain N risk indexes;
and selecting suppliers with risk indexes larger than the first risk assessment index according to N risk indexes corresponding to the N supplier sets as the updated suppliers.
It should be noted that the sequence of the embodiments of the present application is merely for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the present application is not intended to limit the invention to the particular embodiments of the present application, but to limit the scope of the invention to the particular embodiments of the present application.
The specification and drawings are merely exemplary of the application and are to be regarded as covering any and all modifications, variations, combinations, or equivalents that are within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the present application and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (8)
1. A full life cycle vendor business management method, the method comprising:
identifying a business process of a target enterprise to obtain a business supply chain, wherein the business supply chain comprises a plurality of business nodes;
the connection provider cloud management platform identifies the service nodes, and the service nodes are taken as blocks to correspondingly acquire provider storage blocks corresponding to each service node;
historical demand collection is carried out on the plurality of corresponding service nodes in the target enterprise, so that a service demand change curve is obtained;
historical production data acquisition is carried out on each supplier in the supplier storage block to obtain a supply variable change curve;
predicting based on the service demand change curve and the supply quantity change curve to obtain a prediction matching degree set corresponding to each service node;
and acquiring matching suppliers corresponding to the service nodes from the predicted matching degree set, outputting a plurality of matching suppliers corresponding to the service nodes respectively, carrying out long-term supply identification on the matching suppliers, and storing the long-term supply identification in a long-term supply management module.
2. The method of claim 1, wherein the predicting is performed based on the traffic demand profile and the supply volume profile to obtain a set of predicted matches for each traffic node, the method further comprising:
acquiring a supply period of the service supply chain, taking a time node corresponding to each supply period as a prediction node, and establishing a plurality of prediction nodes;
carrying out Markov chain prediction on the service demand change curve corresponding to each service node according to the plurality of prediction nodes, and outputting demand prediction indexes corresponding to the plurality of prediction nodes;
carrying out Markov chain prediction on the supply quantity change curve corresponding to each supplier according to the plurality of prediction nodes, and outputting supply prediction indexes corresponding to the plurality of prediction nodes;
and matching the demand prediction indexes corresponding to the plurality of prediction nodes with the supply prediction indexes corresponding to the plurality of prediction nodes to obtain the prediction matching degree of each provider under the service node, and the like, and outputting a prediction matching degree set corresponding to each service node.
3. The method of claim 2, wherein the method further comprises:
acquiring the supply fault frequency of each supplier;
and setting state transfer factors of all nodes in the plurality of prediction nodes based on the supply fault frequency, and optimizing a supply quantity change curve corresponding to each supplier according to the state transfer factors, wherein the state transfer factors are fault disturbance factors.
4. The method of claim 1, wherein identifying the business process for the target enterprise results in a business supply chain, the method further comprising:
acquiring a plurality of importance indexes by identifying the service importance of each service node in the service supply chain, and acquiring important service nodes with the importance indexes larger than a preset importance index according to the plurality of importance indexes;
continuously predicting the important service nodes to obtain a secondary prediction matching degree set;
and carrying out long-term supply identification on the plurality of matched suppliers according to the secondary prediction matching degree set and the prediction matching degree set.
5. The method of claim 1, wherein the vendor cloud management platform further comprises a risk identification module coupled to the long-term vendor management module, the method further comprising:
performing fusion training according to a plurality of risk indexes, and establishing a risk identification module, wherein the plurality of risk indexes comprise delivery cycle accuracy, product supply defect degree and emergency delivery feedback degree;
performing risk identification on each identification provider stored in the long-term provider management module according to the risk identification module to obtain a first risk assessment index;
performing risk identification on each identification provider when the identification provider is not stored in the long-term provider management module according to the risk identification module, and acquiring a second risk assessment index;
and if the first risk assessment index is larger than the second risk assessment index, updating the corresponding identification provider.
6. The method of claim 5, wherein if the first risk assessment indicator is greater than the second risk assessment indicator, updating the corresponding identified provider, the method further comprising:
if the first risk assessment index is larger than the second risk assessment index, screening and updating suppliers from the supplier storage blocks corresponding to the service nodes by using a digital twin technology;
and updating and replacing the identification provider by the updating provider, wherein the updating provider is a provider with a risk index larger than the first risk assessment index in the provider storage block.
7. The method of claim 6, wherein the method further comprises:
acquiring a feature set of the identification provider;
matching the provider storage blocks corresponding to the service nodes according to the characteristic set of the identified provider by establishing a digital twin network, and outputting N provider sets with similarity greater than preset similarity;
performing risk identification on the N supplier sets according to the risk identification module to obtain N risk indexes;
and selecting suppliers with risk indexes larger than the first risk assessment index according to N risk indexes corresponding to the N supplier sets as the updated suppliers.
8. A full life cycle vendor business management system, said system comprising:
the system comprises a supply chain acquisition module, a service supply chain generation module and a service management module, wherein the supply chain acquisition module is used for identifying a service flow of a target enterprise to obtain a service supply chain, and the service supply chain comprises a plurality of service nodes;
the storage block obtaining module is used for connecting with the provider cloud management platform to identify the plurality of service nodes, and correspondingly obtaining the provider storage block corresponding to each service node by taking the plurality of service nodes as blocks;
the change curve acquisition module is used for acquiring historical requirements of the corresponding service nodes in the target enterprise to obtain a service requirement change curve;
the supply quantity change curve obtaining module is used for acquiring historical production data of each supplier in the supplier storage block to obtain a supply quantity change curve;
the matching degree set obtaining module is used for predicting based on the service demand change curve and the supply quantity change curve to obtain a prediction matching degree set corresponding to each service node;
and the matching provider storage module is used for acquiring matching providers of corresponding service nodes from the prediction matching degree set, outputting a plurality of matching providers respectively corresponding to the service nodes, carrying out long-term supply identification on the matching providers, and storing the long-term supply identification into the long-term provider management module.
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