CN116070920A - All-channel intelligent operation and maintenance management method and system - Google Patents
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Abstract
The invention provides a full-channel intelligent operation and maintenance management method and system, which relate to the technical field of enterprise operation and maintenance management, and are used for constructing a client file based on basic information of a target client, further carrying out enterprise planning data analysis, setting a preset evaluation time interval and combining the client file to obtain first sales reference comparison data; annual sales reference analysis is carried out on the client files to generate second sales reference comparison data, data integration optimization of the sales reference comparison data is carried out on the basis of analysis data to obtain estimated reference data, order auxiliary parameters are generated on the basis of real-time inventory data, real-time order quantity data and the estimated reference data, order suggestion of target clients is carried out on the basis of the estimated reference data, the technical problems that an enterprise marketing management method in the prior art is complex, cost is high, management efficiency is low, accurate management and control of the enterprise cannot be carried out are solved, order data of the enterprise are generated by obtaining the auxiliary order parameters, and product management efficiency and accuracy can be effectively improved.
Description
Technical Field
The invention relates to the technical field of enterprise operation and maintenance management, in particular to a full-channel intelligent operation and maintenance management method and system.
Background
In order to adapt to the development of market economy, market research is required, the marketing management strategy of an enterprise is formulated according to market supply and demand, and meanwhile, products are optimized and updated according to the demand, so that the enterprise can better adapt to the demand of the market development, long-term sustainable development is realized, the market is in a real-time dynamic fluctuation state for the supply and demand of the products, the marketing management strategy of the enterprise is required to be pertinently adjusted based on the real-time state so as to ensure the normal operation of the enterprise, and at present, marketing management is mainly carried out through professional personnel through the analysis of the development rule of the market economy, and due to the large analysis data quantity, certain manpower and financial resources are wasted in the management process.
In the prior art, because the marketing management method of the enterprise is complex, the cost is high, the management efficiency is low, and the enterprise cannot be accurately managed and controlled.
Disclosure of Invention
The application provides a full-channel intelligent operation and maintenance management method and system, which are used for solving the technical problems that the marketing management method of enterprises in the prior art is complex, the cost is high, the management efficiency is low, and the accurate management and control of the enterprises cannot be performed.
In view of the above problems, the present application provides a method and a system for intelligent operation and maintenance management of a whole channel.
In a first aspect, the present application provides a method for managing intelligent operation and maintenance of a whole channel, where the method includes: collecting basic information of a target client, and constructing a client file of the target client based on the basic information; obtaining enterprise planning data of the target client, and analyzing the enterprise planning data to obtain analysis data; setting a preset evaluation time interval, and obtaining first sales reference comparison data through the preset evaluation time interval and the client file; annual sales reference analysis is carried out based on the client file, and second sales reference comparison data are generated; performing data integration optimization on the first sales reference comparison data and the second sales reference comparison data through the analysis data to obtain estimated reference data; acquiring real-time inventory data and real-time order quantity data of the target customer, and generating an order auxiliary parameter based on the real-time inventory data, the real-time order quantity data and the estimated reference data; and carrying out order proposal of the target client through the order auxiliary parameters.
In a second aspect, the present application provides a full channel intelligent operation and maintenance management system, the system comprising: the archive construction module is used for collecting basic information of a target client and constructing a client archive of the target client based on the basic information; the data analysis module is used for obtaining enterprise planning data of the target client and analyzing the enterprise planning data to obtain analysis data; the first data acquisition module is used for setting a preset evaluation time interval and obtaining first sales reference comparison data through the preset evaluation time interval and the client file; the second data acquisition module is used for carrying out annual sales reference analysis based on the client file and generating second sales reference comparison data; the data optimization module is used for carrying out data integration optimization on the first sales reference comparison data and the second sales reference comparison data through the analysis data to obtain estimated reference data; the parameter generation module is used for obtaining real-time inventory data and real-time order quantity data of the target clients and generating order auxiliary parameters based on the real-time inventory data, the real-time order quantity data and the estimated reference data; and the proposal acquisition module is used for carrying out the order proposal of the target client through the order auxiliary parameters.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the full-channel intelligent operation and maintenance management method, a client file of a target client is constructed based on basic information of the target client, and enterprise planning data of the target client are analyzed to obtain analysis data; setting a preset evaluation time interval, and obtaining first sales reference comparison data through the preset evaluation time interval and the client file; annual sales volume reference analysis is carried out based on the client files, second sales volume reference comparison data is generated, data integration optimization of the first sales volume reference comparison data and the second sales volume reference comparison data is carried out based on the analysis data, estimated reference data is obtained, order auxiliary parameters are generated based on real-time inventory data of the target clients, real-time order volume data and the estimated reference data, order suggestion of the target clients is carried out through the order auxiliary parameters, the technical problems that an enterprise marketing management method is complex, high in cost and low in management efficiency, and accurate management and control of the enterprise cannot be carried out in the prior art are solved, order data of the enterprise is generated by acquiring the auxiliary order parameters, and product management efficiency and accuracy can be effectively improved.
Drawings
FIG. 1 is a flow chart of a method for managing intelligent operation and maintenance of a whole channel;
FIG. 2 is a schematic diagram of a process for obtaining estimated reference data in a full channel intelligent operation and maintenance management method;
FIG. 3 is a schematic diagram of a feedback evaluation tag acquiring process in a full channel intelligent operation and maintenance management method;
fig. 4 is a schematic structural diagram of a full-channel intelligent operation and maintenance management system.
Reference numerals illustrate: the system comprises a file construction module 11, a data analysis module 12, a first data acquisition module 13, a second data acquisition module 14, a data optimization module 15, a parameter generation module 16 and a suggestion acquisition module 17.
Detailed Description
The application provides a full-channel intelligent operation and maintenance management method and system, which constructs a client file based on basic information of a target client, further analyzes enterprise planning data, sets a preset evaluation time interval and combines the client file to obtain first sales reference comparison data; annual sales reference analysis is carried out on the client files to generate second sales reference comparison data, data integration optimization of the sales reference comparison data is carried out on the basis of analysis data to obtain estimated reference data, order auxiliary parameters are generated on the basis of real-time inventory data, real-time order quantity data and the estimated reference data, order advice of target clients is carried out on the basis of the estimated reference data, and the technical problems that a marketing management method of an enterprise is complex, high in cost and low in management efficiency, and accurate management and control of the enterprise cannot be carried out in the prior art are solved.
Example 1
As shown in fig. 1, the present application provides a full channel intelligent operation and maintenance management method, which includes:
step S100: collecting basic information of a target client, and constructing a client file of the target client based on the basic information;
specifically, in order to ensure the sustainable stable development of the enterprise, the adaptive analysis can be performed based on the live condition of the enterprise and the supply and demand of the society, the enterprise planning and the market competitiveness are used as references, the product order suggestion of the enterprise is performed, the basic information of the target user is firstly collected, the target client refers to the enterprise to be subjected to marketing analysis, the enterprise direction of the target client is obtained, the enterprise direction comprises enterprise scale, various production products and corresponding sales channels, sales volume and the like, the information is stored as the basic information of the target client, the construction of the client file of the target client is further performed based on the basic information, for example, the classification and integration processing of the basic information can be performed based on time sequence in units of product category, the client file of the target client is generated, and the acquisition of the client file provides a basic information basis for the analysis management of the enterprise to be performed later.
Step S200: obtaining enterprise planning data of the target client, and analyzing the enterprise planning data to obtain analysis data;
specifically, the enterprise planning data of the target customer is collected, under market competition, in order to ensure sustainable development of an enterprise, technical improvement is performed on a product to improve product quality and further improve market competitiveness of the product, the enterprise planning data mainly refer to planning in quality, including short-term planning and long-term planning, short-term benefits are considered, long-term development of the enterprise is further emphasized, the enterprise planning data is obtained, the enterprise planning data is further analyzed, specific execution steps, corresponding execution time limit and execution results in the enterprise planning data are determined, the analysis data are obtained, and data support is provided for reference data determination of subsequent production sales by obtaining the analysis data.
Step S300: setting a preset evaluation time interval, and obtaining first sales reference comparison data through the preset evaluation time interval and the client file;
step S400: annual sales reference analysis is carried out based on the client file, and second sales reference comparison data are generated;
specifically, the predetermined evaluation time interval is set, where the predetermined evaluation time interval refers to a time interval in which sales analysis and evaluation are performed once, for example, a month or a quarter may be used as the predetermined evaluation time interval, the client file is divided based on the predetermined evaluation time interval, the client file of the predetermined evaluation time interval is determined, and identified based on a time sequence, and further data classification and integration processing is performed to obtain the first sales reference comparison data, where the first sales reference comparison data is further obtained by extracting sales records corresponding to each time period with the client file as reference data, further performing product annual condition analysis on the target client, determining an annual condition evaluation result based on the sales records, further performing product competitive analysis prediction on the target client, performing annual sales reference analysis with a product competitive trend prediction result as an influence parameter, and obtaining the second sales reference comparison data.
Further, the step S400 of the present application further includes:
step S410: acquiring a sales record set of the same year and month according to the client file, and carrying out year identification;
step S420: annual quotation evaluation is carried out based on the target customer product, and an annual quotation evaluation result is generated;
step S430: and carrying out sales trend analysis through the annual market evaluation result and the sales record set, and obtaining the second sales reference comparison data through the sales trend analysis result.
Specifically, the sales records of the same annual month are extracted based on the client file, identification is performed based on a time sequence, the sales records comprise the sales of products corresponding to a plurality of products in the client file and the percentage of the sales of the products to the social demands, further, annual quotation evaluation is performed on the products of the target clients to generate the annual quotation evaluation result, wherein the higher the sales is, the higher the occupation ratio is, the better the corresponding annual evaluation is, further, the product competitiveness evaluation of the target clients is performed based on the annual quotation evaluation result and the sales records, competitive trend prediction is performed based on the evaluation result, sales trend analysis is performed with the prediction result as a reference, the sales trend analysis result is generated as the second sales reference comparison data, and reference information is provided for product production planning of the subsequent enterprises through sales trend prediction.
Further, step S430 of the present application further includes:
step S431: constructing a corresponding relation between the annual quotation evaluation result and the sales volume record set;
step S432: performing product competitiveness evaluation of the target client based on the corresponding relation construction result to generate a competitiveness evaluation parameter;
step S433: performing competitive trend prediction of the target client based on the competitive evaluation parameters to generate a competitive trend prediction result;
step S434: and obtaining the sales trend analysis result through the competitive trend prediction result.
Specifically, a corresponding relation between the annual market evaluation result and the sales volume record set is constructed, in general, the annual market of the product is in direct proportion to the sales volume, the better the market is, the higher the corresponding sales volume is, data acquisition is carried out on the sales volume of the product of the same industry and the same type of product and the corresponding sales volume based on the product of the target customer, competitive evaluation is carried out on the product of the target customer based on the actual market demand of the product, the competitive evaluation parameter is generated, the competitive market is in inverse proportion to the market demand and in direct proportion to the production sales volume of the product of the same type, competitive trend prediction is carried out on the target customer based on the competitive evaluation parameter, a competitive trend prediction curve is constructed by taking the actual data as a reference, the competitive trend prediction result is generated in a certain time period in the future based on the current competitive trend, the target customer is analyzed by taking the competitive trend prediction result as the reference, the competitive trend analysis result is obtained, and the competitive trend is accurately analyzed, and the competitive trend is inversely proportional to the sales volume is improved.
Step S500: performing data integration optimization on the first sales reference comparison data and the second sales reference comparison data through the analysis data to obtain estimated reference data;
specifically, the analysis data is obtained by carrying out data analysis on enterprise planning data of the target enterprise, data integration optimization is carried out on the first sales reference comparison data and the second sales reference comparison data based on the analysis data, the first sales reference comparison data is sales data corresponding to the client files in the preset time evaluation interval, the second sales reference data refers to sales prediction data of the target client determined by taking the client files as reference data, deviation correction of the reference data is carried out based on the three types of data, the estimated reference data is obtained, the estimated reference data is predicted product sales and quality, and the data accuracy of the estimated reference data can be improved by carrying out data integration optimization on the reference data.
Further, as shown in fig. 2, step S500 of the present application further includes:
step S510: setting a planning constraint level set;
step S520: judging whether cross-level planning exists in the enterprise planning of the target client according to the planning constraint level set and the analysis data;
step S530: when the enterprise planning of the target client has cross-grade planning, generating cross-grade influence parameters;
step S540: and carrying out data integration optimization on the first sales reference comparison data and the second sales reference comparison data based on the cross-grade influence parameters.
Specifically, setting the planning constraint level set based on enterprise planning of the target client, where the planning constraint level set refers to constraint levels set for product quality of the target client, and product optimization scales determined based on real-time conditions of an enterprise, such as suitability range and service life of the product, are determined, meanwhile, the enterprise planning needs to be performed step by step according to reality conditions, and further, whether cross-level planning exists in the enterprise planning of the target client based on the planning constraint level set and the analysis data, that is, when the planning of the enterprise planning of the target client exceeds the planning constraint level, a certain influence is caused on predicted product sales to a certain extent, the cross-level influence parameter is generated, and data integration optimization is performed on the first sales reference comparison data and the second sales reference comparison data based on the cross-level influence parameter, so that accuracy of finally determined estimated reference data can be effectively improved by performing cross-level analysis of the enterprise planning.
Step S600: acquiring real-time inventory data and real-time order quantity data of the target customer, and generating an order auxiliary parameter based on the real-time inventory data, the real-time order quantity data and the estimated reference data;
step S700: and carrying out order proposal of the target client through the order auxiliary parameters.
Specifically, the real-time inventory data and the real-time order quantity data are collected for the target customer, the real-time inventory data, the real-time order quantity data and the estimated reference data of the target customer are used as references to generate the order auxiliary parameters, the product optimization sorting of the partners is performed according to the order auxiliary parameters, further, the goods source screening is performed, the order suggestion is generated based on the order auxiliary parameters, the order suggestion is a preferred partner enterprise with the best product suitability and quality with the target enterprise, the order suggestion is sent to the target enterprise, the goods source determination is performed based on the order suggestion, and the product quality of the target customer can be ensured to a certain extent by improving the product quality of the partner enterprise.
Further, step S600 of the present application further includes:
step S610: constructing an early warning time threshold according to the real-time inventory data;
step S620: judging whether corresponding order data exists at the early warning time threshold node;
step S630: and when the corresponding order data does not exist in the early warning time threshold node, carrying out real-time early warning of inventory shortage.
Specifically, the construction of the early warning time threshold is performed based on the real-time inventory data, the early warning time threshold refers to rated sales time, normal sales in the early warning time threshold should be guaranteed by the real-time inventory data, whether corresponding order data exist in the early warning time threshold node or not is judged, whether sales in preset time can be met or not is judged, when the corresponding order data do not exist in the early warning time threshold node, insufficient inventory of products is indicated, the risk of shortage exists, early warning information can be generated to perform early warning and warning, and accordingly the target enterprises can be reminded of timely replenishing inventory sources to guarantee normal continuous sales of the products.
Further, step S700 of the present application further includes:
step S710: collecting feedback evaluation data of a cooperative enterprise;
step S720: constructing a feedback evaluation tag based on the feedback evaluation data;
step S730: matching the feedback evaluation labels based on the order auxiliary parameters, and generating sequential enterprise recommendation data based on a matching result;
step S740: and feeding back the sequential enterprise recommendation data to the target client.
Specifically, feedback evaluation data of the cooperative enterprise is collected, the feedback evaluation data refers to feedback evaluation of related products performed on a partner of the target client, such as quality, sales surface, service life and the like of product parts, mapping correspondence between the cooperative enterprise products and the feedback evaluation data is further performed, further construction of a feedback evaluation label is performed, the feedback evaluation label corresponds to the cooperative enterprise products and can be used as visual evaluation of the cooperative enterprise products, further, the order auxiliary parameters and the feedback evaluation label are matched, a matching result is obtained, sequential ordering is performed based on the superiority and inferiority, sequential enterprise recommendation data is generated, the sequential enterprise recommendation data is further fed back to the target client, and the target client can refer to the sequential enterprise recommendation data to perform screening determination of the cooperative enterprise to determine an optimal cooperative enterprise so as to ensure product quality.
Further, as shown in fig. 3, step S720 of the present application further includes:
step S721: obtaining the cooperation order quantity data of a cooperation enterprise, and taking the cooperation order quantity data as first evaluation data;
step S722: obtaining brand data of a demand party of a partner enterprise, and taking the brand data as second evaluation data;
step S723: performing value degree identification of the feedback evaluation data according to the first evaluation data and the second evaluation data;
step S724: and constructing the feedback evaluation label through the feedback evaluation data with the value identification.
Specifically, the cooperation enterprise is subjected to cooperation order quantity data acquisition, the cooperation order quantity data of a plurality of cooperation enterprises are determined and used as the first evaluation data, the brand data of a demand party of the cooperation enterprise is acquired, the brand data are used as the second evaluation data, the product brand, model parameters and the like of the cooperation enterprise are included, the first evaluation data and the second evaluation data are used as references to carry out the value identification of the feedback evaluation data, the product quality of the cooperation enterprise affects the product of the target customer to a certain extent, the construction of the feedback evaluation label is further carried out based on the feedback evaluation data with the value identification, and then the recommendation of the cooperation enterprise is carried out according to the feedback evaluation label.
Example two
Based on the same inventive concept as the method for managing intelligent operation and maintenance of all channels in the foregoing embodiments, as shown in fig. 4, the present application provides a system for managing intelligent operation and maintenance of all channels, where the system includes:
the archive construction module 11 is used for collecting basic information of a target client and constructing a client archive of the target client based on the basic information;
a data parsing module 12, where the data parsing module 12 is configured to obtain enterprise planning data of the target client, and parse the enterprise planning data to obtain parsed data;
the first data acquisition module 13 is configured to set a predetermined evaluation time interval, and obtain first sales reference comparison data through the predetermined evaluation time interval and the client file;
the second data acquisition module 14 is configured to perform annual sales reference analysis based on the client profile, and generate second sales reference comparison data;
the data optimization module 15 is configured to perform data integration optimization of the first sales reference comparison data and the second sales reference comparison data through the analysis data, so as to obtain estimated reference data;
a parameter generation module 16, wherein the parameter generation module 16 is configured to obtain real-time inventory data and real-time order volume data of the target customer, and generate an order assistance parameter based on the real-time inventory data, the real-time order volume data, and the pre-estimated reference data;
Further, the system further comprises:
the record identification module is used for obtaining a sales record set of the same year and month according to the client file and carrying out year identification;
the product evaluation module is used for performing annual quotation evaluation based on the product of the target client and generating an annual quotation evaluation result;
and the trend analysis module is used for carrying out sales trend analysis through the annual market evaluation result and the sales record set, and obtaining the second sales reference comparison data through the sales trend analysis result.
Further, the system further comprises:
the relation construction module is used for constructing the corresponding relation between the annual quotation evaluation result and the sales volume record set;
the evaluation parameter generation module is used for carrying out product competitiveness evaluation of the target client based on the corresponding relation construction result to generate a competitiveness evaluation parameter;
the trend prediction module is used for predicting the competitive trend of the target client based on the competitive evaluation parameter and generating a competitive trend prediction result;
and the analysis result acquisition module is used for acquiring the sales volume trend analysis result through the competitive trend prediction result.
Further, the system further comprises:
the level setting module is used for setting a planning constraint level set;
the planning judgment module is used for judging whether cross-grade planning exists in the enterprise planning of the target client according to the planning constraint grade set and the analysis data;
the influence parameter generation module is used for generating cross-grade influence parameters when the cross-grade planning exists in the enterprise planning of the target client;
and the data integration optimization module is used for performing data integration optimization on the first sales reference comparison data and the second sales reference comparison data based on the cross-grade influence parameters.
Further, the system further comprises:
the data acquisition module is used for acquiring feedback evaluation data of the cooperative enterprises;
the label construction module is used for constructing a feedback evaluation label based on the feedback evaluation data;
the label matching module is used for matching the feedback evaluation labels based on the order auxiliary parameters and generating sequential enterprise recommendation data based on a matching result;
and the data feedback module is used for feeding back the sequential enterprise recommendation data to the target client.
Further, the system further comprises:
the first evaluation data acquisition module is used for acquiring the cooperation order quantity data of the cooperation enterprise, and taking the cooperation order quantity data as first evaluation data;
the second evaluation data acquisition module is used for acquiring brand data of a demand party of the cooperative enterprise, and taking the brand data as second evaluation data;
the data identification module is used for carrying out value identification on the feedback evaluation data according to the first evaluation data and the second evaluation data;
and the evaluation label construction module is used for constructing the feedback evaluation label through the feedback evaluation data with the value identification.
Further, the system further comprises:
the threshold value construction module is used for constructing an early warning time threshold value according to the real-time inventory data;
the data judging module is used for judging whether corresponding order data exists at the early warning time threshold node;
and the real-time early warning module is used for carrying out real-time early warning of insufficient inventory when the corresponding order data does not exist in the early warning time threshold node.
Through the foregoing detailed description of a full-channel intelligent operation and maintenance management method, those skilled in the art can clearly know a full-channel intelligent operation and maintenance management method and system in this embodiment, and for the device disclosed in the embodiment, since the device corresponds to the method disclosed in the embodiment, the description is simpler, and relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The method for managing the intelligent operation and maintenance of the whole channel is characterized by comprising the following steps:
collecting basic information of a target client, and constructing a client file of the target client based on the basic information;
obtaining enterprise planning data of the target client, and analyzing the enterprise planning data to obtain analysis data;
setting a preset evaluation time interval, and obtaining first sales reference comparison data through the preset evaluation time interval and the client file;
annual sales reference analysis is carried out based on the client file, and second sales reference comparison data are generated;
performing data integration optimization on the first sales reference comparison data and the second sales reference comparison data through the analysis data to obtain estimated reference data;
acquiring real-time inventory data and real-time order quantity data of the target customer, and generating an order auxiliary parameter based on the real-time inventory data, the real-time order quantity data and the estimated reference data;
and carrying out order proposal of the target client through the order auxiliary parameters.
2. The method of claim 1, wherein the generating second sales reference comparison data based on the annual sales reference analysis of the customer profile further comprises:
acquiring a sales record set of the same year and month according to the client file, and carrying out year identification;
annual quotation evaluation is carried out based on the target customer product, and an annual quotation evaluation result is generated;
and carrying out sales trend analysis through the annual market evaluation result and the sales record set, and obtaining the second sales reference comparison data through the sales trend analysis result.
3. The method of claim 2, wherein the method further comprises:
constructing a corresponding relation between the annual quotation evaluation result and the sales volume record set;
performing product competitiveness evaluation of the target client based on the corresponding relation construction result to generate a competitiveness evaluation parameter;
performing competitive trend prediction of the target client based on the competitive evaluation parameters to generate a competitive trend prediction result;
and obtaining the sales trend analysis result through the competitive trend prediction result.
4. The method of claim 1, wherein the method further comprises:
setting a planning constraint level set;
judging whether cross-level planning exists in the enterprise planning of the target client according to the planning constraint level set and the analysis data;
when the enterprise planning of the target client has cross-grade planning, generating cross-grade influence parameters;
and carrying out data integration optimization on the first sales reference comparison data and the second sales reference comparison data based on the cross-grade influence parameters.
5. The method of claim 1, wherein the method further comprises:
collecting feedback evaluation data of a cooperative enterprise;
constructing a feedback evaluation tag based on the feedback evaluation data;
matching the feedback evaluation labels based on the order auxiliary parameters, and generating sequential enterprise recommendation data based on a matching result;
and feeding back the sequential enterprise recommendation data to the target client.
6. The method of claim 5, wherein the method further comprises:
obtaining the cooperation order quantity data of a cooperation enterprise, and taking the cooperation order quantity data as first evaluation data;
obtaining brand data of a demand party of a partner enterprise, and taking the brand data as second evaluation data;
performing value degree identification of the feedback evaluation data according to the first evaluation data and the second evaluation data;
and constructing the feedback evaluation label through the feedback evaluation data with the value identification.
7. The method of claim 1, wherein the method further comprises:
constructing an early warning time threshold according to the real-time inventory data;
judging whether corresponding order data exists at the early warning time threshold node;
and when the corresponding order data does not exist in the early warning time threshold node, carrying out real-time early warning of inventory shortage.
8. A full channel intelligent operation and maintenance management system, the system comprising:
the archive construction module is used for collecting basic information of a target client and constructing a client archive of the target client based on the basic information;
the data analysis module is used for obtaining enterprise planning data of the target client and analyzing the enterprise planning data to obtain analysis data;
the first data acquisition module is used for setting a preset evaluation time interval and obtaining first sales reference comparison data through the preset evaluation time interval and the client file;
the second data acquisition module is used for carrying out annual sales reference analysis based on the client file and generating second sales reference comparison data;
the data optimization module is used for carrying out data integration optimization on the first sales reference comparison data and the second sales reference comparison data through the analysis data to obtain estimated reference data;
the parameter generation module is used for obtaining real-time inventory data and real-time order quantity data of the target clients and generating order auxiliary parameters based on the real-time inventory data, the real-time order quantity data and the estimated reference data;
and the proposal acquisition module is used for carrying out the order proposal of the target client through the order auxiliary parameters.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116822754A (en) * | 2023-08-30 | 2023-09-29 | 亿家商业科创产业管理(湖北)有限公司 | Data specification analysis system based on modularized classification of enterprise service items |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116822754A (en) * | 2023-08-30 | 2023-09-29 | 亿家商业科创产业管理(湖北)有限公司 | Data specification analysis system based on modularized classification of enterprise service items |
| CN116822754B (en) * | 2023-08-30 | 2023-12-15 | 亿家商业科创产业管理(湖北)有限公司 | Data specification analysis system based on modularized classification of enterprise service items |
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