Automobile supply chain order user data management method, system and device and medium

Abstract

The invention relates to an automobile supply chain order user data management method, system and device and a medium, and the method comprises the steps: obtaining multi-channel order data, and carrying out the initial user analysis, and obtaining the initial user data; supply chain management information is acquired, and synchronization processing is performed on the initial user data, so that corresponding order user data can be obtained; acquiring historical order data, and performing order quantity trend prediction on the multi-channel order data and the supply chain management information to obtain corresponding order period prediction information; performing management analysis on the order user data to obtain an initial order management scheme; and performing real-time scheme optimization on the initial order management scheme according to the order period prediction information to obtain a supply chain order management strategy. According to the invention, the overall operation efficiency of the supply chain can be improved, and stock overstock can be effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile supply chain order management, and in particular to an automobile supply chain order user data management method, system, device and medium. Background Art

[0002] As one of the core links in the automotive manufacturing industry, automotive supply chain management plays a vital role in improving production efficiency, reducing costs and meeting customer needs. With the rapid development of the automotive market and the increasing diversification of consumer demand, how to effectively manage order user data and achieve precise and intelligent operation of the supply chain has become one of the focuses of industry research. Traditional management methods lack in-depth analysis of supply chain management information and historical order data, and cannot accurately predict order volume trends, thus affecting the overall efficiency of the supply chain. Finally, existing methods often lack the ability to optimize in real time, making it difficult to respond to market changes and rapid changes in user needs in a timely manner. Summary of the invention

[0003] The main purpose of the present invention is to provide an automobile supply chain order user data management method, system, device and medium, which can improve the overall operational efficiency of the supply chain and effectively reduce inventory backlogs.

[0004] To achieve the above object, the present invention provides a method for managing user data of automobile supply chain orders, comprising: Obtain multi-channel order data and conduct initial user analysis to obtain initial user data; Acquire supply chain management information and synchronize the initial user data to obtain corresponding order user data; Acquire historical order data, and perform order quantity trend forecasting on the multi-channel order data and the supply chain management information to obtain corresponding order cycle forecasting information; Performing management analysis on the order user data to obtain an initial order management plan; The initial order management solution is optimized in real time according to the order cycle forecast information to obtain a supply chain order management strategy.

[0005] Furthermore, the obtaining of multi-channel order data and performing initial user analysis to obtain initial user data includes: Acquire corresponding channel data from corresponding multiple order channels, and integrate them to obtain the multi-channel order data; Performing user identity identification and matching on the multi-channel order data according to a preset user database to obtain user identity information; Performing group analysis on the user identity information to obtain a user group division result; Extracting features from the user group division results to obtain common features of each user group; Produce a preliminary user portrait based on the common characteristics to obtain initial user portrait data; The initial user portrait data is combined with the user group segmentation result to obtain initial user data.

[0006] Furthermore, the acquiring of supply chain management information and synchronous processing of the initial user data to obtain corresponding order user data includes: Obtaining inventory data, production planning data and logistics distribution data in the supply chain management system, and integrating and processing them to obtain the supply chain management information; Classifying and labeling the initial user data according to the supply chain management information to obtain labeled user data; Performing feature extraction on the labeled user data to obtain a user feature vector; Constructing a user portrait based on the annotated user data to obtain an initial user portrait; Optimizing the initial user portrait according to the user feature vector to obtain a final user portrait; The end-user portrait is associated and matched with the supply chain management information to obtain the order user data.

[0007] Furthermore, the acquiring of historical order data and performing order quantity trend forecasting on the multi-channel order data and the supply chain management information to obtain corresponding order cycle forecasting information includes: Acquire the historical order data from a preset historical database; Performing time series decomposition on the historical order data to obtain trend items, seasonal items, and random items; Reconstructing the historical order data in time series according to the trend item, the seasonal item and the random item to obtain reconstructed historical data; Extracting order features from the multi-channel order data and the supply chain management information to obtain an order feature vector; Preliminarily predicting the order feature vector based on the reconstructed historical data to obtain an initial order quantity prediction result; Performing order cycle analysis on the order quantity forecast result to obtain an order cycle identification result; Performing seasonal adjustments on the order volume forecast results based on the multi-channel order data and the supply chain management information to obtain adjusted order volume forecast results; The adjusted order quantity forecast result and the order cycle identification result are integrated and analyzed to obtain the order cycle forecast information.

[0008] Furthermore, the management analysis of the order user data to obtain an initial order management solution includes: Performing order periodic classification processing on the order user data to obtain multiple order period categories; Performing feature statistics on each of the order cycle categories to obtain order quantity features of each category; Performing supply and demand balance analysis on the order cycle categories based on the order quantity characteristics to obtain supply and demand difference data for each category; Dynamically allocate supply chain resources based on the supply-demand difference data to obtain a preliminary resource allocation plan; Performing multi-constraint optimization on the preliminary resource allocation plan to obtain an optimized resource allocation strategy; An order processing flow analysis is performed on the optimized resource allocation strategy based on the order user data to obtain an initial order management solution.

[0009] Furthermore, the initial order management solution is optimized in real time according to the order cycle forecast information to obtain a supply chain order management strategy, including: Performing a cycle analysis on the initial order management plan based on the order cycle forecast information to obtain an order cycle fluctuation rule; Dynamically adjusting the parameters of the initial order management scheme according to the order cycle fluctuation law to obtain order management parameters; Optimizing resource allocation according to the order management parameters to obtain a resource allocation plan; Evaluate and analyze supplier configuration based on the resource allocation plan to obtain a supplier configuration plan; The initial order management plan is optimized and reconstructed according to the supplier configuration plan and the resource allocation plan to obtain the supply chain order management strategy.

[0010] The present invention also provides an automobile supply chain order user data management system, which is applied to any one of the above automobile supply chain order user data management methods, comprising: A collection module, which is used to obtain multi-channel order data and perform initial user analysis to obtain initial user data; An analysis module, the analysis module is used to obtain supply chain management information and synchronize the initial user data to obtain corresponding order user data; A correlation module, the correlation module is used to obtain historical order data, and perform order quantity trend forecasting on the multi-channel order data and the supply chain management information to obtain corresponding order cycle forecasting information; A processing module, the processing module is used to manage and analyze the order user data to obtain an initial order management plan; A control module is used to optimize the initial order management plan in real time according to the order cycle forecast information to obtain a supply chain order management strategy.

[0011] The present invention also provides an automobile supply chain order user data management device, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of the automobile supply chain order user data management method described in any one of the above.

[0012] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0013] The present invention provides an automobile supply chain order user data management method, system, device and medium, which have the following beneficial effects: By acquiring and integrating multi-channel order data and conducting initial user analysis, we can fully grasp user needs, overcome the limitations of single-channel data analysis, and improve the depth of understanding of user behavior. By synchronously processing supply chain management information and initial user data, we can achieve precise management of order user data and provide more reliable data support for supply chain decision-making. Combining historical order data with multi-channel order data and supply chain management information to predict order volume trends effectively improves the ability to predict changes in market demand and provides a scientific basis for inventory management and production planning. By managing and analyzing order user data, formulating initial order management plans, and optimizing real-time plans based on order cycle forecast information, we can achieve dynamic adjustment of supply chain order management strategies and effectively respond to market changes and rapid changes in user needs. This comprehensive analysis and real-time optimization method not only improves the overall operational efficiency of the supply chain, but also effectively reduces inventory backlogs, balances production plans, and improves customer satisfaction, thereby significantly improving the management effect and competitiveness of the automotive supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of a method for managing user data of an automobile supply chain order provided by the present invention; Figure 2 This is a structural diagram of an automobile supply chain order user data management system provided by the present invention; Figure 3 It is a structural diagram of an automobile supply chain order user data management device provided by the present invention.

[0015] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] The present invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0018] Reference Figure 1 As shown, the present invention provides 1. A method for managing user data of automobile supply chain orders, characterized by comprising: Step S1: Obtain multi-channel order data and perform initial user analysis to obtain initial user data; Step S2: Acquire supply chain management information and synchronize the initial user data to obtain corresponding order user data; Step S3: Obtain historical order data, and perform order volume trend forecasting on multi-channel order data and supply chain management information to obtain corresponding order cycle forecast information; Step S4: Perform management analysis on the order user data to obtain an initial order management plan; Step S5: Optimize the initial order management plan in real time based on the order cycle forecast information to obtain a supply chain order management strategy.

[0019] Based on the above steps, the detailed process is as follows: Step S1: Collect raw channel data from multiple sales channel platforms. Integrate different raw channel data into a unified format to ensure data consistency and comparability.

[0020] Conduct preliminary analysis on the integrated data, including calculation of indicators such as purchase frequency, average order amount, product preferences, etc., to understand the basic behavior patterns of users.

[0021] Based on the analysis results, users are preliminarily classified, such as high-value customers, potential customers, general customers, etc.

[0022] Step S2: Integrate the supply chain management information with the initial user data to establish a unified data relationship. Update the order status, such as feasibility, delivery status, etc., based on the supply chain information. Analyze the degree of match between user order requirements and current supply chain capabilities to identify potential problems. Perform quality checks on the processed data to ensure accuracy and consistency.

[0023] Step S3: Extract order data from a certain period in the past (e.g., recent years) from the data warehouse or historical database. Clean, standardize, and extract features from the historical data to ensure that it is consistent with the current data format. Perform time series decomposition on the historical order data to identify trends, seasonality, and cyclical patterns.

[0024] Combine multi-channel order data and supply chain management information to analyze the various factors that affect order volume.

[0025] Based on historical data and multi-factor analysis results, an order volume trend prediction relationship is constructed.

[0026] Based on the order volume trend forecast relationship, generate order volume forecast and order cycle forecast information for the future period.

[0027] Step S4: Integrate the initial user data, order user data and order cycle forecast information to form a comprehensive data view.

[0028] Based on user behavior, order characteristics and forecast information, users are grouped in a refined manner, such as high-frequency order users, large-amount order users, seasonal order users, etc.

[0029] Analyze various characteristics of orders, such as order size, frequency, product mix, geographic distribution, etc., to identify order patterns and characteristics. Combine supply chain management information to assess the pressure of different types of orders on various links of the supply chain, such as production capacity, inventory levels, logistics requirements, etc. Identify potential order fulfillment risks, such as inventory shortages, production delays, logistics bottlenecks, etc.

[0030] Based on the above analysis results, formulate the initial order management plan, including inventory strategy, production plan, logistics arrangement, customer service strategy, etc.

[0031] Step S5: Establish a real-time data monitoring system to continuously track order status, supply chain status, and market changes. Compare actual order status with forecast results and analyze the causes of deviations.

[0032] Dynamically adjust the initial order management plan based on real-time data and reasons for deviation.

[0033] Conduct multi-scenario supply chain response simulations for different order cycle forecast scenarios to evaluate the effectiveness of different strategies. Use optimization algorithms (such as genetic algorithms, simulated annealing, etc.) to optimize order management solutions and balance cost, efficiency, and customer satisfaction.

[0034] Based on the optimization results, formulate the final supply chain order management strategy and convert it into executable operational guidelines.

[0035] The present invention provides a method for managing user data of automobile supply chain orders. By acquiring and integrating multi-channel order data and performing initial user analysis, the method can fully grasp user needs, overcome the limitations of single-channel data analysis, and improve the depth of understanding of user behavior. By synchronously processing supply chain management information and initial user data, the precise management of order user data is realized, providing more reliable data support for supply chain decision-making. The order volume trend forecasting of multi-channel order data and supply chain management information is combined with historical order data, which effectively improves the ability to predict changes in market demand and provides a scientific basis for inventory management and production planning. By managing and analyzing order user data, formulating an initial order management plan, and optimizing the plan in real time based on order cycle forecast information, the dynamic adjustment of supply chain order management strategy is realized, effectively responding to market changes and rapid changes in user needs. This comprehensive analysis and real-time optimization method not only improves the overall operational efficiency of the supply chain, but also effectively reduces inventory backlogs, balances production plans, and improves customer satisfaction, thereby significantly improving the management effect and competitiveness of the automobile supply chain.

[0036] In one embodiment, multi-channel order data is obtained and initial user analysis is performed to obtain initial user data, including: Obtain corresponding channel data from multiple order channels. These order channels include but are not limited to official websites, mobile applications, telephone ordering systems, physical stores, etc. Integrate the data collected from various channels to form multi-channel order data in a unified format. During the integration process, the data will be cleaned and standardized to ensure data consistency and availability.

[0037] For the integrated multi-channel order data, user identity recognition and matching are performed based on the preset user database. The user database stores basic information of known users, such as name, contact information, historical orders, etc. By comparing the information in the order data with the records in the database, the user identity corresponding to the order is determined. In the case where an exact match cannot be made, a fuzzy matching algorithm is used for approximate recognition. The result of recognition and matching is the user identity information.

[0038] Perform group analysis on the obtained user identity information to obtain the user group segmentation results. Group analysis uses a clustering algorithm to divide users into different groups based on their attribute characteristics (such as age, gender, frequency, etc.). The segmentation criteria can be adjusted according to business needs to ensure the rationality and practicality of the segmentation results.

[0039] Perform feature extraction on the user group segmentation results to obtain the common features of each user group. The feature extraction process uses statistical analysis to extract representative features from the user data of each group. These common features can summarize the typical behavior patterns of each user group.

[0040] Based on the extracted common features, the user is initially profiled to generate initial user profile data. The user profile includes descriptions of multiple dimensions such as the user's basic information, behavioral characteristics, preferences, etc. In the initial profile process, a relatively comprehensive user description is formed by combining the user's personal data with the common characteristics of the group to which they belong.

[0041] The initial user portrait data is combined with the user group segmentation results to obtain the initial user data.

[0042] This embodiment improves the integrity and consistency of data by uniformly processing multi-channel order data, laying a solid foundation for subsequent analysis. This method overcomes the limitations of traditional single-channel data analysis and makes user behavior analysis more comprehensive and accurate. The use of user identity recognition and matching technology effectively solves the problem of cross-channel user identification and improves the relevance and availability of user data. This method not only improves the accuracy of user portraits, but also provides a reliable basis for the formulation of personalized marketing strategies. Through group analysis and feature extraction, refined classification and feature summary of users are achieved, enabling enterprises to better understand the needs and behavior patterns of different user groups. This analysis method helps enterprises formulate targeted marketing strategies and improve marketing efficiency and effectiveness. By combining individual user information with group characteristics, the initial user data generated reflects both individual characteristics and group commonalities.

[0043] In one embodiment, the supply chain management information is obtained and the initial user data is synchronously processed to obtain the corresponding order user data, including: In the step of obtaining supply chain management information, inventory data, production plan data and logistics distribution data are extracted from the supply chain management system. Inventory data includes information such as the inventory quantity and inventory location of various automotive parts; production plan data includes information such as production plans and production progress of various models; logistics distribution data includes information such as parts transportation routes and delivery time. These data are integrated and processed to form complete supply chain management information, providing a basis for subsequent user data processing.

[0044] Based on the acquired supply chain management information, the initial user data is classified and labeled. Initial user data refers to the original user information collected from various channels, including basic user information, car purchase preferences, consumption capacity, etc. Based on the vehicle model production plan, inventory status, etc. in the supply chain management information, users are labeled to obtain labeled user data.

[0045] Feature extraction of labeled user data is to describe user characteristics more accurately. Key features are extracted from labeled user data, such as user age, occupation type, family structure, etc., and these features are converted into numerical vectors to form user feature vectors. This step provides data support for subsequent user portrait optimization.

[0046] The construction of user portraits is based on annotated user data. Using data mining technology, we analyze the user's consumption behavior, interests and hobbies, and construct an initial user portrait. The initial user portrait contains multi-dimensional information such as the user's basic attributes, consumption ability, and car purchase preferences, providing a basis for precision marketing and personalized services.

[0047] In order to improve the accuracy of the user portrait, the user feature vector obtained above is used to optimize the initial user portrait. By comparing the difference between the user feature vector and the initial user portrait, the various indicators in the user portrait are adjusted and improved, and finally a more accurate final user portrait is obtained.

[0048] After obtaining the end-user portrait, it is associated and matched with the supply chain management information. The matching process considers the information in the user portrait and compares and analyzes it with the vehicle model inventory, production plan and other data in the supply chain management information. Through this matching, order user data is generated, including user information, intended vehicle model, possible order time and other details, providing data support for subsequent precision marketing and inventory management.

[0049] This embodiment integrates inventory, production planning and logistics distribution data to form complete supply chain management information, providing a comprehensive foundation for user data processing and improving the accuracy and efficiency of data processing. The initial user data is classified and labeled, and combined with supply chain management information, to achieve accurate matching of user demand and supply capacity, effectively improving the accuracy of order forecasting. The use of feature extraction and user portrait optimization technology not only improves the accuracy of user feature description, but also dynamically adjusts user portraits to adapt to changes in user needs, providing a reliable basis for personalized marketing. By associating and matching the end-user portrait with the supply chain management information, detailed order user data is generated, providing enterprises with accurate user insights and order forecasting capabilities.

[0050] In one embodiment, historical order data is obtained, and order quantity trend forecasting is performed on multi-channel order data and supply chain management information to obtain corresponding order cycle forecasting information, including: Get historical order data from the preset historical database. The historical database contains order records for a certain period in the past, including order time, quantity, type and other information. The acquired historical order data provides a basis for subsequent analysis.

[0051] After obtaining the historical order data, perform time series decomposition on it. Time series decomposition is the process of decomposing time series data into trend items, seasonal items, and random items. The trend item reflects the long-term trend of the data, the seasonal item reflects the periodic changes, and the random item includes irregular fluctuations. Through this decomposition, we can better understand the composition and change patterns of historical order data.

[0052] After completing the time series decomposition, the historical order data is reconstructed according to the obtained trend term, seasonal term and random term to obtain the reconstructed historical data. During the reconstruction process, each component may be appropriately adjusted to eliminate the influence of outliers or noise, so that the reconstructed data can better reflect the real order change trend.

[0053] Order feature extraction is performed on multi-channel order data and supply chain management information to obtain order feature vectors. Multi-channel order data includes order information from different sales channels, such as online shopping malls, physical stores, etc. Supply chain management information includes inventory levels, production capacity, logistics status, etc. During the feature extraction process, factors that have a significant impact on order prediction, such as order frequency, order size, seasonal factors, etc., are selected and quantified into feature vectors.

[0054] Based on the reconstructed historical data, the order feature vector is initially predicted to obtain the initial order volume forecast result. This step usually uses machine learning algorithms, such as time series prediction models or regression models, to predict the order volume in the future based on the pattern of historical data and the information of feature vectors.

[0055] After obtaining the initial forecast results, we conduct order cycle analysis to obtain order cycle identification results. Order cycle analysis aims to discover the cyclical change patterns of order volume, such as regular fluctuations on a weekly, monthly or quarterly basis. This helps to more accurately predict future order trends.

[0056] Based on multi-channel order data and supply chain management information, seasonal adjustments are made to the order volume forecast results to obtain adjusted order volume forecast results. Seasonal adjustments take into account special factors in different periods, such as holiday effects, promotional activities, etc., to make the forecast results closer to the actual situation.

[0057] The adjusted order volume forecast results and order cycle identification results are integrated and analyzed to obtain order cycle forecast information. This step comprehensively considers the predicted order volume and the identified cyclical characteristics to form the final forecast result. Order cycle forecast information includes the expected order volume in the future, the possible fluctuation range, and the cyclical change pattern.

[0058] This embodiment can more accurately capture the long-term trends, seasonal changes and random fluctuations of order data by decomposing and reconstructing the historical order data in time series, and provide more reliable basic data for subsequent forecasts. Combining multi-channel order data and supply chain management information for feature extraction enables the prediction model to fully consider various influencing factors, thereby improving the comprehensiveness and accuracy of the prediction. Through order cycle analysis and seasonal adjustment, it is possible to identify and adapt to the cyclical changes in order volume and fluctuations in special periods, making the prediction results closer to the actual situation. The order cycle forecast information obtained by the final integrated analysis provides a comprehensive and accurate reference for supply chain management decisions, helps to optimize inventory management, production planning and logistics arrangements, improve supply chain operation efficiency, reduce costs, and enhance the company's ability to respond to market changes.

[0059] In one embodiment, the order user data is managed and analyzed to obtain an initial order management solution, including: The order user data is classified by order cycle, and the orders are divided into multiple order cycle categories according to their generation time, such as daily orders, weekly orders, monthly orders, etc. The division of order cycle categories can be flexibly set according to actual business needs to facilitate subsequent analysis.

[0060] Perform feature statistics on each order cycle category and calculate the order volume features of each category. Order volume features include but are not limited to statistical indicators such as total order volume, average order volume, peak order volume, and valley order volume. These features can reflect the quantity distribution of orders in different cycle categories.

[0061] Based on the order volume characteristics, the supply and demand balance analysis is performed on the order cycle categories, and the supply and demand difference data of each category is calculated. The supply and demand difference data reflects the gap between the actual order volume and the expected supply capacity, which can be used to guide the subsequent resource allocation.

[0062] Based on the supply and demand difference data, supply chain resources are dynamically allocated and a preliminary resource allocation plan is generated. The plan includes preliminary allocation suggestions for resources in various links such as production, warehousing, and logistics, aiming to alleviate the contradiction between supply and demand.

[0063] The preliminary resource allocation plan is optimized under multiple constraints to obtain the optimized resource allocation strategy. The multiple constraints include cost constraints, time requirements, quality standards and other factors. Through the optimization algorithm, a better allocation plan is obtained under the premise of meeting various constraints.

[0064] The order processing flow is analyzed based on the order user data to optimize the resource allocation strategy and generate an initial order management plan. This plan comprehensively considers order characteristics, resource allocation and processing flow, and provides specific guidance for subsequent order management.

[0065] This embodiment can fully grasp the order distribution characteristics under different time scales by periodically classifying and feature counting the order user data, providing a reliable data basis for subsequent supply and demand analysis. Supply and demand balance analysis based on order volume characteristics can accurately identify supply and demand differences and provide precise guidance for resource allocation. Dynamic allocation of supply chain resources and multi-constraint optimization can maximize resource utilization efficiency and effectively alleviate supply and demand contradictions while meeting various restrictions. By analyzing the order processing flow of the optimized resource allocation strategy, a feasible initial order management plan can be generated to improve the accuracy and efficiency of order processing. The entire process achieves comprehensive coverage from data analysis to resource optimization to order management, which helps to improve the overall operational efficiency of the supply chain and enhance the company's ability to respond quickly to changes in market demand.

[0066] In one embodiment, the initial order management solution is optimized in real time according to the order cycle forecast information to obtain a supply chain order management strategy, including: When conducting a periodic analysis of the initial order management plan, time series analysis methods, such as moving average method and exponential smoothing method, are used to analyze the periodic changes of historical order data. By identifying the seasonal fluctuations and long-term trends of order volume, the order cycle fluctuation pattern is obtained. For example, the order volume of a certain auto parts may show a periodic characteristic of rising in the second quarter of each year and falling in the fourth quarter.

[0067] Based on the order cycle fluctuation pattern obtained, the key parameters in the initial order management plan are dynamically adjusted. The adjusted parameters include safety stock level, order point, order quantity, etc. For example, the safety stock level is increased during the peak order period and appropriately reduced during the trough period to balance inventory costs and out-of-stock risks. Through this dynamic adjustment, more flexible order management parameters are obtained.

[0068] Based on the adjusted order management parameters, various resources in the supply chain are optimally allocated. Resources include raw materials, production equipment, storage space, transportation tools, etc. The goal of optimization is to maximize resource utilization and minimize costs while meeting order requirements. For example, adjust the staffing of the production line based on order forecasts, and reasonably arrange the raw material procurement plan. Finally, a detailed resource allocation plan is obtained.

[0069] Based on the resource allocation plan, the existing supplier configuration is evaluated and analyzed. The evaluation indicators include the supplier's production capacity, quality stability, delivery timeliness, etc. Through comparative analysis, the supplier relationship that needs to be adjusted is identified, such as adding new alternative suppliers, reducing dependence on certain suppliers, etc. Finally, an optimized supplier configuration plan is formed.

[0070] Combine the supplier configuration plan with the resource allocation plan to comprehensively optimize and reconstruct the initial order management plan. The goal of optimization is to improve the efficiency and accuracy of order fulfillment and reduce the overall supply chain cost. Specific measures include adjusting the order allocation strategy, optimizing inventory layout, improving logistics plans, etc. Through this series of optimizations, a complete supply chain order management strategy is finally obtained.

[0071] This embodiment can achieve flexibility and adaptability of supply chain management by dynamically optimizing the initial order management plan based on order cycle prediction. Parameters are adjusted according to the fluctuation law of the order cycle, so that the order management strategy can be adjusted in time according to changes in market demand, improving the accuracy and efficiency of order fulfillment. By optimizing resource allocation and evaluating and analyzing supplier configuration, efficient utilization of supply chain resources is achieved and overall operating costs are reduced. This dynamic optimization method can not only cope with short-term market fluctuations, but also continuously optimize supply chain performance and enhance the long-term competitiveness of enterprises through continuous data analysis and strategy adjustment.

[0072] Reference Figure 2 As shown, the present invention also provides an automobile supply chain order user data management system, characterized in that the automobile supply chain order user data management method applied to any one of the above items includes: The acquisition module is used to obtain multi-channel order data and perform initial user analysis to obtain initial user data; Analysis module, which is used to obtain supply chain management information and synchronize the initial user data to obtain the corresponding order user data; The association module is used to obtain historical order data and perform order volume trend forecasting on multi-channel order data and supply chain management information to obtain corresponding order cycle forecast information; Processing module, which is used to manage and analyze order user data and obtain an initial order management plan; The control module is used to optimize the initial order management plan in real time according to the order cycle forecast information to obtain the supply chain order management strategy.

[0073] The automobile supply chain order user data management system provided by the present invention can fully grasp user needs by acquiring and integrating multi-channel order data and performing initial user analysis, overcome the limitations of single-channel data analysis, and improve the depth of understanding of user behavior. By synchronously processing supply chain management information and initial user data, accurate management of order user data is achieved, providing more reliable data support for supply chain decision-making. Combining historical order data with multi-channel order data and supply chain management information to predict order volume trends, effectively improve the ability to predict changes in market demand, and provide a scientific basis for inventory management and production planning. By managing and analyzing order user data, formulating an initial order management plan, and optimizing the real-time plan based on order cycle forecast information, dynamic adjustment of supply chain order management strategies is achieved, effectively responding to market changes and rapid changes in user needs. This comprehensive analysis and real-time optimization method not only improves the overall operational efficiency of the supply chain, but also effectively reduces inventory backlogs, balances production plans, and improves customer satisfaction, thereby significantly improving the management effect and competitiveness of the automobile supply chain.

[0074] Reference Figure 3 As shown, the present invention also provides an automobile supply chain order user data management device, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of any one of the above-mentioned methods for managing user data of automobile supply chain orders.

[0075] In this embodiment, the processor and the memory may be connected via a bus or other means. The memory may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a read-only memory, a flash memory, a hard disk, or a solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of the present invention.

[0076] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0077] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and conciseness of description, the specific working process of the system and each module described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0078] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for managing user data of automobile supply chain orders, characterized in that: include: Obtain multi-channel order data and conduct initial user analysis to obtain initial user data; Acquire supply chain management information and synchronize the initial user data to obtain corresponding order user data; Acquire historical order data, and perform order quantity trend forecasting on the multi-channel order data and the supply chain management information to obtain corresponding order cycle forecasting information; Performing management analysis on the order user data to obtain an initial order management plan; The initial order management solution is optimized in real time according to the order cycle forecast information to obtain a supply chain order management strategy.

2. The automobile supply chain order user data management method according to claim 1 is characterized in that: The obtaining of multi-channel order data and performing initial user analysis to obtain initial user data includes: Acquire corresponding channel data from corresponding multiple order channels, and integrate them to obtain the multi-channel order data; Performing user identity identification and matching on the multi-channel order data according to a preset user database to obtain user identity information; Performing group analysis on the user identity information to obtain a user group division result; Extracting features from the user group division results to obtain common features of each user group; Conduct a preliminary user portrait based on the common characteristics to obtain initial user portrait data; The initial user portrait data is combined with the user group segmentation result to obtain initial user data.

3. The automobile supply chain order user data management method according to claim 1, characterized in that: The acquiring of supply chain management information and synchronous processing of the initial user data to obtain corresponding order user data includes: Obtaining inventory data, production planning data and logistics distribution data in the supply chain management system, and integrating and processing them to obtain the supply chain management information; Classifying and labeling the initial user data according to the supply chain management information to obtain labeled user data; Performing feature extraction on the labeled user data to obtain a user feature vector; Constructing a user portrait based on the annotated user data to obtain an initial user portrait; Optimizing the initial user portrait according to the user feature vector to obtain a final user portrait; The end-user portrait is associated and matched with the supply chain management information to obtain the order user data.

4. The automobile supply chain order user data management method according to claim 1, characterized in that: The acquiring of historical order data and performing order quantity trend forecasting on the multi-channel order data and the supply chain management information to obtain corresponding order cycle forecast information includes: Acquire the historical order data from a preset historical database; Performing time series decomposition on the historical order data to obtain trend items, seasonal items, and random items; Reconstructing the historical order data in time series according to the trend item, the seasonal item and the random item to obtain reconstructed historical data; Extracting order features from the multi-channel order data and the supply chain management information to obtain an order feature vector; Preliminarily predicting the order feature vector based on the reconstructed historical data to obtain an initial order quantity prediction result; Performing order cycle analysis on the order quantity forecast result to obtain an order cycle identification result; Performing seasonal adjustments on the order volume forecast results based on the multi-channel order data and the supply chain management information to obtain adjusted order volume forecast results; The adjusted order quantity forecast result and the order cycle identification result are integrated and analyzed to obtain the order cycle forecast information.

5. The automobile supply chain order user data management method according to claim 1 is characterized in that: The step of managing and analyzing the order user data to obtain an initial order management solution includes: Performing order periodic classification processing on the order user data to obtain multiple order period categories; Performing feature statistics on each of the order cycle categories to obtain order quantity features of each category; Performing supply and demand balance analysis on the order cycle categories based on the order quantity characteristics to obtain supply and demand difference data for each category; Dynamically allocate supply chain resources based on the supply-demand difference data to obtain a preliminary resource allocation plan; Performing multi-constraint optimization on the preliminary resource allocation plan to obtain an optimized resource allocation strategy; An order processing flow analysis is performed on the optimized resource allocation strategy based on the order user data to obtain an initial order management solution.

6. The automobile supply chain order user data management method according to claim 1, characterized in that: The real-time optimization of the initial order management solution based on the order cycle forecast information to obtain a supply chain order management strategy includes: Performing a cycle analysis on the initial order management plan based on the order cycle forecast information to obtain an order cycle fluctuation rule; Dynamically adjusting the parameters of the initial order management scheme according to the order cycle fluctuation law to obtain order management parameters; Optimizing resource allocation according to the order management parameters to obtain a resource allocation plan; Evaluate and analyze supplier configuration based on the resource allocation plan to obtain a supplier configuration plan; The initial order management plan is optimized and reconstructed according to the supplier configuration plan and the resource allocation plan to obtain the supply chain order management strategy.

7. An automobile supply chain order user data management system, characterized in that: The automobile supply chain order user data management method applied to any one of claims 1 to 6 above comprises: A collection module, which is used to obtain multi-channel order data and perform initial user analysis to obtain initial user data; An analysis module, the analysis module is used to obtain supply chain management information and synchronize the initial user data to obtain corresponding order user data; A correlation module, the correlation module is used to obtain historical order data, and perform order quantity trend forecasting on the multi-channel order data and the supply chain management information to obtain corresponding order cycle forecasting information; A processing module, the processing module is used to manage and analyze the order user data to obtain an initial order management plan; A control module is used to optimize the initial order management plan in real time according to the order cycle forecast information to obtain a supply chain order management strategy.

8. An automobile supply chain order user data management device, characterized in that: include: Memory, used to store programs; A processor is used to execute the program to implement each step of the automobile supply chain order user data management method as described in any one of claims 1-6.

9. A storage medium, characterized in that: Computer instructions are stored, and the computer instructions are used to make a computer execute the method according to any one of claims 1 to 6.

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