A precise commodity planning and distribution system and method based on member tag data

Abstract

The application discloses a kind of based on member label data's accurate commodity planning and goods distribution system and method, it is related to big data intelligent decision-making and commodity operation technical field, the system includes: member data acquisition module, obtains member basic information and purchase commodity characteristic information;Label generation module, the information obtained is converted into member portrait label and commodity characteristic label;Data storage module, store structured member label data set;Goods demand analysis module, execute type-based demand quantification calculation and label decomposition;Plan rolling adjustment module, monitor sales deviation and trigger dynamic adjustment;Purchasing production plan generation module, generate purchase order and production plan;Single store goods distribution module, match goods to target store;Logistics interface module, send goods distribution list to warehousing logistics system.The application improves demand prediction accuracy, enhances the scientific nature of plan adjustment, and realizes the matching of supply and preference, thereby optimizing inventory efficiency and reducing operating costs.

Description

Technical Field

[0001] This invention relates to the field of big data intelligent decision-making and commodity operation technology, and in particular to a precise commodity planning and distribution system and method based on member tag data. Background Technology

[0002] In the fast fashion menswear direct-sale chain industry, the existing merchandise planning and distribution models have the following technical problems: 1. Demand quantification is ambiguous. Traditional solutions do not differentiate between existing and new members when calculating demand, relying solely on overall sales data for estimation. Membership structure changes dynamically, and different member types have different needs. Traditional methods cannot accurately grasp these characteristics and are ill-suited to the demand differences arising from changes in membership structure.

[0003] 2. Lack of precise breakdown of tag proportions. The industry lacks a quantitative correlation between purchase quantity and tag dimensions, including style and color. This lack of correlation leads to a mismatch between inventory levels and member preference needs, resulting in inventory discrepancies. For example, there may be insufficient stock of men's clothing in a specific color, while other colors are stocked up.

[0004] 3. Lack of clear rules for plan adjustments. Current plan adjustments lack unified standards, clear deviation thresholds, and adjustment cycles, relying on subjective human judgment, resulting in delayed and inaccurate adjustments. When market demand changes significantly, relevant personnel may fail to make timely or accurate adjustments.

[0005] 4. Inventory and cost issues are prominent. The above-mentioned defects combined lead to a mismatch between supply and demand, manifested as inventory backlog tying up capital and storage space, stockouts causing customer loss, and frequent transfers increasing logistics costs and operational complexity. Inventory and cost issues are difficult to resolve effectively. Summary of the Invention

[0006] In view of this, the present invention proposes a precise product planning and distribution system and method based on member tag data, in order to solve the problem in the prior art that product planning and distribution is difficult to accurately adapt to actual needs.

[0007] The specific technical solution of this invention is as follows: A precise product planning and distribution system based on member tag data includes: The member data collection module is used to obtain basic member information and purchase characteristics information from store terminal equipment; The tag generation module is used to convert the acquired information into member profile tags and product feature tags, and associate and bind them with member attribute data; The data storage module is used to store structured member tag datasets; The product demand analysis module is used to perform categorized demand quantification calculations and label decomposition. The rolling adjustment module is used to monitor sales deviations and trigger dynamic adjustments. The procurement and production planning generation module is used to generate purchase orders and production plans based on updated planning data; The single-store distribution module is used to automatically match goods to target stores based on the planning scheme; The logistics integration module is used to convert the sorting list into a standardized format and send it to the warehousing and logistics system.

[0008] Specifically, the product demand analysis module further includes: a unit for calculating the demand of existing members, used to calculate the expected purchase quantity of existing members based on the total number of historical members, repurchase rate, and average cross-selling rate; a unit for calculating the demand of new members, used to calculate the expected purchase quantity of new members based on the number of new members and average cross-selling rate; a tag proportion decomposition unit, used to decompose the proportion of each tag dimension for existing members and new members based on historical purchase records or alternative data sources and calculate the demand quantity; and a weight fusion unit, used to perform weighted calculation of the demand quantity of existing members and the demand quantity of new members for the same tag dimension according to preset fusion rules, and generate the final product planning scheme.

[0009] Specifically, the rolling adjustment module further includes: an inventory deviation monitoring unit, used to calculate the inventory deviation value in real time or periodically, and determine whether the preset inventory deviation threshold has been reached, and generate an adjustment signal; a label proportion deviation monitoring unit, used to calculate the label proportion deviation value, and determine whether the preset label proportion deviation threshold has been reached, and generate an adjustment signal; and a plan adjustment execution unit, used to receive the adjustment signal and adjust the future inventory quantity or label proportion target according to the deviation direction.

[0010] A precise product planning and distribution method based on member tag data includes: Collect member data to obtain basic member information and purchase product characteristics; Generate tags by converting the acquired information into member profile tags and product feature tags, and then associate and bind them with member attribute data; Store the data by storing the structured member tag dataset into the database; Analyze product demand, perform categorized demand quantification and tag decomposition, including calculating demand from existing members, calculating demand from new members, decomposing tag proportions and weighting integration; The plan is adjusted on a rolling basis, and inventory deviation and label ratio deviation are monitored. Dynamic adjustments are triggered when preset thresholds are reached. Generate procurement and production plans, transforming them into purchase orders and production plans based on updated planning data; Single-store product allocation: automatically matching products to target stores based on the planning scheme; Logistics integration involves converting the sorting list into a standardized format and sending it to the warehousing and logistics system.

[0011] Specifically, the analysis of product demand includes: calculating the expected purchase quantity of existing members based on the total number of historical members, repurchase rate, and average cross-selling rate through the existing member demand calculation unit; calculating the expected purchase quantity of new members based on the number of new members and average cross-selling rate through the new member demand calculation unit; decomposing the proportion of each tag dimension for existing members based on historical purchase records and calculating the demand quantity through the tag proportion decomposition unit, and decomposing the proportion for new members based on historical data or alternative data sources and calculating the demand quantity through the weight fusion unit; and weighting the demand quantity for the same tag dimension according to preset fusion rules through the weighted calculation unit to generate the final product planning scheme.

[0012] Specifically, calculating the demand of existing members involves retrieving the total number of historical members, the repurchase rate of historical members, and the average cross-selling rate of existing members for a single store. Then, a formula is applied to calculate the expected purchase quantity of existing members, which is the total number of historical members multiplied by the repurchase rate and then multiplied by the average cross-selling rate.

[0013] Specifically, the calculation of new member demand involves: retrieving the number of new members and the average cross-selling rate of new members within a preset time period for a single store, and applying a formula to calculate the expected purchase quantity of new members, which is the number of new members multiplied by the average cross-selling rate.

[0014] Specifically, when decomposing the tag proportions for new members, if there is insufficient historical data for new members, a preset alternative data source is called for decomposition. This alternative data source is the average value of new member tag data from stores of the same size in the same city or from all stores of the company.

[0015] Specifically, the rolling adjustment plan includes: calculating the inventory deviation value through the inventory deviation monitoring unit, determining whether the preset inventory deviation threshold has been reached, and generating an adjustment signal; calculating the label percentage deviation value through the label percentage deviation monitoring unit, determining whether the preset label percentage deviation threshold has been reached, and generating an adjustment signal; and receiving the signal through the plan adjustment execution unit and adjusting the future inventory quantity or label percentage target according to the deviation direction.

[0016] Specifically, adjusting future inventory levels involves: if the inventory deviation signal indicates a negative deviation, reducing future inventory levels proportionally to the deviation; if it indicates a positive deviation, increasing future inventory levels proportionally to the deviation. Adjusting the label percentage target involves: directly adjusting the future label percentage target based on the recent actual label percentage, and recalculating the required quantity.

[0017] The beneficial effects of this invention are as follows: 1. By differentiating between the needs of old and new members and combining them with weighted fusion rules, the accuracy of demand forecasting is improved, making the forecast results more consistent with the actual composition of the store's members.

[0018] 2. By setting deviation thresholds and adjustment rules, and automatically triggering plan adjustments based on monitoring results, the systematization of plan adjustments is improved, and errors caused by manual intervention are reduced.

[0019] 3. By combining accurate demand forecasting, dynamic adjustment, and tag-based automated distribution, we ensure that product supply accurately matches member preferences and improve product sales efficiency.

[0020] 4. By using a dynamic quantity control mechanism, we can control overstocking and stockouts at the source, thereby optimizing inventory management efficiency.

[0021] 5. By accurately matching and optimizing inventory, reduce ineffective inventory occupation and resource waste, and reduce resource consumption caused by frequent transfers. Detailed Implementation

[0022] To make the technical problems, solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0023] This invention proposes a precise product planning and distribution system and method based on member tag data.

[0024] This invention's system is deployed on a server and consists of multiple functional modules working collaboratively. Specifically, it includes: a member data collection module, a tag generation module, a data storage module, a product demand analysis module, a rolling plan adjustment module, a procurement and production plan generation module, a single-store distribution module, a logistics integration module, server hardware, and store terminal equipment. The server hardware includes necessary central processing units (CPUs), memory, and storage devices. The server hardware is electrically connected to all functional modules and units to provide operational support. The store terminal equipment includes cash registers and member management terminals. The store terminal equipment is communicatively connected to the member data collection module, and data generated by the store terminal equipment is transmitted to the member data collection module via a communication interface.

[0025] The product demand analysis module is further subdivided into a demand calculation unit for existing members, a demand calculation unit for new members, a tag proportion decomposition unit, and a weight fusion unit. The rolling plan adjustment module is further subdivided into an inventory deviation monitoring unit, a tag proportion deviation monitoring unit, and a plan adjustment execution unit.

[0026] The connections between the modules are as follows: the output of the member data collection module is electrically connected to the input of the tag generation module; the output of the tag generation module is electrically connected to the input of the data storage module; and the output of the data storage module is electrically connected to the inputs of the product demand analysis module and the rolling adjustment plan module, respectively.

[0027] Within the product demand analysis module, the outputs of both the old member demand calculation unit and the new member demand calculation unit are electrically connected to the input of the tag proportion decomposition unit, while the output of the tag proportion decomposition unit is electrically connected to the input of the weight fusion unit.

[0028] Within the rolling adjustment module, both the inventory deviation monitoring unit and the label percentage deviation monitoring unit are connected to the plan adjustment execution unit.

[0029] The output of the weight fusion unit is electrically connected to the input of the plan adjustment execution unit within the plan rolling adjustment module; the output of the plan adjustment execution unit is electrically connected to the input of the procurement and production plan generation module; the output of the procurement and production plan generation module is electrically connected to the input of the single-store distribution module; and the output of the single-store distribution module is electrically connected to the input of the logistics docking module.

[0030] The method of this invention is performed as follows: Step S10, the specific implementation of the member data collection module is as follows: When a member makes a purchase at the store through a cash register or member management terminal, the member data collection module obtains two types of information from the store terminal equipment in real time. The first type is basic member information, specifically including quantifiable personal attributes such as the member's age, height, and weight. The second type is the characteristic information of the purchased goods, specifically including the category of the purchased goods (e.g., tops, pants), price range (e.g., low price, mid price, high price), fit characteristics (e.g., slim fit, loose fit), style characteristics (e.g., business, casual, sports), fabric composition (e.g., cotton, blend, chemical fiber), and design elements (e.g., prints, patchwork, embroidery).

[0031] Step S20, the tag generation module is implemented as follows: The tag generation module receives data transmitted from the member data collection module, and according to a preset rule mapping table, converts the acquired raw member basic information into structured member profile tags (e.g., divided into "youth" and "middle-aged" tags based on age); simultaneously, it converts the characteristic information of purchased goods into corresponding product feature tags (e.g., category tag "shirt", price range tag "mid-price 199-399", style tag "casual", fabric tag "pure cotton", and design element tag "small area print"). The tag generation module also associates and binds the collected member attribute data (including "old member" or "new member" tags used to identify member type, historical records of the member's historical purchase count used to calculate repurchase rate, and related records of the number of items included in each purchase used to calculate average cross-selling rate) with the generated member profile tags and product feature tags to form a complete member tag dataset. All generated tag data and association relationships are transmitted to the data storage module for storage. Throughout this process, the terminology used is strictly standardized. For example, the proportion of members purchasing multiple items is uniformly expressed as "average cross-selling rate," and the proportion of members making repeat purchases is uniformly expressed as "repurchase rate."

[0032] Step S30, the data storage module is implemented as follows: It receives a structured member tag dataset (containing member profile tags, product feature tags, and their associated member attribute data) from the tag generation module and stores it in the server's storage device, establishing corresponding database tables or a data warehouse to ensure efficient querying and retrieval of the data by subsequent modules. The data storage module provides data access interfaces for the product demand analysis module and the rolling plan adjustment module.

[0033] Step S40, the specific implementation of the product demand analysis module is as follows: the product demand analysis module obtains relevant historical data of a specified single store from the data storage module, and performs categorized demand quantification calculation and tag decomposition.

[0034] Step S41, the specific operation of the old member demand calculation unit is as follows: Retrieve historical data for the single store, including the total number of historical members (the number of members registered at the beginning of the statistical period), the overall repurchase rate of these historical members (the proportion of historical members making repeat purchases within the statistical period), and the average cross-selling rate of old members (the average number of items purchased by old members per transaction). Then, apply the following formula to accurately calculate the total number of items expected to be purchased by the old member group in the store over a future period: Expected purchase quantity of existing members in a single store = Total number of historical members in a single store × Repurchase rate of historical members in a single store × Average cross-selling rate of existing members; Step S42, the specific operation of the new member demand calculation unit is as follows: Retrieve the number of new members for the single store in the past 30 days (or adjust to the past 20 days, past 45 days, etc., depending on the actual situation), and the average cross-selling rate of new members (the average number of items purchased by new members in their first or initial transactions). Then, apply the following formula to accurately calculate the total number of items expected to be purchased by the new member group in the store in the future: Expected purchase quantity of new members in a single store = Number of new members added in the past N days in a single store × Average cross-selling rate of new members; N can be 30, 20, 45, etc.

[0035] Step S43, the specific operation of the label proportion decomposition unit is as follows: For existing members, based on the product feature tag data corresponding to the historical purchase records of existing members in a single store over a period of time (e.g., the past 6 months or 12 months), statistical analysis is performed on the proportion of each major tag dimension (such as style, fabric, design elements, etc.) in their total purchase volume (e.g., the proportion of products tagged "casual style" in existing members' purchase records is 65%, and the proportion of products tagged "cotton fabric" is 70%). Furthermore, the accurate expected demand quantity corresponding to each tag dimension is calculated based on the expected purchase quantity of existing members (e.g., the expected demand quantity of "casual style" for existing members = the expected purchase quantity of existing members in a single store × 65%).

[0036] For new members, if the new members of a single store have sufficient historical purchase data (e.g., data coverage exceeds a preset threshold), the tag dimension proportion decomposition and accurate quantity calculation will be performed based on their historical purchase records. If the new members of a single store have insufficient historical data (e.g., too few new stores or new members), a preset alternative data source will be called for decomposition. The alternative data source can be the average of new member tag data of stores of the same city and scale, or the average of new member tag data of all stores in the entire company.

[0037] Step S44, the specific operation of the weighted fusion unit is as follows: It receives the estimated demand quantities for each tag dimension of existing members and the estimated demand quantities for each tag dimension of new members from the tag proportion decomposition unit. Based on the preset fusion rules (typically 70% weight for existing members + 30% weight for new members; alternative ratios can be adjusted to 60% for existing members + 40% for new members or 80% for existing members + 20% for new members, etc., depending on the characteristics of the store's existing and new member composition), it performs a weighted calculation on the demand quantities of existing members and new members for the same tag dimension. The specific formula is: The final demand for a specific tag dimension in a single store = the demand for that tag dimension from existing members × the weight of existing members. +Quantity required for this tag dimension for new members × Weight of new members; After performing this calculation on all tag dimensions, a final merchandise planning scheme based on member tags and incorporating the needs of both new and existing members is generated for each store. This scheme clarifies the target inventory levels for each key product feature tag dimension. All individual store merchandise planning schemes are ultimately aggregated and transmitted to the data storage module or directly to subsequent modules to form the company's overall merchandise planning scheme.

[0038] Step S50, the specific implementation of the rolling adjustment module is as follows: the rolling adjustment module continuously monitors the deviation between sales performance and the plan, and triggers dynamic adjustment when the preset threshold is reached.

[0039] Step S51, the specific operation of the inventory deviation monitoring unit is as follows: It retrieves the planned sales volume (i.e., the target inventory quantity in the plan or its time-decomposed sales plan) and actual sales volume data of each store and each tag dimension product (or product batches divided by the plan) from the data storage module in real time or periodically (e.g., daily). The inventory deviation value is calculated using the formula: Inventory Deviation = (Actual Sales Volume - Planned Sales Volume) / Planned Sales Volume × 100%. This unit continuously judges whether the absolute value of the inventory deviation reaches or exceeds the preset inventory deviation threshold (typically ±10%; it can also be adjusted to ±8% or ±12% or other alternative thresholds depending on the product category characteristics). If the absolute value of the inventory deviation of a certain tag dimension product in a certain store is ≥ the threshold (e.g., ≥10%), an adjustment signal is generated and transmitted to the planned adjustment execution unit. The signal must include information such as the deviation direction (positive deviation means actual sales volume > planned sales volume, negative deviation means actual sales volume < planned sales volume), the deviation magnitude, the stores involved, and the product tag dimension.

[0040] Step S52, the specific operation of the label proportion deviation monitoring unit is as follows: Retrieve the sales data of a specified single store for the past 15 days from the data storage module, and calculate the actual sales proportion of each product feature label dimension during this period (for example, among the products sold in the past 15 days, products with the "splicing design" label account for 14%). Simultaneously, retrieve the historical label proportion data corresponding to that single store (i.e., the proportion of that label dimension obtained in step S40, for example, a historical proportion of 10%). Calculate the label proportion deviation value using the following formula: Tag percentage deviation = Recent actual tag percentage - Historical tag percentage; The label proportion deviation monitoring unit continuously determines whether the absolute value of the label proportion deviation for each label dimension reaches or exceeds the preset label proportion deviation threshold (typically ±3%; it can also be adjusted to ±2% or ±4% as alternative thresholds). If the absolute value of the label proportion deviation for a certain label dimension in a single store is ≥ the threshold (e.g., ≥3%), an adjustment signal is generated and transmitted to the planned adjustment execution unit. The signal must include information such as the deviation direction, deviation magnitude, affected stores, product label dimensions, recent actual proportion, and historical proportion.

[0041] Step S53, the specific operation of the planned adjustment execution unit is: receiving adjustment signals from the inventory deviation monitoring unit and / or the label proportion deviation monitoring unit.

[0042] For inventory deviation signals: If the signal indicates a negative deviation (actual sales < planned sales, risk of inventory backlog), then reduce the planned inventory quantity for that label dimension of the single store for the next 2 months by the deviation ratio (i.e., the calculated deviation value, such as -15%). The adjustment formula (the deviation value is negative, so the calculation result is a reduction) is as follows: Adjusted inventory level for the next two months = Original planned inventory level for the next two months × (1 + inventory deviation value); If the signal indicates a positive deviation (actual sales > planned sales, stockout risk), then the planned inventory of the product for that label dimension in the single store will be increased for the next two months according to the deviation ratio. The formula is the same as above (if the deviation value is positive, the calculation result is an increase).

[0043] For the label percentage deviation signal: Based on the recent actual label percentage indicated by the signal (e.g., "splicing design" has an actual percentage of 14%) and the deviation direction (e.g., +4%), directly adjust the target percentage of the label dimension in the product planning for the next month to the recent actual percentage value (e.g., increase the target percentage of the "splicing design" label for the next month from 10% to 14%), and recalculate the accurate demand quantity for the label dimension based on the adjusted percentage (which needs to be combined with the total demand forecast for the store during this period).

[0044] After the execution unit completes its calculations, the updated single-store product plan (including adjusted inventory data for the next two months and / or label percentage data for the next month) is transmitted to the data storage module for updating, and the update process of the procurement and production plan generation module is triggered simultaneously. The company's overall plan is also updated accordingly.

[0045] Step S56, the specific implementation of the procurement and production plan generation module, involves receiving the company's overall merchandise planning data updated by the rolling adjustment module. This module, based on the final target inventory levels (including dynamically adjusted results) for each product feature tag dimension explicitly listed in the plan, and combining this with basic data such as the bill of materials (BOM), supplier information, production cycle, and quality standards, transforms it into specific, executable purchase orders and production plans. Specific operations include: summarizing the needs of each individual store by tag dimension to determine the specific styles, specifications, and quantities of the required goods; generating purchase orders or production instructions based on supplier allocation rules and delivery cycle requirements; and specifying the quality acceptance standards and expected delivery time for each batch of goods. The generated detailed procurement and production plan is synchronously transmitted to the company's internal procurement department and external partner manufacturers through the system interface.

[0046] Step S57, the specific implementation of the single-store distribution module, is as follows: When the purchased or produced goods actually arrive at the warehouse, the Warehouse Management System (WMS) or relevant personnel enter the characteristic label information (such as category, style, fabric, design elements, etc.) of the arriving goods into the system or automatically transmit it to this module through an interface (such as scanning the RFID / barcode associated label of the goods). This module retrieves the final merchandise planning scheme of each single store stored in the data storage module, which has been calculated and dynamically adjusted in the aforementioned steps (clearly listing the required quantity of each single store for each dimension of product characteristic labels). Based on this, the system automatically matches the target store to which each arriving product should be allocated according to the preset distribution algorithm rules (such as allocation according to demand ratio, considering store level, etc.), generating a detailed distribution list. This list must include information such as store code, product code / characteristic label, and allocation quantity.

[0047] Step S58, the logistics integration module specifically implements the following: receiving the distribution list generated by the single-store distribution module. This module converts the distribution list into a standardized data format (such as EDI, API messages) that meets the requirements of the Warehouse Management System / Transportation Management System (WMS / TMS), and automatically sends it to the WMS and / or TMS through the system interface. Based on the received distribution instructions, the WMS / TMS system executes specific picking, packing, and shipping operations to accurately deliver goods to each target store, completing the entire precise distribution process based on member tags.

[0048] Through the coordinated operation of the various modules of the above system and the strict implementation of the methods, an effective response has been achieved to the dynamic changes in membership structure and the rapid iteration of consumer preferences in industries such as fast fashion menswear. Its core value lies in: 1. By calculating the demand of old members and new members in a refined and formulaic way (number of old members = number of historical members × repurchase rate × old cross-linking rate; number of new members = number of new members × new cross-linking rate) and combining it with weighted integration (such as 70% + 30%), the accuracy of demand forecasting is significantly improved, avoiding the ambiguity of traditional experience estimation and conforming to the actual structure of store members. 2. By setting clear and quantifiable deviation thresholds (such as inventory deviation ±10% and label percentage deviation ±3%) and adjustment rules (adjusting inventory deviation to the quantity for the next 2 months and label deviation to the percentage for the next 1 month), and automatically triggering adjustments based on monitoring results, the scientific nature and objectivity of plan adjustments are greatly enhanced, and the blindness and error of subjective human intervention are reduced. 3. Ultimately, the combination of accurate demand forecasting, scientific dynamic adjustment, and tag-based automated distribution ensures a high degree of matching between supply and member preferences (especially new and old members in structural changes), thereby effectively improving sell-through rates. At the same time, the dynamic quantity control mechanism reduces overstocking and stockouts from the source, achieving efficient inventory optimization and significantly reducing the costs of ineffective inventory occupation, resource waste, and frequent transfers.

[0049] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A precise product planning and distribution system based on member tag data, characterized in that, include: The member data collection module is used to obtain basic member information and purchase characteristics information from store terminal equipment; The tag generation module is used to convert the acquired information into member profile tags and product feature tags, and associate and bind them with member attribute data; The data storage module is used to store structured member tag datasets; The product demand analysis module is used to perform categorized demand quantification calculations and label decomposition. The rolling adjustment module is used to monitor sales deviations and trigger dynamic adjustments. The procurement and production planning generation module is used to generate purchase orders and production plans based on updated planning data; The single-store distribution module is used to automatically match goods to target stores based on the planning scheme; The logistics integration module is used to convert the sorting list into a standardized format and send it to the warehousing and logistics system.

2. The precise product planning and distribution system based on member tag data as described in claim 1, characterized in that, The product demand analysis module further includes: a unit for calculating the demand of existing members, used to calculate the expected purchase quantity of existing members based on the total number of historical members, repurchase rate, and average cross-selling rate; a unit for calculating the demand of new members, used to calculate the expected purchase quantity of new members based on the number of new members and average cross-selling rate; a tag proportion decomposition unit, used to decompose the proportion of each tag dimension for existing members and new members based on historical purchase records or alternative data sources and calculate the demand quantity; and a weight fusion unit, used to perform weighted calculation of the demand quantity of existing members and the demand quantity of new members for the same tag dimension according to preset fusion rules, and generate the final product planning scheme.

3. The precise product planning and distribution system based on member tag data as described in claim 1, characterized in that, The rolling adjustment module further includes: an inventory deviation monitoring unit, used to calculate the inventory deviation value in real time or periodically, and determine whether the preset inventory deviation threshold has been reached, and generate an adjustment signal; a label proportion deviation monitoring unit, used to calculate the label proportion deviation value, and determine whether the preset label proportion deviation threshold has been reached, and generate an adjustment signal; and a plan adjustment execution unit, used to receive the adjustment signal and adjust the future inventory quantity or label proportion target according to the deviation direction.

4. A precise product planning and distribution method based on member tag data, characterized in that, include: Collect member data to obtain basic member information and purchase product characteristics; Generate tags by converting the acquired information into member profile tags and product feature tags, and then associate and bind them with member attribute data; Store the data by storing the structured member tag dataset into the database; Analyze product demand, perform categorized demand quantification and tag decomposition, including calculating demand from existing members, calculating demand from new members, decomposing tag proportions and weighting integration; The plan is adjusted on a rolling basis, and inventory deviation and label ratio deviation are monitored. Dynamic adjustments are triggered when preset thresholds are reached. Generate procurement and production plans, transforming them into purchase orders and production plans based on updated planning data; Single-store product allocation: automatically matching products to target stores based on the planning scheme; Logistics integration involves converting the sorting list into a standardized format and sending it to the warehousing and logistics system.

5. The precise product planning and distribution method based on member tag data as described in claim 4, characterized in that, The analysis of product demand includes: calculating the expected purchase quantity of existing members based on the total number of historical members, repurchase rate, and average cross-selling rate using the existing member demand calculation unit; calculating the expected purchase quantity of new members based on the number of new members and average cross-selling rate using the new member demand calculation unit; decomposing the proportion of each tag dimension for existing members based on historical purchase records and calculating the demand quantity using the tag proportion decomposition unit, and decomposing the proportion for new members based on historical data or alternative data sources and calculating the demand quantity using the weighted fusion unit according to preset fusion rules to generate the final product planning scheme.

6. The precise product planning and distribution method based on member tag data as described in claim 5, characterized in that, The specific steps to calculate the demand of existing members are as follows: retrieve the total number of historical members, the repurchase rate of historical members, and the average cross-selling rate of existing members for a single store, and apply the formula to calculate the expected purchase quantity of existing members. This formula is the total number of historical members multiplied by the repurchase rate and then multiplied by the average cross-selling rate.

7. The precise product planning and distribution method based on member tag data as described in claim 5, characterized in that, The calculation of new member demand involves retrieving the number of new members and the average cross-selling rate of new members within a preset time period for a single store, and then applying a formula to calculate the expected purchase quantity of new members, which is the number of new members multiplied by the average cross-selling rate.

8. The precise product planning and distribution method based on member tag data as described in claim 5, characterized in that, When decomposing tag proportions for new members, if there is insufficient historical data for new members, a preset alternative data source is called for decomposition. This alternative data source is the average value of new member tag data from stores of the same size in the same city or from all stores of the company.

9. The precise product planning and distribution method based on member tag data as described in claim 4, characterized in that, The rolling adjustment plan includes: calculating the inventory deviation value through the inventory deviation monitoring unit, determining whether the preset inventory deviation threshold has been reached, and generating an adjustment signal; calculating the label percentage deviation value through the label percentage deviation monitoring unit, determining whether the preset label percentage deviation threshold has been reached, and generating an adjustment signal; and receiving the signal through the plan adjustment execution unit and adjusting the future inventory quantity or label percentage target according to the deviation direction.

10. The precise product planning and distribution method based on member tag data as described in claim 9, characterized in that, The specific adjustments to future inventory levels are as follows: if the inventory deviation signal indicates a negative deviation, the future inventory level will be reduced proportionally to the deviation; if it indicates a positive deviation, the future inventory level will be increased proportionally to the deviation. The specific adjustments to the label percentage target are as follows: the future label percentage target will be directly adjusted based on the recent actual label percentage, and the required quantity will be recalculated.