Stock-up conflict management method based on contract constraint and returned goods grading reuse

Abstract

The invention relates to the field of e-commerce supply chain management, discloses a stock conflict management method based on contract constraint and return grading reuse, and aims to solve the problems of high contract performance risk, waste of return resources and unbalanced stock distribution in traditional multi-platform stock. The method comprises the steps of collecting multi-source data and preprocessing the multi-source data to generate a multi-dimensional data set; the contract terms are analyzed and quantified into priority dynamic scores; the returned commodities are graded according to the commodity state and the like, and a reuse quantitative model is constructed to calculate the effective reuse number; the optimal stock quantity is calculated in combination with the contract priority, the reuse quantity and the supplier productivity; the stock conflict is eliminated through a scene-dividing rule; and full-link closed-loop iterative optimization is carried out. According to the method, the contract performance compliance and the returned goods reuse rate are improved, the optimal allocation of stock resources is realized, and an accurate and long-acting technical scheme is provided for multi-platform stock management.

Description

Technical Field

[0001] This invention relates to the field of e-commerce supply chain management, specifically to a method for managing inventory conflicts based on contractual constraints and graded reuse of returned goods. Background Technology

[0002] With the booming development of the e-commerce industry, multi-platform distribution and multi-channel operation have become core business models for e-commerce companies to increase market share and expand their user base. E-commerce companies typically conduct sales on multiple e-commerce platforms simultaneously, and inventory management, as a core link in the e-commerce supply chain, directly determines the company's fulfillment efficiency, inventory costs, and user experience. Under the multi-platform operation model, inventory management faces multiple challenges. The activity contracts of each platform have differentiated constraints, including sales commitments, out-of-stock penalty rules, fulfillment time requirements, platform subsidy policies, and inventory turnover assessment clauses. Traditional inventory management solutions often rely on manual interpretation of contracts and formulation of inventory plans, which is not only inefficient but also unable to respond to changes in contract terms in real time, easily leading to high penalties for non-compliance with contract performance. E-commerce promotional activities are often accompanied by large-scale returns. Existing return management often adopts a crude model of uniform scrapping or low-price clearance, without hierarchical management based on product status, reuse costs, and reuse cycles, nor does it combine the return tolerance clauses of each platform's contracts to achieve precise reuse, resulting in serious waste of return resources and exacerbating the cost pressure of new inventory.

[0003] Therefore, in order to solve the problems existing in the prior art, this invention proposes a stock preparation conflict management method based on contractual constraints and graded reuse of returned goods. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a method for managing inventory conflicts based on contractual constraints and graded reuse of returned goods.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A stock preparation conflict management method based on contractual constraints and tiered reuse of returned goods includes: The data collection and processing steps involve collecting multi-platform activity contract data, basic product data, cross-platform real-time order data, inventory dynamic data, logistics timeliness data, and return data, and performing data preprocessing to generate a multidimensional dataset. The contract analysis and quantification process involves analyzing the core constraints of contract data from various platforms, monitoring changes in contract terms in real time, and quantifying constraints such as penalty amounts, cooperation levels, performance timeliness, platform subsidies, and inventory turnover assessments into dynamic contract priority scores using a weighted algorithm. The steps for graded reuse of returned goods are as follows: returned goods are graded based on their status, reuse cost, and reuse cycle; the contract compatibility score is calculated by combining the return period, defect tolerance, and return penalty rules of each platform's contract; and a reuse quantification model is constructed to calculate the effective reuse quantity. The inventory calculation process takes contract priority dynamic score, real-time order data, effective reuse quantity and supplier capacity as constraints, and uses an optimization algorithm to calculate the optimal inventory quantity that maximizes cross-platform benefits. The inventory conflict management process involves identifying conflicts between contractual constraints across multiple platforms and conflicts between inventory needs, and allocating the optimal inventory quantity through scenario-based conflict resolution rules combined with a cross-platform shared inventory scheduling mechanism.

[0006] As a further improvement of the present invention, the contract parsing and quantification steps include: semantically parsing the contract texts of various platforms using a natural language processing model to extract basic data corresponding to core constraint clauses. These core constraint clauses include penalty clauses, cooperation level clauses, performance timeliness clauses, platform subsidy clauses, and inventory turnover assessment clauses. The extracted basic data undergoes standardized preprocessing, with penalty amounts normalized to a range, cooperation levels quantified to a range, performance timeliness deviation rate converted, platform subsidies corrected for effective fulfillment ratio, and inventory turnover assessment quantified to achieve compliance rate. This yields the basic quantitative values ​​corresponding to each clause. Simultaneously, a clause change monitoring submodule is constructed to collect contract clause change records in real time and calculate change stability coefficients. Finally, these are integrated to form intermediate data for contract constraint quantification, providing input parameters for calculating the dynamic score of contract priority.

[0007] As a further improvement of the present invention, the formula for calculating the dynamic score of contract priority is as follows: ; Where P is the dynamic score of contract priority, τ is the basic decay coefficient in the time dimension, t0 is the initial time of contract effectiveness (Unix timestamp value at the time of contract signing), t1 is the current statistical time (Unix timestamp value at the time of real-time calculation), and α is the time decay factor for clause changes. Let be the stability coefficient of the contract terms at time t. , Let t be the quantified value of the changes in contract terms at time t. Let K be the total number of initial contract terms, K be the total number of penalty scenarios in the contract, and denoted as a positive integer obtained after the natural language processing model parses the contract. Let be the importance coefficient of the k-th type of penalty clause after being quantified by natural language processing, representing the importance level of the penalty clause. F kLet be the agreed amount for the k-th type of penalty scenario, γ be the non-linear adjustment index of the penalty amount, C be the quantitative value of the platform-merchant cooperation level, quantified by the platform's cooperation rating system to a value from 0 to 1, β be the weighting amplification coefficient of the cooperation level, S be the total subsidy amount from the platform for this contract, η be the effective fulfillment coefficient of the platform subsidy, obtained by fitting historical subsidy fulfillment data, and T be the total amount of the platform subsidy for this contract. max This represents the theoretical maximum threshold for the performance period. The probability density of performance timeliness function, , This represents the platform's average fulfillment time. Z represents the standard deviation of delivery time, M is the total number of indicators for inventory turnover assessment, and Z is a positive integer. m This represents the actual value of the m-th inventory turnover assessment item. Let m be the weighting coefficient for the inventory turnover assessment item. This refers to the historical average performance time under this contract. For the normalization function of multidimensional indicators, , The original matrix of the contract's constraint dimensions indicators, min The global minimum value of the original index matrix is ​​max. P represents the global maximum value of the original index matrix, with a range of [0,10]. P∈[0,2) represents extremely low contract priority, P∈[2,5) represents medium contract priority, P∈[5,8) represents relatively high contract priority, and P∈[8,10] represents extremely high contract priority.

[0008] As a further improvement of the present invention, the returned goods classification and reuse step includes classifying returned goods into four levels: Level 1 is an immediate reuse level that is unopened, has no reuse cost, and has a reuse period of less than 24 hours; Level 2 is a simple reuse level with minor defects, a reuse cost of less than 5% of the product price, and a reuse period of 24 to 72 hours; Level 3 is a component reuse level with intact core components, a reuse cost of 5% to 20%, and a reuse period of one to seven days; and Level 4 is a non-reusable level with core function failure and a reuse cost of more than 20%. The reuse quantification model calculates the effective reuse quantity through return prediction quantity, classification qualification rate, reuse willingness rate, and contract compatibility score.

[0009] As a further improvement of the present invention, the reuse quantization model is configured as follows: ; Among them, Q eff Q represents the effective reuse quantity of returned goods. pre The quantity of returned goods is predicted, and k is the grade number of the returned goods. Let be the comprehensive effective coefficient for the k-th level of returns. , where ρ k Let θ be the graded pass rate for the k-th level of returns. k The reuse rate for returns at level k. To adapt the contract to a correction factor, , For platform contract adaptation score, This represents the theoretical maximum value of the contract fit score.

[0010] As a further improvement of the present invention, the inventory quantity calculation step includes: calculating the basic inventory demand of orders based on the real-time order volume and the order growth value in the next 24 hours of each platform; weighting the basic inventory demand of orders on each platform with the dynamic score of contract priority; allocating the effective reuse quantity to the corresponding platform according to the contract adaptation score of each platform to offset the new inventory quantity of that platform; summarizing the inventory demand of each platform; if the total demand exceeds the supplier's maximum capacity, it is reduced proportionally according to the contract priority weight; at the same time, 5% elastic inventory quantity is reserved to cope with sudden order fluctuations, so as to achieve the optimal inventory quantity determination that maximizes cross-platform benefits.

[0011] As a further improvement of the present invention, the total inventory constraint formula is configured as follows: ; Among them, Q i This is for the final inventory level of each platform. O i for The real-time order quantity of the i-th platform, g i Let P be the predicted order growth rate for the i-th platform over the next 24 hours. i Let q be the dynamic score for contract priority on the i-th platform, n be the total number of platforms, and q be the value of q. eff,i Q represents the number of returns that can be reused and allocated to the i-th platform. total For total inventory across platforms, C sup This is the maximum capacity of the supplier.

[0012] As a further improvement of the present invention, the scenario-based conflict resolution rules include: when the platform's daily growth rate is less than a threshold preset based on the historical average daily order growth rate, the inventory is allocated from high to low according to the product of the contract priority dynamic score and the platform's order conversion rate; when the daily order growth rate is greater than the preset threshold, the inventory is sorted according to the stockout penalty cost and the fulfillment timeliness urgency coefficient, and a flexible inventory is preset, wherein the fulfillment timeliness urgency coefficient is the ratio of the platform's contractually agreed fulfillment timeliness to the current average logistics timeliness; when the daily return rate of goods is greater than a threshold preset based on the historical average daily return rate, the returned goods are sorted according to the product of the net value of returned goods reuse and the platform's inventory turnover efficiency, and high-level returned goods are allocated to platforms with high inventory turnover efficiency, and cross-platform shared inventory only collects first-level and second-level returned reuse goods and general standard products, and the scheduling trigger condition is that the cross-platform scheduling cost is less than the target platform's stockout penalty cost.

[0013] As a further improvement of the present invention, it also includes a full-link closed-loop iterative optimization step. After each batch of activities, the actual inventory revenue, contract fulfillment rate, return reuse rate, stockout rate, backlog rate and conflict resolution success rate are collected. Through multi-dimensional optimization and evaluation, the contract constraint quantitative weight coefficient, the return reuse quantitative model graded qualification rate calibration value and the inventory quantity calculation algorithm objective function weight are updated in reverse. Among them, the contract fulfillment rate calibration coefficient is strongly correlated with the actual fulfillment data during the peak promotion period, and the return reuse rate calibration value is strongly correlated with the actual reuse data during the peak return period, so as to complete the dynamic collaborative optimization of the parameters of each module.

[0014] The beneficial effects of this invention are as follows: By intelligently parsing and prioritizing contract terms, and combining this with dynamic monitoring of term changes, abstract contract constraints are transformed into weights for inventory preparation decisions, effectively reducing the risk of penalties caused by human interpretation bias and improving the contract fulfillment rate across multiple platforms; a four-level classification system for returned goods is constructed, and the effective reuse quantity is calculated based on the platform contract adaptability, changing the traditional extensive handling mode of returns, improving the reuse rate of returned resources, and significantly reducing inventory procurement and inventory backlog costs by using returned goods reuse to offset new inventory preparation; the full-link closed-loop iterative optimization mechanism can reverse-correct core parameters based on the performance data of each batch of activities, enabling inventory preparation decisions to dynamically iterate with business scenarios and continuously improve the adaptability of the solution and the accuracy of decisions. Attached Figure Description

[0015] Figure 1 This is a flowchart of a stock preparation conflict management method based on contractual constraints and graded reuse of returned goods, according to the present invention.

[0016] Figure 2 This is a flowchart illustrating the calculation of return forecast volume in an embodiment of the present invention.

[0017] Figure 3 This is a flowchart of the multi-dimensional optimization evaluation process according to an embodiment of the present invention. Detailed Implementation

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Identical components are denoted by the same reference numerals. It should be noted that the terms "front," "rear," "left," "right," "upper," and "lower" used in the following description refer to directions in the accompanying drawings, and the terms "bottom surface," "top surface," "inner," and "outer" refer to directions toward or away from the geometric center of a specific component, respectively.

[0019] This invention proposes a method for managing inventory conflicts based on contractual constraints and tiered reuse of returned goods, such as... Figures 1 to 3 As shown, it includes: The data collection and processing steps involve collecting multi-platform activity contract data, basic product data, cross-platform real-time order data, inventory dynamic data, logistics timeliness data, and return data, and performing data preprocessing to generate a multidimensional dataset. During the data collection phase, the system integrates and aggregates data across the entire value chain using multi-source data interfaces. This includes multi-platform activity contract data covering various cooperation agreements and supplementary clauses signed between e-commerce platforms and merchants, encompassing explicit sales targets and breach of contract compensation standards, as well as implicit inventory turnover assessment details and subsidy disbursement conditions. Basic product data covers core information such as product category attributes, basic costs, specifications, and historical sales lifecycle. Cross-platform real-time order data provides real-time data on order generation, payment, and pending shipment status across various platforms. Dynamic inventory data includes real-time inventory levels at each warehousing node, in-transit inventory transportation progress, and the order association status of occupied inventory. Logistics timeliness data covers the regular delivery cycle of various logistics channels, peak-period timeliness fluctuation patterns, and reverse logistics turnaround time. Return data includes traceability information for returned goods, reasons for return, appearance and functional inspection results, and return warehousing time.

[0020] After data collection, standardized preprocessing is required to eliminate data heterogeneity. For missing data, the system will perform linear interpolation to complete the data based on the historical time-series patterns of data from the same product category and platform, ensuring the temporal continuity of the data. For outlier data, statistical methods will be used to identify and remove it. Specifically, a reasonable fluctuation range will be defined based on the dispersion of the data distribution. Data that exceeds the range and has no reasonable business motivation will be marked and verified. After verification, it will be corrected or removed. For data with different dimensions, normalization processing will be performed to unify the range. For example, fines that differ significantly between different platforms will be converted into dimensionless values ​​relative to the company's profit capacity, and delivery times of different logistics channels will be converted into ratios relative to the industry average delivery time. At the same time, the system will use the order generation time as a benchmark to complete the temporal alignment of multi-source data, ensuring the traceability of the correlation between different types of data in the same time dimension. Finally, a structured multidimensional dataset will be formed, providing a unified and high-quality data input for subsequent calculations and analyses.

[0021] The contract analysis and quantification process involves analyzing the core constraints of contract data from various platforms, monitoring changes in contract terms in real time, and quantifying constraints such as penalty amounts, cooperation levels, performance timeliness, platform subsidies, and inventory turnover assessments into dynamic contract priority scores using a weighted algorithm. Specifically, such as Figures 1 to 3 As shown, the contract parsing and quantification steps include: semantically parsing the contract texts of various platforms using a natural language processing model to extract basic data corresponding to core constraint clauses. These core constraint clauses include penalty clauses, cooperation level clauses, performance timeliness clauses, platform subsidy clauses, and inventory turnover assessment clauses. The extracted basic data undergoes standardized preprocessing, with penalty amounts normalized to a range, cooperation levels quantified to a range, performance timeliness deviation rate converted, platform subsidies corrected for effective fulfillment ratio, and inventory turnover assessment quantified for compliance rate. This yields the basic quantitative values ​​corresponding to each clause. Simultaneously, a clause change monitoring submodule is constructed to collect contract clause change records in real time and calculate change stability coefficients. Finally, these are integrated to form intermediate data for contract constraint quantification, providing input parameters for calculating the dynamic score of contract priority.

[0022] In this step, this application uses a Natural Language Processing (NLP) model to segment and syntactically analyze the contract text, transforming the unstructured text into recognizable semantic units. Then, through pre-defined clause entity recognition rules, it accurately locates the information corresponding to core constraint clauses. For example, for penalty clauses, the model can identify the triggering conditions and corresponding compensation standards for different breach scenarios; for cooperation level clauses, it can extract the platform's grading standards for merchants and the resource support levels corresponding to each level; for performance timeliness clauses, it can capture the delivery time requirements for different order types; for platform subsidy clauses, it can parse the subsidy disbursement nodes and accounting methods; and for inventory turnover assessment clauses, it can identify the assessment cycle and achievement thresholds, ultimately achieving accurate extraction of basic data corresponding to various core constraint clauses.

[0023] In the standardized preprocessing stage, this embodiment performs dimensional transformation on the extracted basic data to make it computable. For fine data, a range normalization method is used. Based on the proportion of the company's historical fine expenditures to net profit and combined with the common fine ranges of similar businesses in the industry, the upper and lower limits of the quantitative data for this dimension are determined, converting the fine amounts of different platforms and scenarios into relative values ​​within a unified range. For cooperation level data, the level identifier is converted into a quantitative value between 0 and 1 based on the resource allocation such as traffic support ratio and event registration permissions corresponding to each platform level. The higher the level, the closer the value is to 1. For performance timeliness data, it is converted using the timeliness deviation rate, i.e., based on the platform's agreed timeliness. Based on the benchmark, and considering the deviation between the actual timeliness of historical performance and the benchmark value, a quantitative indicator representing the difficulty of achieving the timeliness is generated. For platform subsidy data, an effective fulfillment ratio is introduced for correction. This ratio is determined based on the ratio of the actual amount received for each batch of subsidies in history to the amount agreed upon in the agreement, thereby correcting the deviation of the theoretical subsidy value. For inventory turnover assessment data, the compliance rate is quantified, that is, by combining the ratio of the actual turnover times in the historical assessment period to the turnover times required for the assessment, a value representing the difficulty of achieving the assessment target is generated. Through the above series of processing, the basic quantitative values ​​corresponding to each clause can be generated.

[0024] Simultaneously, this embodiment constructs a clause change monitoring submodule to achieve dynamic perception of contractual constraints. This submodule connects to the contract clause update interfaces of various platforms in real time and also periodically scans the version change records of archived contract texts. For clause changes, it first identifies the type and extent of the change, and then calculates a change stability coefficient. This coefficient considers both change frequency and the scope of change impact; the higher the change frequency and the wider the scope of changes involving high-weight clauses, the lower the stability coefficient, thus characterizing the degree of fluctuation in contractual constraints. Finally, the basic quantitative value and the stability coefficient are integrated into intermediate data for contract constraint quantification. This type of data can directly provide adaptable input parameters for subsequent quantitative calculations of contract priorities.

[0025] In the quantification of dynamic contract priority scores, this embodiment achieves weighted integration by comprehensively considering the actual impact of various constraint clauses. The weight allocation for each dimension is not a fixed value but is dynamically adjusted based on the review results of historical business data. For example, the weight of penalty amounts is determined by the proportion of historical penalty expenditures on the profit of a single batch of activities; the higher the proportion, the greater the weight. The weight of cooperation level is determined by combining the traffic conversion and brand exposure value that different platform levels can bring to merchants. The weight of fulfillment timeliness is determined by the order negative review rate and customer churn rate caused by historical non-compliance with timeliness standards. The weight of platform subsidies is determined by the proportion of subsidy amount supplementing activity profits. The weight of inventory turnover assessment is determined by the degree of platform resource deduction caused by non-compliance with assessment standards. Through the dynamic adaptation of multi-dimensional weights, the scientific quantification of dynamic contract priority scores can be achieved. This score can intuitively reflect the constraint priority of different platform contracts on inventory preparation decisions.

[0026] The formula for calculating the dynamic score of contract priority is as follows: ; To achieve quantitative evaluation of contract constraints across multiple platforms, this application designs a core technical indicator: dynamic contract priority score. This score characterizes the priority weight of activity contracts on each platform in the allocation of inventory resources. Here, P is the dynamic contract priority score, τ is the basic decay coefficient over time (range [0.05, 0.2]), t0 is the initial contract effective time (Unix timestamp at the time of contract signing), t1 is the current statistical time (Unix timestamp at the time of real-time calculation), and α is the time decay factor for clause changes (range [0.01, 0.1]). Let be the stability coefficient of the contract terms at time t. , Let t be the quantified value of the changes in contract terms at time t. Let K be the total number of initial contract terms, K be the total number of penalty scenarios in the contract, and denoted as a positive integer obtained after the natural language processing model parses the contract. Let be the importance coefficient of the k-th type of penalty clause after natural language processing, representing the importance level of the penalty clause, with a value range of [0,1]. F k Let T be the agreed amount for the k-th type of penalty scenario, in yuan; γ be the non-linear adjustment index of the penalty amount, ranging from [1.2, 1.8]; C be the quantitative value of the platform-merchant cooperation level, quantified by the platform's cooperation rating system to a value between 0 and 1; β be the weight amplification coefficient of the cooperation level, ranging from [1.5, 2.5]; S be the total subsidy amount from the platform for this contract, in yuan; η be the effective fulfillment coefficient of the platform subsidy, ranging from [0.7, 1], obtained by fitting historical subsidy fulfillment data; and T be the total amount of the platform subsidy for this contract. max This represents the theoretical maximum threshold for performance timeliness, expressed in hours, and ranges from [48, 168]. To fulfill the contract The probability density function of timeliness, , This represents the platform's average fulfillment time. For performance The effective standard deviation, M is the total number of indicators for inventory turnover assessment, which is a positive integer, Z m This represents the actual value of the m-th inventory turnover assessment item. Let m be the weighting coefficient for the inventory turnover assessment item. For the historical level under this contract All performance time limits, For the normalization function of multidimensional indicators, , For the contract The original matrix of each constraint dimension index, min The global minimum value of the original index matrix is ​​max. This represents the global maximum value of the original index matrix.

[0027] The dynamic contract priority score P calculated by the above formula has a range of [0, 10]. Different ranges correspond to different contract priority levels and inventory resource allocation strategies: When P ∈ [0, 2), the contract priority is extremely low, indicating that the platform's contract constraints are lenient and the cooperation value is limited, and inventory resources can be prioritized for other platforms; when P ∈ [2, 5), the contract priority is medium, indicating that the contract constraints and cooperation benefits are balanced, and regular inventory resources can be allocated; when P ∈ [5, 8), the contract priority is relatively high, indicating that the contract penalty cost is high and the subsidy is large, and core inventory needs need to be guaranteed; when P ∈ [8, 10], the contract priority is extremely high, indicating that the contract cooperation level is high and the inventory turnover requirements are strict, and inventory resources need to be prioritized and flexible inventory reserved to cope with fluctuations in performance time.

[0028] The steps for graded reuse of returned goods are as follows: returned goods are graded based on their status, reuse cost, and reuse cycle; the contract compatibility score is calculated by combining the return period, defect tolerance, and return penalty rules of each platform's contract; and a reuse quantification model is constructed to calculate the effective reuse quantity. Specifically, such as Figures 1 to 3 As shown, the returned goods tiered reuse step includes classifying returned goods into four levels: Level 1 is unopened, has no reuse cost, and a reuse period of less than 24 hours for immediate reuse; Level 2 is slightly defective, has a reuse cost of less than 5% of the product price, and a reuse period of 24 to 72 hours for simple reuse; Level 3 is core components intact, has a reuse cost of 5% to 20%, and a reuse period of one to seven days for component reuse; and Level 4 is core functions failed, has a reuse cost of more than 20%, and is not reusable. The reuse quantification model calculates the effective reuse quantity through return prediction quantity, tiered qualification rate, reuse willingness rate, and contract compatibility score.

[0029] In this step, this embodiment connects the product inspection equipment and manual verification process at the warehouse end to construct a multi-dimensional status assessment matrix. For determining the product status, visual inspection equipment is first used to identify appearance indicators such as the integrity of the outer packaging, surface scratches, and stains. Then, functional testing equipment verifies the normal operation of core functions. Simultaneously, product traceability information is used to confirm whether the product is genuine and whether it is within its warranty period. Specifically, "unopened" refers to the original packaging being undamaged, the seal intact, and without signs of resealing; "minor defects" refers to minor marks on the appearance that do not affect use, and the core functions are normal; "intact core components" means that although the product has some degree of external damage or non-core component failure, the core functional modules can be restored with simple repairs; "core function failure" means that the core module cannot operate normally and the repair cost is too high.

[0030] Reuse costs are not simply financial costs, but rather a comprehensive cost encompassing labor inspection costs, repair costs, packaging replacement costs, and secondary logistics delivery costs. The calculation logic involves first calculating the basic costs at each stage, and then combining this with the product's base selling price to determine the cost percentage. For example, no reuse cost means the product can be directly resold without any repair or packaging replacement; a reuse cost less than 5% of the product's selling price indicates that only simple outer packaging replacement or surface cleaning is required for reuse.

[0031] The reuse cycle is calculated from the moment goods are returned to the warehouse and inspected, to the moment they are reused and ready for shelving. The duration depends on the complexity of the reuse process. For example, for goods requiring immediate reuse, no additional processing is needed; only an inventory update is required before shelving, so the cycle can be controlled within 24 hours. For goods requiring component reuse, the process is more complex due to the need for core component repair and functional debugging, extending the cycle to one to seven days.

[0032] Based on the comprehensive evaluation of the above three dimensions, the system will form a four-level classification system for returned goods. For goods of different levels, the system will automatically match the corresponding storage areas and processing priorities. Instantly reusable and simple reusable goods will be stored in the priority reusable storage area near the outbound gate, component reusable goods will be transferred to the repair storage area, and non-reusable goods will be classified into the scrap or dismantling storage area, laying the foundation for subsequent reuse matching.

[0033] Regarding contract suitability scores, this embodiment retrieves contract constraint data generated during the initial contract parsing phase from various platforms, extracting clauses related to the reuse of returned goods. These primarily include three core indicators: return deadline, defect tolerance, and return penalty rules. For the return deadline dimension, the system assesses the match between the expected completion time of reuse of the returned goods and the platform contract's stipulated period for relisting returned goods. If the reuse completion time is earlier than the contractually agreed deadline, the suitability of this dimension is high. For the defect tolerance dimension, the system compares the actual defect level of the returned goods with the defect level allowed by the platform contract. For example, some platform contracts allow slightly defective goods to be listed, while some platforms only accept flawless goods; the system determines the suitability of this dimension accordingly. For the return penalty rules dimension, the system assesses the potential penalty risk if returned goods are returned due to status issues after reuse; the lower the risk, the higher the suitability of this dimension. This embodiment assigns dynamic weights to three types of indicators. The weights are determined based on the degree of influence of each indicator on inventory preparation and fulfillment. For example, for platforms with strict penalty rules, the weight of the return penalty rule dimension will be increased accordingly. Through multi-dimensional weighted evaluation, the system will generate a corresponding contract compatibility score for each type of returned goods and the contracts of each platform. The higher the score, the stronger the reusability and compatibility of the returned goods with the corresponding platform contract.

[0034] The core of the reuse quantification model is to determine the actual quantity of returned goods at each level that can be used for inventory deduction through the collaborative evaluation of multiple dimensions. The return forecast is obtained by dynamically correcting multi-dimensional historical and real-time data. The system first collects various types of historical and real-time business data, including the historical return rate of the product category, the scale and intensity of promotional activities on various platforms in the current period, the sales life cycle stage of the product, the peak return periods and quantities of similar past activities, and user profiles of existing orders and their correlation with return tendencies. At the same time, it simultaneously collects real-time data on returns that have occurred in the current period. Based on the above data sources, the system adopts a time-series forecasting model, combined with the periodic fluctuation pattern of the product category return rate, The system first calculates the initial forecast of returned goods within the current promotional period, based on the positive correlation between the scale of promotional activities and the return decay / growth characteristics corresponding to different stages of the product lifecycle, thus clarifying the distribution trend of return volume at different times. During the activity, the system compares the real-time return data with the initial forecast at preset time intervals (e.g., every 12 hours). If the deviation between the actual return volume and the forecast exceeds a preset threshold, the system dynamically adjusts the return forecast for the remaining period based on the deviation magnitude and real-time business drivers (e.g., sudden fluctuations in product reputation or logistics anomalies), ensuring the adaptability of the forecast to the actual business scenario and providing an accurate base for calculating the effective reuse quantity. The evaluation of reuse willingness rate needs to combine platform-side demand feedback and merchant inventory strategies. For example, some platforms have a low acceptance of returned goods of specific categories, or merchants have adopted a strategy of prioritizing the stocking of new products for a particular platform. These factors will affect the reuse willingness of returned goods of corresponding levels. The system will integrate such information to generate the reuse willingness rate for each level of goods. The graded pass rate refers to the percentage of returned goods of a certain grade that meet the reuse standards for that grade after testing. The determination requires two processes: initial inspection and re-inspection. The initial inspection is completed by automated equipment, while the re-inspection is completed by professional personnel. Only goods that pass both processes can be included in the qualified category. Goods that fail will be downgraded or classified as non-reusable, thereby ensuring the quality stability of reusable goods.

[0035] The reuse quantization model is configured as follows: ; Among them, Q eff Q represents the effective reuse quantity of returned goods. pre The quantity of returned goods is predicted, and k is the grade number of the returned goods. Let be the comprehensive effective coefficient for the k-th level of returns. , where ρ k Let θ be the graded pass rate for the k-th level of returns. k The reuse rate for returns at level k. To adapt the contract to a correction factor, , For platform contract adaptation score, This represents the theoretical maximum value of the contract fit score.

[0036] The effective reuse quantity Q of returned goods calculated by the above formula eff The range is [0, Q] pre Different value ranges correspond to different efficiency of returned resource reuse and deduction value for inventory. When Q eff ∈[0,0.2Q pre When Q = 0, it indicates that the graded pass rate, reuse willingness rate, or contract fit score of returned goods are at a low level, and the returned resources can only achieve a small amount of effective reuse, with limited deduction effect on new inventory; when Q = 0, it indicates that the graded pass rate, reuse willingness rate, or contract fit score of returned goods are at a low level, and the returned resources can only achieve a small amount of effective reuse, with limited deduction effect on new inventory; eff ∈[0.2Q pre 0.6Q pre When Q) indicates that the comprehensive reuse conditions of returned goods have met the basic standards, effective reuse on a regular scale can be achieved, and it can offset some of the new inventory needs of the corresponding platform; when Q eff ∈[0.6Q pre Q pre When the return is at a certain level, it indicates that the quality of the returned goods and their platform compatibility are at a high level, which can maximize the effective reuse of returned resources and significantly reduce the cost of new inventory preparation and the risk of inventory backlog on the corresponding platform.

[0037] The inventory calculation process takes contract priority dynamic score, real-time order data, effective reuse quantity and supplier capacity as constraints, and uses an optimization algorithm to calculate the optimal inventory quantity that maximizes cross-platform benefits. Specifically, such as Figures 1 to 3 As shown, the inventory preparation calculation steps include: calculating the basic inventory preparation demand for orders based on the real-time order volume and the order growth value in the next 24 hours for each platform; weighting the basic inventory preparation demand for orders on each platform with the dynamic score of contract priority; allocating the effective reuse quantity to the corresponding platform according to the contract adaptation score of each platform to offset the new inventory preparation quantity of that platform; summarizing the inventory preparation demand of each platform; if the total demand exceeds the supplier's maximum capacity, it is reduced proportionally according to the contract priority weight; at the same time, 5% flexible inventory preparation quantity is reserved to cope with sudden order fluctuations, so as to achieve the optimal inventory preparation quantity determination that maximizes cross-platform benefits.

[0038] The generation of basic order preparation requirements is not based on a single-dimensional order statistics, but rather on a comprehensive evaluation system based on real-time order data and future demand forecasts. For the collection of real-time order volume, the management method configuration system in this embodiment interfaces with the order management interfaces of various platforms to achieve real-time synchronization of order data across all statuses. The collected order data includes not only paid orders awaiting shipment, but also generated but unpaid high-intent orders. Furthermore, invalid orders that have been canceled or refunded are removed to ensure the accuracy of the order base.

[0039] The generation of the order growth rate forecast for the next 24 hours relies on the collaborative analysis of multi-dimensional data. The management method configuration system in this embodiment integrates historical order growth patterns for the same platform and product category, the real-time promotional intensity of current promotional activities, the conversion rate of user browsing and adding to cart, and industry trends. It then uses a time-series forecasting model to dynamically calculate the growth rate. For example, if the advertising exposure of a platform's current promotional activity increases by 50% compared to the same period in history, and the product add-to-cart rate increases by 30%, the corresponding order growth rate forecast will be adjusted upwards accordingly. This ensures that basic inventory needs can cover potential order increases, laying a scientific foundation for subsequent inventory allocation.

[0040] Contract priority weighting is a key technical means to ensure the performance of high-constraint platforms. Its core is to convert the dynamically generated contract priority scores into actionable inventory allocation weights. These weights are not fixed values ​​but are dynamically adapted based on historical performance data. Specifically, the management method configuration system in this embodiment reviews data such as the performance compliance rate and penalty expenditure ratio of different priority platforms in historical batches, and calibrates the weight coefficients. For example, for high-priority platforms that have historically incurred high penalties due to insufficient inventory, their weight ratio will be appropriately increased. In the specific implementation of weighting, the management method configuration system in this embodiment first calculates the initial proportion of each platform's basic inventory demand to the total basic demand, and then adjusts this proportion based on the dynamic contract priority score. Platforms with higher priority scores will have their final inventory allocation ratio increased proportionally from the initial proportion. Simultaneously, the management method configuration system in this embodiment sets a weight cap to prevent a single platform from consuming too many inventory resources, thus preventing other platforms from having their needs unmet, thereby achieving an initial balance in the inventory demand of multiple platforms.

[0041] The allocation of effective reuse quantities relies on a precise matching mechanism based on contract compatibility scores. The system first calculates the proportion of each platform's contract compatibility score to the total compatibility score of all platforms, and then allocates the total effective reuse quantity to the corresponding platforms according to this proportion. Platforms with higher compatibility scores will receive more quotas for returned and reused goods. This is because the return terms of these platforms are more closely matched with the status, cycle, and other attributes of the reused goods, resulting in a lower risk of secondary returns or penalties after reuse, thus maximizing the value of reused resources.

[0042] During the inventory deduction process, the system sets clear deduction priorities, prioritizing the use of returned and reused goods to offset the regular inventory needs of each platform, and then offsetting potential incremental inventory needs. During the deduction process, the inventory ledgers of each platform are updated simultaneously, and the inventory status and platform of reused goods are marked in real time to ensure that the deduction process is traceable and verifiable, avoiding inventory discrepancies caused by chaotic allocation of reused goods.

[0043] When the total inventory demand of all platforms, after weighted allocation and reuse deduction, exceeds the supplier's maximum capacity, the system will activate a capacity limit adaptation mechanism. The core of this mechanism is to achieve a reasonable reduction in inventory volume while ensuring core fulfillment needs. The system will first classify the inventory demand of each platform, distinguishing between core needs for fulfilling basic orders and non-core needs for handling incremental orders. During the reduction process, non-core needs will be compressed proportionally first, while core needs will be reduced at a differentiated ratio based on the contract priority score. The higher the priority score of a platform, the lower the reduction ratio of its core needs. For platforms with the highest priority, the inventory demand corresponding to their core orders can enjoy reduction exemption, thereby ensuring the basic fulfillment capabilities of highly constrained platforms.

[0044] After the reduction is completed, the system will generate a capacity adaptation report, which will clarify the reduction range of inventory on each platform, the reasons for the reduction, and the corresponding performance guarantee plan. This report will be synchronized with merchants and suppliers to ensure that both parties reach a consensus on the inventory quota.

[0045] To cope with uncontrollable factors such as sudden order fluctuations and logistics delays, the system will reserve a flexible inventory level during the final inventory level determination stage. The proportion of the flexible inventory level is set as a fixed percentage of the total inventory level. It comes from a portion of the regular inventory quota allocated to each platform and will be managed in a separate warehouse area to avoid confusion with regular inventory.

[0046] Regarding the activation rules for flexible inventory replenishment, the system sets trigger thresholds. When the real-time order volume on a platform exceeds the preset proportion of the approved inventory replenishment within a short period of time, or when delays occur in the logistics channel leading to inventory shortages, flexible inventory replenishment can be automatically activated to supplement the inventory. Simultaneously, the system establishes a replenishment mechanism for flexible inventory replenishment. After flexible inventory replenishment is activated, replenishment instructions are sent to suppliers in real time to ensure that the flexible inventory replenishment can quickly recover to the preset level, providing continuous assurance for subsequent order fulfillment.

[0047] The total inventory constraint formula is configured as follows: ; Among them, Q i This is for the final inventory level of each platform. O i for The real-time order quantity of the i-th platform, g i Let P be the predicted order growth rate for the i-th platform over the next 24 hours. i Let q be the dynamic score for contract priority on the i-th platform, n be the total number of platforms, and q be the value of q. eff,i Q represents the number of returns that can be reused and allocated to the i-th platform. total For total inventory across platforms, C supThis refers to the supplier's maximum capacity, which is the maximum quantity of goods that the supplier can provide within the corresponding stock preparation period.

[0048] When Q i When Q = 0, it indicates that the effective reuse quantity of returned goods allocated by the platform has fully covered its basic order inventory requirements, and no additional inventory is needed, which can significantly reduce the platform's inventory costs; when Q i In (0, O) i ×(1+g i )× When this is the case, it indicates that the platform needs to combine its own order requirements with the contract. Priority is given to acquiring some new inventory, and the reuse of returned goods can only offset part of the inventory pressure. When Q i When the value approaches the upper limit, it indicates that the platform's contracts have extremely high priority, and the proportion of its basic inventory needs in the total demand reaches its maximum. Therefore, the platform's inventory quota should be prioritized.

[0049] When Q total equal and less than When Q is in a certain timeframe, it indicates that the total inventory demand of all platforms does not exceed the combined supply capacity of supplier production capacity and the effective reuse quantity of returned goods, and can fully meet the inventory demand of all platforms. total equal When Q reaches this point, it indicates that the total inventory demand of all platforms has exceeded the overall supply capacity. Therefore, the inventory levels of each platform need to be reduced proportionally according to contract priority weights. At this time, the total inventory level reaches the combined upper limit of production capacity and reusable resources, achieving optimal allocation of inventory resources while ensuring supply capacity. total When the index approaches the lower limit, it indicates that the inventory ratio of low-priority platforms dominates the total inventory volume, and the contract fulfillment priority of the overall inventory plan is too low. The weight allocation strategy needs to be adjusted according to actual business needs.

[0050] The inventory conflict management process involves identifying conflicts between contractual constraints across multiple platforms and conflicts between inventory needs, and allocating the optimal inventory quantity through scenario-based conflict resolution rules combined with a cross-platform shared inventory scheduling mechanism.

[0051] Specifically, such as Figures 1 to 3As shown, the scenario-based conflict resolution rules include: when the platform's daily growth rate is less than a preset threshold based on the historical average daily order growth rate, the inventory is allocated from high to low according to the product of the contract priority dynamic score and the platform's order conversion rate; when the daily order growth rate is greater than the preset threshold, the inventory is sorted according to the stockout penalty cost and the fulfillment timeliness urgency coefficient, and a flexible inventory is preset. The fulfillment timeliness urgency coefficient is the ratio of the platform's contractually agreed fulfillment timeliness to the current average logistics timeliness; when the daily return rate of goods is greater than a preset threshold based on the historical average daily return rate, the returned goods are sorted according to the product of the net value of returned goods reuse and the platform's inventory turnover efficiency, and high-level returned goods are allocated to platforms with high inventory turnover efficiency. Cross-platform shared inventory only collects first-level and second-level returned reuse goods and general standard products. The scheduling trigger condition is that the cross-platform scheduling cost is less than the target platform's stockout penalty cost.

[0052] Actual inventory preparation revenue is the net profit after comprehensively considering various cost and profit items, including total sales across platforms, inventory preparation costs, out-of-stock penalty expenses, logistics costs, net revenue from returns and reuse, and inventory backlog losses. The system automatically matches the platform and business process to which each cost and profit item belongs, ensuring that revenue data is traceable to specific inventory preparation decisions. Contract fulfillment rate needs to be statistically analyzed by sales period. Different statistical methods will be used for regular sales periods and peak promotion periods. The fulfillment rate during peak promotion periods will be additionally linked to the fulfillment timeliness during peak order periods. The system will classify and label the fulfillment data of different periods to provide data support for subsequent targeted calibration. Return reuse rate needs to be calculated in conjunction with the grading system of returned goods, that is, each level will be statistically analyzed separately. The ratio of the actual reuse quantity of returned goods to the total reusable quantity is used, and reuse data during peak return periods is also marked to clarify the adaptation efficiency of returned goods during that period. The stockout rate is associated with the contract priority of the corresponding platform, and the proportion of stockout orders on platforms with different priorities is calculated. The backlog rate is calculated based on the turnover characteristics of the product category, and the proportion of inventory exceeding actual demand for each category is recorded, along with the storage cycle and degree of damage of backlogged goods. The conflict resolution success rate is determined by the scheduling response time and scheduling results of cross-platform inventory conflicts, that is, the proportion of successfully resolved conflict cases to total conflict cases is calculated, and the resource loss cost during the scheduling process is recorded to measure the actual effectiveness of the conflict resolution mechanism.

[0053] Multi-dimensional optimization and evaluation is the core bridge to achieving accurate parameter correction. Its core lies in constructing a two-layer evaluation logic that combines indicator correlation weights and benchmark threshold comparisons. The system dynamically sets weights based on the impact of each indicator on inventory preparation decisions. For example, contract fulfillment rate, directly linked to platform penalties and cooperation levels, receives a higher weight than other indicators. Fulfillment data during peak sales periods, due to large order volumes and stringent constraints, receives a higher sub-weight within the contract fulfillment rate dimension. Similarly, data from peak return periods in the return reuse rate, due to concentrated return volumes and high adaptation difficulties, also receives a dedicated sub-weight, ensuring that the evaluation focus aligns with core business needs. The generation of evaluation benchmark thresholds combines two types of data: historical data and benchmark data. The system uses two benchmarks: the average of similar activities and the inventory fulfillment benchmarks of merchants of similar size within the industry. It determines a reasonable benchmark range through data fitting and sets a tolerance range for deviations. If a batch of indicators exceeds the tolerance range, the system determines that the parameters in the corresponding stage need targeted correction. The system compares the actual data of each batch with the benchmark thresholds to pinpoint the core source of indicator deviations. For example, if the contract fulfillment rate during a major promotional period is significantly lower than the benchmark value, it determines that the weighting of the fulfillment timeliness dimension in the contract constraint quantification stage is unreasonable. If the reuse rate during a peak return period does not meet expectations, it determines that the calibration of the graded pass rate in the return reuse quantification model has a deviation, using this as a guide for parameter correction.

[0054] Specifically, such as Figures 1 to 3 As shown, it also includes a closed-loop iterative optimization step. After each batch of activities, actual inventory revenue, contract fulfillment rate, return reuse rate, stockout rate, backlog rate, and conflict resolution success rate are collected. Through multi-dimensional optimization and evaluation, the contract constraint quantitative weight coefficient, the return reuse quantitative model graded qualification rate calibration value, and the inventory quantity calculation algorithm objective function weight are updated in reverse. Among them, the contract fulfillment rate calibration coefficient is strongly correlated with the actual fulfillment data during the peak promotion period, and the return reuse rate calibration value is strongly correlated with the actual reuse data during the peak return period, thus completing the dynamic collaborative optimization of the parameters of each module.

[0055] The multi-dimensional optimization evaluation includes: first, collecting data from the entire inventory preparation process (including inventory preparation revenue, contract fulfillment rate, return reuse rate, etc.), and then stratifying and labeling core indicators according to business periods (peak sales periods, peak return periods, and regular sales periods) to ensure a strong correlation between data and business scenarios; second, defining reasonable deviation tolerance ranges for each indicator based on the historical average of similar activities and benchmark values ​​of merchants of similar scale in the industry, and clarifying evaluation reference standards; third, allocating weights according to the degree of influence of indicators on inventory preparation decisions, setting higher sub-weights for core business scenario indicators such as fulfillment rate during peak sales periods and reuse rate during peak return periods, focusing on the evaluation of key links; fourth, comparing actual data with benchmark thresholds to pinpoint the core causes of indicator deviations, such as a low fulfillment rate during peak sales periods corresponding to insufficient weight in contract constraint quantification, and a failure to meet return reuse rate standards corresponding to deviations in the preset value of graded qualification rate; and fifth, binding the attribution results with the parameters of each module of the technical solution, clarifying the specific correction directions for parameters such as contract constraint quantification weight, return reuse quantification calibration value, and inventory preparation algorithm objective function weight, to achieve precise linkage between evaluation and optimization.

[0056] The foregoing has illustrated and described the basic features, principles, and advantages of the present invention. It should be noted that the present invention is not limited to the above embodiments, but only to some embodiments. Any improvements and additions made without departing from the spirit and scope of the present invention are considered to be within the scope of protection of the present invention.

Claims

1. A method for managing inventory conflicts based on contractual constraints and tiered reuse of returned goods, characterized in that, include: The data collection and processing steps involve collecting multi-platform activity contract data, basic product data, cross-platform real-time order data, inventory dynamic data, logistics timeliness data, and return data, and performing data preprocessing to generate a multidimensional dataset. The contract analysis and quantification process involves analyzing the core constraints of contract data from various platforms, monitoring changes in contract terms in real time, and quantifying constraints such as penalty amounts, cooperation levels, performance timeliness, platform subsidies, and inventory turnover assessments into dynamic contract priority scores using a weighted algorithm. The steps for graded reuse of returned goods are as follows: returned goods are graded based on their status, reuse cost, and reuse cycle; the contract compatibility score is calculated by combining the return period, defect tolerance, and return penalty rules of each platform's contract; and a reuse quantification model is constructed to calculate the effective reuse quantity. The inventory calculation process takes contract priority dynamic score, real-time order data, effective reuse quantity and supplier capacity as constraints, and uses an optimization algorithm to calculate the optimal inventory quantity that maximizes cross-platform benefits. The inventory conflict management process involves identifying conflicts between contractual constraints across multiple platforms and conflicts between inventory needs, and allocating the optimal inventory quantity through scenario-based conflict resolution rules combined with a cross-platform shared inventory scheduling mechanism.

2. The inventory conflict management method based on contractual constraints and graded reuse of returned goods according to claim 1, characterized in that, The contract parsing and quantification steps include: semantically parsing the contract texts of various platforms using a natural language processing model to extract basic data corresponding to core constraint clauses. These core constraint clauses include penalty clauses, cooperation level clauses, performance timeliness clauses, platform subsidy clauses, and inventory turnover assessment clauses. The extracted basic data undergoes standardized preprocessing, with penalty amounts normalized to a range, cooperation levels quantified to a range, performance timeliness deviation rate converted, platform subsidies corrected for effective fulfillment ratio, and inventory turnover assessment compliance rate quantified. This yields the basic quantitative values ​​corresponding to each clause. Simultaneously, a clause change monitoring submodule is constructed to collect contract clause change records in real time and calculate change stability coefficients. Finally, these are integrated to form intermediate data for contract constraint quantification, providing input parameters for calculating the dynamic score of contract priority.

3. The inventory conflict management method based on contractual constraints and graded reuse of returned goods according to claim 2, characterized in that, The formula for calculating the dynamic score of contract priority is as follows: ； Where P is the dynamic score of contract priority, τ is the basic decay coefficient in the time dimension, t0 is the initial time of contract effectiveness (Unix timestamp value at the time of contract signing), t1 is the current statistical time (Unix timestamp value at the time of real-time calculation), and α is the time decay factor for clause changes. Let be the stability coefficient of the contract terms at time t. , Let t be the quantified value of the changes in contract terms at time t. Let K be the total number of initial contract terms, K be the total number of penalty scenarios in the contract, and denoted as a positive integer obtained after the natural language processing model parses the contract. Let be the importance coefficient of the k-th type of penalty clause after being quantified by natural language processing, representing the importance level of the penalty clause. F k Let be the agreed amount for the k-th type of penalty scenario, γ be the non-linear adjustment index of the penalty amount, C be the quantitative value of the platform-merchant cooperation level, quantified by the platform's cooperation rating system to a value from 0 to 1, β be the weighting amplification coefficient of the cooperation level, S be the total subsidy amount from the platform for this contract, η be the effective fulfillment coefficient of the platform subsidy, obtained by fitting historical subsidy fulfillment data, and T be the total amount of the platform subsidy for this contract. max This represents the theoretical maximum threshold for the performance period. The probability density of performance timeliness function, , This represents the platform's average fulfillment time. Z represents the standard deviation of delivery time, M is the total number of indicators for inventory turnover assessment, and Z is a positive integer. m This represents the actual value of the m-th inventory turnover assessment item. Let m be the weighting coefficient for the inventory turnover assessment item. This refers to the historical average performance time under this contract. For the normalization function of multidimensional indicators, , The original matrix of the contract's constraint dimensions indicators, min The global minimum value of the original index matrix is ​​max. P represents the global maximum value of the original index matrix, with a range of [0,10]. P∈[0,2) represents extremely low contract priority, P∈[2,5) represents medium contract priority, P∈[5,8) represents relatively high contract priority, and P∈[8,10] represents extremely high contract priority.

4. The inventory conflict management method based on contractual constraints and graded reuse of returned goods according to claim 1, characterized in that, The returned goods tiered reuse process includes classifying returned goods into four levels: Level 1 is unopened, has no reuse cost, and a reuse period of less than 24 hours for immediate reuse; Level 2 is slightly defective, has a reuse cost of less than 5% of the product price, and a reuse period of 24 to 72 hours for simple reuse; Level 3 is component reuse with intact core components, a reuse cost of 5% to 20%, and a reuse period of one to seven days; and Level 4 is non-reusable with core functional failure and a reuse cost of more than 20%. The reuse quantification model calculates the effective reuse quantity through return prediction, tiered qualification rate, reuse willingness rate, and contract compatibility score.

5. A method for managing inventory conflicts based on contractual constraints and tiered reuse of returned goods, as described in claim 1, is characterized in that... The reuse quantization model is configured as follows: ； Among them, Q eff Q represents the effective reuse quantity of returned goods. pre The quantity of returned goods is predicted, and k is the grade number of the returned goods. Let be the comprehensive effective coefficient for the k-th level of returns. , where ρ k Let θ be the graded pass rate for the k-th level of returns. k The reuse rate for returns at level k. To adapt the contract to a correction factor, , For platform contract adaptation score, This represents the theoretical maximum value of the contract fit score.

6. The inventory conflict management method based on contractual constraints and graded reuse of returned goods according to claim 1, characterized in that, The inventory preparation calculation steps include: calculating the basic inventory preparation demand for orders based on the real-time order volume and the order growth value in the next 24 hours for each platform; weighting the basic inventory preparation demand for orders on each platform with the dynamic score of contract priority; allocating the effective reuse quantity to the corresponding platform according to the contract adaptation score of each platform to offset the new inventory preparation quantity of that platform; summarizing the inventory preparation demand of each platform; if the total demand exceeds the supplier's maximum capacity, it is reduced proportionally according to the contract priority weight; at the same time, 5% elastic inventory preparation quantity is reserved to cope with sudden order fluctuations, so as to achieve the optimal inventory preparation quantity determination that maximizes cross-platform benefits.

7. A method for managing inventory conflicts based on contractual constraints and tiered reuse of returned goods, as described in claim 6, is characterized in that... The total inventory constraint formula is configured as follows: ； Among them, Q i To determine the final inventory level for each platform, O i for The real-time order quantity of the i-th platform, g i Let P be the predicted order growth rate for the i-th platform over the next 24 hours. i Let q be the dynamic score for contract priority on the i-th platform, n be the total number of platforms, and q be the value of q. eff,i Q represents the number of returns that can be reused and allocated to the i-th platform. total For total inventory across platforms, C sup This is the maximum capacity of the supplier.

8. The inventory conflict management method based on contractual constraints and graded reuse of returned goods according to claim 1, characterized in that, The scenario-based conflict resolution rules include: when the platform's daily growth rate is less than a preset threshold based on the historical average daily order growth rate, the inventory is allocated from high to low based on the product of the contract priority dynamic score and the platform's order conversion rate; when the daily order growth rate is greater than the preset threshold, the inventory is sorted based on the stockout penalty cost and the fulfillment timeliness urgency coefficient, and a flexible inventory is preset. The fulfillment timeliness urgency coefficient is the ratio of the platform's contractually agreed fulfillment timeliness to the current average logistics timeliness; when the daily return rate of goods is greater than a preset threshold based on the historical average daily return rate, the returned goods are sorted based on the product of the net value of returned goods reuse and the platform's inventory turnover efficiency, and high-level returned goods are allocated to platforms with high inventory turnover efficiency. Cross-platform shared inventory only collects first-level and second-level returned reuse goods and general standard products. The scheduling trigger condition is that the cross-platform scheduling cost is less than the target platform's stockout penalty cost.

9. A method for managing inventory conflicts based on contractual constraints and tiered reuse of returned goods, as described in claim 1, is characterized in that... It also includes a closed-loop iterative optimization step across the entire chain. After each batch of activities, actual inventory revenue, contract fulfillment rate, return reuse rate, stockout rate, backlog rate, and conflict resolution success rate are collected. Through multi-dimensional optimization and evaluation, the contract constraint quantitative weight coefficient, the return reuse quantitative model graded qualification rate calibration value, and the inventory quantity calculation algorithm objective function weight are updated in reverse. Among them, the contract fulfillment rate calibration coefficient is strongly correlated with the actual fulfillment data during the peak promotion period, and the return reuse rate calibration value is strongly correlated with the actual reuse data during the peak return period, thus completing the dynamic collaborative optimization of the parameters of each module.

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