Downstream receipt plan generation method based on quota protocol

Abstract

The invention provides a method for planning and generating downstream receipts based on a quota protocol, and relates to the technical field of enterprise resource planning and purchasing management. The quota agreement comprises providable suppliers of each material in different organizations, a minimum batch, a maximum batch and a price of each supplier, an initial distribution proportion of each supplier and a unique identifier of the quota agreement; creating a purchase plan, wherein the purchase plan comprises purchase organizations, material IDs, the total number of plans and other purchase information; querying and matching quota protocols according to the purchase plan information, and screening out quota protocols meeting conditions; optimizing the allocation proportion of the quota protocol based on Markov decision to obtain an optimized allocation proportion; and associating the purchase plan with a quota protocol, dividing the total quantity of the purchase plan into a plurality of parts according to the optimized distribution proportion, and generating a corresponding downstream document for each divided part.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of enterprise resource planning and procurement management, and particularly relates to a plan generation downstream document method based on a quota agreement. BACKGROUND

[0002] In an enterprise procurement management system, a procurement plan is the starting point of the procurement process, and its core task is to determine the procurement quantity and time according to the material demand. In the traditional procurement management mode, when the quantity of a procurement plan needs to be allocated to multiple suppliers, it is usually necessary to create independent procurement plan documents for each supplier, and each document corresponds to a supplier and a specific procurement quantity. This approach has obvious efficiency problems: first, the user needs to manually create multiple plan documents, which is tedious and prone to errors; second, the quantity allocation between different plan documents lacks systematic planning, making it difficult to achieve optimal supplier selection and cost control; third, when the procurement demand changes, multiple documents need to be adjusted synchronously, resulting in high maintenance costs. Although existing systems attempt to solve this problem through quota management, most of them only stay at the static proportion allocation stage and cannot dynamically adjust the allocation strategy according to real-time supplier capabilities, inventory status, cost, and other factors, resulting in low resource utilization and difficulty in optimizing procurement costs. SUMMARY

[0003] The application provides a plan generation downstream document method based on a quota agreement to solve one of the above technical problems.

[0004] The technical solution adopted by the application is as follows: The application provides a plan generation downstream document method based on a quota agreement, which includes: Initializing a quota agreement, the quota agreement including available suppliers of each material in different organizations, minimum and maximum quantities and prices of each supplier, initial allocation proportions of each supplier, and a unique identifier of the quota agreement; Creating a procurement plan, the procurement plan including a procurement organization, a material ID, a total quantity, and other procurement information; Querying and matching the quota agreement according to the procurement plan information, and screening out quota agreements that meet the conditions; Optimizing the allocation proportions of the quota agreement based on Markov decision, to obtain optimized allocation proportions; Associating the procurement plan with the quota agreement, and dividing the total quantity of the procurement plan into multiple parts according to the optimized allocation proportions, and generating corresponding downstream documents for each divided part.

[0005] According to one embodiment of the present application, the initialization of the quota agreement includes the available suppliers of each material in different organizations, the minimum batch size, the maximum batch size and the price of each supplier, the initial allocation ratio of each supplier, and the unique identification of the quota agreement, including: The available suppliers of each material in different organizations are a preset supplier list, which is maintained by an organization authority management module; The minimum batch size, the maximum batch size and the price of each supplier are parameters dynamically adjusted according to historical procurement data and market quotations; The initial allocation ratio of each supplier is calculated based on historical procurement quantity and supplier performance score.

[0006] According to one embodiment of the present application, the other procurement information in the procurement plan includes procurement time requirement, quality requirement and delivery location.

[0007] According to one embodiment of the present application, the step of querying and matching the quota agreement according to the procurement plan information to screen out the quota agreement meeting the conditions includes the screening conditions, specifically: the procurement organization is consistent with the organization in the quota agreement, the material ID is matched, and the total quantity of the plan is between the minimum batch size and the maximum batch size of the supplier.

[0008] According to one embodiment of the present application, in the step of optimizing the allocation ratio of the quota agreement based on the Markov decision, the decision state includes real-time inventory of the supplier, delivery cycle, historical compliance rate and current market demand forecast; The reward value calculation method of the Markov decision is: reward value = α × total cost + β × supplier utilization rate + γ × on-time delivery rate - δ × default penalty, wherein α, β, γ, δ are preset weight coefficients.

[0009] According to one embodiment of the present application, in the step of associating the procurement plan with the quota agreement, the association method includes user self-defined configuration or system automatic matching.

[0010] According to one embodiment of the present application, in the step of dividing the total quantity of the procurement plan into multiple parts according to the optimized allocation ratio, the division method is to allocate the total quantity of the plan to each supplier in proportion to ensure that each division part meets the minimum batch size requirement of the supplier.

[0011] According to one embodiment of the present application, in the step of generating corresponding downstream documents for each division part, the downstream documents include purchase orders and contracts, which are automatically sent to the corresponding suppliers after being generated.

[0012] The second aspect embodiment of the present application provides a computer readable storage medium, which stores a program, and the program is executed by a processor to implement the steps in the method.

[0013] The third aspect embodiment of the present application provides an electronic device, which comprises a memory, a processor and a program stored in the memory and executable on the processor, and the processor implements the steps in the method when executing the program.

[0014] Thanks to the above technical solutions, the present application has the following beneficial effects: The present application predefines the available suppliers and their batch constraints, prices and initial allocation ratios of each material under different organizational institutions through the initialization of the quota agreement, providing a data basis for intelligent allocation of procurement plans and avoiding the repeated labor of reconfiguring supplier information each time of procurement. By creating a procurement plan containing the procurement organization, material ID and total quantity, and automatically querying the matching quota agreement according to these information, the present application realizes the rapid docking of procurement demand and supplier resources, reducing the workload of manual screening and matching. By optimizing the allocation ratio of the quota agreement based on Markov decision, the present application comprehensively considers dynamic factors such as real-time inventory, delivery cycle, historical compliance rate of suppliers and market demand forecast, and can calculate the optimal allocation ratio, so that the procurement decision is not only based on static data, but also can adapt to real-time changes of the market and supply chain, effectively reducing the total procurement cost and improving the utilization rate and on-time delivery rate of suppliers. By associating the procurement plan with the optimized quota agreement, and automatically dividing the total quantity into multiple parts according to the optimized ratio, the present application generates corresponding downstream documents for each divided part, realizing the automatic generation of multiple downstream documents from a single procurement plan, completely solving the cumbersome problem of creating multiple procurement plan documents in the traditional way, and significantly improving the execution efficiency and intelligent level of the procurement process. BRIEF DESCRIPTION OF DRAWINGS

[0015] The accompanying drawings, which are included to provide a further understanding of the present application, constitute a part of the present application and illustrate the illustrative embodiments of the present application and their description serve to explain the present application, and do not constitute improper limitations on the present application. In the drawings: Figure 1 A flowchart of a plan generation downstream document method based on a quota agreement provided by an embodiment of the present application is shown in the figure; Figure 2 A structural diagram of an electronic device provided by an embodiment of the present application is shown in the figure.

[0016] Reference signs: 810, processor; 820, communication interface; 830, memory; 840, communication bus. DETAILED DESCRIPTION

[0017] In order to more clearly illustrate the overall concept of the present application, a detailed description is given below in an illustrative manner in conjunction with the accompanying drawings.

[0018] The following description sets forth many specific details to facilitate a thorough understanding of the present application. However, the present application may also be implemented in other ways than those described herein, and therefore, the scope of protection of the present application is not limited by the specific embodiments disclosed below. It should be noted that the embodiments of the present application and the features of each embodiment may be combined with each other unless there is a conflict.

[0019] In this application, unless otherwise expressly specified and limited, a first feature "above" or "below" a second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0020] Example 1 like Figure 1 As shown in the figure, a method for generating downstream documents based on a quota agreement plan includes: Initialize the quota agreement. The quota agreement includes the available suppliers of each material in different organizations, the minimum batch, maximum batch and price of each supplier, the initial allocation ratio of each supplier, and the unique identifier of the quota agreement.

[0021] As mentioned above, quota agreements serve as a bridge between procurement plans and supplier resources. Their core function is to define the available suppliers and their supply capacity boundaries for specific materials within a specific organization. The "available suppliers for each material in different organizations" ensures clear organizational boundaries for procurement activities and avoids cross-organizational misoperations. The "minimum batch size for each supplier" refers to the minimum order quantity a supplier can accept. Orders below this limit may result in the supplier refusing to accept an order or increasing unit costs. The "maximum batch size" refers to the maximum quantity a supplier can stably supply within a specific period. Exceeding this limit may result in delivery delays. "Price" information is used for cost accounting and price comparison analysis. The "initial allocation ratio for each supplier" provides the system with default allocation weights, serving as a starting point for subsequent optimization. The "unique identifier for the quota agreement" is used to accurately track and manage each agreement within the system, ensuring data consistency and traceability.

[0022] For example, in a manufacturing enterprise, for a processor material with model "CPU-888" under its North China procurement organization, the system initializes a quota agreement containing three suppliers: supplier A has a minimum batch size of 500, a maximum batch size of 2000, a unit price of 800 yuan, and an initial allocation ratio of 40%; supplier B has a minimum batch size of 300, a maximum batch size of 1500, a unit price of 820 yuan, and an initial allocation ratio of 35%; and supplier C has a minimum batch size of 200, a maximum batch size of 1000, a unit price of 810 yuan, and an initial allocation ratio of 25%. Each quota agreement is assigned a unique 32-bit GUID, such as "1a2b3c4d-...". When subsequent procurement plans are created, the system can quickly identify available suppliers and their constraints based on this agreement, laying the foundation for subsequent intelligent allocation.

[0023] It should be noted that in specific implementation scenarios, on the basis of the above scheme, the group level can define a general quota agreement, and each subsidiary can make individual adjustments to the agreement parameters based on local procurement strategies or regional supplier conditions, forming a sub-level quota agreement. In addition, the quota agreement can support time dimension management, i.e., setting different supplier lists or allocation ratios for different time periods (such as quarters or years) to adapt to changes in supplier cooperation cycles. At the same time, the system can support version control of the quota agreement, recording the change history of the agreement for auditing and tracing. Supplier qualification information can also be introduced as an additional field of the quota agreement, and only suppliers that have passed specific certification can participate in the quota allocation of specific materials, thereby enhancing procurement compliance.

[0024] A procurement plan is created, which includes a procurement organization, a material ID, a total planned quantity, and other procurement information.

[0025] As mentioned above, users create procurement plans through the system as input for subsequent automatic matching, optimization, and generation of downstream documents. The procurement plan, as the core document of procurement management, contains "procurement organization" to define the management attribution of procurement activities, ensuring consistency with the organization information in the quota agreement and avoiding cross-organizational data confusion; "material ID" is the unique code of the material, used for accurate matching of the corresponding material record in the quota agreement, and is a key identifier for automation association; "total planned quantity" is the total quantity of this procurement demand, which will be used as the basis for subsequent quantity segmentation and allocation, directly affecting the selection and optimization results of the quota agreement; "other procurement information" covers the auxiliary information required in the procurement process, providing more comprehensive context support for subsequent decision-making, ensuring that the generated downstream documents meet the actual business requirements.

[0026] For example, continuing with the manufacturing enterprise example, a procurement staff creates a procurement plan in the system, specifying the procurement organization as "North China Procurement Center", the material ID as "CPU-888", and the total planned quantity as 2500 pieces. At the same time, additional information such as "expected delivery date before October 15, 2025", "quality standard needs to meet ISO 9001", and "delivery location is Beijing warehouse" is supplemented in "other procurement information". After receiving the procurement plan, the system will automatically index the procurement organization and material ID to search for matching protocol records in the quota agreement library, and verify whether the total quantity of 2500 pieces meets the quantity constraints of each supplier, and then start the subsequent optimization and allocation process.

[0027] It should be noted that in specific implementation scenarios, the system can allow users to create multiple procurement plans for materials at a time based on the above scheme, and the system will perform the subsequent quota matching and optimization process for each material respectively. In addition, the procurement plan can support integration with the demand forecasting module, automatically converting demand quantities generated by external systems such as sales forecasts and production plans into procurement plans, reducing manual entry. The procurement plan can also introduce priority identifiers (such as high, medium, and low) as weight factors in the subsequent Markov decision optimization process to prioritize the allocation of supplier resources for high-priority plans. At the same time, the system can support state management of procurement plans (such as draft, submitted, allocated, and generated documents) to realize visual tracking of the entire process.

[0028] According to the procurement plan information, the quota agreement is queried and matched, and the quota agreement that meets the conditions is filtered out.

[0029] As described above, through the automatic screening mechanism of the system, the protocol record that completely matches the current procurement plan is found from the preset quota agreement library. The system uses the core fields in the procurement plan as query conditions to perform exact matching. First, the "procurement organization" is used for preliminary screening to ensure that the selected quota agreement belongs to the same management entity and avoid cross-organizational data interference. Second, the "material ID" is used for exact matching to find all available quota agreements for the material in the current organization. Finally, and most importantly, the system verifies whether the "total planned quantity" meets the quantity constraint condition of at least one supplier in the quota agreement, i.e., the planned quantity must be greater than or equal to the minimum quantity of the supplier and less than or equal to the maximum quantity of the supplier. Only the quota agreement that meets all three conditions will be selected as a candidate for subsequent optimization and allocation, ensuring the feasibility and effectiveness of the subsequent process.

[0030] For example, continuing the previous example, after receiving a purchase plan with a purchase organization of "North China Purchase Center", a material ID of "CPU-888", and a planned total quantity of 2500, the system begins to perform query matching. First, the system filters all quota agreements related to "CPU-888" under "North China Purchase Center"; second, for the filtered agreements, the system checks the batch quantity constraints of each supplier: Is the minimum batch quantity of supplier A 500 ≤ 2500 ≤ the maximum batch quantity 2000? Not satisfied (2500 > 2000); Is the minimum batch quantity of supplier B 300 ≤ 2500 ≤ the maximum batch quantity 1500? Not satisfied (2500 > 1500); Is the minimum batch quantity of supplier C 200 ≤ 2500 ≤ the maximum batch quantity 1000? Not satisfied (2500 > 1000). At this time, it is found that no single supplier can meet the demand of 2500. The system further checks whether there is a quota agreement that supports joint supply of multiple suppliers, i.e., multiple suppliers are defined in the agreement and the sum of their maximum batch quantities is greater than or equal to 2500. If such an agreement exists, the agreement is successfully filtered out and enters the subsequent optimization process. If not, the system prompts the user that the plan quantity cannot be matched and needs to be adjusted or a new quota agreement needs to be configured.

[0031] It should be noted that in specific implementation scenarios, when exact matching cannot find a quota agreement that meets the conditions, the system can start a fuzzy matching mode, recommend suppliers with batch quantity constraints close to the plan quantity, or suggest adjusting the plan quantity to the nearest feasible batch quantity range. In addition, the comprehensive score of the supplier (such as quality, service, historical fulfillment rate) can be introduced as an additional weight for matching, and the supplier combination with a high score is preferred. The system can also support multi-level matching strategies, such as automatically attempting to match the next level (such as the group level or the backup supplier library) when the preferred quota agreement cannot be met. At the same time, the matching process can record detailed log information, including the reasons for successful / failure matching, the list of suppliers participating in matching, etc., to provide data support for subsequent agreement optimization and decision analysis.

[0032] Based on the Markov decision, the allocation ratio of the quota agreement is optimized to obtain an optimized allocation ratio.

[0033] As mentioned above, the initial allocation ratio preset in the quota agreement is intelligently optimized through a Markov decision process to adapt to the dynamically changing procurement environment and business needs. The system constructs the current procurement task and supplier state into a decision state, which not only contains static information in the quota agreement (such as supplier's batch constraint, price, initial allocation ratio), but also integrates real-time dynamic information such as supplier's current inventory level, delivery cycle, historical performance, etc. Based on this state, the system defines a series of executable actions, such as adjusting the allocation ratio of a certain supplier to increase or decrease by a certain amplitude, or reallocating the ratio among multiple suppliers. By calculating the comprehensive benefits (reward values) that each action may bring, the system selects the optimal action that can maximize long-term benefits, thereby obtaining a set of optimized allocation ratios. This process realizes the transition from static and fixed ratio allocation to dynamic and intelligent optimization decision, ensuring efficient use of procurement resources and comprehensive optimization of procurement goals.

[0034] For example, continuing the previous example of procuring 2500 pieces of "CPU-888" processors. The system has screened a quota agreement containing suppliers A, B, and C, with initial allocation ratios of 40%, 35%, and 25% respectively. In the optimization phase, the system constructs the decision state: it finds that supplier A has sufficient inventory recently and a historical fulfillment rate as high as 98%, but the lowest price (800 yuan); supplier B has the highest price (820 yuan), but the shortest delivery cycle; supplier C has inventory shortages and a warning of lack of stock. The system evaluates adjustment actions: if the initial ratio is maintained, the total cost is low but some orders may be delayed due to lack of stock in C; if the 25% share allocated to C is transferred to A, although the supply stability can be guaranteed, A's maximum batch (2000 pieces) can only handle 80% of the total, and the remaining part still needs to be allocated. After Markov decision calculation, the system obtains the optimal solution: adjust the allocation ratio to A: 60% (1500 pieces), B: 30% (750 pieces), C: 10% (250 pieces). This solution not only takes full advantage of A's low cost and high fulfillment advantage, but also guarantees part of the fast delivery demand through B, while only relying on C to a small extent, reducing the risk of stock shortage, and achieving a balance between cost, efficiency, and risk.

[0035] It should be noted that in a specific implementation scenario, on the basis of the above scheme, the system can allow the user to manually adjust the weight coefficients of each index (cost, utilization rate, on-time rate, etc.) in the optimization target according to the current procurement strategy (such as cost priority, delivery priority, or risk avoidance priority), so that the optimization result is more in line with the actual business demand. In addition, online learning capability can be introduced. The system automatically collects actual execution results (such as real delivery time, actual cost, supplier performance, etc.) after each execution of the procurement plan and completion of the downstream documents, and uses these feedback data to update the Markov decision model, so that the system can continuously learn and adapt to changes in supplier capabilities and fluctuations in market environment. Visualization of the optimization process can also be supported to present the basis for proportion adjustment and expected effects to the user, enhancing the transparency and credibility of decision-making.

[0036] Associate the procurement plan with the quota agreement, and divide the total quantity of the procurement plan into multiple parts according to the optimized allocation proportion, and generate corresponding downstream documents for each divided part.

[0037] As described above, the optimized procurement strategy is converted into specific business operations. The system first formally associates the procurement plan with the quota agreement optimized by Markov decision, establishes a binding relationship between the two, and ensures the traceability of subsequent operations. After the association is completed, the system accurately divides the total quantity of the procurement plan into multiple sub-quantities according to the optimized allocation proportion, each sub-quantity corresponding to a supplier, and ensuring that each sub-quantity meets the minimum batch size and maximum batch size constraints of the supplier. The division process supports full division (completely allocating the total quantity) or partial division (only allocating part of the quantity, the remaining quantity can be left for later processing). After the division is completed, the system automatically generates corresponding downstream documents such as purchase orders or purchase contracts for each sub-quantity, which contain complete information such as supplier information, material information, allocated quantity, price, delivery requirements, etc. The entire process realizes the automated and batched generation from one procurement plan to multiple downstream documents, completely avoiding the tedious operation of manually creating documents repeatedly.

[0038] For example, continuing the previous example, the system has successfully associated the plan to purchase 2500 units of "CPU-888" with the optimized quota agreement (A: 60%, B: 30%, C: 10%). Subsequently, the system proportionally divides the total quantity of 2500 units: 1500 units to supplier A (2500 x 60%), 750 units to supplier B (2500 x 30%), and 250 units to supplier C (2500 x 10%). The system verifies that each divided quantity is within the corresponding supplier's batch range: 500 ≤ 1500 ≤ 2000 for A, which satisfies; 300 ≤ 750 ≤ 1500 for B, which satisfies; and 200 ≤ 250 ≤ 1000 for C, which satisfies. After verification, the system automatically generates three purchase orders: Order 1 to supplier A with a quantity of 1500 units and a unit price of 800 yuan; Order 2 to supplier B with a quantity of 750 units and a unit price of 820 yuan; and Order 3 to supplier C with a quantity of 250 units and a unit price of 810 yuan. The three orders are created simultaneously and enter a pending state, and the procurement personnel can submit them with one click to complete the entire procurement process.

[0039] It should be noted that in specific implementation scenarios, the system can support "priority order" division based on the above-mentioned scheme, i.e., preferentially allocating quantities to higher-ranked suppliers until their maximum batch is exhausted, and then allocating to lower-ranked suppliers; or support "cost-optimal" division, automatically finding the combination scheme with the lowest total cost. In addition, users can customize the generation rules of downstream documents, such as setting the approval process for generating orders, selecting contract templates, automatically filling in delivery addresses and payment terms, etc. The system can also support a preview function for generating documents, allowing users to confirm the division results and document content before formal submission. At the same time, all generated downstream documents are bidirectionally associated with the original procurement plan, supporting tracing back to the source plan from any document, and achieving closed-loop management of the entire process.

[0040] According to an embodiment of the present application, the initialization quota agreement includes the available suppliers of each material in different organizational units, the minimum batch, the maximum batch, and the price of each supplier, the initial allocation ratio of each supplier, and the unique identifier of the quota agreement, which includes: The available suppliers of each material in different organizational units are a preset supplier list, and the supplier list is maintained by an organizational unit permission management module; The minimum batch, the maximum batch, and the price of each supplier are parameters dynamically adjusted according to historical procurement data and market conditions; The initial allocation ratio of each supplier is calculated based on historical procurement quantities and supplier performance scores.

[0041] As mentioned above, the initialization of the quota agreement is the basic preparation for the operation of the system, and this step establishes a structured data record for guiding the subsequent quantity allocation of the procurement plan. The quota agreement contains the available suppliers of each material under different organizational units, and these suppliers exist in the form of a preset list, ensuring that only audited and authorized suppliers can participate in the procurement allocation. The supplier list is uniformly maintained by the organizational authority management module, and administrators of different organizations can only manage the supplier information within their own organization, ensuring the security of the data and the clarity of the organizational boundaries.

[0042] The quota agreement also records the minimum quantity, maximum quantity and price of each supplier for a specific material. The minimum quantity refers to the minimum order quantity that the supplier can accept, and below this quantity may lead to no transaction or increase the unit cost; the maximum quantity represents the maximum quantity that the supplier can stably supply within a certain period, and exceeding this limit may affect the delivery cycle; the price information is used for subsequent cost accounting and price comparison analysis. These parameters are not fixed but dynamically adjusted according to historical procurement data and market conditions, for example, when the market price fluctuates greatly or the supplier's production capacity changes, the system can update the relevant parameters according to the latest data, so that the quota agreement always reflects the current actual supply capacity.

[0043] In addition, the quota agreement also sets the initial allocation ratio of each supplier, which serves as the default weight for quantity allocation before optimization. This initial allocation ratio is not arbitrarily set by humans, but is calculated based on historical procurement quantity and supplier performance score. Historical procurement quantity reflects the closeness of cooperation between the enterprise and the supplier, and the greater the procurement quantity usually represents the more stable cooperation relationship; the supplier performance score combines the on-time delivery rate, product quality pass rate, after-sales service response speed and other multi-dimensional evaluation results. By combining these two factors for calculation, the initial allocation ratio obtained is more reasonable and objective, providing a high-quality starting point for subsequent optimization based on Markov decision. The quota agreement also sets a unique identifier for accurate identification and tracking of each agreement within the system, ensuring the accuracy and traceability of data processing.

[0044] According to one embodiment of the present application, the other procurement information in the procurement plan includes procurement time requirement, quality requirement and delivery location.

[0045] As mentioned above, other procurement information in the procurement plan is a supplementary business parameter in addition to the procurement organization, material ID and total quantity of the plan, which is used to fully describe the specific needs of this procurement. Among them, the procurement time requirement refers to the time node or time range that the buyer expects the supplier to complete the delivery, which is used to evaluate whether the delivery capacity of the supplier matches the demand, and is one of the important bases for subsequent optimization of the distribution ratio. The quality requirement refers to the technical standard, inspection specification or industry certification that the procurement material needs to meet, to ensure that the purchased material meets the production or use standard, and this information can be used to filter suppliers with corresponding qualifications or historical quality performance when matching suppliers. The delivery location refers to the specific physical location where the procurement material needs to be delivered, such as a warehouse or production workshop, which not only affects the calculation of logistics cost, but also is used to judge whether the supplier has the ability to supply to this location, especially when multiple regions are involved in supply, the matching of the delivery location directly affects the generation and execution of downstream documents. These information together constitutes a complete procurement demand portrait, providing necessary context support for the system to realize accurate matching, intelligent optimization and compliance execution.

[0046] According to one embodiment of the present application, the querying and matching of the quota agreement according to the procurement plan information includes the following screening conditions: the procurement organization is consistent with the organization in the quota agreement, the material ID is matched, and the total quantity of the plan is between the minimum batch and the maximum batch of the supplier.

[0047] As mentioned above, querying and matching the quota agreement according to the procurement plan information is the process of automatically finding the quota agreement suitable for the current procurement demand by the system. This process sets clear screening conditions to ensure that the matched quota agreement meets the requirements in the three key dimensions of organization, material and quantity. First, the system checks whether the procurement organization in the procurement plan is consistent with the organization defined in the quota agreement, and only the agreement belonging to the same organization will be included in the candidate range, so as to avoid misuse of supplier resources across organizations. Secondly, the system checks whether the material ID in the procurement plan is completely matched with the material ID recorded in the quota agreement, to ensure that the purchased material has a pre-set supplier and supply rule in the agreement. Finally, the system verifies whether the total quantity of the procurement plan meets the batch constraint condition of at least one supplier in the quota agreement, i.e. the total quantity of the plan must be greater than or equal to the minimum batch of the supplier, and less than or equal to the maximum batch. Only the quota agreement that meets the above three conditions will be screened out by the system as the basis for subsequent optimization of the distribution ratio. This matching process ensures the logical consistency and execution feasibility between the procurement plan and the quota agreement, providing accurate data support for subsequent intelligent segmentation and downstream document generation.

[0048] According to one embodiment of the present application, in the step of optimizing the allocation proportion of the quota agreement based on the Markov decision, the decision state comprises real-time inventory of the supplier, delivery cycle, historical fulfillment rate and current market demand prediction; The reward value of the Markov decision is calculated in the following manner: reward value = a x total cost + b x supplier utilization rate + g x delivery punctuality rate - d x default penalty, wherein a, b, g and d are preset weight coefficients.

[0049] As described above, in the process of optimizing the allocation proportion of the quota agreement based on the Markov decision, the decision state is the basis for the system to make intelligent judgments. This state not only contains static information in the quota agreement, but also integrates multiple dynamic business factors. Among them, the real-time inventory of the supplier reflects the current material supply capacity of the supplier, and the inventory is sufficient, the supply risk is low, and the inventory is tight, which may affect delivery; the delivery cycle indicates the time required by the supplier from order to completion of delivery, and the supplier with shorter cycle is more suitable for emergency demand; the historical fulfillment rate is the proportion of on-time delivery of the supplier based on the statistics of past purchase orders, which is used to evaluate the reliability of the supplier; and the current market demand prediction reflects the overall demand trend of the material in the future period of time, which is used to adjust the procurement strategy in advance to respond to market changes. These dynamic factors and static information such as price and batch in the quota agreement together constitute a complete decision state, enabling the system to make more reasonable allocation decisions based on real-time business environment.

[0050] In this decision process, the system evaluates the pros and cons of each possible allocation action by calculating the reward value. The calculation of the reward value takes into account multiple optimization objectives: total cost refers to the total procurement expenditure calculated based on the current allocation proportion, and the lower the cost, the higher the score; supplier utilization rate represents the ratio of actual allocation amount of each supplier to its maximum supply capacity, aiming to fully utilize the supplier's capacity and avoid resource idling; delivery punctuality rate is based on the historical performance of the supplier, and the higher the rate, the more reliable the delivery; and default penalty is for behaviors that violate constraints, such as allocation quantity exceeding the maximum batch or being lower than the minimum batch, and each occurrence deducts the corresponding score. The reward value is calculated in a weighted sum manner, i.e. reward value = a x total cost + b x supplier utilization rate + g x delivery punctuality rate - d x default penalty, wherein a, b, g and d are preset weight coefficients, used to adjust the relative importance of each index in the overall evaluation. Through this reward mechanism, the system can automatically select the allocation proportion that maximizes the overall benefit, achieving a balance between cost, efficiency, risk and resource utilization.

[0051] According to one embodiment of the present application, in the step of associating the procurement plan with the quota agreement, the association manner comprises user self-defined configuration or system automatic matching.

[0052] As mentioned above, associating procurement plans with quota agreements is a key step in achieving intelligent allocation of procurement quantities. This step provides two ways of association to adapt to different business scenarios and user needs. The first way is user-defined configuration, that is, the system presents the filtered quota agreements to the user, who manually adjusts the allocation proportion of each supplier or selects a specific combination of suppliers for association according to the actual business situation. This way is suitable for scenarios with special procurement strategies, the need for human intervention, or system recommendation results that do not meet current needs, giving users full control. The second way is automatic matching by the system, that is, after completing quota agreement filtering and allocation proportion optimization, the system automatically binds the procurement plan with the optimal quota agreement without human intervention. This way is suitable for standardized and high-frequency procurement tasks, which can significantly improve operational efficiency and reduce human errors. The coexistence of the two association methods ensures the level of system intelligence while retaining the necessary flexibility, allowing users to choose the appropriate mode according to actual needs to ensure that procurement plans can be accurately and efficiently converted into subsequent execution actions.

[0053] According to an embodiment of the present application, in the step of dividing the total quantity of the procurement plan into multiple parts according to the optimized allocation proportion, the division method is to allocate the total quantity to each supplier in proportion, ensuring that each division part meets the minimum batch requirement of the supplier.

[0054] As mentioned above, dividing the total quantity of the procurement plan into multiple parts according to the optimized allocation proportion is the core operation of task decomposition. This division method is based on the optimized allocation proportion calculated by the system, which splits the total quantity of the procurement plan according to the proportion occupied by each supplier, with each division part corresponding to a supplier and its allocated quantity. During the division process, the system strictly checks whether the allocated quantity of each supplier meets the minimum batch requirement, that is, the quantity allocated to a supplier cannot be lower than the minimum order quantity set by the supplier in the quota agreement. If the allocated quantity of a supplier is lower than the minimum batch due to proportion calculation, the system will automatically adjust, such as merging the quantity to other eligible suppliers or triggering a re-optimization of the allocation proportion, until all division parts meet the batch constraint. This division method ensures that each generated procurement task is feasible in actual execution, avoiding rejection or additional pricing by suppliers due to small order quantities, and ensuring smooth execution of the procurement process. At the same time, the division result is automatically associated with supplier information, prices, and other data in the quota agreement, providing an accurate data basis for subsequent generation of downstream documents.

[0055] According to an embodiment of the present application, in the step of generating corresponding downstream documents for each division part, the downstream documents include procurement orders and contracts, which are automatically sent to the corresponding suppliers after generation.

[0056] As mentioned above, generating corresponding downstream documents for each split is the final output link of the procurement plan execution. The system automatically generates the relevant business documents based on the split results determined in the previous steps, i.e., the specific quantities allocated to each supplier. These downstream documents mainly include purchase orders and purchase contracts, where the purchase order is used to specify the execution details of this procurement, such as materials, quantities, prices, delivery times and locations, and the purchase contract further solidifies the rights and obligations of both parties, including payment conditions, quality standards, and legal terms for breach of contract. When generating the documents, the system automatically fills in the relevant information from the procurement plan and the quota agreement to ensure data consistency and accuracy. After the documents are generated, the system automatically sends the purchase orders and contracts to the designated receiving channels of the corresponding suppliers through pre-set communication mechanisms, such as email, enterprise interface or electronic signature platform, to achieve efficient information transmission. This process does not require manual creation and sending one by one, significantly improving the efficiency of procurement execution, reducing operational delays and human errors, and achieving full-process automation from planning to execution.

[0057] The second aspect of the application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method of any one of the embodiments of the first aspect.

[0058] Figure 2 An example of an entity structure diagram of an electronic device is shown in Figure 2 As shown, the electronic device can include a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other through the communication bus 840. The processor 810 can invoke the logical instructions in the memory 830 to execute the method in any one of the embodiments of the first aspect, which includes: initializing a quota agreement, the quota agreement including available suppliers for each material in different organizational units, minimum and maximum quantities and prices for each supplier, initial allocation ratios for each supplier, and a unique identifier for the quota agreement; creating a procurement plan, the procurement plan including a procurement organization, a material ID, a total planned quantity, and other procurement information; querying and matching the quota agreement according to the procurement plan information to filter out the quota agreement that meets the conditions; optimizing the allocation ratio of the quota agreement based on Markov decision, to obtain an optimized allocation ratio; associating the procurement plan with the quota agreement, and dividing the total quantity of the procurement plan into multiple parts according to the optimized allocation ratio, and generating corresponding downstream documents for each split.

[0059] Further, the logic instructions in the memory 830 described above can be implemented in the form of software functional units and sold or used as independent products, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or parts of the present application that essentially contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and various media that can store program codes.

[0060] In another aspect, the present application also provides a computer program product, which comprises a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program can be executed by a processor to enable a computer to execute the method provided by the above-mentioned methods, and the method comprises: initializing a quota agreement, the quota agreement comprising available suppliers of each material in different organizations, minimum batch quantity, maximum batch quantity and price of each supplier, initial allocation ratio of each supplier, and unique identifier of the quota agreement; creating a purchase plan, the purchase plan comprising a purchase organization, a material ID, a total planned quantity, and other purchase information; querying and matching the quota agreement according to the purchase plan information, and screening the quota agreement meeting the conditions; optimizing the allocation ratio of the quota agreement based on Markov decision, to obtain an optimized allocation ratio; associating the purchase plan with the quota agreement, and dividing the total quantity of the purchase plan into multiple parts according to the optimized allocation ratio, and generating a corresponding downstream document for each divided part.

[0061] In another aspect, the present application also provides a non-transitory computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method provided by the above-mentioned methods, and the method comprises: initializing a quota agreement, the quota agreement comprising available suppliers of each material in different organizations, minimum batch quantity, maximum batch quantity and price of each supplier, initial allocation ratio of each supplier, and unique identifier of the quota agreement; creating a purchase plan, the purchase plan comprising a purchase organization, a material ID, a total planned quantity, and other purchase information; querying and matching the quota agreement according to the purchase plan information, and screening the quota agreement meeting the conditions; The distribution proportion of the quota agreement is optimized based on a Markov decision, and an optimized distribution proportion is obtained. The purchase plan is associated with the quota agreement, the total quantity of the purchase plan is divided into multiple parts according to the optimized distribution proportion, and a corresponding downstream document is generated for each divided part.

[0062] The places not mentioned in the application can be implemented by using or referring to the existing technology.

[0063] Each of the embodiments in the specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each of the embodiments mainly describes the differences from other embodiments.

[0064] The above only describes the embodiments of the application and is not intended to limit the application. The application can have various changes and modifications for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the application shall be included in the scope of the claims of the application.

Claims

1. A method for generating downstream documents based on a quota agreement plan, characterized in that: include: Initialize the quota agreement, which includes the suppliers of each material in different organizations, the minimum batch, maximum batch, and price of each supplier, the initial allocation ratio of each supplier, and the unique identifier of the quota agreement; Create a purchase plan, which includes the purchasing organization, material ID, total planned quantity, and other purchase information; Query and match quota agreements based on procurement plan information, and select quota agreements that meet the requirements; The allocation ratio of the quota agreement is optimized based on Markov decision making to obtain the optimized allocation ratio; Associate the purchase plan with the quota agreement, split the total purchase plan quantity into multiple parts based on the optimized allocation ratio, and generate corresponding downstream documents for each split.

2. The method according to claim 1, characterized in that The initial quota agreement includes the suppliers of each material in different organizations, the minimum batch, maximum batch and price of each supplier, the initial allocation ratio of each supplier, and the unique identifier of the quota agreement, including: The suppliers of each material in different organizations may be a preset supplier list, and the supplier list is maintained by the organization authority management module; The minimum batch, maximum batch and price of each supplier are parameters that are dynamically adjusted based on historical procurement data and market conditions; The initial allocation ratio for each supplier is calculated based on historical procurement volume and supplier performance scores.

3. The method according to claim 1, characterized in that Other procurement information in the procurement plan includes procurement time requirements, quality requirements and delivery location.

4. The method according to claim 1, wherein The querying and matching of quota agreements based on the procurement plan information to select quota agreements that meet the conditions includes: screening conditions, specifically: the procurement organization is consistent with the organizational structure in the quota agreement, the material ID matches, and the total planned quantity is between the minimum batch and the maximum batch of the supplier.

5. The method according to claim 4, characterized in that In the step of optimizing the allocation ratio of the quota agreement based on Markov decision making, the decision status includes the supplier's real-time inventory, delivery cycle, historical fulfillment rate and current market demand forecast; The reward value of the Markov decision is calculated as follows: reward value = α×total cost + β×supplier utilization rate + γ×on-time delivery rate-δ×default penalty, where α, β, γ, and δ are preset weight coefficients.

6. The method according to claim 1, characterized in that In the step of associating the procurement plan with the quota agreement, the associating method includes user-defined configuration or system automatic matching.

7. The method according to claim 1, characterized in that In the step of dividing the total quantity of the procurement plan into multiple parts according to the optimized allocation ratio, the division method is: the total quantity of the plan is distributed to each supplier in proportion, ensuring that each divided part meets the supplier's minimum batch requirement.

8. The method according to claim 1, characterized in that In the step of generating corresponding downstream documents for each segmented part, the downstream documents include purchase orders and contracts, which are automatically sent to the corresponding suppliers after being generated.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method according to any one of claims 1 to 8 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps in the method according to any one of claims 1 to 8 are implemented.

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