A method for generating downstream documents based on a quota agreement plan

Abstract

The application provides a method for generating downstream bills based on a quota agreement, relates to the technical field of enterprise resource planning and procurement management, and comprises the following steps: 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 proportion of each supplier, and unique identification of the quota agreement; creating a procurement plan, the procurement plan comprising 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 quota agreements meeting the conditions; optimizing the allocation proportion of the quota agreement based on Markov decision, to obtain an optimized allocation proportion; 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 proportion, and generating corresponding downstream bills 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 method for generating downstream documents 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 remain at the static proportional 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 method for generating downstream documents based on a quota agreement to solve one of the above technical problems.

[0004] The technical solution adopted by the application is as follows:

[0005] The application provides a method for generating downstream documents based on a quota agreement, which includes:

[0006] 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;

[0007] Creating a procurement plan, the procurement plan including a procurement organization, a material ID, a total quantity, and other procurement information;

[0008] Querying and matching the quota agreement according to the procurement plan information, and screening out quota agreements that meet the conditions;

[0009] Optimizing the allocation proportions of the quota agreement based on Markov decision, to obtain optimized allocation proportions;

[0010] 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.

[0011] According to an 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, which includes:

[0012] The available suppliers of each material in different organizations are a preset supplier list, which is maintained by an organization authority management module;

[0013] 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;

[0014] The initial allocation ratio of each supplier is calculated based on historical procurement quantity and supplier performance score.

[0015] According to an embodiment of the present application, the other procurement information in the procurement plan includes procurement time requirement, quality requirement and delivery location.

[0016] According to an embodiment of the present application, the screening of the quota agreement that meets the conditions according to the procurement plan information 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.

[0017] According to an embodiment of the present application, in the step of optimizing the allocation ratio of the quota agreement based on Markov decision, the decision state includes real-time inventory of the supplier, delivery cycle, historical compliance rate and current market demand forecast;

[0018] 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, where α, β, γ, δ are preset weight coefficients.

[0019] According to an 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.

[0020] 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 ratio, the division method is: the total quantity of the plan is allocated to each supplier in proportion, ensuring that each division part meets the minimum batch size requirement of the supplier.

[0021] According to an embodiment of the present application, in the step of generating a corresponding downstream document for each division part, the downstream document includes a purchase order and a contract, which is automatically sent to the corresponding supplier after generation.

[0022] 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.

[0023] 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.

[0024] Due to the adoption of the above technical solutions, the present application has the following beneficial effects:

[0025] The present application provides a data basis for intelligent allocation of procurement plans by initializing the quota agreement, pre-defining the available suppliers and their batch constraints, prices, and initial allocation ratios of each material under different organizational institutions, avoiding the repeated labor of reconfiguring supplier information for each procurement. By creating a procurement plan containing the procurement organization, material ID, and total quantity, and automatically querying the matching quota agreement according to this information, the procurement demand is quickly connected with the supplier resources, reducing the workload of manual screening and matching. By optimizing the allocation ratio of the quota agreement based on Markov decision, considering dynamic factors such as real-time inventory, delivery cycle, historical compliance rate, and market demand forecast of the supplier, the optimal allocation ratio can be calculated, so that the procurement decision is not only based on static data, but also adapts to the 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 corresponding downstream documents are generated 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

[0026] The accompanying drawings, which are included to provide a further understanding of the present application, constitute a part of the present application, and the illustrative embodiments of the present application and their descriptions serve to explain the present application and do not constitute improper limitations on the present application. In the drawings:

[0027] 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;

[0028] Figure 2 A structural schematic diagram of an electronic device provided by an embodiment of the present application is shown in the figure.

[0029] Reference signs:

[0030] 810, processor; 820, communication interface; 830, memory; 840, communication bus. DETAILED DESCRIPTION

[0031] In order to more clearly illustrate the overall concepts of the present application, the following will be described in detail with reference to the accompanying drawings.

[0032] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be apparent to one skilled in the art that the present application can be practiced without the specific details and other implementations can be employed. In other instances, well-known methods have not been described in detail in order to avoid obscuring the present application. It will be appreciated that embodiments of the present application can be used in combination with other embodiments unless otherwise specified herein.

[0033] In the present application, unless specifically stated and limited otherwise, the first feature is "on" or "under" the second feature can be that the first and second features are in direct contact, or the first and second features are indirectly in contact through an intermediate medium. In the description of the present specification, the description referring to the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples.

[0034] Embodiment 1

[0035] As shown in FIG. 1, a method for generating downstream documents based on a quota agreement plan includes: Figure 1

[0036] Initializing a quota agreement, the quota agreement including available suppliers of each material in different organizations, minimum batch size, maximum batch size and price of each supplier, initial allocation ratio of each supplier, and unique identifier of the quota agreement.

[0037] ​As mentioned above, the quota agreement serves as a bridge connecting the procurement plan and the supplier resources, and its core function is to define the available suppliers and their supply capacity boundaries for a specific material under a specific organization. Among them, "the available suppliers of each material in different organizations" ensures the clear boundaries of the procurement organization, avoiding cross-organizational misoperation; "the minimum batch size of each supplier" refers to the minimum order quantity that the supplier can undertake, and below this quantity may lead to the supplier's refusal to accept the order or increase the unit cost; "the maximum batch size" refers to the maximum quantity that the supplier can stably supply within a certain period, and exceeding this limit may cause delivery delay; "the price" information is used for cost accounting and price comparison analysis; "the initial allocation ratio of each supplier" provides a default allocation weight for the system as a starting point for subsequent optimization; "the unique identifier of the quota agreement" is used for precise tracking and management of each agreement within the system, ensuring data consistency and traceability.

[0038] For example, in a manufacturing enterprise, under its North China procurement organization, for the processor material with model "CPU-888", the system initializes a quota agreement containing three suppliers: the minimum batch size of supplier A is 500 pieces, the maximum batch size is 2000 pieces, the unit price is 800 yuan, and the initial allocation ratio is 40%; the minimum batch size of supplier B is 300 pieces, the maximum batch size is 1500 pieces, the unit price is 820 yuan, and the initial allocation ratio is 35%; the minimum batch size of supplier C is 200 pieces, the maximum batch size is 1000 pieces, the unit price is 810 yuan, and the initial allocation ratio is 25%. Each quota agreement is assigned a unique 32-bit GUID, such as "1a2b3c4d-...". When creating a procurement plan later, the system can quickly identify the available suppliers and their constraint conditions based on this agreement, laying the foundation for subsequent intelligent allocation.

[0039] 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, 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, only suppliers who have passed specific certification can participate in the quota allocation of specific materials, thereby enhancing procurement compliance.

[0040] Create a procurement plan, which includes procurement organization, material ID, plan total quantity, and other procurement information.

[0041] As described above, the user creates a procurement plan through the system as the input basis 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, ensure consistency with the organization information in the quota agreement, and avoid cross-organizational data confusion; "material ID" is the unique code of the material, used to accurately match the corresponding material record in the quota agreement, and is the key identifier for automatic 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, and directly affects the selection and optimization results of the quota agreement; "other procurement information" covers the auxiliary information required in the procurement process, provides more comprehensive context support for subsequent decision-making, and ensures that the generated downstream documents meet the actual business requirements.

[0042] For example, continuing with the example of a manufacturing enterprise, a procurement personnel creates a procurement plan in the system, specifies 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 it by procurement organization and material ID to find matching agreement records in the quota agreement library, and verify whether it meets the batch constraints of each supplier based on the total quantity of 2500 pieces, and then start the subsequent optimization and allocation process.

[0043] It should be noted that in specific implementation scenarios, the system can allow the user to create procurement plans for multiple materials at a time based on the above-mentioned 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 forecasting and production planning into procurement plans, reducing manual input. 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), realizing visual tracking of the entire process.

[0044] According to the procurement plan information, the quota agreement is queried and matched, and the quota agreement that meets the conditions is selected.

[0045] As mentioned above, the system automatically finds a matching agreement record from the pre-set agreement library by a screening mechanism. The system performs an exact match with the core fields in the procurement plan as query conditions. First, the system performs a preliminary screening by "procurement organization" to ensure that the selected agreement belongs to the same management entity, avoiding cross-organizational data interference. Second, the system performs an exact match based on "material ID" to find all available agreement records for the material in the current organization. Finally, and most importantly, the system checks whether the "total planned quantity" meets the batch constraint conditions of at least one supplier in the agreement, i.e., the planned quantity must be greater than or equal to the minimum batch of the supplier and less than or equal to the maximum batch. Only the agreement that meets all three conditions will be screened out as a candidate for subsequent optimization and allocation, ensuring the feasibility and effectiveness of the subsequent process.

[0046] For example, based on the preceding example, after receiving a procurement plan with a procurement organization of "North China Procurement Center", a material ID of "CPU-888", and a total planned quantity of 2500, the system begins to perform query matching. First, the system screens all agreements about "CPU-888" under "North China Procurement Center"; second, for the screened agreements, the system checks the batch constraints of each supplier: Is the minimum batch of 500 pieces ≤ 2500 ≤ the maximum batch of 2000 pieces for supplier A? No (2500 > 2000); Is the minimum batch of 300 pieces ≤ 2500 ≤ the maximum batch of 1500 pieces for supplier B? No (2500 > 1500); Is the minimum batch of 200 pieces ≤ 2500 ≤ the maximum batch of 1000 pieces for supplier C? No (2500 > 1000). At this time, it is found that a single supplier cannot meet the demand of 2500. The system further checks whether there is an agreement that supports joint supply by multiple suppliers, i.e., multiple suppliers are defined in the agreement and the sum of their maximum batches is greater than or equal to 2500. If such an agreement exists, the agreement is successfully screened out and enters the subsequent optimization process. If not, the system prompts the user that no match is found and the plan quantity needs to be adjusted or a new agreement needs to be configured.

[0047] It should be noted that in a specific implementation scenario, on the basis of the above scheme, when the exact match cannot find a quota agreement that meets the conditions, the system can start a fuzzy matching mode, recommend suppliers with batch constraints close to the planned quantity, or suggest adjusting the planned quantity to the nearest feasible batch 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 combination of suppliers with high scores is preferentially recommended. The system can also support multi-level matching strategies, such as automatically attempting to match to 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 matching success / failure, the list of suppliers participating in matching, etc., to provide data support for subsequent agreement optimization and decision analysis.

[0048] The allocation ratio of the quota agreement is optimized based on Markov decision to obtain an optimized allocation ratio.

[0049] As described above, the initial allocation ratio preset in the quota agreement is intelligently optimized through the 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 the batch constraints, price, and initial allocation ratio of the supplier), but also integrates real-time dynamic information such as the current inventory level, delivery cycle, and historical performance of the supplier. 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-making, ensuring efficient use of procurement resources and comprehensive optimization of procurement goals.

[0050] For example, continue the previous example of purchasing 2500 pieces of "CPU-888" processors. The system has screened out the 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 is found that supplier A has sufficient inventory recently and a historical fulfillment rate as high as 98%, but the price is the lowest (800 yuan); supplier B has the highest price (820 yuan), but the delivery cycle is the shortest; supplier C has a tight inventory and a warning of stockout. The system evaluates the adjustment action: if the initial ratio is maintained, the total cost is low but some orders may be delayed due to C's stockout; 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 accommodate 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), and 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 stockout, and achieving a balance between cost, efficiency, and risk.

[0051] It should be noted that in specific implementation scenarios, on the basis of the above scheme, the system can allow users 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 needs. In addition, online learning capability can be introduced, and 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 and expected effect of the proportion adjustment to the user, enhancing the transparency and credibility of the decision.

[0052] 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 ratio, and generate corresponding downstream documents for each divided part.

[0053] As mentioned above, the optimized procurement strategy is translated into specific business operations. The system first formally associates the procurement plan with the optimized quota agreement, establishing a binding relationship between the two to ensure traceability of subsequent operations. After the association is complete, the system accurately divides the total quantity of the procurement plan into multiple sub-quantities according to the optimized allocation ratio, with each sub-quantity corresponding to a supplier and ensuring that each sub-quantity meets the minimum and maximum batch constraints of the supplier. The division process supports full division (completely allocating the total quantity) or partial division (only allocating part of the quantity, with the remaining quantity left for subsequent processing). After the division is complete, the system automatically generates corresponding downstream documents such as purchase orders or purchase contracts for each sub-quantity, including complete information such as supplier information, material information, allocated quantity, price, delivery requirements, etc. The entire process realizes the automated and batch generation from a procurement plan to multiple downstream documents, completely avoiding the tedious operation of manually creating duplicate documents.

[0054] For example, based on the previous example, the system has successfully associated the procurement plan for 2500 units of "CPU-888" with the optimized quota agreement (A: 60%, B: 30%, C: 10%). Subsequently, the system divides the total quantity of 2500 units according to the ratio: 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 batch range of the corresponding supplier: A's 500 ≤ 1500 ≤ 2000, which is satisfied; B's 300 ≤ 750 ≤ 1500, which is satisfied; and C's 200 ≤ 250 ≤ 1000, which is satisfied. After verification, the system automatically generates three purchase orders: Order 1 to supplier A, quantity 1500 units, unit price 800 yuan; Order 2 to supplier B, quantity 750 units, unit price 820 yuan; and Order 3 to supplier C, quantity 250 units, unit price 810 yuan. The three orders are created simultaneously and enter the pending confirmation state, and the procurement personnel can submit them with one click to complete the entire procurement process.

[0055] It should be noted that in a specific implementation scenario, on the basis of the above scheme, the system can support division according to "priority order", i.e. preferentially allocating quantities to the top-ranked suppliers until their maximum batch is exhausted, and then allocating to the secondary suppliers; or support division according to "cost optimization", automatically finding the combination scheme with the lowest total cost. In addition, the user can customize the generation rules of the downstream documents, such as setting the approval process for generating orders, selecting the contract template, automatically filling in the delivery address and payment terms, etc. The system can also support the preview function of generating documents, allowing the user to confirm the segmentation result and document content before formal submission. At the same time, all generated downstream documents are bidirectionally associated with the original procurement plan, supporting tracing from any document to the source plan to realize closed-loop management of the whole process.

[0056] According to one embodiment of the present application, the initialization quota agreement includes the available suppliers of each material in different organizational units, 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, which includes:

[0057] 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 authority management module;

[0058] The minimum batch, maximum batch and price of each supplier are parameters dynamically adjusted according to historical procurement data and market conditions;

[0059] The initial allocation ratio of each supplier is calculated based on historical procurement quantity and supplier performance score.

[0060] As mentioned above, the initialization of the quota agreement is the basic preparation work for the system to run, and this step establishes a structured data record to guide the quantity allocation of the subsequent procurement plan. The quota agreement contains the available suppliers of each material in different organizational units, and these suppliers exist in the form of a preset list to ensure that only audited and authorized suppliers can participate in procurement allocation. The supplier list is uniformly maintained by the organizational unit authority management module, and the 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.

[0061] The quota agreement also records the minimum batch, maximum batch and price of each supplier for a specific material. The minimum batch refers to the minimum order quantity that the supplier can accept, below which it may lead to no transaction or increase the unit cost; the maximum batch represents the maximum quantity that the supplier can stably supply within a certain period, exceeding which 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 market prices fluctuate greatly or supplier 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.

[0062] In addition, the quota agreement also sets the initial allocation ratio of each supplier, which serves as the default weight for quantity allocation and is used for preliminary allocation before optimization. This initial allocation ratio is not arbitrarily set by humans, but is calculated based on historical procurement volume and supplier performance score. Historical procurement volume reflects the closeness of cooperation between the enterprise and the supplier, and the larger the procurement volume usually represents the more stable the cooperation relationship; the supplier performance score integrates 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, 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 has a unique identifier for precise identification and tracking of each agreement within the system, ensuring the accuracy and traceability of data processing.

[0063] According to an embodiment of the present application, the other procurement information in the procurement plan includes procurement time requirement, quality requirement and delivery location.

[0064] 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, which is used to fully describe the specific demand of this procurement. Among them, the procurement time requirement refers to the time node or time range that the procurement party 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.

[0065] 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 is between the minimum batch and the maximum batch of the supplier.

[0066] 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, 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, that is, the total quantity 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.

[0067] 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.

[0068] 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.

[0069] As mentioned 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 more sufficient the inventory is, the lower the supply risk is, and the more nervous the inventory is, which may affect delivery; the delivery cycle indicates the time required by the supplier from order acceptance 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.

[0070] 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 is, the higher the score is; supplier utilization rate indicates the ratio of actual allocation amount of each supplier to its maximum supply capacity, aiming to fully utilize the supplier capacity and avoid resource idling; delivery punctuality rate is based on the historical performance of the supplier, and the higher it is, the more reliable the delivery is; 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, which are 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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 must not 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 constraints. 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.

[0075] 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.

[0076] As mentioned above, generating corresponding downstream documents for each split part is the final output link of the procurement plan execution. The system automatically generates the corresponding business documents based on the split results determined in the previous steps, i.e., the specific quantity 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 the materials, quantity, price, delivery time and location of this procurement, and the purchase contract further solidifies the rights and obligations of both parties, including payment conditions, quality standards, breach of contract responsibilities and other legal terms. When the system generates the documents, it 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 the 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.

[0077] The second aspect embodiment of the present 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 implements the method in any of the embodiments of the first aspect when executing the program.

[0078] Figure 2 An example of a schematic diagram of the physical structure 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 of the embodiments of the first aspect, which includes:

[0079] 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;

[0080] creating a procurement plan, the procurement plan including a procurement organization, a material ID, a total planned quantity, and other procurement information;

[0081] querying and matching the quota agreement according to the procurement plan information, and screening the quota agreement that meets the conditions;

[0082] optimizing the allocation ratio of the quota agreement based on Markov decision, to obtain an optimized allocation ratio;

[0083] 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 ratio, and generate corresponding downstream documents for each divided part.

[0084] In addition, the logical instructions in the memory 830 described above can be implemented in the form of a software function unit and sold or used as an independent product, and can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or part 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.

[0085] In another aspect, the present application also provides a computer program product, which includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and the computer program is executed by a processor, so that the computer can execute the method provided by the above-mentioned methods. The method includes:

[0086] Initializing the quota agreement, the quota agreement including 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;

[0087] Creating a procurement plan, the procurement plan including a procurement organization, a material ID, a total quantity, and other procurement information;

[0088] Querying and matching the quota agreement according to the procurement plan information, and screening the quota agreement meeting the conditions;

[0089] Optimizing the allocation ratio of the quota agreement based on Markov decision, and obtaining the optimized allocation ratio;

[0090] 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 divided part.

[0091] 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. The method includes:

[0092] initializing a quota agreement, the quota agreement including 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 identification of the quota agreement;

[0093] creating a purchase plan, the purchase plan including a purchase organization, a material ID, a total quantity, and other purchase information;

[0094] querying and matching the quota agreement according to the purchase plan information, and screening the quota agreement meeting the conditions;

[0095] optimizing the allocation ratio of the quota agreement based on Markov decision, and obtaining an optimized allocation ratio;

[0096] 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 corresponding downstream documents for each divided part.

[0097] Any place not described in the present application can be realized by using or referring to the existing technology.

[0098] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment mainly describes the difference from other embodiments.

[0099] The above only describes the embodiments of the present application and is not used to limit the present application. The present application can have various changes and variations for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the scope of claims of the present application.

Claims

1. A method for generating downstream documents based on a quota agreement, characterized in that, include: Initialize the quota agreement, which includes the available suppliers for each material in different organizations, the minimum and maximum batch sizes and prices for each supplier, the initial allocation ratio for each supplier, and a unique identifier for the quota agreement. The minimum batch size refers to the minimum order quantity that the supplier can accept, and the maximum batch size refers to the maximum quantity that the supplier can stably supply within a specific period. Create a procurement plan, which includes the procurement organization, material ID, total planned quantity, and other procurement information. Quota agreements are queried and matched based on procurement plan information, and quota agreements that meet the criteria are selected. The specific screening criteria are: the purchasing organization is consistent with the organization in the quota agreement, the material ID matches, and the planned total quantity is between the supplier's minimum and maximum batch sizes. The allocation ratio of the quota agreement is optimized based on Markov decision-making to obtain the optimized allocation ratio. Decision status includes supplier 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. The procurement plan is linked to the quota agreement, and the total quantity of the procurement plan is divided into multiple parts according to the optimized allocation ratio, generating corresponding downstream documents for each part.

2. The method according to claim 1, characterized in that, The initial quota agreement includes the available suppliers for each material in different organizations, the minimum and maximum batch sizes and prices for each supplier, the initial allocation ratio for each supplier, and a unique identifier for the quota agreement, including: The suppliers available for each material in different organizations are based on a preset supplier list, which is maintained through the organization permission management module. The minimum order quantity, maximum order quantity, and price of each supplier are parameters that are dynamically adjusted based on historical procurement data and market conditions. The initial allocation ratios for each supplier are calculated based on historical purchase volumes 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, characterized in that, The step of associating the procurement plan with the quota agreement can be achieved through user-defined configuration or automatic system matching.

5. 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 as follows: the total quantity of the plan is allocated to each supplier proportionally to ensure that each part meets the supplier's minimum batch requirement.

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

7. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-6.

8. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1-6.

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