Order commitment period dynamic optimization method and system based on demand prediction and real-time performance capability verification

Abstract

The invention relates to the technical field of supply chain digital management, and provides an order commitment period dynamic optimization method based on demand prediction and real-time performance capability verification, and the method comprises the steps: obtaining multi-source heterogeneous demand data and a supply chain state data flow in real time; performing priority dynamic calibration and conflict resolution on the demand through a rule engine; calling a supply and demand matching engine, executing real-time parallel performance capability verification, inputting a verification result and a preset multi-dimensional SLA rule set into an integrated multi-target optimization model, and outputting a Pareto optimal solution set by the multi-target optimization model through iterative solution by taking synchronous minimization of commitment delay, resource vacancy rate and SLA default risk as targets, generating a committed delivery period and a resource occupation scheme; and outputting a commitment result, and comparing planned and actually executed data streams to form closed-loop feedback so as to adaptively adjust a priority rule and optimize a model weight.

Description

Technical Field

[0001] This invention relates to digital supply chain management, specifically to a method and system for dynamically optimizing order commitment periods based on demand forecasting and real-time fulfillment capability verification. Background Technology

[0002] In order management within the manufacturing industry, accurately and quickly committing to delivery dates is crucial for enhancing customer satisfaction and supply chain competitiveness. Existing technologies primarily utilize static lead time management. When the sales department receives a customer order, it typically calculates the committed delivery date directly based on the fixed production and procurement cycles preset in the ERP system, plus a certain safety buffer period.

[0003] However, static calculations ignore the factory's actual capacity load and material availability, and lack overall planning, which often leads to the failure to meet promised delivery dates in actual production, resulting in frequent delays. This can easily lead to a situation where forecasted orders occupy capacity, but after formal orders are received, there is no available capacity. Summary of the Invention

[0004] This application proposes a dynamic optimization method and system for order commitment period based on demand forecasting and real-time fulfillment capability verification. This method addresses the technical problems of low delivery commitment accuracy, slow response speed, and supply-demand imbalance caused by neglecting real-time capacity bottlenecks, material constraints, and forecast data in complex manufacturing environments when existing order management technologies face these issues.

[0005] To achieve the above objectives, this application provides the following technical solution: Firstly, this application proposes a dynamic optimization method for order commitment period based on demand forecasting and real-time fulfillment capability verification, including the following steps: S1: Real-time acquisition of multi-source heterogeneous demand data and supply chain status data streams; S2: Dynamically prioritize and resolve conflicts of requirements through a rules engine; S3: Invoke the supply and demand matching engine to perform real-time parallel fulfillment capability verification, including: Based on the bill of materials, real-time inventory map, work-in-process status, and the occupied and available capacity of key resources, supply and demand matching simulation is conducted to assess the feasibility of orders and the estimated completion time. When the simulation results show that the demand cannot be directly met, an alternative solution matching algorithm is run according to the pre-configured business and production rules to generate feasible alternative fulfillment solutions. For key resource constraints or material bottlenecks identified in the simulation verification, structured constraint information is generated for outputting commitment results or passing them to the downstream planning system for in-depth scheduling. S4: Input the verification results and the preset multi-dimensional SLA rule set into an integrated multi-objective optimization model. The multi-objective optimization model aims to simultaneously minimize the commitment delay, resource idle rate and SLA default risk. It iteratively solves and outputs the Pareto optimal solution set, and generates the commitment delivery date and resource usage scheme accordingly. S5: Output the promised results and form a closed-loop feedback by comparing the planned and actual data flow to adaptively adjust the priority rules in S2 and the optimization model weights in S4.

[0006] In conjunction with the first aspect, S1 includes: Lightweight edge data agents are deployed at IoT gateways in enterprise order management systems, production execution systems, and key logistics nodes; among them, The edge data agent runs in event listening mode. When a new sales order, forecast update, work order completion, or inventory change occurs, it immediately encapsulates the summary information of the changed data into a standardized event message and pushes it to the central processing platform in real time through a high-throughput enterprise-grade streaming data bus. The central processing platform includes a dynamically updated global data view, which collects heterogeneous demand data from multiple sources and outputs corresponding supply chain status data streams.

[0007] In conjunction with the first aspect, S2 includes: Automatically add customer level, product strategy level, urgency of need, and expected profit contribution tag to each demand; The rules engine has a built-in dynamic weight calculation model. The dynamic weight calculation model adjusts the weight coefficient of each tag in the priority score calculation in real time according to the current total global demand to be committed and the overall tension of the supply chain. When the capacity is sufficient, it executes profit-guarantee orders, and when the capacity is tight, it executes high-strategy-level customer orders.

[0008] In conjunction with the first aspect, S3 includes: Based on the aggregated resource load view and material availability time series, a time-bucket-based capacity consumption and material deduction algorithm is used to simulate the impact on global resources and materials if the order demand is accepted, and to calculate its earliest possible completion time. During the simulation, the core limiting factors that cause delays in order completion time are tracked and recorded, and marked as key constraints for the order. By aggregating simulation results from multiple orders, frequently tagged common constraint resources or materials are identified.

[0009] In conjunction with the first aspect, S4 includes: The Pareto optimal solution set is pushed to the interactive visualization decision-making platform. The interactive visualization decision-making platform uses two-dimensional or three-dimensional graphs to visualize the performance of each potential solution in three target dimensions: commitment delay, resource idle rate, and SLA default risk, and intuitively presents the trade-off relationship between the targets. By responding to any solution point on the 3D map, or by defining an acceptable target value range, the platform will automatically recommend solutions that fall within that range. When the decision-maker finally selects a solution, they need to select from a preset list or manually enter the main considerations for this decision on the interface provided by the platform.

[0010] In conjunction with the first aspect, when the supply chain status data flow involves multiple production plants, real-time simulation and multi-objective optimization models need to collaboratively handle cross-plant order distribution and complete shipment constraints. Specifically, this includes: before the simulation, based on the order's destination, product type, and preset factory capacity tags, using a hybrid logic that includes static rules and dynamic load assessment, pre-allocating one or more candidate fulfillment factories for each order requirement; during the parallel simulation instance simulation of each order, the simulation will be performed simultaneously within its candidate factory set, and the bottlenecks and expected completion times under each factory scheme will be recorded to generate a kit tracking number.

[0011] In conjunction with the first aspect, S5 includes: Based on each output commitment result, continuously monitor the actual fulfillment data flow of the corresponding batch of orders, and trigger an effectiveness evaluation when an order is closed or a key milestone event occurs; Calculate the deviations between the actual and promised forecasts for the corresponding batch of orders in terms of indicators such as average delivery delay, resource utilization, and SLA achievement rate. Based on the bias data, the corresponding snapshot of the decision-making scenario features, and the version of the rules and model parameters used at that time, they are collectively packaged into a training sample; The incremental learning engine periodically uses newly added training sample sets to fine-tune the weight calculation model in the rule engine and the target weights in the multi-objective optimization model. After the adjusted new parameter version passes the historical backtesting test, it is marked as the effective version and applied to real-time decision-making.

[0012] In conjunction with the first aspect, the execution monitoring phase following the output of the commitment result: Based on the commitment scheme, a fulfillment time baseline including key checkpoints is generated for each order; By continuously comparing the actual progress with the baseline through real-time data stream, an abnormal event is immediately triggered once a deviation exceeds a preset threshold. Among them, abnormal events are categorized and input into the intelligent handling suggestion engine; The intelligent handling suggestion engine is linked to the performance capability simulation module, which simulates the insertion of remedial measures into the current plan and quickly generates one or more adjustment plans that are expected to reduce deviations; The adjustment plan will then be fed back to the corresponding adjustment end and integrated into the interactive visual decision-making platform as a high-priority pending item until the matter is completed or the platform is closed abnormally.

[0013] In conjunction with the first aspect, the supply and demand matching simulation and the alternative matching algorithm can be guided from an order fulfillment knowledge base upon startup; The knowledge base is constructed by analyzing historical order commitment and fulfillment data, recording typical fulfillment cycles, common bottleneck resources, and successful alternatives for different products, customers, and process paths; When conducting simulation verification, similar cases are retrieved from the knowledge base based on the characteristics of the current order, their typical cycles are used as the initial reference for the simulation, and historical common bottlenecks are used as key monitoring constraints. When performing alternative matching, priority is given to recommending alternatives with high historical verification success rates and strong relevance from the knowledge base.

[0014] Secondly, this application proposes a dynamic optimization system for order commitment periods based on demand forecasting and real-time fulfillment capability verification, including: Multi-source data acquisition module: Real-time acquisition of multi-source heterogeneous demand data and supply chain status data streams; The rules engine processing module dynamically prioritizes and resolves conflicts in requirements using the rules engine. Fulfillment verification module: Invokes the supply and demand matching engine to perform real-time parallel fulfillment capability verification, including: Based on the bill of materials, real-time inventory map, work-in-process status, and the occupied and available capacity of key resources, supply and demand matching simulation is conducted to assess the feasibility of orders and the estimated completion time. When the simulation results show that the demand cannot be directly met, an alternative solution matching algorithm is run according to the pre-configured business and production rules to generate feasible alternative fulfillment solutions. For key resource constraints or material bottlenecks identified in the simulation verification, structured constraint information is generated for outputting commitment results or passing them to the downstream planning system for in-depth scheduling. Iterative solution module: Input the verification results and the preset multi-dimensional SLA rule set into an integrated multi-objective optimization model. The multi-objective optimization model aims to simultaneously minimize the commitment delay, resource idle rate and SLA default risk. It outputs the Pareto optimal solution set through iterative solution and generates the commitment delivery date and resource usage plan accordingly. The results output module is used to output the promised results and form a closed-loop feedback by comparing the planned and actual data streams to adaptively adjust the priority rules in the rule engine processing module and the optimization model weights in the iterative solution module.

[0015] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0016] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0018] In the attached diagram: Figure 1 This is a flowchart of a method for dynamically optimizing the order commitment period based on demand forecasting and real-time fulfillment capability verification in an embodiment of the present invention. Figure 2 This is a system composition diagram of an order commitment period dynamic optimization system based on demand forecasting and real-time fulfillment capability verification in an embodiment of the present invention; Figure 3 This is a schematic diagram of the data acquisition process for multi-source heterogeneous demand data in an embodiment of the present invention. Detailed Implementation

[0019] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0020] Example 1: This application proposes a dynamic optimization method for order commitment period based on demand forecasting and real-time fulfillment capability verification, including the following steps: S1: Real-time acquisition of multi-source heterogeneous demand data and supply chain status data streams; S2: Dynamically prioritize and resolve conflicts of requirements through a rules engine; S3: Invoke the supply and demand matching engine to perform real-time parallel fulfillment capability verification, including: Based on the bill of materials, real-time inventory map, work-in-process status, and the occupied and available capacity of key resources, supply and demand matching simulation is conducted to assess the feasibility of orders and the estimated completion time. When the simulation results show that the demand cannot be directly met, an alternative solution matching algorithm is run according to the pre-configured business and production rules to generate feasible alternative fulfillment solutions. For key resource constraints or material bottlenecks identified in the simulation verification, structured constraint information is generated for outputting commitment results or passing them to the downstream planning system for in-depth scheduling. S4: Input the verification results and the preset multi-dimensional SLA rule set into an integrated multi-objective optimization model. The multi-objective optimization model aims to simultaneously minimize the commitment delay, resource idle rate and SLA default risk. It iteratively solves and outputs the Pareto optimal solution set, and generates the commitment delivery date and resource usage scheme accordingly. S5: Output the promised results and form a closed-loop feedback by comparing the planned and actual data flow to adaptively adjust the priority rules in S2 and the optimization model weights in S4.

[0021] In this application: Step S1 deploys a lightweight edge data agent in the OMS order creation interface, ERP forecasting module, MES work order completion report node, and WMS inventory change transaction point. This agent, in a monitoring mode, captures key event information such as order information, version update information, work order status information, and material inventory information. The multi-source heterogeneous demand data includes not only formally placed sales orders from customers but also internal demand forecasts generated based on market analysis, stock orders generated due to trade shows or inventory preparation, and long-term framework contract demands. The supply chain status data stream includes current finished goods inventory data, in-transit supply data, procurement supply plans, and current work-in-process tasks and real-time load status of each production line. This breaks down information silos in traditional ERP systems. Traditional OMS often makes commitments based on T+1 static data snapshots, leading to a disconnect between committed results and reality. The above solution, through an event-driven streaming architecture, ensures that decision-making data is synchronized with the physical world, improving data confidence.

[0022] Step S2 prioritizes all requests according to preset business rules before the data enters the calculation engine. Rule dimensions include: customer level, request amount, order urgency, and delivery date. For example, urgent orders from strategic customers will be assigned the highest priority P1, while ordinary forecast orders might be marked as P3. If multiple requests compete for the same resource at the same time, the rule engine will resolve conflicts based on this priority, determining the initial allocation intention of the resource. The calculation engine has a built-in dynamic weight calculation model. It dynamically adjusts the weight of each label in the comprehensive priority score calculation based on the current total amount of global pending requests and the overall utilization rate of key resources. The dynamic weight vector is: ; It is a vector biased towards profit weights. It is a vector that is biased towards customer strategy and urgency. This represents the supply chain tension coefficient. Conflict resolution refers to the situation where multiple demands compete for the same limited resource, such as materials, and are ranked according to priority scores. The demand with the highest score obtains temporary pre-allocation of the resource. For demands that fail to pre-allocate, alternative matching or re-simulation in S3 is triggered. Traditional fixed priority rules cannot adapt to changes in the supply chain status. This invention, through a dynamic weighting model, enables the system to automatically pursue profit maximization when capacity is ample and automatically ensure strategic customers and timely delivery when capacity is tight.

[0023] Step S3 involves a multi-level simulation verification process for each requirement or requirement package: The inventory and work-in-progress matching simulation employs a time-series-based backward consumption algorithm. The engine starts with the customer's requested delivery date and iterates backward through the bill of materials. Specifically, it checks the available inventory of finished goods and materials at each level, as well as the estimated completion quantity of work-in-progress orders. Available supply is deducted in chronological order to meet demand, and the earliest material node that cannot meet demand and its shortage time are recorded. If the entire process is feasible, an inventory-based commitment plan is generated.

[0024] The coarse-grained capacity verification of critical resources is performed using a time-bucket capacity consumption algorithm. For each requirement that needs to be processed through critical resources, based on its standard working hours or resource usage, the first time bucket with sufficient remaining capacity is found on the resource capacity calendar, starting from its material availability time, and that capacity is occupied. This allows for the simulation of all pending requirements' occupancy of critical resources and the calculation of the resource readiness time for each requirement.

[0025] Alternative solution matching is triggered simultaneously when the two aforementioned solutions are activated. It explores a pre-configured rule base, including rules for material substitution, process route substitution, and supply source substitution. Based on rule priority, it attempts to generate one or more alternative fulfillment paths. Each path re-activates the two aforementioned solutions to verify its feasibility and calculates the alternative cost. At the end of the simulation, it outputs the earliest estimated completion time for each demand, the critical supply nodes it depends on, a list of identified critical constraints, and a list of alternative solutions.

[0026] Step S4 inputs the verification results and the preset multi-dimensional SLA rule set into an integrated multi-objective optimization model. After verification in S3, several feasible execution schemes are obtained.

[0027] These solutions are then input into a multi-objective optimization model. The objective function of the multi-objective optimization model has three dimensions: Minimize promised delays (deviations from the customer's requested delivery date); Minimize resource idle rate (avoid frequent production line switching to rush production, resulting in wasted capacity); Minimize the risk of SLA default (e.g., if a specific contract has a high penalty for delay).

[0028] The model solves the problem through weighted iterations, outputting a Pareto-optimal solution set. Decision-makers can select the optimal solution from this set based on their current strategic priorities, such as whether they prioritize delivery speed or production cost. This solution ultimately determines the specific promised delivery date and the corresponding resource allocation plan; specifically, it includes which factory's capacity and which batch of material inventory to utilize.

[0029] In step S5, the system will feed back the selected promised delivery date to the sales department and issue a resource allocation instruction to the production department.

[0030] During subsequent order fulfillment, the system continuously monitors actual progress (such as actual start-up time and actual material consumption). If monitoring reveals that actual production progress lags behind the plan generated in S4, the system will record the deviation. When the deviation accumulates to a certain level, the system automatically triggers a closed-loop feedback mechanism. For example, if a certain type of order is frequently delayed, the system will automatically reduce the effective capacity coefficient of the relevant resources in that process route in the model, or increase the priority weight of such orders in S2, ensuring that the model can make more accurate predictions and commitments when similar situations occur again.

[0031] This embodiment achieves the transformation from static query to dynamic inference through the above steps. In particular, bottleneck step identification and material substitution optimization are introduced in S3, and combined with the multi-objective model in S4, the problems of inaccurate commitment and unreasonable resource allocation in the prior art are solved.

[0032] Example 2: S1 includes: Lightweight edge data agents are deployed at IoT gateways in enterprise order management systems, production execution systems, and key logistics nodes; among them, The edge data agent runs in event listening mode. When a new sales order, forecast update, work order completion, or inventory change occurs, it immediately encapsulates the summary information of the changed data into a standardized event message and pushes it to the central processing platform in real time through a high-throughput enterprise-grade streaming data bus. The central processing platform includes a dynamically updated global data view, which collects heterogeneous demand data from multiple sources and outputs corresponding supply chain status data streams.

[0033] In the embodiments of this application, the edge data agent program operates in an event-listening mode. For example, when a new sales order (PO) is generated in the OMS, a work order is completed in the MES, or a material inventory changes in the WMS, the agent program immediately captures these incremental changes. The agent program encapsulates summary information of the changed data (rather than the full data) into standardized Kafka or MQTT event messages. These messages are pushed to the central processing platform in real time through a high-throughput enterprise-grade streaming data bus. The central processing platform maintains a dynamically updated global data view. This view integrates external data (such as customer POs, internal sales forecasts, and framework contract requirements) and internal status data (such as real-time finished goods inventory, in-transit supply, procurement supply, real-time load of each production line, and equipment status). Through S1, the system breaks down information silos, providing a unified, real-time, and accurate data foundation for subsequent precise calculations.

[0034] Example 3: S2 includes: Automatically add customer level, product strategy level, urgency of need, and expected profit contribution tag to each demand; The rules engine has a built-in dynamic weight calculation model. The dynamic weight calculation model adjusts the weight coefficient of each tag in the priority score calculation in real time according to the current total global demand to be committed and the overall tension of the supply chain. When the capacity is sufficient, it executes profit-guarantee orders, and when the capacity is tight, it executes high-strategy-level customer orders.

[0035] In this application, the system periodically calculates the ratio of the current total global uncommitted demand to the overall available capacity of the supply chain, i.e., the tension index: When the index is low and production capacity is ample: The model increases the weight of expected profit contribution while decreasing the weight of demand urgency and customer level. The strategy at this time prioritizes profit, maximizing corporate revenue by accepting high-margin orders.

[0036] When the index is high and capacity is tight: The model automatically increases the weight of customer tier and demand urgency, and decreases the weight of expected profit contribution. At this time, the system's strategy shifts to prioritizing delivery and strategic needs, ensuring the urgent needs of strategic customers are met, even if these orders have lower profit margins. Through this dynamic adjustment, the rule engine calculates the final weight score for each demand and ranks them accordingly. When resource conflicts occur (such as multiple orders competing for the same batch of materials), the system uses this dynamic score to resolve the conflict, ensuring that the company's resource allocation aligns with the current optimal business strategy within a specific period.

[0037] Example 4: S3 includes: Based on the aggregated resource load view and material availability time series, a time-bucket-based capacity consumption and material deduction algorithm is used to simulate the impact on global resources and materials if the order demand is accepted, and to calculate its earliest possible completion time. During the simulation, the core limiting factors that cause delays in order completion time are tracked and recorded, and marked as key constraints for the order. By aggregating simulation results from multiple orders, frequently tagged common constraint resources or materials are identified.

[0038] Specifically, by calling the supply and demand matching engine to perform real-time parallel fulfillment capability verification, after receiving one or more pending order requirements, it quickly and in parallel simulates the global impact of these requirements on the supply chain after they are accepted, accurately calculates their earliest possible completion time, and intelligently diagnoses key bottlenecks in the fulfillment process.

[0039] The supply and demand matching engine is a microservice based on in-memory computing. It includes an aggregated snapshot view that maintains the global supply chain state and parallel simulation executors. When a batch of order demands arrives, the engine creates an independent simulation task for each demand, which is executed in parallel across the computing cluster. The simulation tasks share the same global state snapshot as the starting point for the simulation, and perform calculations by reading, simulating, and writing pre-defined results, finally aggregating all simulation results.

[0040] The resource load view is a simplified, order-commitment-oriented resource capacity model. It does not include all production equipment and workers, but focuses on key or bottleneck resources predefined by the production planning department that affect order delivery dates; for example, special heat treatment furnaces, precision testing stations, and painting lines. The resource load view is updated in real-time by the streaming data from step S1. When the planning system issues a work order that occupies a resource, or when this engine simulates and pre-occupies a portion of the capacity, the committed capacity in the corresponding time bucket increases synchronously.

[0041] Material availability time series is a timeline that describes the quantity of all relevant materials that will be available for use at future points in time.

[0042] The time-bucket-based capacity consumption and material deduction algorithm is used to calculate the earliest possible completion time of an order under the current supply chain status. It is a deterministic, rule-based discrete event simulation, but it uses time buckets as the smallest unit for extrapolation, rather than second-level event-driven simulation, in order to ensure calculation speed.

[0043] Algorithm flow: Initialization: Set the current required completion time to the customer's requested delivery date. Set up a material requirements stack, initially containing finished product P and a required quantity Q.

[0044] cycle: a. Processing Material Requirements: Retrieve a material requirement (material X, quantity N) from the top of the stack. In the material availability time series, starting from the current required completion time, search backwards to find the first "available event" that can provide a sufficient quantity N, and deduct the quantity N. Record the kitting time of this material. If X is a raw material, its kitting time is the time when processing can begin.

[0045] b. Decompose the BOM: If material X is a finished product or a semi-finished product, obtain its BOM list. For each sub-material Y (quantity n) required by production unit X, generate the new material requirements (Y, N). n) Push it onto the stack.

[0046] c. Consider resource constraints (critical steps): If production X requires critical resource R, the standard processing time is the standard processing time.

[0047] Calculate the required total capacity: Total capacity = N Standard processing time.

[0048] In the load view of resource R, look backwards from the standard processing time at a given point in time, accumulating the remaining available capacity in consecutive time buckets until the accumulated capacity is greater than or equal to the total capacity. Find the latest start time that can meet the demand.

[0049] Update the load view to pre-allocate the total capacity in the corresponding time bucket after the latest start time.

[0050] Calculate the actual completion time of X: Actual completion time = Latest start time + Current required completion time.

[0051] Return to step 2.a with the actual completion time as the current required completion time for its sub-material requirements.

[0052] Calculate the final assembly / shipment time: Once all the underlying materials are assembled and processed by their own key resources, the completion time of the final product is the earliest possible completion time of the order.

[0053] During the simulation, the core limiting factors causing order completion time delays are tracked and recorded, and marked as critical constraints for the order. In the aforementioned algorithm flow, triggers are set up that activate when, during reverse material lookup, the kitting time of a material is significantly later than the initial preset time. Similarly, during reverse resource capacity lookup, if the start time needs to be significantly advanced to meet capacity requirements, resulting in a start time earlier than the kitting time, a trigger is activated. A list of critical constraints is maintained for each simulation task. Each time a trigger is activated, a constraint record is added to the list. Each record contains detailed constraint information and an estimated contribution to the total order cycle delay.

[0054] By aggregating simulation results from multiple orders, frequently marked common constraint resources or materials are identified. During aggregation, after a batch of orders completes parallel simulation, the supply and demand matching engine collects the simulation results and key constraint markings for all orders. In the common constraint analysis process, an empty constraint calculator is created to iterate through the simulation results of all orders. For each order's marked key constraints, a constraint threshold, i.e., common constraints, is finally set based on the frequency of each constraint's occurrence.

[0055] Example 5: S4 includes: The Pareto optimal solution set is pushed to the interactive visualization decision-making platform. The interactive visualization decision-making platform uses two-dimensional or three-dimensional graphs to visualize the performance of each potential solution in three target dimensions: commitment delay, resource idle rate, and SLA default risk, and intuitively presents the trade-off relationship between the targets. By responding to any solution point on the 3D map, or by defining an acceptable target value range, the platform will automatically recommend solutions that fall within that range. When the decision-maker finally selects a solution, they need to select from a preset list or manually enter the main considerations for this decision on the interface provided by the platform.

[0056] In this application, the Pareto optimal solution set is pushed to an interactive visual decision-making platform.

[0057] Interactive visualization decision-making platforms display the aforementioned solution sets in two-dimensional or three-dimensional graphs. For example, a three-dimensional scatter plot can be used, where the X-axis represents commitment delays, the Y-axis represents resource availability, and the size or color intensity of the bubbles represents the risk of SLA default. The visualization intuitively shows the trade-offs between objectives.

[0058] The platform supports multiple interactive operations: Click-to-search: Decision-makers can directly click on any solution point on the map, and the sidebar will instantly display the detailed parameters of that solution, such as the specific delivery date, production line usage, and material costs.

[0059] Region Delineation and Recommendation: Decision-makers can drag and drop the mouse to define a rectangular region on the map, setting an acceptable range of target values. The platform will automatically search for and highlight all Pareto optimal solutions falling within that region. If the region is empty, it will indicate that no perfectly matching solution exists and recommend the closest boundary solution.

[0060] Decision Basis Recording: When a decision-maker finally selects a solution, the platform displays a decision basis entry interface. The decision-maker can choose from a preset list or enter specific text descriptions themselves. The system binds and stores this basis with the solution for subsequent closed-loop feedback and auditing.

[0061] Example 6: When the supply chain status data flow involves multiple production plants, real-time simulation and multi-objective optimization models need to collaboratively handle cross-plant order distribution and kit delivery constraints. Specifically, this includes: before the simulation, based on the order's destination, product type, and preset factory capacity tags, using a hybrid logic that includes static rules and dynamic load assessment, pre-allocating one or more candidate fulfillment factories for each order requirement; during the parallel simulation instance simulation of each order, the simulation will be performed simultaneously within its candidate factory set, and the bottlenecks and expected completion times under each factory scheme will be recorded to generate a kit tracking number.

[0062] In this application, when the supply chain status data flow involves multiple production plants, the simulation deduction in step S3 and the multi-objective optimization model in step S4 need to collaboratively handle cross-plant order distribution and kitting constraints. The specific implementation is as follows: Pre-allocation of candidate fulfillment plants: Before the simulation, one or more candidate fulfillment factories are pre-assigned to each order requirement based on the order's destination, product type, and preset factory capacity labels, using a hybrid logic that includes static rules and dynamic load assessment.

[0063] Parallel simulation instance derivation: When performing parallel simulation instance derivation for each order, simulations will be conducted simultaneously within its candidate factory set. That is, for the same order, simulation instances will be created in parallel for the Tianjin factory and the Beijing factory.

[0064] Bottleneck and Completion Time Recording: During the simulation, the bottleneck steps and estimated completion times for each factory scheme are recorded.

[0065] Kitting Tracking Number Generation: Based on simulation results, a kitting tracking number is generated. This number is associated with the production plans of the order at each factory, as well as the final kitting and delivery plan, ensuring that materials across factories can be kitted and delivered on time.

[0066] Example 7: S5 includes: Based on each output commitment result, continuously monitor the actual fulfillment data flow of the corresponding batch of orders, and trigger an effectiveness evaluation when an order is closed or a key milestone event occurs; Calculate the deviations between the actual and promised forecasts for the corresponding batch of orders in terms of indicators such as average delivery delay, resource utilization, and SLA achievement rate. Based on the bias data, the corresponding snapshot of the decision-making scenario features, and the version of the rules and model parameters used at that time, they are collectively packaged into a training sample; The incremental learning engine periodically uses newly added training sample sets to fine-tune the weight calculation model in the rule engine and the target weights in the multi-objective optimization model. After the adjusted new parameter version passes the historical backtesting test, it is marked as the effective version and applied to real-time decision-making.

[0067] In this application, the actual fulfillment data flow of the corresponding batch of orders is continuously monitored based on the committed results of each output. When an order is closed or a key milestone event occurs, an effectiveness evaluation is automatically triggered. This is achieved by calculating the deviations between the actual values ​​and the committed predicted values ​​for the corresponding batch of orders in metrics such as average delivery delay, resource utilization, and SLA achievement rate.

[0068] The deviation data and corresponding snapshots of decision-making scenario features—including the demand data features at that time, the supply chain status snapshot, and the version of rules and model parameters used at that time—are collectively packaged into a training sample. The incremental learning engine periodically uses the newly added training sample set to fine-tune the weight calculation model in the rule engine and the objective weights in the multi-objective optimization model. The adjusted new parameter version, after passing historical backtesting, is marked as the effective version and applied to real-time decision-making. This ensures that as data accumulates, the predictive model and decision rules become increasingly accurate.

[0069] Example 8: Execution monitoring phase after outputting commitment results: Based on the commitment scheme, a fulfillment time baseline including key checkpoints is generated for each order; By continuously comparing the actual progress with the baseline through real-time data stream, an abnormal event is immediately triggered once a deviation exceeds a preset threshold. Among them, abnormal events are categorized and input into the intelligent handling suggestion engine; The intelligent handling suggestion engine is linked to the performance capability simulation module, which simulates the insertion of remedial measures into the current plan and quickly generates one or more adjustment plans that are expected to reduce deviations; The adjustment plan will then be fed back to the corresponding adjustment end and integrated into the interactive visual decision-making platform as a high-priority pending item until the matter is completed or the platform is closed abnormally.

[0070] In this application, during the execution monitoring phase after the output of the commitment result: Based on the commitment scheme, the system generates a fulfillment time baseline for each order, which includes key checkpoints such as process start time and material availability time.

[0071] The actual progress is continuously compared with the baseline through real-time data streams. Once a deviation (such as production delay) is detected that exceeds a preset threshold, an abnormal event is immediately triggered.

[0072] Abnormal events are categorized (such as material delays and equipment failures) and input into the intelligent handling suggestion engine. This engine, in conjunction with the fulfillment capability simulation module, simulates the insertion of remedial measures (such as activating standby capacity or activating alternative routes) into the current plan, quickly generating one or more adjustment plans that are expected to reduce deviations.

[0073] The adjustment plan is fed back to the corresponding adjustment end and integrated into the interactive visual decision-making platform as a high-priority pending item, for decision-makers to confirm and implement until the matter is completed or abnormally closed.

[0074] Example 9: When the supply and demand matching simulation and the alternative matching algorithm are started, they can be guided from an order fulfillment knowledge base; The knowledge base is constructed by analyzing historical order commitment and fulfillment data, recording typical fulfillment cycles, common bottleneck resources, and successful alternatives for different products, customers, and process paths; When conducting simulation verification, similar cases are retrieved from the knowledge base based on the characteristics of the current order, their typical cycles are used as the initial reference for the simulation, and historical common bottlenecks are used as key monitoring constraints. When performing alternative matching, priority is given to recommending alternatives with high historical verification success rates and strong relevance from the knowledge base.

[0075] Specifically, the order fulfillment knowledge base is an independent data and computing service module. It consists of two parts: an offline knowledge mining engine and an online knowledge retrieval and service engine. The former is responsible for periodically analyzing historical data, extracting knowledge, and updating the knowledge base; the latter responds to query requests from the supply and demand matching engine and provides guidance information during real-time decision-making.

[0076] The order fulfillment knowledge base is built based on historical order commitment records extracted from system logs and actual fulfillment records of corresponding orders extracted from systems such as MES, WMS, and TMS. Case alignment and feature engineering link commitment records with fulfillment records through order numbers to form a complete order fulfillment case.

[0077] During simulation verification, knowledge guidance is employed. When a new order needs to be simulated and verified, the online service engine performs feature extraction, similarity matching, and knowledge acquisition guidance. During the guided simulation, the retrieved typical fulfillment cycle is used as the initial time anchor for backpropagation in the S3 simulation algorithm. This accelerates simulation convergence, especially during iterative optimization. A list of common bottlenecks is passed to the simulation engine. During runtime, the simulation engine pays special attention to these resources or materials. For example, when the simulation extrapolates to resources in the list, more detailed queuing data is recorded; when materials come from the list, more stringent availability checks are triggered.

[0078] During the knowledge-guided process of alternative matching, when the alternative matching algorithm in S3 is triggered, it first queries the knowledge graph of alternatives. While exploring each possible alternative path, it simultaneously queries the knowledge graph for the historical success rate and average additional cost of the path or key alternative relationships on the path.

[0079] Alternative solutions = weighting of estimated additional costs (1 / Estimated Additional Costs) + Weight of Historical Success Rate Historical success rate.

[0080] The final list of alternative solutions will be sorted by the alternative itself, not just by the estimated cost. Historically proven successful solutions will be recommended at the top. Each time an order is fulfilled, the actual alternative used and its outcome (success / failure, actual additional cost) will be treated as a new data point and fed back to the offline mining engine to update the edge weights in the knowledge graph; for example, increasing the number of successes or updating the average cost.

[0081] Example 10: Second aspect, such as Figure 2 As shown, this application proposes a dynamic optimization system for order commitment period based on demand forecasting and real-time fulfillment capability verification, including: Multi-source data acquisition module: Real-time acquisition of multi-source heterogeneous demand data and supply chain status data streams; The rules engine processing module dynamically prioritizes and resolves conflicts in requirements using the rules engine. Fulfillment verification module: Invokes the supply and demand matching engine to perform real-time parallel fulfillment capability verification, including: Based on the bill of materials, real-time inventory map, work-in-process status, and the occupied and available capacity of key resources, supply and demand matching simulation is conducted to assess the feasibility of orders and the estimated completion time. When the simulation results show that the demand cannot be directly met, an alternative solution matching algorithm is run according to the pre-configured business and production rules to generate feasible alternative fulfillment solutions. For key resource constraints or material bottlenecks identified in the simulation verification, structured constraint information is generated for outputting commitment results or passing them to the downstream planning system for in-depth scheduling. Iterative solution module: Input the verification results and the preset multi-dimensional SLA rule set into an integrated multi-objective optimization model. The multi-objective optimization model aims to simultaneously minimize the commitment delay, resource idle rate and SLA default risk. It outputs the Pareto optimal solution set through iterative solution and generates the commitment delivery date and resource usage plan accordingly. The results output module is used to output the promised results and form a closed-loop feedback by comparing the planned and actual data streams to adaptively adjust the priority rules in the rule engine processing module and the optimization model weights in the iterative solution module.

[0082] All of the above modules are deployed on a cloud platform with a microservice architecture, and the modules interact with each other through an API gateway. In particular, the incremental learning engine unit uses online learning algorithms to achieve rapid iterative updates of weight parameters. The knowledge graph guidance unit utilizes a graph database to store entity relationships and uses a subgraph retrieval algorithm to assist the simulation engine in parameter initialization and path optimization.

[0083] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for dynamically optimizing order commitment period based on demand forecasting and real-time fulfillment capability verification, characterized in that, Includes the following steps: S1: Real-time acquisition of multi-source heterogeneous demand data and supply chain status data streams; S2: Dynamically prioritize and resolve conflicts of requirements through a rules engine; S3: Invoke the supply and demand matching engine to perform real-time parallel fulfillment capability verification, including: Based on the bill of materials, real-time inventory map, work-in-process status, and the occupied and available capacity of key resources, supply and demand matching simulation is conducted to assess the feasibility of orders and the estimated completion time. When the simulation results show that the demand cannot be directly met, an alternative solution matching algorithm is run according to the pre-configured business and production rules to generate feasible alternative fulfillment solutions. For key resource constraints or material bottlenecks identified in the simulation verification, structured constraint information is generated for outputting commitment results or passing them to the downstream planning system for in-depth scheduling. S4: Input the verification results and the preset multi-dimensional SLA rule set into an integrated multi-objective optimization model. The multi-objective optimization model aims to simultaneously minimize the commitment delay, resource idle rate and SLA default risk. It iteratively solves and outputs the Pareto optimal solution set, and generates the commitment delivery date and resource usage scheme accordingly. S5: Output the promised results and form a closed-loop feedback by comparing the planned and actual data flow to adaptively adjust the priority rules in S2 and the optimization model weights in S4.

2. The method for dynamically optimizing order commitment period based on demand forecasting and real-time fulfillment capability verification as described in claim 1, characterized in that, S1 includes: Lightweight edge data agents are deployed at IoT gateways in enterprise order management systems, production execution systems, and key logistics nodes; among them, The edge data agent runs in event listening mode. When a new sales order, forecast update, work order completion, or inventory change occurs, it immediately encapsulates the summary information of the changed data into a standardized event message and pushes it to the central processing platform in real time through a high-throughput enterprise-grade streaming data bus. The central processing platform includes a dynamically updated global data view, which collects heterogeneous demand data from multiple sources and outputs corresponding supply chain status data streams.

3. The method for dynamically optimizing order commitment period based on demand forecasting and real-time fulfillment capability verification as described in claim 1, characterized in that, S2 includes: Automatically add customer level, product strategy level, urgency of need, and expected profit contribution tag to each demand; The rules engine has a built-in dynamic weight calculation model. The dynamic weight calculation model adjusts the weight coefficient of each tag in the priority score calculation in real time according to the current total global demand to be committed and the overall tension of the supply chain. When the capacity is sufficient, it executes profit-guarantee orders, and when the capacity is tight, it executes high-strategy-level customer orders.

4. The method for dynamically optimizing order commitment period based on demand forecasting and real-time fulfillment capability verification as described in claim 1, characterized in that, S3 includes: Based on the aggregated resource load view and material availability time series, a time-bucket-based capacity consumption and material deduction algorithm is used to simulate the impact on global resources and materials if the order demand is accepted, and to calculate its earliest possible completion time. During the simulation, the core limiting factors that cause delays in order completion time are tracked and recorded, and marked as key constraints for the order. By aggregating simulation results from multiple orders, frequently tagged common constraint resources or materials are identified.

5. The method for dynamic optimization of order commitment period based on demand forecasting and real-time fulfillment capability verification as described in claim 1, characterized in that, S4 includes: The Pareto optimal solution set is pushed to the interactive visualization decision-making platform. The interactive visualization decision-making platform uses two-dimensional or three-dimensional graphs to visualize the performance of each potential solution in three target dimensions: commitment delay, resource idle rate, and SLA default risk, and intuitively presents the trade-off relationship between the targets. By responding to any solution point on the 3D map, or by defining an acceptable target value range, the platform will automatically recommend solutions that fall within that range. When the decision-maker finally selects a solution, they need to select from a preset list or manually enter the decision-making basis on the interface provided by the platform.

6. The method for dynamically optimizing order commitment period based on demand forecasting and real-time fulfillment capability verification as described in claim 1, characterized in that, When the supply chain status data flow involves multiple production plants, real-time simulation and multi-objective optimization models are needed to collaboratively handle cross-plant order distribution and complete shipment constraints. Specifically, this includes: before the simulation, based on the order's destination, product type, and preset factory capacity labels, using a hybrid logic that includes static rules and dynamic load assessment, pre-assigning one or more candidate fulfillment factories for each order requirement; When performing parallel simulation instance derivation for each order, simulation will be carried out simultaneously within its candidate factory set, and the bottlenecks and expected completion times under each factory scheme will be recorded to generate a complete set tracking number.

7. The method for dynamic optimization of order commitment period based on demand forecasting and real-time fulfillment capability verification as described in claim 1, characterized in that, S5 includes: Based on each output commitment result, continuously monitor the actual fulfillment data flow of the corresponding batch of orders, and trigger an effectiveness evaluation when an order is closed or a key milestone event occurs; Calculate the deviations between the actual and promised forecasts for the corresponding batch of orders in terms of indicators such as average delivery delay, resource utilization, and SLA achievement rate. Based on the bias data, the corresponding snapshot of the decision-making scenario features, and the version of the rules and model parameters used at that time, they are collectively packaged into a training sample; The incremental learning engine periodically uses newly added training sample sets to fine-tune the weight calculation model in the rule engine and the target weights in the multi-objective optimization model. After the adjusted new parameter version passes the historical backtesting test, it is marked as the effective version and applied to real-time decision-making.

8. The method for dynamic optimization of order commitment period based on demand forecasting and real-time fulfillment capability verification as described in claim 1, characterized in that, The execution monitoring phase after outputting the commitment result also includes: Based on the commitment scheme, a fulfillment time baseline including key checkpoints is generated for each order; By continuously comparing the actual progress with the baseline through real-time data stream, an abnormal event is immediately triggered once a deviation exceeds a preset threshold. Among them, abnormal events are categorized and input into the intelligent handling suggestion engine; The intelligent handling suggestion engine is linked to the performance capability simulation module, which simulates the insertion of remedial measures into the current plan and quickly generates one or more adjustment plans that are expected to reduce deviations; The adjustment plan will then be fed back to the corresponding adjustment end and integrated into the interactive visual decision-making platform as a high-priority pending item until the matter is completed or the platform is closed abnormally.

9. The method for dynamically optimizing order commitment period based on demand forecasting and real-time fulfillment capability verification as described in claim 1, characterized in that, The supply and demand matching simulation and the alternative matching algorithm can be guided from an order fulfillment knowledge base when started. The knowledge base is constructed by analyzing historical order commitment and fulfillment data, recording typical fulfillment cycles, common bottleneck resources, and successful alternatives for different products, customers, and process paths; When conducting simulation verification, similar cases are retrieved from the knowledge base based on the characteristics of the current order, their typical cycles are used as the initial reference for the simulation, and historical common bottlenecks are used as key monitoring constraints. When performing alternative matching, priority is given to recommending alternatives with high historical verification success rates and strong relevance from the knowledge base.

10. A dynamic optimization system for order commitment period based on demand forecasting and real-time fulfillment capability verification, characterized in that, include: Multi-source data acquisition module: Real-time acquisition of multi-source heterogeneous demand data and supply chain status data streams; The rules engine processing module dynamically prioritizes and resolves conflicts in requirements using the rules engine. Fulfillment verification module: Invokes the supply and demand matching engine to perform real-time parallel fulfillment capability verification, including: Based on the bill of materials, real-time inventory map, work-in-process status, and the occupied and available capacity of key resources, supply and demand matching simulation is conducted to assess the feasibility of orders and the estimated completion time. When the simulation results show that the demand cannot be directly met, an alternative solution matching algorithm is run according to the pre-configured business and production rules to generate feasible alternative fulfillment solutions. For key resource constraints or material bottlenecks identified in the simulation verification, structured constraint information is generated for outputting commitment results or passing them to the downstream planning system for in-depth scheduling. Iterative solution module: Input the verification results and the preset multi-dimensional SLA rule set into an integrated multi-objective optimization model. The multi-objective optimization model aims to simultaneously minimize the commitment delay, resource idle rate and SLA default risk. It outputs the Pareto optimal solution set through iterative solution and generates the commitment delivery date and resource usage plan accordingly. The results output module is used to output the promised results and form a closed-loop feedback by comparing the planned and actual data streams to adaptively adjust the priority rules in the rule engine processing module and the optimization model weights in the iterative solution module.

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