Simple MRP system and method based on report platform

Abstract

The invention relates to a simple MRP system and method based on a report platform, which can solve the problems of data isolation, inflexible inventory management and the like of the traditional MRP system. The system integrates multi-source data by using an ETL tool, can dynamically calculate safe stock, sets parameters according to material classification, and triggers replenishment in combination with stock and arrival conditions. The conflict pre-judgment module can model supplier delivery time and generate a multi-scheme comparison report. Real-time neat set rate monitoring can analyze according to materials / processes, and production interruption can be prevented by using a work order state machine. And the rules can be continuously optimized through substitute optimization, inventory coefficient self-calibration and the like.

Description

Technical Field

[0001] The present invention relates to the field of enterprise resource management, and in particular to a simple MRP system and method based on a report platform. Background Art

[0002] In the field of material requirement planning (MRP) in traditional manufacturing, the existing technologies generally have the following technical bottlenecks: First, the phenomenon of multi-system data islands is serious, and there is a lack of real-time data interaction mechanism between systems such as PLM, WMS, and SRM, resulting in the inventory status update lagging behind actual business changes. Secondly, the safety stock calculation mostly adopts a fixed parameter mode, and fails to adjust the threshold according to the material characteristics and supply chain dynamics, resulting in the coexistence of inventory backlog and material shortage risks. Furthermore, the supplier delivery date forecast relies on manual experience judgment, lacks a quantitative analysis model based on historical data, and is difficult to cope with supply chain fluctuations of urgent orders. In addition, the traditional procurement strategy optimization lacks a real-time linkage mechanism with inventory status and supplier production capacity. Especially when responding to sudden demands, the existing system often cannot quickly generate emergency plans including cost comparisons. In particular, the existing MRP system's monitoring of production set rate still remains at the post-statistical level, lacks early warning and root cause analysis capabilities based on real-time material status, resulting in frequent production interruptions. These technical defects seriously restrict the company's response speed to complex supply chain environments and the efficiency of resource allocation. Summary of the invention

[0003] In order to solve the above technical problems, the technical solution adopted by the simplified MRP method based on the report platform of the present invention comprises the following steps: Step S100: Multi-source data fusion interface construction: Use ETL tools to establish data pipelines with PLM product lifecycle management, WMS warehouse management system, and SRM supplier relationship management system, and use incremental extraction to obtain the following data in real time: WMS system data: including real-time available inventory, quantity to be shipped, and storage location distribution data, used to dynamically update inventory status; SRM system data: push undelivered order details and supplier confirmed delivery date for demand and supply matching analysis; PLM system data: Synchronously update the BOM material list version and process route change records to ensure that the production plan is consistent with the design changes; Step S110: Dynamic safety stock calculation: Configure differentiated parameters based on material classification: Conventional materials: use 30-day moving average consumption (M) and 7-day transit cycle (N); Key materials: Use 20-day moving average consumption (M) and 9-day transit cycle (N); Every morning the system automatically executes the following formula to calculate the actual balance: Actual balance = Max(0, (available inventory − reserved quantity) + (∑N-day arrival quantity) − (safety factor × M-day consumption average)); if the balance is lower than the preset threshold, a replenishment suggestion is triggered; Step S120: Abnormal value processing mechanism: Establish a material-level historical data fluctuation baseline and trigger an abnormal work order when the following situations are detected: (1) The calculated value for a single day deviates from the baseline ,in is the standard deviation; (2) Deviation from the baseline in the same direction for three consecutive days ; The abnormal work order is pushed to the planner's workbench, and the baseline data must be updated and the safety stock calculation must be re-executed after manual review and confirmation.

[0004] As a further solution of the present invention, a method for predicting conflicts and generating alternative solutions is also provided, comprising the following steps: Step S200: Supplier delivery date modeling: Build a supplier material dimension database to store the following information: The actual delivery date data set of historical orders, including minimum, maximum, and median values; the supplier capacity fluctuation coefficient, which is expressed in standard deviation Indicates; Expedited channel availability indicator; Step S210: Conflict detection rule: When the time window is required The warning is triggered when the following conditions are met: Yellow warning: If and , generate conventional alternatives; Red alert: If , activate the emergency plan; Step S220: generating alternative solutions: Perform the following actions in order of priority: S221: Activate supplier expedited channel: Calculate expedited fee: and compare with standard cost; S222: Call the list of alternative suppliers: compare the price difference , select alternative suppliers that can meet the delivery deadline; S223: Enable safety stock contingency: Calculate replenishment costs , assess the impact of inventory depletion; The system generates a decision support report containing a comparison matrix of costs and delivery dates for each plan for planners to review and approve.

[0005] As a further solution of the present invention, a method for intelligently recommending a procurement strategy is also included, and the method comprises the following steps: Step S300: Centralized purchasing trigger: Centralized procurement is triggered when the following conditions are met within the T+7 period: (1) Total demand quantity ≥ minimum order quantity (MOQ) × 80%; (2) Supplier grade ≥ Class B; Use the EOQ (economic order quantity) model to calculate the optimal purchase quantity: , where D is the period demand, S is the purchase cost, and H is the holding cost; Generate economic analysis reports including transport batch optimization suggestions; Step S310: Emergency Procurement Agreement: When the complete set rate is <90% and the delivery margin is ≤3 days: S311: Activate the shortlist of alternate suppliers and give priority to suppliers in the framework agreement; S312: Invoke the emergency purchase clause in the agreement and negotiate to shorten the delivery period; S313: Start the price difference compensation model: , generate an emergency purchase order that requires approval from the supply chain director; Step S320: Visual negotiation interface: integrate supplier delivery time heat map, display the following dimensions: On-time delivery rate: color gradient logo, including red <90%, yellow 90-95%, green >95%; Capacity saturation: Transparency indicates the load level, including: 0%-30% low load, 30%-70% normal, and above 70% high load; Support dragging and dropping to adjust the demand time window, and display the probability of achieving supplier commitment in real time.

[0006] As a further solution of the present invention, a real-time uniformity rate monitoring method is also included, and the method comprises the following steps: Step S400: Multi-dimensional uniform calculation: Material dimension: complete set rate = Σ (available quantity ∩ demand time window) / total demand quantity × 100%; Process dimension: Critical path completeness rate = Π (critical material completeness mark) × 100%. The system refreshes data every 15 minutes. If the fluctuation exceeds ±5%, an early warning is triggered. Step S410: Work order state machine management: define state transition rules: Green work order: automatically generate MES work order number and synchronize process parameters to the production system; Yellow work order: Start the batch feeding plan, the first batch feeding quantity = full set quantity × 0.8 (safety factor), and set the feeding checkpoint; Red work order: freeze the work order, start root cause analysis, expand the material shortage impact tree along the BOM, and associate the abnormal warehousing, quality inspection delays and supplier delivery deviation records within the past 72 hours; Step S420: Performance feedback mechanism: Establish supplier KPI deduction rules: Delivery delayed by 1 day: 2 points will be deducted from the performance score for that month; Unqualified batch: 5 points will be deducted and on-site audit will be triggered; The system generates a supplier capability matrix every month, which includes three-dimensional evaluations of quality index, delivery accuracy, and emergency responsiveness.

[0007] As a further solution of the present invention, a dynamic rule base update mechanism is also included, comprising the following steps: Step S500: Optimization of substitute materials: construct a substitution relationship map and calculate the substitution success rate every day: If the success rate difference of the same group of replacement materials is >15%, execute: S501: Reorganize BOM substitution priority; S502: Update the material matching rule base; S503: Send a change proposal to the process department; Step S510: Inventory coefficient self-calibration: Use the rolling window method to evaluate the frequency of shortages every quarter: If the frequency exceeds the baseline value by 20%, iterative adjustment is started: , limit the adjustment range to ±15%; Step S520: Version control: Create a rule change log to record the following information: Modify timestamps, trigger condition snapshots, impact scope assessment, and rollback test cases; The data of the last five versions is retained to support comparative analysis of historical strategies.

[0008] As a further solution of the present invention, a simple MRP system based on a report platform includes the following modules: Data fusion interface module, used to establish data pipelines with PLM, WMS, and SRM systems through ETL tools to achieve incremental extraction and real-time synchronization of multi-source data; The dynamic safety stock module is equipped with a material classification parameter library and a moving average calculation engine to perform real-time calculation of the balance and replenishment trigger based on the safety factor and the in-transit cycle; The abnormal monitoring and processing module has a built-in fluctuation baseline analysis algorithm and 3σ detection model, generates abnormal work orders and links the manual review workflow; The conflict prediction center includes a supplier delivery date database, an early warning rule engine, and an alternative solution generator to achieve intelligent matching of demand time windows and supply capabilities. The procurement strategy optimization module integrates the EOQ calculation model, emergency protocol library and supplier heat map visualization components to support centralized procurement and emergency procurement decisions; The completeness rate monitoring center is equipped with a multi-dimensional computing unit and a work order state machine to achieve real-time completeness analysis of materials / process dimensions and traceability of root causes of material shortages; The rule self-learning module includes an alternative relationship graph analyzer, an inventory coefficient iteration algorithm, and a version control log to achieve dynamic optimization and version backtracking of business rules.

[0009] Each module is connected through a unified data bus and deeply integrated with the reporting platform to form a closed-loop material requirement planning management system.

[0010] Beneficial effects: The present invention realizes real-time information synchronization through cross-system data integration, significantly improves the dynamic visibility of inventory, and effectively eliminates information delays in multi-platform collaboration. The adaptive inventory management mechanism based on material characteristics and supply chain status can dynamically balance inventory levels and supply risks, and ensure production continuity while avoiding excessive stockpiling. Intelligently predict supplier delivery dates and combine real-time production capacity data to provide a flexible procurement strategy with multi-source alternatives for sudden demand, greatly enhancing the resilience of the supply chain. The innovative production material complete set monitoring system can identify risk nodes before material shortages occur through a predictive early warning mechanism, and cooperate with automated root cause analysis to effectively prevent production line shutdowns. This technical system comprehensively optimizes procurement decision-making efficiency and resource allocation accuracy, systematically reduces inventory holding and operating maintenance costs, and enables enterprises to maintain rapid response and stable production capabilities in a complex and changing supply chain environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A flowchart of the steps of a simple MRP method based on a report platform of the present invention; Figure 2 The present invention is a module schematic diagram of a simple MRP system based on a report platform. DETAILED DESCRIPTION

[0012] The present invention will be further described in detail below in conjunction with the embodiments.

[0013] See also Figure 1 As shown, it is a flow chart of the steps of a simple MRP method based on a report platform of the present invention, and the specific steps are as follows: Multi-source data fusion interface construction: Step S100: ETL tool configuration: deploy Apache NiFi as a data pipeline tool, configure API interfaces with PLM, WMS, and SRM systems, set incremental extraction strategies, and poll every 15 minutes; Data mapping and cleaning: Perform field mapping on inventory data (available quantity, reserved quantity) in the WMS system, undelivered orders (including supplier delivery date) in the SRM system, and BOM version change records in the PLM system to filter out invalid data; Real-time synchronization: The cleaned data is stored in a distributed database (such as MongoDB) and pushed to the dynamic safety inventory module through a message queue (Kafka).

[0014] Further, dynamic safety stock calculation: Step S110: Parameter configuration: Configure parameters for material classification in the system background: Conventional materials: M = 30-day moving average consumption, N = 7-day transit period; Key materials: M = 20-day moving average consumption, N = 9-day transit cycle; Remaining amount calculation: The calculation engine (based on Python script) is executed at 2:00 a.m. every day. The formula is: Actual balance = Max(0, (available inventory − reserved quantity) + (∑N-day arrival quantity) − (safety factor × M-day consumption average)); Replenishment trigger: If the remaining quantity is lower than the preset threshold (such as the general material threshold = 100 pieces), a replenishment work order is automatically generated and pushed to the procurement system.

[0015] Further, outlier processing mechanism: Step S120: Baseline establishment: Based on the historical consumption data of the past 90 days, the average daily consumption is calculated by material and standard deviation ; Anomaly detection: Real-time monitoring of daily calculation values, triggering a work order if the following conditions are met: (1) The calculated value for a single day deviates from the baseline ,in is the standard deviation; (2) Deviation from the baseline in the same direction for three consecutive days ; Manual review: Exception work orders are pushed to the planner's workbench (web interface), and the baseline is updated after manual confirmation of data accuracy, and the safety stock calculation is re-executed.

[0016] Further, conflict prediction and alternative solution generation: Steps S200-S220: Supplier delivery date modeling: Build a MySQL database table to store supplier historical delivery date data (minimum, maximum, median) and capacity fluctuation coefficient ( ); Warning rules: When the demand time window satisfy: Yellow warning: If and , generate conventional alternatives (such as activating an expedited channel); Red alert: If , activate the emergency plan (call backup suppliers); Solution generation: The system calls the list of alternative suppliers and calculates the expedited fee ( )、Spread( )、Restocking cost( ), generate a PDF report containing the cost-delivery matrix for approval.

[0017] Furthermore, intelligent purchase strategy recommendation: Steps S300-S320: Centralized procurement trigger: When the total demand in the T+7 period is ≥MOQ×80% and the supplier level is ≥B, the EOQ model is called to calculate the optimal procurement quantity: , generate transport batch optimization suggestions (such as merging logistics batches); Emergency Purchase Agreement: When the complete set rate is <90% and the delivery time margin is ≤3 days: Give priority to suppliers in the framework agreement and negotiate to shorten delivery time; Start the price difference compensation model, compensation amount = base price × (market volatility index + emergency coefficient), which needs to be approved by the supply chain director; Visual interface: Integrate supplier heat maps in the Dashboard to display on-time delivery rates with color gradients (red / yellow / green). Transparency indicates capacity load. It supports dragging and dropping to adjust demand time windows and refreshes commitment probabilities in real time.

[0018] Further, real-time completeness rate monitoring: Steps S400-S420: Multidimensional computing: Material completeness rate = available quantity ∩ demand time window / total demand quantity × 100%; Process completeness rate = Π (key material completeness mark) × 100%, updated every 15 minutes; Work Order Management: Green work order: automatically synchronized to the MES system and generated a work order number; Yellow work order: The first batch of material feeding is 80% of the full set, and a feeding checkpoint is set; Red work order: freeze the work order and start root cause analysis (linking to abnormal storage and quality inspection delay records within 72 hours); Supplier performance: KPI matrix charts are generated every month, calculated according to the rules of delivery delay (deduction of 2 points / day) and batch failure (deduction of 5 points), triggering on-site audits.

[0019] Further, dynamic rule base update: Steps S500-S520: Substitution material optimization: Calculate the substitution success rate daily. If the difference in the same group is >15%, automatically reorganize the BOM substitution priority and push the change suggestion to the process department; Inventory factor calibration: adjust the safety factor iteratively every quarter according to the shortage frequency formula: Limit adjustment range to ±15%; Version control: record rule change logs (timestamp, trigger conditions, impact scope), retain the latest 5 versions to support backtracking.

[0020] For further information, see Figure 2 FIG. 1 is a schematic diagram of a module of a simple MRP system based on a report platform of the present invention, and the system includes: Data fusion interface module, used to establish data pipelines with PLM, WMS, and SRM systems through ETL tools to achieve incremental extraction and real-time synchronization of multi-source data; The dynamic safety stock module is equipped with a material classification parameter library and a moving average calculation engine to perform real-time calculation of the balance and replenishment trigger based on the safety factor and the in-transit cycle; The abnormal monitoring and processing module has a built-in fluctuation baseline analysis algorithm and 3σ detection model, generates abnormal work orders and links the manual review workflow; The conflict prediction center includes a supplier delivery date database, an early warning rule engine, and an alternative solution generator to achieve intelligent matching of demand time windows and supply capabilities. The procurement strategy optimization module integrates the EOQ calculation model, emergency protocol library and supplier heat map visualization components to support centralized procurement and emergency procurement decisions; The completeness rate monitoring center is equipped with a multi-dimensional computing unit and a work order state machine to achieve real-time completeness analysis of materials / process dimensions and traceability of root causes of material shortages; The rule self-learning module includes an alternative relationship graph analyzer, an inventory coefficient iteration algorithm, and a version control log to achieve dynamic optimization and version backtracking of business rules.

[0021] Each module is connected through a unified data bus and deeply integrated with the reporting platform to form a closed-loop material requirement planning management system.

[0022] System Integration: Each module is integrated with the reporting platform (such as Tableau) through RESTful API to form a closed-loop management: The data bus uses Kafka to implement real-time data streaming; The computing engine is deployed in a Kubernetes cluster and supports high concurrency. The user interface is developed based on Vue.js and supports multi-terminal access.

[0023] The above description is only an embodiment of this specification and is not intended to limit this specification. For those skilled in the art, this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this specification should be included in the scope of the claims of this application.

Claims

1. A simple MRP method based on a reporting platform, characterized in that: The method comprises the following steps: Step S100: Multi-source data fusion interface construction: Use ETL tools to establish data pipelines with PLM product lifecycle management, WMS warehouse management system, and SRM supplier relationship management system, and use incremental extraction to obtain the following data in real time: WMS system data: including real-time available inventory, quantity to be shipped, and storage location distribution data, used to dynamically update inventory status; SRM system data: push undelivered order details and supplier confirmed delivery date for demand and supply matching analysis; PLM system data: Synchronously update the BOM material list version and process route change records to ensure that the production plan is consistent with the design changes; Step S110: Dynamic safety stock calculation: Configure differentiated parameters based on material classification: Conventional materials: use 30-day moving average consumption and 7-day transit cycle; Key materials: Use 20-day moving average consumption and 9-day transit cycle; The system automatically executes the following formula to calculate the actual balance every day: Actual balance = Max(0, (available inventory − reserved quantity) + (∑N-day arrival quantity) − (safety factor × M-day consumption average)); if the balance is lower than the preset threshold, a replenishment suggestion is triggered; Step S120: Abnormal value processing mechanism: Establish a material-level historical data fluctuation baseline and trigger an abnormal work order when the following situations are detected: (1) The calculated value for a single day deviates from the baseline ,in is the standard deviation; (2) Deviation from the baseline in the same direction for three consecutive days ; The abnormal work order is pushed to the planner's workbench, and the baseline data must be updated and the safety stock calculation must be re-executed after manual review and confirmation.

2. A simple MRP method based on a report platform according to claim 1, characterized in that: The invention also includes a method for predicting conflicts and generating alternative solutions, the method comprising the following steps: Step S200: Supplier delivery date modeling: Build a supplier material dimension database to store the following information: The actual delivery date data set of historical orders, including minimum, maximum, and median values; the supplier capacity fluctuation coefficient, which is expressed in standard deviation Indicates; Expedited channel availability indicator; Step S210: Conflict detection rule: When the time window is required The warning is triggered when the following conditions are met: Yellow warning: If and , generate conventional alternatives; Red alert: If , activate the emergency plan; Step S220: generating alternative solutions: Perform the following actions in order of priority: S221: Activate supplier expedited channel: Calculate expedited fee: and compare with standard cost; S222: Call the list of alternative suppliers: compare the price difference , select alternative suppliers that can meet the delivery deadline; S223: Enable safety stock contingency: Calculate replenishment costs , assess the impact of inventory depletion; The system generates a decision support report containing a comparison matrix of costs and delivery dates for each plan for planners to review and approve.

3. A simple MRP method based on a report platform according to claim 1, characterized in that: The invention also includes a procurement strategy intelligent recommendation method, the method comprising the following steps: Step S300: Centralized purchasing trigger: Centralized procurement is triggered when the following conditions are met within the T+7 period: (1) Total demand quantity ≥ minimum order quantity × 80%; (2) Supplier grade ≥ Class B; The economic order quantity model is used to calculate the optimal purchase quantity: , where D is the period demand, S is the purchase cost, and H is the holding cost; Generate economic analysis reports including transport batch optimization suggestions; Step S310: Emergency Procurement Agreement: When the complete set rate is <90% and the delivery margin is ≤3 days: S311: Activate the shortlist of alternate suppliers and give priority to suppliers in the framework agreement; S312: Invoke the emergency purchase clause in the agreement and negotiate to shorten the delivery period; S313: Start the price difference compensation model: , generate an emergency purchase order that requires approval from the supply chain director; Step S320: Visual negotiation interface: integrate supplier delivery time heat map, display the following dimensions: On-time delivery rate: gradient identification, including the first gradient: <90%, the second gradient 90%-95%, and the third gradient >95%; Capacity saturation: Transparency indicates the load level, including: 0%-30% low load, 30%-70% normal, and above 70% high load; Supports dragging and dropping to adjust the demand time window, and displays the probability of achieving supplier commitments in real time.

4. The simplified MRP method based on the report platform according to claim 1 is characterized in that: Also included is a real-time uniformity monitoring method, the method comprising the following steps: Step S400: Multi-dimensional uniform calculation: Material dimension: complete set rate = Σ (available quantity ∩ demand time window) / total demand quantity × 100%; Process dimension: Critical path completeness rate = Π (critical material completeness mark) × 100%. The system refreshes data every 15 minutes. If the fluctuation exceeds ±5%, an early warning is triggered. Step S410: Work order state machine management: define state transition rules: Green work order: automatically generate MES work order number and synchronize process parameters to the production system; Yellow work order: Start the batch feeding plan, the first batch feeding quantity = full set quantity × 0.8, and set the feeding checkpoint; Red work order: freeze the work order, start root cause analysis, expand the material shortage impact tree along the BOM, and associate the abnormal warehousing, quality inspection delays and supplier delivery deviation records within the past 72 hours; Step S420: Performance feedback mechanism: Establish supplier KPI deduction rules: Delivery delayed by 1 day: 2 points will be deducted from the performance score for that month; Unqualified batch: 5 points will be deducted and on-site audit will be triggered; The system generates a supplier capability matrix every month, which includes three-dimensional evaluations of quality index, delivery accuracy, and emergency responsiveness.

5. The simplified MRP method based on the report platform according to claim 1 is characterized in that: It also includes a dynamic rule base update mechanism, including the following steps: Step S500: Optimization of substitute materials: construct a substitution relationship map and calculate the substitution success rate every day: ； If the success rate difference of the same group of replacement materials is >15%, execute: S501: Reorganize BOM substitution priority; S502: Update the material matching rule base; S503: Send a change proposal to the process department; Step S510: Inventory coefficient self-calibration: Use the rolling window method to evaluate the frequency of shortages every quarter: If the frequency exceeds the baseline value by 20%, iterative adjustment is started: , limit the adjustment range to ±15%; Step S520: Version control: Create a rule change log to record the following information: Modify timestamps, trigger condition snapshots, impact scope assessment, and rollback test cases; The data of the last five versions is retained to support comparative analysis of historical strategies.

6. A simple MRP system based on a reporting platform, characterized in that: The system comprises: Data fusion interface module, used to establish data pipelines with PLM, WMS, and SRM systems through ETL tools to achieve incremental extraction and real-time synchronization of multi-source data; The dynamic safety stock module is equipped with a material classification parameter library and a moving average calculation engine to perform real-time calculation of the balance and replenishment trigger based on the safety factor and the in-transit cycle; The abnormal monitoring and processing module has a built-in fluctuation baseline analysis algorithm and 3σ detection model, generates abnormal work orders and links the manual review workflow; The conflict prediction center includes a supplier delivery date database, an early warning rule engine, and an alternative solution generator to achieve intelligent matching of demand time windows and supply capabilities. The procurement strategy optimization module integrates the EOQ calculation model, emergency protocol library and supplier heat map visualization components to support centralized procurement and emergency procurement decisions; The completeness rate monitoring center is equipped with a multi-dimensional computing unit and a work order state machine to achieve real-time completeness analysis of materials / process dimensions and traceability of root causes of material shortages; The rule self-learning module includes an alternative relationship graph analyzer, an inventory coefficient iteration algorithm, and a version control log to achieve dynamic optimization and version backtracking of business rules; Each module is connected through a unified data bus and deeply integrated with the reporting platform to form a closed-loop material requirement planning management system.

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