Method for managing total metal amount of aluminum plate strip foil processing enterprise

Abstract

The invention discloses a total metal amount management method for an aluminum plate strip foil processing enterprise, and aims to solve the problems of poor data timeliness, dependence on manpower, serious information island, weak traceability and the like in metal management of an existing aluminum processing enterprise. The method comprises the following steps: constructing a dynamic cyclic inventory mechanism, and making a differentiated inventory plan based on material value and mobility; intelligent data acquisition and real-time verification are realized through the Internet of Things equipment and the mobile terminal; a unique identification code is given to the material, and a full-life-cycle tracing system is constructed in combination with a digital twinning technology; building a metal management big data platform, integrating multi-system data, and realizing differential root cause analysis and risk early warning through an algorithm model; and a flexible organization assessment mechanism is matched. According to the invention, real-time collection and high-precision monitoring of metal data are realized, the checking efficiency and the account-real conformity rate are improved, the production and operation decision support is strengthened, the requirements of fine management and policy compliance are met, and the method has remarkable economic and social benefits.

Description

Technical Field

[0001] This invention relates to the field of inventory management technology, specifically a method for managing the total amount of metal in aluminum sheet, strip, and foil processing enterprises. Background Technology

[0002] The total metal management of aluminum sheet, strip, and foil processing enterprises is directly related to production efficiency, cost control, and compliant operation. Currently, aluminum processing enterprises generally face the following prominent problems in metal management:

[0003] 1. Poor data timeliness: Most enterprises rely on the static inventory count model at the end of the month. The inventory count results lag behind the actual production and operation progress, making it difficult to quickly reflect the real-time status of metal materials. This makes it impossible to provide timely support for production scheduling, procurement planning and other decisions, which can easily lead to inventory backlog or supply shortage.

[0004] 2. Reliance on manual experience: Material weight estimation, inventory counting, error investigation and other processes rely heavily on manual operation, which not only leads to low work efficiency, but also makes it easy for human error to cause data deviation, affecting the accuracy of the records and increasing management costs;

[0005] 3. Severe information silos: Data is not fully integrated between various business systems such as the production system (MES), warehouse management system, and financial system (ERP), resulting in untimely and inconsistent data transmission and difficulties in collaboration between departments;

[0006] 4. Weak traceability and supervision capabilities: Incomplete records of material flow and lack of full-process tracking make it difficult to achieve full traceability from raw materials to finished products. This not only fails to meet the needs of refined management but also makes it difficult to cope with potential industry policy and regulatory requirements.

[0007] Currently, leading companies in the industry have begun to explore ways to improve metal management efficiency through intelligent transformation (such as the Internet of Things, digital twins, and smart warehousing) and lean management. However, they have yet to develop a complete solution integrating dynamic inventory, intelligent data collection, end-to-end traceability, and data-driven decision-making, thus failing to fundamentally address the aforementioned industry pain points. Therefore, there is an urgent need for a method for managing the total metal volume of aluminum sheet, strip, and foil processing enterprises that balances intelligence, precision, and practicality. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of existing metal management in aluminum processing enterprises, such as data lag, reliance on manual labor, information silos, and weak traceability. It provides a metal total quantity management method that integrates Internet of Things data collection, dynamic cyclic inventory and intelligent analysis and decision-making, so as to realize real-time monitoring, high-precision management, full-process traceability and scientific decision-making of metal materials, and improve the metal management level and market competitiveness of enterprises.

[0009] To achieve the above objectives, the present invention provides the following technical solution:

[0010] A method for managing total metal volume in aluminum sheet, strip, and foil processing enterprises includes the following steps:

[0011] S1 System Setup and Material Coding: Deploy IoT data acquisition devices, build intelligent inventory terminal programs and metal management big data platforms, assign a unique identification code to each batch / piece of material, and establish a digital archive of materials;

[0012] S2 Dynamic Cyclic Inventory Planning: Based on the ABC classification method for materials, the frequency and priority of differentiated inventory counts are determined according to the value and liquidity of materials, and a dynamic cyclic inventory count plan is generated.

[0013] S3 Intelligent Data Acquisition and Real-time Verification: Automatically collects material weight and location data through IoT acquisition devices, obtains material information by scanning identification codes using intelligent inventory terminals, and compares the inventory quantity with the big data platform in real time to mark discrepancies.

[0014] S4 Full-Link Traceability and Status Mapping: Based on unique identifiers, construct a digital trajectory of the entire lifecycle of materials from raw material warehousing, production feeding, work-in-process circulation, finished product warehousing to sales delivery, establish digital twin models of key equipment, and virtually map the status and quantity of materials within the equipment;

[0015] S5 Data-Driven and Intelligent Analysis: The metal management big data platform integrates data from ERP and MES systems to monitor the total metal inventory, turnover rate, and occupancy at each stage in real time. Through algorithmic models, it conducts multi-dimensional root cause analysis of profit and loss differences and provides early warnings of potential risks.

[0016] S6 Optimization Decision-Making and Continuous Improvement: Based on the analysis results, generate suggestions for process, management and workflow optimization, implement improvement measures, and ensure the objectivity of inventory through flexible organization and assessment mechanisms.

[0017] Furthermore, in S1, the IoT data acquisition devices include RFID readers, smart scales, overhead crane sensors, forklift sensors, and weighbridge sensors. The smart inventory terminal program is a mobile APP or a handheld terminal inventory program.

[0018] Furthermore, in S2, the ABC classification method for materials increases the inventory frequency for high-value, high-flow materials, which include high-end finished foil materials and high-end finished sheet and strip materials.

[0019] Furthermore, S3 also includes using image recognition technology to automatically check the standardization of inventory label pasting, including but not limited to label position and integrity.

[0020] Furthermore, in S3, real-time verification includes instantly marking discrepancies between the accounting records and actual inventory on-site, and synchronously recording the discrepancy data to the metal management big data platform.

[0021] Furthermore, in S4, the digital twin model is used to map the material status and quantity within key equipment to assist in verifying work-in-process inventory. Key equipment includes, but is not limited to, smelting furnaces and rolling mills.

[0022] Furthermore, in S5, multi-dimensional root cause analysis includes, but is not limited to, dimensions such as related work teams, equipment, suppliers, and process parameters. Potential risks include, but are not limited to, abnormal inventory fluctuations, material flow bottlenecks, and individual materials continuously exceeding inventory thresholds.

[0023] Furthermore, in S6, the flexible organization and assessment mechanism includes group mutual inventory and blind inventory methods. The assessment indicators include inventory accuracy, inventory efficiency, data timeliness and problem rectification rate, and are accompanied by a reward and punishment mechanism that focuses on continuous improvement and a best practice sharing mechanism.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows: This method for managing the total metal volume of aluminum sheet, strip, and foil processing enterprises has the following advantages:

[0025] 1. Enhanced Management Precision: Through the Internet of Things, data is automatically collected and verified in real time, enabling real-time, high-precision metal data collection and monitoring, reducing manual entry errors, and significantly improving the consistency between accounts and physical inventory; the dynamic cyclical inventory mechanism can detect inventory problems early and prevent the accumulation of problems.

[0026] 2. Efficiency and cost optimization: IoT data collection devices replace manual labor to complete a large amount of data collection work, improving inventory efficiency and reducing labor costs; real-time monitoring of inventory status and turnover rate accelerates inventory turnover, reduces capital occupation, and lowers inventory costs.

[0027] 3. Enhanced decision support capabilities: The metal management big data platform integrates data from multiple systems, providing timely and accurate data support for production planning, cost control, and process optimization; the risk warning function can help enterprises avoid operational risks in advance and improve the scientific nature of decision-making.

[0028] 4. Enhanced traceability and compliance capabilities: Full-cycle digital tracking enables rapid material traceability, meeting quality traceability requirements; it also complies with potential industry policy and regulatory requirements, improving the company's level of compliant operation.

[0029] 5. Establish a continuous improvement mechanism: Multi-dimensional assessment indicators and reward and punishment mechanisms guide employees to pay attention to the entire process of metal management, encourage the sharing of best practices, and form a continuous optimization mechanism based on data analysis and closed-loop management to improve the long-term effectiveness of enterprise management. Attached Figure Description

[0030] Figure 1 This is a process flow diagram of a method for managing the total amount of metal in an aluminum sheet, strip, and foil processing enterprise according to the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] like Figure 1 As shown, a method for managing the total metal volume of an aluminum sheet, strip, and foil processing enterprise includes the following steps:

[0033] S1 System Setup and Material Coding

[0034] (1) Deploy IoT data collection devices: Install IoT data collection devices on key nodes of aluminum ingot warehouses, branch plants, overhead cranes, forklifts, weighbridges and other equipment. The IoT data collection devices include RFID readers, smart scales, position sensors and other devices, which are used to automatically collect material weight and location data.

[0035] (2) Develop or integrate software systems: Develop intelligent inventory terminal programs (mobile APP or handheld terminal programs), build a metal management big data platform, and achieve data interoperability and integration between the big data platform and the company's existing ERP system and MES system;

[0036] (3) Material coding and modeling: Assign a unique identification code (QR code or RFID tag) to each batch / piece of material and establish a digital archive of the material; establish a digital twin model in the area where key equipment such as smelting furnace and rolling mill are located to virtually map the material status within the equipment;

[0037] S2 Dynamic Cyclic Inventory Plan Development

[0038] The material ABC classification method is adopted to classify materials according to their value and liquidity. High-value, high-liquidity materials are assigned a high-frequency inventory count, while medium- and low-value, low-liquidity materials are assigned a relatively low inventory count. This generates a dynamic cyclical inventory count route and plan, replacing the traditional month-end centralized inventory count model.

[0039] S3 Intelligent Data Acquisition and Real-time Verification

[0040] (1) Operators scan the unique identification code of the material through the intelligent inventory terminal program to obtain the basic information of the material. At the same time, the Internet of Things acquisition device automatically collects the real-time weight and location data of the material and synchronizes it to the terminal program.

[0041] (2) The terminal program will compare the collected real-time data with the inventory data in the metal management big data platform in real time, mark the difference between the accounts and the actual inventory on site, and record the difference information to the big data platform in a synchronous manner. The difference information includes the difference value, material information, inventory time and operator information, etc.

[0042] (3) Use image recognition technology to automatically check the standardization of the pasting of inventory labels, including whether the label position is compliant and whether the label is complete and undamaged, to ensure the label is identifiable;

[0043] S4 end-to-end traceability and status mapping

[0044] (1) Based on the unique material identification code, construct a digital trajectory of the entire life cycle from raw material warehousing, production feeding, work-in-process circulation, finished product warehousing to sales delivery, record the material status, operation information and time nodes of each link, and realize rapid traceability of the entire material process;

[0045] (2) By using the digital twin model of key equipment, the real-time status of materials in the equipment is mapped in the virtual space. The real-time status includes information such as inventory, temperature and processing progress, which helps to verify the accuracy of the work-in-process inventory.

[0046] S5 Data Aggregation and Intelligent Analysis

[0047] (1) The metal management big data platform automatically summarizes the data collected from each link and calculates core indicators such as total metal inventory, inventory turnover rate, and metal usage in each link in real time;

[0048] (2) Based on historical data, construct an algorithm model to conduct multi-dimensional root cause analysis on the profit and loss differences that occur during the inventory process, and identify the root cause of the differences by associating factors such as work teams, equipment, suppliers, and process parameters.

[0049] (3) Automatically warn of potential risks such as abnormal inventory fluctuations and material flow bottlenecks through algorithm models, and promptly push warning information to relevant management personnel;

[0050] S6 Optimization Decision-Making and Continuous Improvement

[0051] (1) Based on the analysis results of the big data platform, generate actionable improvement suggestions such as process optimization, management rule adjustment, and inventory strategy optimization;

[0052] (2) Adopt a group mutual inventory and blind inventory organization method to improve the objectivity of inventory results;

[0053] (3) Establish a multi-dimensional assessment indicator system, including inventory accuracy, inventory efficiency, data timeliness, and problem rectification rate, and support a reward and punishment mechanism that emphasizes continuous improvement, encourage employees to share best practices, and form a management closed loop.

[0054] Example

[0055] Taking a large aluminum sheet, strip, and foil processing enterprise (Henan Mingtai Aluminum Co., Ltd.) as an application scenario, the metal total quantity management method of the present invention is implemented, and the specific steps are as follows:

[0056] S1 System Setup and Material Coding

[0057] (1) Hardware deployment: 30 RFID readers were deployed in key areas such as aluminum ingot warehouse, hot rolling plant, cold rolling plant and finished product warehouse. Position sensors and weight acquisition modules were added to 20 overhead cranes and 25 forklifts. Intelligent transformation was carried out on 8 weighbridges, and smart scales and data transmission modules were added to ensure automatic collection and real-time uploading of material weight and location data.

[0058] (2) Software development and integration: Develop an intelligent inventory APP that supports Android and iOS systems and has functions such as QR code / RFID scanning, data collection, real-time comparison, difference marking, and label compliance detection; build a metal management big data platform, adopting a Hadoop distributed storage architecture, and achieve data interoperability with the company's existing ERP system (SAP) and MES system through API interfaces to ensure real-time synchronization of production, warehousing, and financial data;

[0059] (3) Material coding and modeling: All aluminum ingot raw materials, work-in-process, finished foil and plate are assigned a unique RFID tag. The tag contains basic information such as material number, specifications, production batch, and supplier. The Unity3D engine is used to build digital twin models of smelting furnaces (6 units) and cold rolling mills (8 units). The model is driven in real time by sensor data to map the material inventory and processing status in the equipment.

[0060] S2 Dynamic Cyclic Inventory Plan Development

[0061] Materials are classified using the ABC classification method: Category A materials (high-end finished foil and high-end finished sheet and strip) account for 55% of the total inventory value, with an inventory count frequency of once a week; Category B materials (ordinary sheet and regular work-in-process) account for 35% of the total inventory value, with an inventory count frequency of twice a month; and Category C materials (scrap and low-value auxiliary materials) account for 10% of the total inventory value, with an inventory count frequency of once a month. A monthly cyclical inventory plan is automatically generated by the metal management big data platform, clearly defining the inventory scope, time nodes, and routes for each work team.

[0062] S3 Intelligent Data Acquisition and Real-time Verification

[0063] (1) Inventory personnel carry handheld terminals with smart inventory APP installed, scan material RFID tags according to the inventory plan, the terminal automatically obtains material information, and at the same time the Internet of Things collection device synchronizes real-time weight and location data to the APP;

[0064] (2) The APP will compare the collected data with the inventory data in the metal management big data platform. If the difference rate exceeds ±0.5%, it will be automatically marked as a difference item. The inventory personnel will verify the difference on-site and enter the difference description.

[0065] (3) The APP has a built-in image recognition module that automatically captures the image of the label when scanning the label, analyzes whether the label is pasted in the specified area (within 10cm of the upper right corner of the material) and whether the label is complete, and automatically reminds the user to rectify any non-compliant labels.

[0066] S4 end-to-end traceability and status mapping

[0067] (1) By using the unique RFID tag of the material, the material can be queried for information such as the raw material entry time, inspection results, production feeding team, processing parameters of each process, finished product entry time, and sales customers. The traceability response time has been shortened from the original 4 hours to 10 minutes.

[0068] (2) The digital twin model displays the aluminum liquid inventory and temperature in the smelting furnace and the strip thickness and tension in the cold rolling mill in real time. By comparing the results with the on-site manual sampling inspection, the accuracy rate of work-in-process inventory verification is increased to over 98%.

[0069] S5 Data Aggregation and Intelligent Analysis

[0070] (1) The metal management big data platform displays indicators such as total inventory, proportion of A / B / C category materials, and inventory turnover rate in real time, and managers can monitor them in real time through the visual dashboard;

[0071] (2) Based on the random forest algorithm, a root cause analysis model for the difference was constructed. By analyzing historical difference data, it was found that the weight deviation rate of aluminum ingot raw materials of a certain supplier was relatively high (accounting for 35% of the total difference), and the feeding error frequency of a certain shift was relatively high (accounting for 28% of the total difference). A targeted analysis report was formed.

[0072] (3) When the inventory turnover rate of a certain type of material is lower than the warning threshold for three consecutive days, the system will automatically push warning information to the purchasing department and the production department, prompting them to adjust the purchasing plan or production schedule;

[0073] S6 Optimization Decision-Making and Continuous Improvement

[0074] (1) According to the analysis report, the company negotiated with the supplier with high deviation to improve the raw material inspection standards and carried out material feeding operation training for the relevant work teams. After implementation, the material profit and loss difference rate decreased from the original 3.2% to 0.8%;

[0075] (2) A three-group mutual inventory mode is adopted, namely, Group A inventories the area responsible for Group B, Group B inventories the area responsible for Group C, and Group C inventories the area responsible for Group A. At the same time, blind inventory is implemented (the inventory personnel do not know the inventory data), and the inventory accuracy rate is increased from 95% to 99.5%.

[0076] (3) Establish an assessment indicator system, and include inventory accuracy (weight 40%), inventory efficiency (weight 25%), data timeliness (weight 20%), and problem rectification rate (weight 15%) in the monthly assessment of employees. Bonuses will be awarded to teams that have performed well for three consecutive months. Best practice sharing sessions will be organized to form a management mechanism for continuous improvement.

[0077] Through the implementation of this total metal management method, the metal management of this aluminum sheet, strip and foil processing enterprise has shifted from post-event accounting to in-process intervention and pre-event early warning, significantly improving management accuracy and efficiency, reducing inventory capital occupation by 15%, and creating significant economic benefits for the enterprise.

[0078] The above are merely embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for managing the total metal volume of an aluminum sheet, strip, and foil processing enterprise, characterized in that, Includes the following steps: S1 System Setup and Material Coding: Deploy IoT data acquisition devices, build intelligent inventory terminal programs and metal management big data platforms, assign a unique identification code to each batch / piece of material, and establish a digital archive of materials; S2 Dynamic Cyclic Inventory Planning: Based on the ABC classification method for materials, the frequency and priority of differentiated inventory counts are determined according to the value and liquidity of materials, and a dynamic cyclic inventory count plan is generated. S3 Intelligent Data Acquisition and Real-time Verification: Automatically collects material weight and location data through IoT acquisition devices, obtains material information by scanning identification codes using intelligent inventory terminals, and compares the inventory quantity with the big data platform in real time to mark discrepancies. S4 Full-Link Traceability and Status Mapping: Based on unique identifiers, construct a digital trajectory of the entire lifecycle of materials from raw material warehousing, production feeding, work-in-process circulation, finished product warehousing to sales delivery, establish digital twin models of key equipment, and virtually map the status and quantity of materials within the equipment; S5 Data-Driven and Intelligent Analysis: The metal management big data platform integrates data from ERP and MES systems to monitor the total metal inventory, turnover rate, and occupancy at each stage in real time. Through algorithmic models, it conducts multi-dimensional root cause analysis of profit and loss differences and provides early warnings of potential risks. S6 Optimization Decision-Making and Continuous Improvement: Based on the analysis results, generate suggestions for process, management and workflow optimization, implement improvement measures, and ensure the objectivity of inventory through flexible organization and assessment mechanisms.

2. The method for managing the total metal volume of an aluminum plate, strip, and foil processing enterprise according to claim 1, characterized in that, In S1, the IoT data acquisition devices include RFID readers, smart scales, overhead crane sensors, forklift sensors, and weighbridge sensors. The smart inventory terminal program is a mobile APP or a handheld terminal inventory program.

3. The method for managing the total metal volume of an aluminum plate, strip, and foil processing enterprise according to claim 1, characterized in that, In S2, the ABC classification method for materials increases the inventory frequency for high-value, high-flow materials, which include high-end finished foil materials and high-end finished sheet and strip materials.

4. The method for managing the total metal volume of an aluminum plate, strip, and foil processing enterprise according to claim 1, characterized in that, S3 also includes using image recognition technology to automatically check the standardization of inventory label pasting, including but not limited to label position and integrity.

5. The method for managing the total metal volume of an aluminum plate, strip, and foil processing enterprise according to claim 1, characterized in that, In S3, real-time verification includes marking discrepancies between the accounting records and actual inventory on-site and simultaneously recording the discrepancy data to the metal management big data platform.

6. The method for managing the total metal volume of an aluminum sheet, strip, and foil processing enterprise according to claim 1, characterized in that, In S4, the digital twin model is used to map the material status and quantity within key equipment to assist in verifying work-in-process inventory. Key equipment includes, but is not limited to, smelting furnaces and rolling mills.

7. The method for managing the total metal volume of an aluminum plate, strip, and foil processing enterprise according to claim 1, characterized in that, In S5, multi-dimensional root cause analysis includes, but is not limited to, dimensions such as related work teams, equipment, suppliers, and process parameters. Potential risks include, but are not limited to, abnormal inventory fluctuations, material flow bottlenecks, and individual materials continuously exceeding inventory thresholds.

8. The method for managing the total metal volume of an aluminum plate, strip, and foil processing enterprise according to claim 1, characterized in that, In S6, the flexible organization and assessment mechanism includes group mutual inventory and blind inventory methods. The assessment indicators include inventory accuracy, inventory efficiency, data timeliness and problem rectification rate. It is also equipped with a reward and punishment mechanism that focuses on continuous improvement and a best practice sharing mechanism.

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