Material supervision and management system based on data analysis

Abstract

The invention discloses a material supervision and management system based on data analysis, and the system comprises a material management system. The material management system is composed of a data collection module, a waste coefficient calculation module, a state judgment module, a yield prediction module, a consumption statistics module, an inventory comparison module, a variance calculation module, a quality problem judgment module, an optimization direction analysis module, a correlation value calculation module, an evaluation object selection module and a priority value sorting module. The material supervision and management system based on data analysis can automatically adjust a material distribution strategy according to a use state judgment result and ensure that key production links are preferentially guaranteed, so that the overall material use efficiency is remarkably improved, and the material supervision and management efficiency is improved by calculating the rejection rate variance of an analysis object and judging whether the materials have quality problems or not. And a powerful quality monitoring means is provided for enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of material management, and in particular to a material supervision and management system based on data analysis. Background Art

[0002] The material supervision and management system is an indispensable management tool for modern enterprises. It realizes comprehensive, accurate and efficient supervision of internal materials of enterprises through an integrated information management platform. The system integrates multiple key links such as material procurement, inventory, distribution, and recycling, which greatly improves the efficiency and accuracy of material management. In terms of procurement, the material supervision and management system supports functions such as supplier management, procurement plan formulation, price inquiry and comparison, helping enterprises to effectively control procurement costs and progress. Through real-time data analysis, the system can provide enterprises with accurate procurement decision support to ensure the timeliness and economy of material supply. In terms of inventory management, the system monitors the inventory level of materials in real time, supports operations such as warehousing, outbound, transfer, and inventory, and ensures the continuity of material supply and the rationality of inventory. Through advanced barcodes, RFID and other technologies, the system realizes rapid identification and information collection of materials, and improves the transparency and traceability of inventory management. The material supervision and management system also has an intelligent early warning function, which can monitor the inventory status of materials in real time according to preset rules and parameters. Once the materials are lower than the safety inventory or close to expiration, the system will automatically send out an early warning signal to remind management personnel to deal with them in time, effectively avoiding material waste and operational risks.

[0003] For example, the Chinese authorized patent "A Material Management System Based on Data Analysis" with announcement number CN118095665B includes a material management platform, which is communicatively connected to a material supervision module, an inventory analysis module, a quality supervision module, a production evaluation module and a storage module; the material supervision module is used to supervise and analyze the use of materials: item codes and product codes are assigned to items and products in the production workshop, the item code is used to identify the type of item, and the product code is a collection of item codes of all items required to produce the corresponding product.

[0004] Although the above-mentioned existing technologies can supervise and analyze the use of materials, monitor the scrap data of items within each supervision period in a periodic supervision manner, and promptly provide early warning and quality supervision analysis when abnormal use of items occurs, the response speed of the material supervision and early warning system fails to reach the ideal state, resulting in abnormal phenomena occurring during the use of materials that cannot be discovered and handled in a timely manner. This lag not only increases the risk of material waste, but may also cause unnecessary delays to the production progress, thereby affecting the overall production efficiency and operational benefits. Therefore, it does not meet existing needs. In this regard, we propose a material supervision and management system based on data analysis. Summary of the invention

[0005] The purpose of the present invention is to provide a material supervision and management system based on data analysis to solve the problem that the slow response of material supervision warning of the material management system proposed in the above background technology affects abnormal discovery and processing, resulting in reduced production efficiency and operational benefits.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a material supervision and management system based on data analysis, including a material management system, which is composed of a data collection module, a scrap coefficient calculation module, a state determination module, a production prediction module, a consumption statistics module, an inventory comparison module, a variance calculation module, a quality problem determination module, an optimization direction analysis module, a correlation value calculation module, an evaluation object selection module and a priority value sorting module.

[0007] Preferably, the data collection module, scrap coefficient calculation module and status determination module are used together for material use supervision and analysis.

[0008] Preferably, the data collection module is responsible for automatically collecting the scrap quantity and consumption quantity of each item in each supervision period from the production site or the database, the scrap coefficient calculation module is used to calculate the scrap coefficient of each item, that is, the ratio of the scrap quantity to the consumption quantity, based on the collected data, and the status determination module compares the calculated scrap coefficient with a preset scrap threshold value to determine whether the usage status of the item meets the requirements and generates a determination result.

[0009] Preferably, the production forecasting module, consumption statistics module and inventory comparison module are used together for material inventory analysis.

[0010] Preferably, the output forecasting module calculates the planned production output of products within the supervision period based on the production plan and market demand forecast; the consumption statistics module calculates the expected consumption quantity of all items within the supervision period based on the product output and the unit consumption data of the items; the inventory comparison module compares the statistical value with the current inventory to evaluate whether the material inventory is sufficient and generate out-of-stock warnings or replenishment suggestions.

[0011] Preferably, the variance calculation module, quality problem determination module and optimization direction analysis module are used together for material quality supervision and analysis.

[0012] Preferably, the variance calculation module targets a specific analysis object and calculates the variance of its scrap rate relative to all products to obtain an analysis coefficient. The quality problem determination module compares the analysis coefficient with a preset threshold to determine whether the analysis object has a quality problem and generates a determination result. The optimization direction analysis module further analyzes the scrap rate data when there is no quality problem to provide data support for the optimization direction of product production.

[0013] Preferably, the association value calculation module, the evaluation object selection module and the priority value sorting module are used together for the overall quality evaluation and analysis of materials.

[0014] Preferably, the association value calculation module calculates the number of times each item is a component of a product, i.e., the association value; the evaluation object selection module selects items with the highest association values ​​as evaluation objects based on the sizes of the association values; the priority value sorting module calculates the priority values ​​of the evaluation objects based on their scrap rate data during the supervision cycle, sorts them according to the priority values, and generates an evaluation sequence.

[0015] The working method of the material supervision management system based on data analysis includes the following steps:

[0016] Step 1: Material usage supervision analysis: The system first sets a clear supervision cycle and divides it into several supervision periods. At the end of each supervision period, the data collection module automatically collects the scrap quantity and consumption quantity of each item in each supervision period from the production site or database. Then the scrap coefficient calculation module calculates the scrap coefficient of each item based on these data, that is, the ratio of the scrap quantity caused by the item in this period to the total consumption quantity of the item. After the calculation is completed, the status determination module compares the calculated scrap coefficient with the preset scrap threshold to determine whether the usage status of the item in the supervision period meets the requirements. If the scrap coefficient exceeds the threshold, the system marks the relevant items as potential problem items through the status determination module and triggers further quality supervision processes;

[0017] Step 2: Material inventory analysis: To ensure production continuity, the system first calculates the planned production output within the supervision period through the production forecast module based on the production plan and market demand forecast. Then, the consumption statistics module calculates the estimated consumption quantity of all items within the supervision period, i.e., the statistical value, based on the product output and the unit consumption data of the items. Then, the inventory comparison module compares these statistical values ​​with the current inventory, i.e., the inventory value, to assess whether the material inventory is sufficient. If the inventory value is lower than the statistical value, the inventory comparison module immediately marks the corresponding item as a replenishment object and may trigger the replenishment process to ensure that production is not affected.

[0018] Step 3: Material quality supervision and analysis: In order to ensure product quality, the system conducts in-depth quality supervision and analysis on a specific analysis object, i.e., a batch of materials. First, the variance calculation module calculates the variance of the scrap rate of the analysis object relative to all products to obtain the analysis coefficient. Then, the quality problem determination module compares the analysis coefficient with the preset analysis threshold to determine whether the analysis object has quality problems. If the analysis coefficient exceeds the threshold, the quality problem determination module marks the analysis object as a quality abnormality object and may trigger further investigation or treatment measures. If the analysis object has no quality problems, the optimization direction analysis module further analyzes the scrap rate data to provide data support for the optimization direction of product production, i.e., link optimization and production line optimization.

[0019] Step 4: Overall quality assessment and analysis of materials: In order to comprehensively assess the overall quality performance of materials in industrial production, the system first calculates the number of times each item is a component of a product, i.e., the association value, through the association value calculation module. Then, the assessment object selection module selects the item with the highest association value as the assessment object based on the size of the association value. The system then collects the scrap rate data of these assessment objects during the supervision cycle, and calculates their priority values ​​through the priority value sorting module. Finally, the priority value sorting module sorts the assessment objects in order from low to high priority values ​​to form an assessment sequence.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. Through the material use supervision and analysis module, the system of the present invention can monitor and accurately calculate the scrap coefficient of each item in real time, and promptly discover and correct waste in the use process. This refined management not only reduces unnecessary material consumption, but also promotes the optimal allocation of resources. At the same time, the system can also automatically adjust the material allocation strategy according to the use status judgment results to ensure that key production links are given priority, thereby significantly improving the overall material use efficiency. This effect directly reflects the system's advantages in improving production efficiency and helps enterprises achieve more efficient operations.

[0022] 2. The present invention provides enterprises with accurate material demand forecasts by predicting product output, counting expected consumption quantities and comparing them with inventory quantities. This helps enterprises to arrange procurement plans reasonably and avoid capital occupation and inventory backlogs caused by excessive procurement. It also reduces emergency procurement costs and production delay risks caused by out-of-stock situations. In addition, the system can automatically trigger the replenishment process based on out-of-stock warnings to ensure the continuity and stability of material supply, further reducing material management costs. This effect reflects the system's significant contribution to reducing enterprise operating costs.

[0023] 3. The present invention provides a powerful quality monitoring method for enterprises by calculating the scrap rate variance of the analysis object and determining whether there are quality problems with the materials. This helps enterprises to promptly discover and deal with potential quality problems and prevent unqualified products from entering the market, thereby maintaining the brand image and reputation of the enterprise. At the same time, the system can also provide data support for the optimization direction of product production, promote enterprises to continuously improve production processes and procedures, and improve product quality levels. This effect reflects the core role of the system in ensuring the quality of materials and products, and helps enterprises win the trust of customers and market recognition.

[0024] 4. The present invention provides enterprises with intuitive material quality rankings and evaluation sequences by calculating correlation values ​​and priority values, which helps enterprise management to fully understand the overall performance of materials in industrial production, and provides strong support for formulating targeted quality improvement strategies and optimizing material management decisions. Through the decision-making process driven by data analysis, enterprises can more accurately grasp market dynamics and internal operating conditions, and adjust management strategies and directions in a timely manner, thereby improving overall management efficiency and competitiveness. This effect reflects the unique value of the system in optimizing decision-making support and helps enterprises achieve sustainable development and long-term success. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of the material supervision and management system of the present invention;

[0026] Figure 2 This is a schematic diagram of the material use supervision analysis of the present invention;

[0027] Figure 3 It is a schematic diagram of material inventory analysis of the present invention;

[0028] Figure 4 It is a schematic diagram of material quality supervision analysis of the present invention;

[0029] Figure 5 It is a schematic diagram of the overall quality assessment and analysis of materials of the present invention.

[0030] In the figure: 1. Data collection module; 2. Scrap coefficient calculation module; 3. Status determination module; 4. Production prediction module; 5. Consumption statistics module; 6. Inventory comparison module; 7. Variance calculation module; 8. Quality problem determination module; 9. Optimization direction analysis module; 10. Correlation value calculation module; 11. Evaluation object selection module; 12. Priority value sorting module. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0032] See also Figure 1-5 An embodiment of the present invention is as follows: a material supervision and management system based on data analysis, including a material management system, which is composed of a data collection module 1, a scrap coefficient calculation module 2, a state determination module 3, a production prediction module 4, a consumption statistics module 5, an inventory comparison module 6, a variance calculation module 7, a quality problem determination module 8, an optimization direction analysis module 9, a correlation value calculation module 10, an evaluation object selection module 11 and a priority value sorting module 12.

[0033] When using it, first set the supervision cycle, collect scrap and consumption data, calculate the scrap coefficient and determine the usage status of the items. If it exceeds the standard, mark it and trigger supervision. Secondly, predict production, calculate expected consumption, compare inventory to ensure production continuity, and then for specific batches of materials, calculate the scrap rate variance to determine the quality. If it is abnormal, mark and process it. If there is no abnormality, analyze the data to optimize production. Finally, evaluate the overall quality of the materials, calculate the correlation value to select the evaluation object, collect and sort the scrap rate data, and form an evaluation sequence to fully grasp the material quality performance.

[0034] See also Figure 2 The data collection module 1, the scrap coefficient calculation module 2 and the status determination module 3 are used together for material use supervision and analysis. The automatic data collection reduces manual intervention and improves data accuracy and efficiency. Real-time calculation of the scrap coefficient and determination of the use status enable problems to be quickly identified and handled, which helps to optimize the efficiency of material use and reduce waste.

[0035] Furthermore, the data collection module 1 is responsible for automatically collecting the scrap quantity and consumption quantity of each item in each supervision period from the production site or the database, the scrap coefficient calculation module 2 is used to calculate the scrap coefficient of each item based on the collected data, that is, the ratio of the scrap quantity to the consumption quantity, and the state determination module 3 compares the calculated scrap coefficient with the preset scrap threshold value to determine whether the use status of the item meets the requirements and generate a determination result.

[0036] See also Figure 3 The production forecast module 4, the consumption statistics module 5 and the inventory comparison module 6 are used together for material inventory analysis. Based on accurate production plans and market demand forecasts, accurate estimates of material demand are ensured, and automated statistics of expected consumption and real-time inventory are compared to effectively avoid inventory shortages or surpluses, maintain production continuity, and reduce inventory costs.

[0037] Furthermore, the output forecasting module 4 calculates the planned output of products within the supervision period based on the production plan and market demand forecast. The consumption statistics module 5 calculates the expected consumption quantity of all items within the supervision period based on the product output and the unit consumption data of the items. The inventory comparison module 6 compares the statistical value with the current inventory to evaluate whether the material inventory is sufficient and generate out-of-stock warnings or replenishment suggestions.

[0038] See also Figure 4 The variance calculation module 7, the quality problem determination module 8 and the optimization direction analysis module 9 are used together for material quality supervision and analysis. Through refined variance calculation, batches with quality problems can be accurately identified, and measures can be taken quickly. When there are no quality problems, the scrap rate data can be deeply analyzed to provide a scientific basis for production optimization and continuously promote the improvement of product quality and production efficiency.

[0039] Furthermore, the variance calculation module 7 targets a specific analysis object and calculates the variance of its scrap rate relative to all products to obtain an analysis coefficient. The quality problem determination module 8 compares the analysis coefficient with a preset threshold to determine whether the analysis object has a quality problem and generates a determination result. The optimization direction analysis module 9 further analyzes the scrap rate data in the absence of quality problems to provide data support for the optimization direction of product production.

[0040] See also Figure 5 The associated value calculation module 10, the evaluation object selection module 11 and the priority value sorting module 12 are used together for the overall quality evaluation and analysis of materials. By quantifying the importance of items in the product, namely the associated value, key materials are evaluated first to ensure the rationality and effectiveness of resource allocation. Priority value sorting helps identify the materials that need the most attention and provides a clear direction for overall quality improvement and resource optimization.

[0041] Furthermore, the association value calculation module 10 calculates the number of times each item is a component of a product, namely, the association value. The evaluation object selection module 11 selects the item with the highest association value as the evaluation object based on the size of the association value. The priority value sorting module 12 calculates the priority value of the evaluation object based on the scrap rate data of the evaluation object during the supervision period and sorts it according to the priority value to generate an evaluation sequence.

[0042] The working method of the material supervision management system based on data analysis includes the following steps:

[0043] Step 1: Material usage supervision analysis: The system first sets a clear supervision cycle and divides it into several supervision periods. At the end of each supervision period, the data collection module 1 automatically collects the scrap quantity and consumption quantity of each item in each supervision period from the production site or database. Then, the scrap coefficient calculation module 2 calculates the scrap coefficient of each item based on these data, that is, the ratio of the scrap quantity caused by the item in this period to the total consumption quantity of the item. After the calculation is completed, the state determination module 3 compares the calculated scrap coefficient with the preset scrap threshold to determine whether the use status of the item in the supervision period meets the requirements. If the scrap coefficient exceeds the threshold, the system marks the relevant item as a potential problem item through the state determination module 3 and triggers further quality supervision process;

[0044] Step 2: Material inventory analysis: In order to ensure production continuity, the system first calculates the planned production output within the supervision period through the production forecast module 4 based on the production plan and market demand forecast. Then, the consumption statistics module 5 calculates the estimated consumption quantity of all items within the supervision period, i.e., the statistical value, based on the product output and the unit consumption data of the items. Then, the inventory comparison module 6 compares these statistical values ​​with the current inventory, i.e., the inventory value, to evaluate whether the material inventory is sufficient. If the inventory value is lower than the statistical value, the inventory comparison module 6 immediately marks the corresponding item as a replenishment object and may trigger the replenishment process to ensure that production is not affected.

[0045] Step 3: Material quality supervision and analysis: In order to ensure product quality, the system conducts in-depth quality supervision and analysis on a specific analysis object, i.e., a batch of materials. First, the variance calculation module 7 calculates the variance of the scrap rate of the analysis object relative to all products to obtain the analysis coefficient. Then, the quality problem determination module 8 compares the analysis coefficient with the preset analysis threshold to determine whether the analysis object has a quality problem. If the analysis coefficient exceeds the threshold, the quality problem determination module 8 marks the analysis object as a quality abnormality object and may trigger further investigation or treatment measures. If the analysis object has no quality problem, the optimization direction analysis module 9 further analyzes the scrap rate data to provide data support for the optimization direction of product production, i.e., link optimization and production line optimization.

[0046] Step 4: Overall quality assessment and analysis of materials: In order to comprehensively assess the overall quality performance of materials in industrial production, the system first calculates the number of times each item is a component of a product, i.e., the associated value, through the associated value calculation module 10. Then, the assessment object selection module 11 selects the item with the highest associated value as the assessment object based on the size of the associated value. The system then collects the scrap rate data of these assessment objects during the supervision cycle, and calculates their priority values ​​through the priority value sorting module 12. Finally, the priority value sorting module 12 sorts the assessment objects in order from low to high priority values ​​to form an assessment sequence.

[0047] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A material supervision and management system based on data analysis, including a material management system, characterized in that: The material management system is composed of a data collection module (1), a scrap coefficient calculation module (2), a state determination module (3), a production prediction module (4), a consumption statistics module (5), an inventory comparison module (6), a variance calculation module (7), a quality problem determination module (8), an optimization direction analysis module (9), a correlation value calculation module (10), an evaluation object selection module (11) and a priority value sorting module (12).

2. According to claim 1, a material supervision and management system based on data analysis is characterized in that: The data collection module (1), the scrap coefficient calculation module (2) and the status determination module (3) are used together for material use supervision and analysis.

3. A material supervision and management system based on data analysis according to claim 2, characterized in that: The data collection module (1) is responsible for automatically collecting the scrap quantity and consumption quantity of each item in each supervision period from the production site or the database; the scrap coefficient calculation module (2) is used to calculate the scrap coefficient of each item, i.e., the ratio of the scrap quantity to the consumption quantity, based on the collected data; the state determination module (3) compares the calculated scrap coefficient with a preset scrap threshold value, determines whether the use status of the item meets the requirements and generates a determination result.

4. According to claim 1, a material supervision and management system based on data analysis is characterized in that: The production forecasting module (4), consumption statistics module (5) and inventory comparison module (6) are used together for material inventory analysis.

5. A material supervision and management system based on data analysis according to claim 4, characterized in that: The output prediction module (4) calculates the output of products planned to be produced within the supervision period based on the production plan and the market demand forecast. The consumption statistics module (5) calculates the estimated consumption quantity of all items within the supervision period based on the product output and the unit consumption data of the items. The inventory comparison module (6) compares the statistical value with the current inventory to evaluate whether the inventory of materials is sufficient and generate out-of-stock warnings or replenishment suggestions.

6. The material supervision and management system based on data analysis according to claim 1 is characterized by: The variance calculation module (7), the quality problem determination module (8) and the optimization direction analysis module (9) are used together for material quality supervision and analysis.

7. A material supervision and management system based on data analysis according to claim 6, characterized in that: The variance calculation module (7) targets a specific analysis object and calculates the variance of its scrap rate relative to all products to obtain an analysis coefficient. The quality problem determination module (8) compares the analysis coefficient with a preset threshold value to determine whether the analysis object has a quality problem and generates a determination result. The optimization direction analysis module (9) further analyzes the scrap rate data in the case where there is no quality problem, so as to provide data support for the optimization direction of product production.

8. The material supervision and management system based on data analysis according to claim 1 is characterized by: The associated value calculation module (10), the evaluation object selection module (11) and the priority value sorting module (12) are used together for overall quality evaluation and analysis of materials.

9. A material supervision and management system based on data analysis according to claim 8, characterized in that: The association value calculation module (10) calculates the number of times each item is a component of a product, i.e., the association value; the evaluation object selection module (11) selects the item with the highest association value as the evaluation object according to the size of the association value; the priority value sorting module (12) calculates the priority value of the evaluation object based on the scrap rate data of the evaluation object during the supervision cycle, sorts the items according to the priority value, and generates an evaluation sequence.

10. The working method of the material supervision and management system based on data analysis according to any one of claims 1 to 9 is characterized in that: The steps include: Step 1: Material usage supervision analysis: The system first sets a clear supervision cycle and divides it into several supervision periods. At the end of each supervision period, the data collection module (1) automatically collects the scrap quantity and consumption quantity of each item in each supervision period from the production site or database. Then, the scrap coefficient calculation module (2) calculates the scrap coefficient of each item based on these data, that is, the ratio of the scrap quantity caused by the item in this period to the total consumption quantity of the item. After the calculation is completed, the status judgment module (3) compares the calculated scrap coefficient with the preset scrap threshold to determine whether the usage status of the item in the supervision period meets the requirements. If the scrap coefficient exceeds the threshold, the system marks the relevant item as a potential problem item through the status judgment module (3) and triggers further quality supervision process; Step 2: Material inventory analysis: In order to ensure production continuity, the system first calculates the planned production output within the supervision period through the production forecast module (4) based on the production plan and market demand forecast. Then, the consumption statistics module (5) calculates the estimated consumption quantity of all items within the supervision period, i.e., the statistical value, based on the product output and the unit consumption data of the items. Then, the inventory comparison module (6) compares these statistical values ​​with the current inventory, i.e., the inventory value, to evaluate whether the material inventory is sufficient. If the inventory value is lower than the statistical value, the inventory comparison module (6) immediately marks the corresponding item as a replenishment object and may trigger the replenishment process to ensure that production is not affected. Step 3: Material quality supervision and analysis: In order to ensure product quality, the system conducts in-depth quality supervision and analysis on a specific analysis object, i.e., a batch of materials. First, the variance calculation module (7) calculates the variance of the scrap rate of the analysis object relative to all products to obtain the analysis coefficient. Then, the quality problem determination module (8) compares the analysis coefficient with the preset analysis threshold to determine whether the analysis object has a quality problem. If the analysis coefficient exceeds the threshold, the quality problem determination module (8) marks the analysis object as a quality abnormality object and may trigger further investigation or treatment measures. If the analysis object has no quality problem, the optimization direction analysis module (9) further analyzes the scrap rate data to provide data support for the optimization direction of product production, i.e., link optimization and production line optimization. Step 4: Overall quality assessment and analysis of materials: In order to comprehensively assess the overall quality performance of materials in industrial production, the system first calculates the number of times each item is a component of a product, i.e., the associated value, through the associated value calculation module (10). Then, the assessment object selection module (11) selects the item with the highest associated value as the assessment object based on the size of the associated value. The system then collects the scrap rate data of these assessment objects during the supervision cycle and calculates their priority values ​​through the priority value sorting module (12). Finally, the priority value sorting module (12) sorts the assessment objects in order from low to high priority values ​​to form an assessment sequence.

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