Industrial Smart Management Methods Based on MES

Abstract

To provide an industrial smart management method based on an MES (ManufacturingExecutionSystem) for customizing a ManufacturingExecutionSystem suitable for a company.SOLUTION: The method includes obtaining historical production performance data of an enterprise and analyzing the historical production performance data to determine a production process, a production scale, and a product characteristic, analyzing the product characteristic to determine a key control node in the production process, obtaining an enterprise management requirement and determining an actual production requirement and a personnel management requirement based on the enterprise management requirement, the key control node, and the production scale, and developing a management module and integrating a manufacturing execution system based on these requirements. In this way, the manufacturing stage can be determined more accurately, thereby realizing efficient utilization of the manufacturing execution system, optimization of human resources, reduction of trial and error costs, efficient optimization of the production process, and improvement of production efficiency and product quality.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] This application relates to the technical field of industrial management, and particularly to an industrial smart management method based on MES.

Background Art

[0002] MES (Manufacturing Execution System), a manufacturing execution system, is a very important information management system in modern manufacturing. It can respond to and report on real-time events through information transmission, and by using current accurate data for appropriate instructions and processing, production efficiency and quality are improved, costs are reduced, and market competitiveness is enhanced.

[0003] However, due to the large scale, numerous modules, and complex logical relationships of the manufacturing execution system, it was often difficult for enterprises to fully understand its functions and roles when introducing it. Some enterprises lacked an understanding of the system when selecting a model, making it difficult to select a MES that was truly suitable for their own company.

Summary of the Invention

Problems to be Solved by the Invention

[0004] This application provides an industrial smart management method based on MES to solve the above problems and customize a manufacturing execution system suitable for enterprises.

Means for Solving the Problems

[0005] In a first aspect, this application provides an industrial smart management method based on MES, acquiring the past production performance data of an enterprise, analyzing the past production performance data to identify the production process, production scale, and product characteristics; analyzing the product characteristics to identify the key control nodes in the production process; The steps include obtaining corporate management needs and identifying actual production needs and personnel management needs based on the corporate management needs, the key control nodes, and the production scale, Based on the actual production needs and personnel management needs, the steps include developing a management module and integrating it into the manufacturing execution system, The above method, including.

[0006] Alternatively, the step of analyzing past production performance data to identify the production process, production scale, and product characteristics is: The steps include: analyzing the aforementioned past production performance data, identifying the data characteristics of each past production performance data, classifying the past production performance data based on the aforementioned data characteristics, and obtaining the manufacturing stage based on the classification results; The steps include selecting the aforementioned manufacturing stages to obtain a production process, The steps include identifying production data based on the classification results and determining the production volume per unit time based on the said data, The steps include determining the production scale based on the production volume per unit time and the predetermined enterprise plan volume, Based on the classification results, the steps include identifying the test data, Based on the aforementioned inspection data, the step of determining the product acceptance rate, A step of identifying the components of the product based on the production process and the production data, The process includes the step of identifying the product characteristics based on the ingredients of the product and the product's pass rate.

[0007] Alternatively, the step of analyzing the product characteristics and identifying key control nodes in the production process is: The steps include analyzing past production data after classification to identify the unit production time and number of defective products for each production process, The steps include determining the production defect rate per unit time based on the number of defective products and the product characteristics, The steps include obtaining the production task volume for each production process, and determining the production difficulty level for each production process based on the production task volume, the unit production time, and the production defect rate per unit time, The process includes the steps of sorting the production difficulty of each production process and identifying key control nodes based on the sorting results.

[0008] Alternatively, the step of determining the production difficulty of each production process based on the production task volume, the unit production time, and the production defect rate per unit time refers to the following formula:

[0009] Math 1 JPEG0007850487000001.jpg22148 [In the formula, Di represents the difficulty of production, Ti represents the production task quantity of the i-th production process, ti represents the unit production time of the i-th production process, and Ri represents the production defect rate per unit time of the i-th production process.]

[0010] Alternatively, the steps to obtain corporate management needs are: Steps to obtain enterprise architecture information, The steps include: analyzing the aforementioned enterprise architecture information to identify employee types, Based on the aforementioned employee categories, the steps include identifying the job responsibilities for each employee category, Based on the aforementioned job responsibilities, the process involves sending a needs acquisition signal to the manager corresponding to each employee type and receiving the management needs transmitted by the relevant manager. This includes the step of deriving corporate management needs based on the aforementioned job responsibilities and management needs submitted by the relevant manager.

[0011] Alternatively, the step of identifying actual production needs and personnel management needs based on the enterprise management needs, the key control nodes and the production scale is: The steps include determining the unit production quantity based on the aforementioned production scale, A step of determining the amount of materials consumed per unit time and the energy consumption of equipment based on the unit production volume and the production process, The steps include identifying the actual production needs based on the amount of materials consumed and the energy consumption of the equipment, Based on the aforementioned key control nodes, the steps include identifying the personnel needs for each production position, Based on the aforementioned production scale, the steps include identifying the skill needs for each production position, The process includes the step of identifying the personnel management needs based on the aforementioned personnel needs and skills needs.

[0012] Alternatively, the step of developing a management module and integrating it into a manufacturing execution system based on the actual production needs and personnel management needs is: The steps include: constructing an MES simulation system based on the actual production needs described above; Based on the aforementioned personnel management needs, the MES simulation system is operated, and the smoothness of the operation is identified based on the operational results. Based on the smoothness of the aforementioned operations, a step is made to determine whether there are any problems with the current operations, If a problem occurs, the following steps are taken based on the aforementioned operational results: identify and analyze the congested node and identify the operational problem. The process includes the steps of developing a management module and integrating it with the manufacturing execution system based on the actual production needs, personnel management needs, and operational issues.

[0013] Alternatively, the step of identifying the smoothness of operations based on operational results is: Based on the aforementioned operational results, the steps include identifying the throughput and average response time during the operational process, The steps include acquiring and analyzing the construction data of the MES simulation system, and identifying the material data and component data of the MES simulation system based on the data analysis results, Based on the component data, steps of identifying the contact area of the moving parts of the MES simulation system and the relative speed of the moving parts of the MES simulation system; Based on the material data, steps of identifying the friction coefficient between the construction materials and the mass of the MES simulation system; Comprehensively analyzing the component data and the material data, steps of identifying the stiffness coefficient of the MES simulation system; Based on the contact area, the relative speed, the friction coefficient, the stiffness coefficient and the mass, steps of identifying the smoothness of the operation by referring to the following formula, are included.

[0014] Formula 2 JPEG0007850487000002.jpg19145[where C is the smoothness of the operation, T is the throughput, A is the average response time, k is the stiffness coefficient, m is the mass, H is the contact area, μ is the friction coefficient, v is the relative speed of the moving parts of the MES simulation system, p is the medium density of the operating environment of the MES simulation system, c is the medium attenuation coefficient of the operating environment, a is the influence coefficient of k and m on attenuation, β is the influence coefficient of μ, H, v on attenuation, and γ is the influence coefficient of p, c, H on attenuation.]

[0015] Alternatively, based on the unit production volume and the production process, the steps of identifying the material consumption per unit time and the energy consumption of the equipment include the following steps: Based on the production process, steps of identifying the raw materials for production and equipment data Based on the raw materials for production, steps of identifying the material consumption per unit time by referring to the following formula

[0016] Formula 3 JPEG0007850487000003.jpg22139[where Mt is the material consumption per unit time, n is the quantity of the raw materials for production, P is the unit production volume, and Mi is the unit consumption of the i-th type of raw material for production.] Steps to identify equipment usage parameters based on the aforementioned equipment data. Based on the aforementioned equipment usage parameters, the following step determines the energy consumption of the equipment per unit time by referring to the following formula.

[0017] Math 4 JPEG0007850487000004.jpg19124 [In the formula, Te represents the energy consumption of the equipment per unit time, m represents the number of equipment, Wj represents the power of the j-th equipment, and Tj represents the unit execution time of the j-th equipment.]

[0018] In a second embodiment, the present application provides an industrial smart management system based on MES, A data analysis module for acquiring past production performance data of a company, analyzing said past production performance data, and identifying production processes, production scale, and product characteristics, A node identification module for analyzing the aforementioned product characteristics and identifying key control nodes in the production process, A needs analysis module for acquiring corporate management needs and identifying actual production needs and personnel management needs based on said corporate management needs, said key control nodes and said production scale, Based on the aforementioned actual production needs and personnel management needs, a management module was developed, along with a system development module for integrating the manufacturing execution system. The above system, including.

[0019] Alternatively, the data analysis module may be used specifically for the following purposes: The aforementioned past production performance data is analyzed, the data characteristics of each past production performance data are identified, the past production performance data is classified based on the aforementioned data characteristics, and the manufacturing stages are obtained based on the classification results. Selecting the aforementioned manufacturing stages yields a production process. Based on the classification results, production data is identified, and based on the production data, the production volume per unit time is identified. Based on the production volume per unit time and the predetermined enterprise plan volume, the production scale is determined, Based on the classification results, the inspection data is identified, and based on the inspection data, the product acceptance rate is identified. Based on the aforementioned production process and production data, the components of the product are identified. The product characteristics are determined based on the ingredients of the product and the product's acceptance rate.

[0020] Alternatively, the node-specific module may be used specifically for the following purposes: By analyzing past production data after classification, the unit production time and number of defective products for each production process are identified. Based on the number of defective products and the product characteristics, the production defect rate per unit time is determined. The production task volume for each production process is obtained, and the production difficulty level for each production process is identified based on the production task volume, the unit production time, and the production defect rate per unit time. The difficulty level of each production process is sorted, and key control nodes are identified based on the sorting results.

[0021] Alternatively, the node identification module is used specifically for the following purposes when identifying the production difficulty of each production process based on the production task volume, the unit production time, and the production defect rate per unit time.

[0022] Number 5 JPEG0007850487000005.jpg26157 [In the formula, Di represents the difficulty of production, Ti represents the production task quantity of the i-th production process, ti represents the unit production time of the i-th production process, and Ri represents the production defect rate per unit time of the i-th production process.]

[0023] Alternatively, the needs analysis module may be used specifically for the following purposes: Obtain enterprise architecture information, By analyzing the aforementioned enterprise architecture information, employee types are identified. Based on the aforementioned employee categories, the job responsibilities for each employee category are identified. Based on the aforementioned job responsibilities, a needs acquisition signal is sent to the manager corresponding to each employee type, and the management needs transmitted by the relevant manager are received. Based on the aforementioned job responsibilities and the management needs communicated by the relevant managers, corporate management needs are derived.

[0024] Alternatively, the needs analysis module may be used specifically for the following purposes: Based on the aforementioned production scale, the unit production quantity is determined. Based on the aforementioned unit production volume and production process, the amount of materials consumed per unit time and the energy consumption of the equipment are determined. Based on the amount of materials consumed and the energy consumption of the equipment, the actual production needs are identified. Based on the aforementioned key control nodes, the personnel needs for each production position are identified. Based on the aforementioned production scale, the skill needs for each production position are identified. Based on the aforementioned personnel needs and skill needs, the personnel management needs are identified.

[0025] Alternatively, the system development module may be used specifically for the following purposes: Based on the aforementioned actual production needs, we constructed an MES simulation system. Based on the aforementioned personnel management needs, the MES simulation system is operated, and the smoothness of the operation is identified based on the operational results. Based on the smoothness of the aforementioned operations, we will determine whether there are any problems with the current operations. If a problem occurs, based on the aforementioned operational results, identify and analyze the congested node, and identify the operational problem. Based on the aforementioned actual production needs, personnel management needs, and operational issues, a management module will be developed and the manufacturing execution system will be integrated.

[0026] Alternatively, the system development module may be used specifically for the following purposes: Based on the aforementioned operational results, the throughput and average response time during the operational process are identified. The construction data of the aforementioned MES simulation system is acquired and analyzed, and based on the data analysis results, the material data and component data of the aforementioned MES simulation system are identified. Based on the aforementioned component data, the contact area of ​​the movable parts of the MES simulation system and the relative velocity of the movable parts of the MES simulation system are determined. Based on the aforementioned material data, the coefficient of friction between the construction materials and the mass of the MES simulation system are determined. The aforementioned component data and material data are comprehensively analyzed to determine the stiffness coefficient of the MES simulation system. Based on the contact area, relative velocity, friction coefficient, stiffness coefficient, and mass, the smoothness of the operation is determined by referring to the following formula.

[0027] Number 6 JPEG0007850487000006.jpg18145 [In the formula, C is the smoothness of operation, T is the throughput, A is the average response time, k is the stiffness coefficient, m is the mass, H is the contact area, μ is the friction coefficient, v is the relative velocity of the movable parts of the MES simulation system, p is the media density of the operating environment of the MES simulation system, c is the media damping coefficient of the operating environment, a is the influence coefficient of k and m on damping, β is the influence coefficient of μ, H, and v on damping, and γ is the influence coefficient of p, c, and H on damping.]

[0028] Alternatively, the aforementioned needs analysis module may be used specifically for the following purposes: Based on the aforementioned production process, raw materials and equipment data for production are identified. Based on the raw materials used for production, the amount of materials consumed per unit time is determined by referring to the following formula.

[0029] Number 7 JPEG0007850487000007.jpg17133 [In the formula, Mt represents the amount of material consumed per unit time, n represents the quantity of the raw materials for production, P represents the unit production volume, and Mi represents the unit consumption of the i-th type of raw materials for production.] Based on the aforementioned equipment data, equipment usage parameters are identified. Based on the aforementioned equipment usage parameters, the energy consumption of the equipment per unit time is determined by referring to the following formula.

[0030] Number 8 JPEG0007850487000008.jpg21133 [In the formula, Te represents the energy consumption of the equipment per unit time, m represents the number of equipment, Wj represents the power of the j-th equipment, and Tj represents the unit execution time of the j-th equipment.]

[0031] The following brief description of the accompanying drawings, which are necessary for depicting embodiments or the prior art, is provided below to clearly illustrate the embodiments of this application or the technical means within the prior art. The accompanying drawings described below are merely some embodiments of this application, and a person skilled in the art can obtain other drawings based on these accompanying drawings, assuming no creative activity is required. [Brief explanation of the drawing]

[0032] [Figure 1] This is a schematic diagram illustrating an application scenario provided in one embodiment of this application. [Figure 2] This is a flowchart of an industrial smart management method based on an MES provided in one embodiment of this application. [Figure 3] This is a schematic diagram of an industrial smart management system based on an MES provided in one embodiment of this application. [Modes for carrying out the invention]

[0033] To further clarify the purpose, technical means, and advantages of this application, the technical means in the embodiments of this application will be described in detail below with reference to the drawings in the embodiments of this application, although it goes without saying that the embodiments described are only some of the embodiments of this application, not all of them. All other embodiments derived from the embodiments of this application, without any creative activity by a person skilled in the art, are all within the scope of protection of this application.

[0034] As used herein, the term "and / or" includes any and all combinations of one or more related components listed together. For example, A and / or B means either A exists alone, A and B exist together, or B exists alone. As used herein, the symbol " / " generally indicates an "or" relationship between the preceding and following related components, unless otherwise specified.

[0035] The embodiments of this application will be described in detail below with reference to the drawings in the specification.

[0036] Manufacturing execution systems (MESs) are often large, modular, and have complex logical relationships, making it difficult for companies to fully understand their functions and operations during implementation. Some companies lacked understanding of the system during model selection, making it difficult to choose a truly suitable MES. Therefore, identifying a suitable manufacturing execution system based on a company's specific characteristics is key to achieving more efficient industrial smart management.

[0037] Based on this, this application provides an industrial smart management method based on MES, which deeply analyzes a company's past production performance data and provides data support for identifying a company's production needs by more accurately understanding conventional production processes, production scale, and product characteristics. By identifying key control nodes, more effective monitoring and management can be performed during the production process, ensuring product quality and production efficiency. Based on the company's management needs, key control nodes, and production scale, actual production needs and personnel management needs are identified, and manufacturing stages are more accurately identified, improving the utilization rate of the manufacturing execution system, ensuring sufficient human resources on the production line, and avoiding waste of human resources. By developing management modules based on actual production needs and personnel management needs and integrating them with the manufacturing execution system, a dedicated manufacturing execution system is created for the company, reducing trial-and-error costs, more efficiently optimizing the production process, increasing production efficiency, improving product quality, optimizing personnel management, and improving the overall operational efficiency of the company, thereby strengthening the company's market competitiveness.

[0038] Figure 1 is a schematic diagram illustrating an application scenario provided in one embodiment of this application. When a company needs to build a manufacturing execution system, it can use the method provided in this application to build a manufacturing execution system suited to the company's own characteristics, improve management efficiency, and reduce trial-and-error costs.

[0039] Specifically, by applying the method provided in this application to any server, the server interacts with the company's production management system, acquires and analyzes past production performance data and corporate management needs from the company's production management system, accurately grasps the company's actual production and personnel management needs, develops a management module, specializes a dedicated manufacturing execution system for the company, reduces trial-and-error costs, optimizes the production process more efficiently, increases production efficiency, improves product quality, optimizes personnel management, and enhances the company's overall operational efficiency, thereby strengthening the company's market competitiveness.

[0040] For specific implementation methods, please refer to the following embodiments.

[0041] Figure 2 is a flowchart of an industrial smart management method based on an MES provided in one embodiment of this application. The method of this embodiment can be used for a server in the above scenario. As shown in Figure 2, the method includes the following steps.

[0042] S201: A step to obtain past production performance data of a company, analyze that data, and identify the production process, production scale, and product characteristics.

[0043] Past production performance data can be understood as all the data generated during production at a company's factory over a past period, and can be obtained from the company's existing production management system.

[0044] Specifically, a process analysis model is constructed, and historical production data is input into the process analysis model to identify the manufacturing stages of the factory and the dependencies between each stage, thereby obtaining the corresponding production process.

[0045] Using data mining techniques, historical production data is analyzed to identify production rules for each production process. These rules include the unit production volume, production cycle, and downtime for each process. Based on these production rules, the company's production scale is evaluated.

[0046] We analyze past production performance data to identify the number and cause of defective products that occurred in each production process of each production cycle, and then identify product characteristics based on the number and cause of defective products.

[0047] S202: A step to analyze product characteristics and identify key control nodes in the production process.

[0048] Key control nodes can be understood as nodes that require careful consideration, as these nodes may have a significant impact on the pass / fail status of a product, or on the operation of the equipment itself.

[0049] Specifically, after obtaining product characteristics, the locations where these defective products occurred are identified based on the causes of defective products obtained above. The locations where defective products occurred are then totaled, and the number of defective products at each location is determined. A weighted calculation is then performed for each location based on the number of defective products, and important control points are identified.

[0050] S203: Steps to acquire enterprise management needs and identify actual production needs and personnel management needs based on enterprise management needs, key control nodes, and production scale.

[0051] Actual production needs can be understood as the needs that actually require management in the production process at a company's factory. These actual production needs are obtained by comprehensively analyzing the company's management needs, key control nodes, and production scale, and may differ from the production needs stated by those involved with the company.

[0052] The personnel management needs can be understood as the demands submitted by the aforementioned companies regarding personnel management, and are obtained by analyzing the aforementioned past production performance data; however, these may differ from the personnel management needs stated by those involved with the companies.

[0053] Specifically, if a company needs to build a manufacturing execution system, it can voluntarily submit a request for its construction, which includes its management needs. Based on the production processes, production scale, and product characteristics obtained from the analysis of past production performance data, the company's production capacity is evaluated. The company's management needs are then verified by combining this with the company's production capacity, current market demand, and key control nodes. Differences and similarities are identified based on the verification results, and the company's actual production needs are identified based on these differences and similarities.

[0054] Based on the above production process, clarify the positions within the company and the job responsibilities of the corresponding positions, identify the special job responsibilities required for the positions in these nodes based on the operational characteristics of the key control nodes, and identify the personnel management needs based on the above.

[0055] S204: A step to develop a management module and integrate it into the manufacturing execution system based on actual production needs and the aforementioned personnel management needs.

[0056] Specifically, we will identify integration goals based on actual production and personnel management needs. Based on the above construction request, we will identify the company's construction costs, combine the construction costs and integration goals to formulate an integration plan, select suitable technical tools and platforms, develop management modules based on the above integration plan, and integrate a manufacturing execution system that is fully adapted to the company's circumstances.

[0057] The means provided in this embodiment allow for in-depth analysis of a company's past production performance data, providing data support for identifying the company's production needs by gaining a more accurate understanding of conventional production processes, production scale, and product characteristics. By identifying key control nodes, more effective monitoring and management can be performed during the production process, ensuring product quality and production efficiency. Based on the company's management needs, key control nodes, and production scale, actual production and personnel management needs can be identified, and manufacturing stages can be identified more accurately, improving the utilization rate of the manufacturing execution system. By developing management modules based on actual production and personnel management needs and integrating them with the manufacturing execution system, a dedicated manufacturing execution system can be created for the company, reducing trial-and-error costs, optimizing the production process more efficiently, increasing production efficiency, improving product quality, optimizing personnel management, and enhancing the company's overall operational efficiency, thereby strengthening the company's market competitiveness.

[0058] In some embodiments, past production performance data is analyzed, the data characteristics of each past production performance data are identified, the past production performance data is classified based on the data characteristics, the manufacturing stages are obtained based on the classification results, the manufacturing stages are selected to obtain a production process, the production data is identified based on the classification results, the production volume per unit time is identified based on the production data, the production scale is identified based on the production volume per unit time and a predetermined enterprise plan volume, the inspection data is identified based on the classification results, the product acceptance rate is identified based on the inspection data, the product components are identified based on the production process and production data, and the product characteristics are identified based on the product components and the product acceptance rate.

[0059] The manufacturing stage can be understood as the entire process involved in industrial production, and may include not only all production processes included in product manufacturing, but also the raw material procurement stage and the later transportation stage.

[0060] A predetermined enterprise production volume can be understood as the production volume per unit time that a company specifies before production begins, based on an evaluation of factors such as the characteristics of the product and its own production facilities and equipment performance. This represents the production volume under ideal conditions.

[0061] Inspection data can be understood as data corresponding to the stage in the production process where products are inspected. Product acceptance rate can be understood as the product acceptance rate per unit time calculated by analyzing inspection data, or as the acceptance rate after all product inspections have been completed following the current production run.

[0062] The product's ingredients can be understood as the raw materials necessary to produce the above-mentioned product.

[0063] Product characteristics can be understood as properties inherent in the product itself.

[0064] Specifically, natural language processing technology is used to analyze past production data, identifying data characteristics such as time, quantifiers, and materials, and then identifying the data type to which the data corresponds based on these characteristics. For example, time could be the usage time, production time, or equipment start-up and stop-down time of the corresponding material or product. The specific type identified here can be more accurately identified and more detailed data type classification possible by combining it with contextual information of the data.

[0065] After identifying the data type, all manufacturing stages of product production are identified and selected, and the process used to actually produce the product, such as the injection molding process, is identified, while the purchase of raw materials for injection molding is not considered part of the production process.

[0066] Based on the classification results obtained from the above classification, it is determined which data corresponds to the production process and the production volume of the above product per unit time is identified based on the production data. At the same time, it is determined which data corresponds to the inspection process based on the classification results and inspection data is obtained. Using the inspection data, the product acceptance rate when the above product was produced in the past is identified.

[0067] Because there is a discrepancy between the production volume that a company identifies itself and its actual production volume, the actual production volume per unit time and the predetermined company-planned volume that the company itself has planned are combined to derive the company's actual production scale.

[0068] By identifying the raw materials used in each process based on production data and production processes, the components of the product are identified. Combining the analysis results of the product components and product acceptance rates, key product characteristics (e.g., physical properties, chemical properties, lifespan, etc.) are identified.

[0069] The means provided in this embodiment allow for a clearer identification of manufacturing stages and processes by analyzing and classifying past production performance data. By identifying the scale of production based on the production volume per unit time and a predetermined corporate plan volume, it is possible to prevent the problem of the subsequent manufacturing execution system not being suited to the company's production scale due to unclear corporate self-awareness, leading to unnecessary cost waste. Analyzing product characteristics by combining product components and product acceptance rates also helps to gain a deeper understanding of product uniqueness, making it easier to build the functionality of the manufacturing execution system for products to be produced in the future and improving the utilization rate of the manufacturing execution system.

[0070] In some embodiments, the classified historical production data is analyzed to identify the unit production time and number of defective products for each production process, the production defect rate per unit time is identified based on the number of defective products and product characteristics, the production task volume for each production process is obtained, the production difficulty level for each production process is identified based on the production task volume, unit production time, and production defect rate per unit time, the production difficulty level for each production process is sorted, and key control nodes are identified based on the sorting results.

[0071] Unit production time can be understood as the time required to produce a product.

[0072] The number of defective products can be understood as the number of products that were deemed unacceptable out of all products produced.

[0073] The production defect rate can be understood as the percentage of unacceptable products out of all products produced per unit of time.

[0074] Specifically, the past production performance data after classification is analyzed to check for the existence of data of the data type "failure" and "production volume." If such data exists, the number of products judged to be failures during that time period is aggregated based on the failure-type data to obtain the number of defective products for that time period. Using the production volume-type data, the average production value for that time period is calculated, and the unit production time required to produce products in each production process is derived.

[0075] The production defect rate within a unit of time (per hour, per day) is calculated based on the number of defective products and the corresponding production volume. Formula: Defect rate = Number of defective products / Production volume × 100%.

[0076] The production task volume for each production process, i.e., the planned total production volume, is obtained from the production plan. This production plan can be obtained from order demand. The production difficulty of each production process is comprehensively evaluated by combining the unit production time, the production defect rate per unit time, and the production task volume. All production processes are sorted in descending order of production difficulty, or in descending order. Based on the sorting results, several production processes with the highest production difficulty are identified as key control nodes.

[0077] The means provided in this embodiment ensure the objectivity and accuracy of the analysis by analyzing based on classified past production performance data. By comprehensively considering multiple factors such as production task volume, unit production time, and production defect rate per unit time, and quantifying the difficulty of the production process, it becomes easier to compare different production processes and to identify production bottlenecks more clearly and accurately. Subsequently, the construction of the manufacturing execution system is more closely adapted to the actual production process and key control nodes, reducing wasted costs and improving system utilization and management levels.

[0078] In some embodiments, determining the production difficulty of each production process based on the production task volume, unit production time, and production defect rate per unit time refers to the following equation (1).

[0079] Number 9 JPEG0007850487000009.jpg19120 [In the formula, Di represents the difficulty of production, Ti represents the production task quantity of the i-th production process, ti represents the unit production time of the i-th production process, and Ri represents the production defect rate per unit time of the i-th production process.]

[0080] The means provided in this embodiment allows for the design of a formula for calculating the difficulty of production for each production process by comprehensively considering multiple factors such as the amount of production tasks, the unit production time, and the production defect rate per unit time. This ensures that the actual situation of the production process is more comprehensively reflected, that objective comparisons of the difficulty levels between different production processes are possible, that deviations due to subjective judgments are avoided, and that the evaluation of production difficulty becomes more scientific and accurate.

[0081] In some embodiments, enterprise architecture information is acquired, the enterprise architecture information is analyzed to identify employee types, the job responsibilities for each employee type are identified based on the employee types, a needs acquisition signal is sent to the manager corresponding to each employee type based on the job responsibilities, the management needs sent by the manager are received, and enterprise management needs are derived based on the job responsibilities and the management needs sent by the manager.

[0082] Enterprise architecture information can be understood as data related to the enterprise architecture, such as departmental structure, job classifications, and employee hierarchy.

[0083] Needs acquisition signals can be understood as signals sent to each administrator to gather requirements regarding the manufacturing execution system.

[0084] Specifically, enterprise architecture information is obtained through channels such as the company's official website, internal documents, organizational charts, and employee manuals. By analyzing this enterprise architecture information, the job classifications of the aforementioned companies are clarified, and employee types are determined.

[0085] Based on employee type and departmental structure and employee hierarchy within the architecture information, the system identifies job responsibilities for the positions of employees of the relevant type, identifies the managers of the relevant departments based on job responsibilities, and sends a needs acquisition signal to these managers. After receiving the above signal, the managers of the relevant departments receive the management needs they have compiled and integrate the job responsibilities with the management needs transmitted by the relevant managers to obtain the corporate management needs.

[0086] The means provided in this embodiment begin with acquiring enterprise architecture information, proceed through a series of systematic analyses, and ultimately obtain enterprise management needs, ensuring the systematicity and comprehensiveness of the information while avoiding omissions and biases. First, the enterprise architecture information is analyzed to identify employee types. By comprehensively considering job responsibilities and management needs transmitted by the relevant managers, the comprehensiveness and practicality of the management needs are ensured.

[0087] In some embodiments, the unit production volume is identified based on the production scale, the material consumption and equipment energy consumption per unit time are identified based on the unit production volume and production process, the actual production needs are identified based on the material consumption and equipment energy consumption, the personnel needs for each production position are identified based on key control nodes, the skill needs for each production position are identified based on the production scale, and the personnel management needs are identified based on the personnel needs and skill needs.

[0088] Actual production needs can be understood as the actual needs throughout the entire production process.

[0089] Personnel needs can be understood as the characteristics, traits, and qualities that the personnel required at the relevant key control node should possess.

[0090] Skill needs can be understood as the skills that workers required to produce the above-mentioned products should possess.

[0091] Specifically, the production capacity of the above-mentioned companies is evaluated based on the scale of the companies' production, and the unit production volume for each production cycle or period is determined. When the production volume is known based on the unit production volume, the amount of materials consumed on average to produce these products is calculated, and the material consumption is obtained. In the specific implementation method, this is calculated comprehensively based on the total production volume per unit time and the state of material waste.

[0092] Equipment operation data is acquired, the amount of energy consumed during product production is obtained from the equipment operation data, and the equipment energy consumption per unit time is calculated based on the total energy consumption.

[0093] Based on the material consumption and equipment energy consumption obtained above, mathematical analysis methods are used to calculate which aspects of the manufacturing process a company should manage after actual production input. Examples include equipment maintenance cycle management, material replacement, or purchasing management.

[0094] Based on past production performance data, the complexity and workload of each key control node are evaluated, the number of personnel required for each key control node is calculated, and personnel needs are derived. Furthermore, the specialized skills that workers at each key control node should acquire are identified according to its complexity, clarifying the skill needs for each key control node. Similarly, the skill needs of other production processes can be calculated based on the complexity of those processes.

[0095] Based on personnel and skill needs, clarify personnel management objectives, such as improving production efficiency and reducing costs, and identify personnel management needs. For example, refer to Table (1) to create a personnel management plan.

[0096] Table 1 JPEG0007850487000010.jpg51170

[0097] The means provided in this embodiment allow for the identification of unit production volume based on production scale, ensuring that production capacity matches market demand. By identifying material consumption and equipment energy consumption per unit time based on unit production volume and production process, companies can optimize resource allocation, reduce waste, and improve resource utilization efficiency with the help of the constructed manufacturing execution system. By identifying personnel needs for each production position based on key control nodes, critical positions receive sufficient human resource support, improving the stability and efficiency of the production process. Identifying personnel management needs based on personnel needs and skill needs helps to facilitate corporate management, reduce system construction costs, and improve the efficiency of automated management.

[0098] In some embodiments, an MES simulation system is constructed based on actual production needs, the MES simulation system is operated based on personnel management needs, the smoothness of the operation is identified based on the operational results, it is determined whether there are any problems with the current operation based on the smoothness of the operation, and if there are problems, the bottleneck nodes are identified and analyzed based on the operational results, operational problems are identified, and a management module is developed and the manufacturing execution system is integrated based on actual production needs, personnel management needs and operational problems.

[0099] Specifically, the actual production needs are analyzed to obtain several pieces of information such as production scale, product characteristics, production process, key control nodes, and skill requirements disclosed in the above embodiment. An appropriate simulation tool is then selected, and an MES simulation system is constructed to simulate the operational process of the manufacturing execution system using the above data.

[0100] By running and simulating the manufacturing execution system in a simulation environment, various activities and decision-making processes throughout the entire product production cycle are simulated. Key indicators of the simulation process, such as production efficiency, material consumption, and equipment energy consumption, are identified, and the smooth operation of the manufacturing execution system is evaluated based on these key indicators.

[0101] The system determines whether there are bottlenecks at a certain stage through the smoothness of operation, and if so, identifies the bottleneck node using methods such as simulation replay or log analysis. Next, the bottleneck node is analyzed in depth to identify the cause of the bottleneck, such as insufficient data processing capacity or uneven resource allocation. In the specific implementation method, uncertainties and dynamic change factors in the actual production environment, such as equipment failure, material shortages, and personnel changes, are considered, and these factors are added to the simulation environment and simulated to evaluate the processing capacity of the manufacturing execution system facing a series of problems in the actual production environment, and an overall evaluation is performed to determine the smoothness of operation. Subsequently, a management module is developed based on actual production needs, personnel management needs, and operational problems discovered during simulation to integrate the manufacturing execution system.

[0102] The means provided in this embodiment enable the construction of an MES simulation system using actual production needs, the determination of whether the initially constructed system has defects that affect the system's operation, and, if defects are found, immediate action to be taken. Furthermore, by optimizing the MES simulation system built based on actual production needs and personnel management needs, this system can support corporate management more quickly and efficiently, thereby improving overall production efficiency.

[0103] In some embodiments, based on operational results, throughput and average response time during the operational process are identified, construction data of the MES simulation system is acquired and analyzed, material data and component data of the MES simulation system are identified based on the data analysis results, the contact area of ​​the movable parts of the MES simulation system and the relative velocity of the movable parts of the MES simulation system are identified based on the component data, the friction coefficient between construction materials and the mass of the MES simulation system are identified based on the material data, and the stiffness coefficient of the MES simulation system is identified by comprehensively analyzing the component data and material data. Based on the contact area, relative velocity, coefficient of friction, stiffness coefficient, and mass, the smoothness of operation is determined by referring to the following equation (2).

[0104] Number 10 JPEG0007850487000011.jpg21152 [In the formula, C is the smoothness of operation, T is throughput, A is the mean response time, k is the stiffness coefficient, m is the mass, H is the contact area, μ is the friction coefficient, v is the relative velocity of the moving parts of the MES simulation system, p is the media density of the operating environment of the MES simulation system, c is the media damping coefficient of the operating environment, a is the influence coefficient of k and m on damping, β is the influence coefficient of μ, H, and v on damping, and γ is the influence coefficient of p, c, and H on damping.]

[0105] Throughput can be understood as the amount of data successfully transferred within a unit of time by a network, device, port, virtual circuit, or other equipment, and can reflect the data processing capabilities of a manufacturing execution system.

[0106] Average response time can be understood as the average time it takes to process a request from the start of queuing to the end of execution after a specific request operation has been performed through the manufacturing execution system, and it can reflect the speed and efficiency of request processing in the manufacturing execution system.

[0107] Construction data can be understood as all the data necessary to build a manufacturing execution system, and may include hardware-related data and software-related data.

[0108] Material data can be understood as data corresponding to the materials of the hardware necessary to build a manufacturing execution system, and may include attributes such as material type, density, and hardness.

[0109] Component data can be understood as hardware-related data necessary for building a manufacturing execution system, and may include the size, shape, and motion trajectory of each component.

[0110] Moving parts can be understood as components that physically move during the operation of a manufacturing execution system.

[0111] Specifically, after obtaining the operational results, the total number of tasks processed by the MES during the operational process is aggregated from the operational results and used as the throughput. The processing time for each task is aggregated, all processing times are summed up, and the average response time is calculated by dividing this sum by the total number of tasks.

[0112] From the construction process of the MES simulation system, relevant construction data is extracted and analyzed to identify the materials and components used in the construction process and obtain corresponding data. The contact area between each moving part and other moving parts is obtained using geometric calculation methods, and then the relative velocity of the moving parts is obtained from the relative motion relationship between the moving parts. Based on the material data obtained above, the coefficient of friction between these materials is searched for or calculated. The coefficient of friction between these materials can be obtained by testing or by utilizing existing knowledge in materials science.

[0113] After acquiring material data and component data, attribute information for each material or component is obtained to calculate the corresponding mass, and these are summed to determine the mass of the MES simulation system. The component data and material data are comprehensively analyzed, and the stiffness coefficient of the MES simulation system is calculated using engineering mechanics formulas or simulation software.

[0114] After obtaining the contact area, relative velocity, friction coefficient, stiffness coefficient, and mass using the method described above, these parameters are quantified, and equation (2) is designed to calculate the smoothness of operation.

[0115] The means provided in this embodiment allow for a more accurate reflection of the smoothness of the MES simulation system in actual operation by comprehensively considering multiple factors and designing the equations accordingly, thus avoiding bias in evaluation based on a single indicator. By quantifying contact area, relative velocity, friction coefficient, stiffness coefficient, and mass, the evaluation results of the smoothness of operation become more accurate and objective, providing a precise data base for determining whether there are problems with the simulation of the manufacturing execution system.

[0116] In some embodiments, production raw materials and equipment data are identified based on the production process, and the amount of materials consumed per unit time is determined based on the production raw materials by referring to the following formula (3).

[0117] Number 11 JPEG0007850487000012.jpg16139 [In the formula, Mt represents the amount of material consumed per unit time, n represents the quantity of raw materials for production, P represents the unit output, and Mi represents the unit consumption of the i-th type of raw material for production.]

[0118] Based on the equipment data, equipment usage parameters are identified, and based on these equipment usage parameters, the energy consumption of the equipment per unit time is determined by referring to the following equation (4).

[0119] Number 12 JPEG0007850487000013.jpg22134 [In the formula, Te represents the energy consumption of the equipment per unit time, m represents the number of equipment, Wj represents the power of the j-th equipment, and Tj represents the unit execution time of the j-th equipment.]

[0120] The means provided in this embodiment accurately calculates the amount of materials consumed per unit time by designing an equation that takes into account the unit consumption and unit production of all production raw materials. This improves the reliability and practicality of the calculation results and contributes to precise management and optimization of the production process. By designing an equation that takes into account the power consumption and unit execution time of all equipment, the energy consumption of the equipment per unit time is accurately calculated, contributing to the evaluation of the overall energy consumption and operating costs of the equipment.

[0121] Figure 3 is a schematic diagram of an industrial smart management system based on MES provided in one embodiment of this application. As shown in Figure 3, the industrial smart management system 300 based on MES in this embodiment comprises a data analysis module 301, a node identification module 302, a needs analysis module 303, and a system development module 304.

[0122] The data analysis module 301 is used to acquire past production performance data of a company, analyze said past production performance data, and identify production processes, production scale, and product characteristics. The node identification module 302 is used to analyze the product characteristics and identify key control nodes in the production process. Needs analysis module 303 is used to acquire enterprise management needs and to identify actual production needs and personnel management needs based on said enterprise management needs, said key control nodes and said production scale. The system development module 304 is used to develop management modules and integrate the manufacturing execution system based on the actual production needs and personnel management needs.

[0123] Alternatively, the data analysis module 301 may be used specifically for the following purposes: The aforementioned past production performance data is analyzed, the data characteristics of each past production performance data are identified, the past production performance data is classified based on the aforementioned data characteristics, and the manufacturing stages are obtained based on the classification results. Selecting the aforementioned manufacturing stages yields a production process. Based on the classification results, production data is identified, and based on the production data, the production volume per unit time is identified. Based on the production volume per unit time and the predetermined enterprise plan volume, the production scale is determined, Based on the classification results, the inspection data is identified, and based on the inspection data, the product acceptance rate is identified. Based on the aforementioned production process and production data, the components of the product are identified. The product characteristics are determined based on the ingredients of the product and the product's acceptance rate.

[0124] Alternatively, the node-specific module 302 may be used specifically for the following purposes: By analyzing past production data after classification, the unit production time and number of defective products for each production process are identified. Based on the number of defective products and the product characteristics, the production defect rate per unit time is determined. The production task volume for each production process is obtained, and the production difficulty level for each production process is identified based on the production task volume, the unit production time, and the production defect rate per unit time. The difficulty level of each production process is sorted, and key control nodes are identified based on the sorting results.

[0125] Alternatively, the node identification module 302 is used specifically for the following purposes when identifying the production difficulty of each production process based on the production task volume, the unit production time, and the production defect rate per unit time.

[0126] Number 13 JPEG0007850487000014.jpg17130 [In the formula, Di represents the difficulty of production, Ti represents the production task quantity of the i-th production process, ti represents the unit production time of the i-th production process, and Ri represents the production defect rate per unit time of the i-th production process.]

[0127] Alternatively, the needs analysis module 303 may be used specifically for the following purposes: Obtain enterprise architecture information, By analyzing the aforementioned enterprise architecture information, employee types are identified. Based on the aforementioned employee categories, the job responsibilities for each employee category are identified. Based on the aforementioned job responsibilities, a needs acquisition signal is sent to the manager corresponding to each employee type, and the management needs transmitted by the relevant manager are received. Based on the aforementioned job responsibilities and the management needs communicated by the relevant managers, corporate management needs are derived.

[0128] Alternatively, the needs analysis module 303 may be used specifically for the following purposes: Based on the aforementioned production scale, the unit production quantity is determined. Based on the aforementioned unit production volume and production process, the amount of materials consumed per unit time and the energy consumption of the equipment are determined. Based on the amount of materials consumed and the energy consumption of the equipment, the actual production needs are identified. Based on the aforementioned key control nodes, the personnel needs for each production position are identified. Based on the aforementioned production scale, the skill needs for each production position are identified. Based on the aforementioned personnel needs and skill needs, the personnel management needs are identified.

[0129] Alternatively, the system development module 304 may be used specifically for the following purposes: Based on the aforementioned actual production needs, we constructed an MES simulation system. Based on the aforementioned personnel management needs, the MES simulation system is operated, and the smoothness of the operation is identified based on the operational results. Based on the smoothness of the aforementioned operations, we will determine whether there are any problems with the current operations. If a problem occurs, based on the aforementioned operational results, identify and analyze the congested node, and identify the operational problem. Based on the aforementioned actual production needs, personnel management needs, and operational issues, a management module will be developed and the manufacturing execution system will be integrated.

[0130] Alternatively, the system development module 304 may be used specifically for the following purposes: Based on the aforementioned operational results, the throughput and average response time during the operational process are identified. The construction data of the aforementioned MES simulation system is acquired and analyzed, and based on the data analysis results, the material data and component data of the aforementioned MES simulation system are identified. Based on the aforementioned component data, the contact area of ​​the movable parts of the MES simulation system and the relative velocity of the movable parts of the MES simulation system are determined. Based on the aforementioned material data, the coefficient of friction between the construction materials and the mass of the MES simulation system are determined. The aforementioned component data and material data are comprehensively analyzed to determine the stiffness coefficient of the MES simulation system. Based on the contact area, relative velocity, friction coefficient, stiffness coefficient, and mass, the smoothness of the operation is determined by referring to the following formula.

[0131] Number 14 JPEG0007850487000015.jpg19132 [In the formula, C is the smoothness of operation, T is the throughput, A is the average response time, k is the stiffness coefficient, m is the mass, H is the contact area, μ is the friction coefficient, v is the relative velocity of the movable parts of the MES simulation system, p is the media density of the operating environment of the MES simulation system, c is the media damping coefficient of the operating environment, a is the influence coefficient of k and m on damping, β is the influence coefficient of μ, H, and v on damping, and γ is the influence coefficient of p, c, and H on damping.]

[0132] Alternatively, the needs analysis module 303 may be used specifically for the following purposes: Based on the aforementioned production process, raw materials and equipment data for production are identified. Based on the raw materials used for production, the amount of materials consumed per unit time is determined by referring to the following formula.

[0133] Number 15 JPEG0007850487000016.jpg19130 [In the formula, Mt represents the amount of material consumed per unit time, n represents the quantity of the raw materials for production, P represents the unit production volume, and Mi represents the unit consumption of the i-th type of raw material for production.] Based on the aforementioned equipment data, equipment usage parameters are identified. Based on the aforementioned equipment usage parameters, the energy consumption of the equipment per unit time is determined by referring to the following formula.

[0134] Number 16 JPEG0007850487000017.jpg21122 [In the formula, Te represents the energy consumption of the equipment per unit time, m represents the number of equipment, Wj represents the power of the j-th equipment, and Tj represents the unit execution time of the j-th equipment.]

[0135] The system of this embodiment can also be used to carry out the methods of any of the embodiments described above, and since the implementation principles and technical effects are similar, a detailed explanation is omitted here.

Claims

1. An industrial smart management method based on MES, which is executed by a server, The server acquires past production performance data of the company, analyzes the past production performance data, and identifies the production process, production scale, and product characteristics. The server is used to analyze the product characteristics and characterize important nodes in the production process, and identifies key control nodes, including at least one node that significantly affects the pass / fail status of the product and at least one node that significantly affects the operation of the equipment itself. The server acquires enterprise management needs and identifies actual production needs and personnel management needs based on the enterprise management needs, the key control nodes, and the production scale. The server includes the steps of developing a management module and integrating a manufacturing execution system based on the actual production needs and the personnel management needs, The step of the server analyzing the past production performance data and identifying the production process, production scale, and product characteristics is as follows: The server analyzes the past production performance data, identifies the data characteristics of each past production performance data, classifies the past production performance data based on the data characteristics, and obtains the manufacturing stage based on the classification result. The server selects the manufacturing stage to obtain a production process, The server identifies production data based on the classification results and identifies the production volume per unit time based on the production data. The server performs the steps of determining the production scale based on the production volume per unit time and a predetermined corporate plan volume, The server then performs the steps of identifying the inspection data based on the classification results, The server performs the steps of determining the product pass rate based on the inspection data, The server performs the steps of identifying the components of the product based on the production process and the production data, The server includes the step of identifying the product characteristics based on the product's components and its pass rate, The step of the server analyzing the product characteristics and identifying key control nodes in the production process is: The server analyzes the classified historical production data and identifies the unit production time and number of defective products for each production process. The server performs the steps of determining the production defect rate per unit time based on the number of defective products and the product characteristics, The server obtains the production task volume for each production process, and determines the production difficulty level for each production process based on the production task volume, the unit production time, and the production defect rate per unit time. The server includes the steps of sorting the production difficulty of each production process and identifying key control nodes based on the sorting results, The method is characterized in that the step of the server determining the production difficulty of each production process based on the production task volume, the unit production time, and the production defect rate per unit time refers to the following formula. Number 1 [In the formula, Di represents the difficulty of production, Ti represents the amount of production tasks in the i-th production process, ti represents the unit production time of the i-th production process, and Ri represents the defect rate per unit time of the i-th production process.]

2. The step of the server acquiring enterprise management needs is: The server acquires enterprise architecture information, The server performs the step of analyzing the enterprise architecture information to identify the type of employee, The server, based on the employee type, takes the step of identifying the job responsibilities of each employee type, The server, based on the job responsibilities, sends a needs acquisition signal to the administrator corresponding to each employee type, and receives the management needs transmitted from the administrator; The server derives corporate management needs based on the job responsibilities and management needs transmitted from the relevant manager. The method according to claim 1, characterized by including

3. The step of the server identifying actual production needs and personnel management needs based on the enterprise management needs, the key control nodes and the production scale is: The server performs the steps of determining the unit production quantity based on the production scale, The server takes the step of determining the amount of materials consumed per unit time and the energy consumption of the equipment based on the unit production volume and the production process, The server identifies the actual production needs based on the amount of materials consumed and the energy consumption of the equipment. The server, based on the key control node, identifies the personnel needs for each production position. The server, based on the production scale, identifies the skill needs of each production position. The server, based on the personnel needs and the skill needs, identifies the personnel management needs. The method according to claim 1, characterized by including

4. The step of the server developing a management module and integrating a manufacturing execution system based on the actual production needs and the personnel management needs is: The server constructs an MES simulation system based on the actual production needs, The server operates the MES simulation system based on the personnel management needs and identifies the smoothness of the operation based on the operational results. The server, based on the smoothness of the operation, determines whether there are any problems with the current operation. If the aforementioned server has a problem, the steps include identifying and analyzing the out-of-sync node based on the operational results and identifying the operational problem, The server develops a management module and integrates the manufacturing execution system based on the actual production needs, personnel management needs, and operational problems. The method according to claim 1, characterized by including

5. The step of the server determining the smoothness of operation based on the operational results is: The server, based on the operational results, determines the throughput and average response time during the operational process. The server acquires and analyzes the construction data of the MES simulation system, and based on the data analysis results, identifies the material data and component data of the MES simulation system. The server performs the steps of determining, based on the component data, the contact area of ​​the movable parts of the MES simulation system and the relative velocity of the movable parts of the MES simulation system. The server performs the steps of determining the coefficient of friction between the construction materials and the mass of the MES simulation system based on the material data, The server comprehensively analyzes the component data and the material data to determine the stiffness coefficient of the MES simulation system. The server determines the smoothness of operation by referring to the following formula based on the contact area, relative speed, friction coefficient, stiffness coefficient, and mass, The method according to claim 4, characterized by including Math 2 [In the formula, C represents the smoothness of operation, T represents throughput, A represents the average response time, k represents the stiffness coefficient, m represents the mass, H represents the contact area, μ represents the friction coefficient, v represents the relative velocity of the movable parts of the MES simulation system, p represents the media density of the operating environment of the MES simulation system, c represents the media damping coefficient of the operating environment, a represents the influence coefficients of k and m on damping, β represents the influence coefficients of μ, H, and v on damping, and γ represents the influence coefficients of p, c, and H on damping.]

6. The method according to claim 3, characterized in that the step of the server determining the amount of materials consumed per unit time and the energy consumption of equipment based on the unit production volume and the production process includes the following steps. The server then takes the step of identifying raw materials and equipment data for production based on the production process. The server determines the amount of material consumed per unit time based on the raw materials for production by referring to the following formula. Math 3 [In the formula, Mt represents the amount of material consumed per unit time, n represents the quantity of the raw materials for production, P represents the unit production volume, and Mi represents the unit consumption of the i-th type of raw material for production.] The server then performs the step of identifying equipment usage parameters based on the equipment data. The server determines the energy consumption of the equipment per unit time by referring to the following formula based on the equipment usage parameters. Math 4 [In the formula, Te represents the energy consumption of the equipment per unit time, m represents the number of equipment units, Wj represents the power of the j-th equipment unit, and Tj represents the unit operating time of the j-th equipment unit.]

7. An industrial smart management system based on MES for carrying out the method described in any one of Claims 1 to 6, A data analysis module for acquiring past production performance data of a company, analyzing said past production performance data, and identifying production processes, production scale, and product characteristics, A node identification module for analyzing the aforementioned product characteristics and identifying key control nodes in the production process, A needs analysis module for acquiring corporate management needs and identifying actual production needs and personnel management needs based on said corporate management needs, said key control nodes and said production scale, Based on the aforementioned actual production needs and personnel management needs, a management module was developed, along with a system development module for integrating the manufacturing execution system. An industrial smart management system based on MES, characterized by including [the following].

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