Intelligent factory design method based on digital lean and multiple closed-loop verification

Abstract

The invention discloses an intelligent factory design method based on digital lean and multiple closed-loop verification. The method comprises the steps of determining production elements and product technological processes of different types of production lines suitable for a factory, and performing verification optimization through digital lean; determining a standardization system and an intelligent manufacturing standard conforming to an intelligent factory, and sequentially performing automation, informatization, digitization and intelligent improvement on the production line in combination with the optimized production elements and the product technological process; after intelligent improvement is completed, factory operation performance indicators are simulated to verify and optimize the optimized production elements and product technological processes again; and determining a simulation model according to the re-optimized production elements, the product technological process and an intelligent transformation output result. Through multi-verification optimization of the production elements, the production elements and the product technological process meet the lean requirement, and then it is guaranteed that the designed intelligent factory can improve the project construction implementation success rate of the intelligent factory and the optimal production efficiency, cost, delivery time and quality after production.

Description

Technical Field

[0001] The present application relates to the field of factory intelligence technology, and specifically to a smart factory design method based on digital lean and multiple closed-loop verification. Background Art

[0002] Smart factories use various modern technologies to realize the automation of factory office, management and production, so as to strengthen and standardize enterprise management, reduce work errors, plug various loopholes, improve work efficiency, ensure safe production, provide decision-making references, strengthen external contacts, and expand international markets. Smart factories realize the mutual coordination and cooperation between people and machines, and their essence is human-machine interaction.

[0003] Many companies have begun to transform their factories and automate production lines to achieve automation and mass production, gradually transforming factories into smart factories. However, most of the existing factory intelligent transformations only focus on upgrading specific product production lines or parts of production lines based on the needs of the company itself, and are not applicable to different production lines in the same factory. The reason is that it is impossible to determine the factors for the intelligent transformation of factories, which leads to the current standards not meeting the transformation and upgrading of all production lines in the factory.

[0004] In the existing technology, there are endless concepts such as intelligent manufacturing, industrial Internet platform, enterprise cloud, artificial intelligence, digital factory, etc., which are proposed to achieve the intelligent transformation of factories. For most small and medium-sized manufacturers, these concepts are numerous and complex, and it is difficult to choose. In addition, the concepts of automation, digitization and intelligence are often confused. Enterprises need to understand their needs, find corresponding products, and formulate appropriate processes, otherwise the investment of funds and materials may not get the expected return. In addition, enterprises will also encounter technical application problems in the process of promoting intelligent manufacturing. There are data islands between equipment, between equipment and people, and between systems and platforms. There are delay errors in information transmission, which affect the formulation and execution of decisions and greatly slow down the process of intelligent manufacturing transformation. Therefore, the difficulty in building smart factories lies not only in the application and integration of technologies and concepts, but also in the clarity of the transformation process. Summary of the invention

[0005] In order to solve the above technical problems, this application proposes the following technical solutions: In a first aspect, an embodiment of the present application provides a smart factory design method based on digital lean and multiple closed-loop verification, including: Determine the production factors and product process flows applicable to different types of production lines in the factory; Verify and optimize production factors and product process flow through digital leanness, so that the optimized production factors and product process flow of the production line meet the leanness requirements of the production line; Determine the standardized system and intelligent manufacturing standards that meet the requirements of intelligent factories; Combining the optimized production factors and product process flow, the standardized system of intelligent factories and intelligent manufacturing standards, the production line is improved in automation, informatization, digitization and intelligence in turn; After the production line is improved through intelligence, the optimized production factors are verified and optimized again by simulating the factory operating performance indicators; Finally, a simulation model is determined based on the re-optimized production factors and the intelligent transformation output results, and the simulation model is used to generate a corresponding intelligent factory design based on the input information.

[0006] In a possible implementation, determining production factors and product process flows applicable to different types of production lines in a factory includes: Determine the production factors and product process flows involved in different production lines, including: product process flow analysis, operation flow, physical flow, process control, basic equipment information, staffing and time, production line balance working hours calculation, energy flow and staff efficiency; Determine the basic information of the production factors and product process flow, wherein the basic information of the product process flow analysis includes work sections, processes, work stations and work positions; the operation flow includes motion analysis through video or captured images, motion decomposition, cycle parameters and time units; the physical flow includes batch flow, single piece flow and transmission mode; the process control includes input factors 5M1E, process control parameters, output performance quality characteristics, full process quality traceability and data elements; the basic equipment information includes equipment name, equipment brand specifications, theoretical production capacity, equipment quantity, equipment cost ROI and equipment overall efficiency OEE; the staffing and time include actual production capacity of a single person, staffing, attendance method and working hours; the production line balance working time calculation includes standard beat, working time calculation, slack rate, budget cycle, production line balance rate, process cycle, manual simultaneous operation, machine working time and human-machine collaborative working time; the energy flow includes water, electricity, natural gas, coal and steam; the staff efficiency includes staff saturation and per capita labor efficiency.

[0007] In a possible implementation, the verification and optimization of production factors and product process flow through digital leanness, so that the optimized production factors and product process flow of the production line meet the leanness requirements of the production line, includes: Determine digital lean optimization indicators, including: value-added activities, product material process and equipment and LCIA; Decompose each optimization index to obtain index optimization factors, where: the value-added activities include: necessary value-added, not necessary value-added and not necessary value-added; the product material process includes product design packaging, supply chain materials, new process and process description window optimization; the equipment and LCIA include equipment performance, labor-saving mechanism, auxiliary positioning device and fixture; Verification and optimization are carried out based on the optimization factors of the indicators combined with the production factors and product process flows covered by different types of production lines.

[0008] In a possible implementation, the determining whether the smart factory meets the standardization system and smart manufacturing standards includes: Determine the standardized system that meets the requirements of smart factories, including: standardization of traditional production operations: QMS, EMS, 5M1E, and standardization of smart manufacturing systems: Lighthouse Factory, CMMM; Determine the smart manufacturing standards that meet the requirements of smart factories, including: data collection standards, equipment procurement standards, software and hardware integration standards, and database standards.

[0009] In a possible implementation, the production line is improved in automation, informatization, digitization and intelligence in turn by combining the optimized production factors and product process flow, the standardization system of the intelligent factory and the intelligent manufacturing standards, including: Determine the required degree of automation for the production line based on the form of automation, degree of automation, data collection equipment, sensor types and data interaction methods; Improve the level of informatization required for production lines from the perspective of data collection methods, data flows, system integration, databases, industrial software apps and data platforms; Determine the production line digital big data model and / or 3D algorithm and / or DIKW model; Finally, the determined degree of automation, degree of informatization and digital model or algorithm is combined to generate intelligent scenario application simulation twin AI and intelligent manufacturing field research and development and service model to realize the intelligentization of production line.

[0010] In a possible implementation, after the production line is improved through intelligence, the optimized production factors and product process flow are verified and optimized again by simulating factory operating performance indicators, including: Identify the business performance indicator factors, including: sustainability, productivity, agility, speed to market and customization; The optimized production factors and product process flow are verified and optimized again through the performance results of the operating performance indicator factors.

[0011] In a possible implementation, the optimized production factors and product process flow are re-verified and optimized through the performance results of the operating performance indicator factors, including: Optimize production factors and product process flow again based on the sustainable development of green factories, green products, carbon footprint, ESG, waste reduction, water reduction and energy efficiency; Optimize production factors and product process flow again based on productivity increase of factory output, productivity improvement, OEE improvement, product cost reduction, operating cost reduction and quality cost reduction; Optimize production factors and product process flow again with the criteria of reducing inventory, shortening delivery time, shortening line change time and improving on-time delivery; Optimize production factors and product process flow again based on the shortened time to market and reduced design iteration time; The production factors and product process flow are further optimized based on customized configuration precision commission and batch reduction.

[0012] In the embodiment of the present application, the production factors and product process flow involved in the factory production line are determined and optimized, and then the optimal production factors and product process flow are used in combination with the standardization system of the intelligent factory and the intelligent manufacturing standard to sequentially automate, informatize, digitize and intelligentize the production line, and finally obtain a simulation model, which is universal for the intelligent transformation of different types of factories. In addition, the production factor information in the design process is verified and optimized multiple times, so that the optimized production factors and product process flow meet the lean requirements, thereby ensuring that the designed and planned intelligent factory can improve the success rate of the construction and implementation of the intelligent factory project and the optimal production efficiency, cost, delivery time and quality after production. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flowchart of a smart factory design method based on digital lean and multiple closed-loop verification provided in an embodiment of the present application; Figure 2 A smart factory design framework diagram based on digital lean and multiple closed-loop verification is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0014] The present solution is described below in conjunction with the accompanying drawings and specific implementation methods.

[0015] See also Figure 1 The smart factory design method based on digital lean and multiple closed-loop verification provided in this embodiment includes: S101, determine the production factors and product process flows applicable to different types of production lines in the factory.

[0016] Due to the different types of products, the production factors and product process flows considered by the production lines in the factory are also different. For example, the process production line will consider product process flow analysis, operation flow, process control, personnel allocation and time, energy flow and personnel efficiency, etc., while the discrete production line will consider equipment information, production line balance working hours calculation, energy flow and personnel efficiency, etc. It can be seen that the production factors considered by different types of production lines are overlapping, so in this embodiment, when determining the production factors, the factors involved in the production of all different types of production lines are covered as much as possible.

[0017] Specifically, see Figure 2 In this embodiment, the production factors and product process flows involved in different production lines are determined, including: product process flow analysis, operation flow, physical flow, process control, basic equipment information, staffing and time, production line balance working hours calculation, energy flow and staff efficiency.

[0018] Further, the basic information of the production factors and product process flow is determined, wherein the basic information of the product process flow analysis includes work sections, processes, work stations and work positions, the work flow includes motion analysis through video or captured images, motion decomposition, cycle parameters and time units, the physical flow includes batch flow, single-piece flow and transmission mode, the process control includes input factors 5M1E, process control parameters, output performance quality characteristics, full process quality traceability and data elements, the basic information of the equipment includes equipment name, equipment brand specifications, theoretical production capacity, equipment quantity, equipment cost ROI and equipment overall efficiency OEE, the staffing and time include actual production capacity of a single person, staffing, attendance method and working hours, the production line balance working time calculation includes standard beat, working time calculation, allowance rate, budget cycle, production line balance rate, process cycle, manual simultaneous operation, machine working time and human-machine collaborative working time, the energy flow includes water, electricity, natural gas, coal and steam, and the staff efficiency includes staff saturation and per capita labor efficiency.

[0019] S102, verify and optimize production factors and product process flow through digital lean, so that the optimized production factors and product process flow of the production line meet the lean requirements of the production line.

[0020] The core principles of lean manufacturing include value, value stream, process, pull, and continuous improvement. Among them, value refers to the price that customers are willing to pay for products or services, value stream refers to all the steps that products or services go through in the production process, process refers to the production process of products or services, pull refers to production based on customer needs, and continuous improvement refers to the continuous optimization of the production process to improve efficiency and quality.

[0021] The implementation steps of lean include the following aspects: Determine the goal: clarify the implementation goals of lean, including improving production efficiency, reducing costs, improving quality, etc. Value stream analysis: Through value stream analysis, understand the production process of products or services, identify and eliminate unnecessary steps. Make a plan: Make a lean implementation plan, determine improvement measures, timetables and responsible persons. Implement improvements: According to the plan, implement various improvement measures, including training employees, optimizing processes, improving equipment, etc. Evaluate the effect: Evaluate the improvement effect, monitor production performance indicators, and continuously optimize improvement measures.

[0022] In this embodiment, the digital lean optimization indicators are determined, including: value-added activities, product material processes, equipment and LCIA. Each optimization indicator is decomposed to obtain the indicator optimization factors, where: the value-added activities include: necessary value-added, not necessary value-added, and not more value-added than necessary; the product material processes include product design packaging, supply chain materials, new processes and process description window optimization; the equipment and LCIA include equipment performance (MTBF, MTTR), labor-saving mechanisms, auxiliary positioning devices and fixtures. Verification and optimization are carried out according to the indicator optimization factors combined with the production factors and product process flows covered by different types of production lines.

[0023] S103, determine the standardization system and intelligent manufacturing standards that meet the requirements of intelligent factories.

[0024] Smart factory standards refer to a series of specifications and guidelines developed to achieve efficient and intelligent production and management in the context of industrial intelligence. These standards cover various systems and standards in all aspects from design, construction, delivery to production management, logistics, etc.

[0025] Specifically, in this embodiment, the standardized system that meets the requirements of the intelligent factory is determined, including: standardization of traditional production operations: QMS, EMS, 5M1E, and standardization of intelligent manufacturing systems: lighthouse factories, CMMM. The intelligent manufacturing standards that meet the requirements of the intelligent factory are determined, including: data collection standards, equipment purchase standards, software and hardware integration standards, and database standards.

[0026] S104, combining the optimized production factors and product process flow, the standardization system of the intelligent factory and the intelligent manufacturing standards, the production line is improved in automation, informatization, digitization and intelligence in turn.

[0027] Specifically, in this embodiment, the required automation level of the production line is determined from the form of automation, degree of automation, data acquisition equipment, sensor types and data interaction methods. The required informatization level of the production line is improved from the data acquisition method, data flow, system integration level, database, industrial software app and data platform; Determine the digital big data model, 3D algorithm and DIKW model of the production line; finally, combine the determined degree of automation, degree of informatization and digital model or algorithm to generate intelligent scenario application simulation twin AI and intelligent manufacturing field research and development and service model to realize the intelligentization of the production line.

[0028] S105, after the production line is improved through intelligence, the factory operating performance indicators are simulated to re-verify and optimize the optimized production factors and product process flow.

[0029] The intelligent transformation of a factory is ultimately reflected in the operating performance indicators. If the operating performance indicators deteriorate, it does not meet the purpose of the intelligent transformation of the factory.

[0030] In this embodiment, the operating performance indicator factors are determined to include: sustainable development, productivity, agility, speed to market and customization; the optimized production factors are re-verified and optimized through the performance results of the operating performance indicator factors.

[0031] Specifically, in this embodiment, the production factors are optimized again based on the sustainable development of green factories, green products, carbon footprint, ESG, waste reduction, water consumption reduction and energy efficiency. The production factors are optimized again based on the productivity of increased factory output, improved productivity, improved OEE, reduced product costs, reduced operating costs and reduced quality costs. The production factors are optimized again based on the agility of reduced inventory, shortened delivery time, shortened line change time and improved delivery on time. The production factors are optimized again based on the shortened time to market and the reduced design iteration time. The production factors and product process flow are optimized again based on the customized configuration accuracy commission and batch reduction.

[0032] S106, finally determining a simulation model based on the re-optimized production factors and product process flow and the intelligent transformation output results, wherein the simulation model is used to generate a corresponding intelligent factory design based on the input information.

[0033] In S105, after optimizing the production factors again by combining multiple factors such as sustainable development, productivity, agility, speed to market and customization, the production factors fully meet the requirements of lean manufacturing. At this time, the simulation model is determined by combining the optimal lean manufacturing factors and the output results of intelligent transformation, so that a traditional production factory can be quickly designed into an intelligent factory that meets its needs.

[0034] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and subsequent associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0035] The above is only a specific implementation of the present application. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. The protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A smart factory design method based on digital lean and multiple closed-loop verification, characterized in that: include: Determine the production factors and product process flows applicable to different types of production lines in the factory; Verify and optimize production factors and product process flow through digital leanness, so that the optimized production factors and product process flow of the production line meet the leanness requirements of the production line; Determine the standardized system and intelligent manufacturing standards that meet the requirements of intelligent factories; Combining the optimized production factors and product process flow, the standardized system of intelligent factories and intelligent manufacturing standards, the production line is improved in automation, informatization, digitization and intelligence in turn; After the production line is improved through intelligence, the factory operating performance indicators are simulated to re-verify and optimize the optimized production factors and product process flow; Finally, a simulation model is determined based on the optimized production factors, product process flow and intelligent transformation output results. The simulation model is used to generate a corresponding intelligent factory design based on the input information.

2. The smart factory design method based on digital lean and multiple closed-loop verification according to claim 1 is characterized in that: The determination of production factors and product process flows applicable to different types of production lines in the factory includes: Determine the production factors and product process flows involved in different production lines, including: product process flow analysis, operation flow, physical flow, process control, basic equipment information, staffing and time, production line balance working hours calculation, energy flow and staff efficiency; Determine the basic information of the production factors and product process flow, wherein the basic information of the product process flow analysis includes work sections, processes, work stations and work positions; the operation flow includes motion analysis through video or captured images, motion decomposition, cycle parameters and time units; the physical flow includes batch flow, single piece flow and transmission mode; the process control includes input factors 5M1E, process control parameters, output performance quality characteristics, full process quality traceability and data elements; the basic equipment information includes equipment name, equipment brand specifications, theoretical production capacity, equipment quantity, equipment cost ROI and equipment overall efficiency OEE; the staffing and time include actual production capacity of a single person, staffing, attendance method and working hours; the production line balance working time calculation includes standard beat, working time calculation, slack rate, budget cycle, production line balance rate, process cycle, manual simultaneous operation, machine working time and human-machine collaborative working time; the energy flow includes water, electricity, natural gas, coal and steam; the staff efficiency includes staff saturation and per capita labor efficiency.

3. The smart factory design method based on digital lean and multiple closed-loop verification according to claim 2 is characterized in that: The verification and optimization of production factors and product process flow through digital leanness, so that the optimized production factors and product process flow of the production line meet the lean requirements of the production line, includes: Determine digital lean optimization indicators, including: value-added activities, product material process and equipment and LCIA; Decompose each optimization index to obtain index optimization factors, where: the value-added activities include: necessary value-added, not necessary value-added and not necessary value-added; the product material process includes product design packaging, supply chain materials, new process and process description window optimization; the equipment and LCIA include equipment performance, labor-saving mechanism, auxiliary positioning device and fixture; Verification and optimization are carried out based on the optimization factors of the indicators combined with the production factors and product process flows covered by different types of production lines.

4. The smart factory design method based on digital lean and multiple closed-loop verification according to claim 3 is characterized in that: The determination of compliance with the standardized system and intelligent manufacturing standards of intelligent factories includes: Determine the standardized system that meets the requirements of smart factories, including: standardization of traditional production operations: QMS, EMS, 5M1E, and standardization of smart manufacturing systems: Lighthouse Factory, CMMM; Determine the smart manufacturing standards that meet the requirements of smart factories, including: data collection standards, equipment procurement standards, software and hardware integration standards, and database standards.

5. The smart factory design method based on digital lean and multiple closed-loop verification according to claim 4 is characterized in that: Combined with the optimized production factors and product process flow, the standardized system of intelligent factories and intelligent manufacturing standards, the production line is improved in automation, informatization, digitization and intelligence, including: Determine the required degree of automation for the production line based on the form of automation, degree of automation, data collection equipment, sensor types and data interaction methods; Improve the level of informatization required for production lines from the perspective of data collection methods, data flows, system integration, databases, industrial software apps and data platforms; Determine the production line digital big data model and / or 3D algorithm and / or DIKW model; Finally, the determined degree of automation, degree of informatization and digital model or algorithm is combined to generate intelligent scenario application simulation twin AI and intelligent manufacturing field research and development and service model to realize the intelligentization of production line.

6. The smart factory design method based on digital lean and multiple closed-loop verification according to claim 5 is characterized in that: After the production line is improved through intelligence, the optimized production factors and product process flow are verified and optimized again by simulating the factory operating performance indicators, including: Identify the business performance indicator factors, including: sustainability, productivity, agility, speed to market and customization; The optimized production factors and product process flow are verified and optimized again through the performance results of the operating performance indicator factors.

7. The smart factory design method based on digital lean and multiple closed-loop verification according to claim 6 is characterized in that: The optimized production factors and product process flow are re-verified and optimized through the performance results of the above-mentioned operating performance indicator factors, including: Optimize production factors and product process flow again based on the sustainable development of green factories, green products, carbon footprint, ESG, waste reduction, water reduction and energy efficiency; Optimize production factors and product process flow again based on productivity increase of factory output, productivity improvement, OEE improvement, product cost reduction, operating cost reduction and quality cost reduction; Optimize production factors and product process flow again with the criteria of reducing inventory, shortening delivery time, shortening line change time and improving on-time delivery; Optimize production factors and product process flow again based on the shortened time to market and reduced design iteration time; The production factors and product process flow are further optimized based on customized configuration precision commission and batch reduction.

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