A digital diagnosis and selection method for manufacturing industry

Abstract

The present invention belongs to the technical field of digital diagnosis and selection for enterprises, and specifically relates to a digital diagnosis and selection method for manufacturing industry. By collecting monthly active product models Y, sub-components C at various levels of product structure, planning responsible personnel R and adjustment factors N, and then bringing them into the formula to quickly calculate the monthly operating data flux T, the shortcomings of the existing technology are overcome, the digital diagnosis and selection work of the enterprise is improved, and a large amount of manpower and material resources are saved. At the same time, as the data collected by big data increases, the comparison table is further refined in the future, and a more intelligent selection can be upgraded, which has very good intelligence and scalability.

Description

Technical Field

[0001] The invention belongs to the technical field of digital diagnosis and selection for enterprises, and specifically relates to a digital diagnosis and selection method for manufacturing industry. Background Art

[0002] The purpose of digital diagnosis for enterprises is to fully understand the current digital level of the enterprise, including technical infrastructure, degree of digitalization of business processes, data management capabilities, and many other aspects. This helps to discover the strengths and weaknesses of the enterprise in the digitalization process and provide a basis for subsequent transformation strategies. It can also identify the problems and pain points of the enterprise in the digital transformation process, such as poor compatibility between systems, hidden dangers in data security, and insufficient digital skills of employees.

[0003] Existing diagnostic methods include:

[0004] 1 Questionnaire survey: Design a questionnaire covering multiple dimensions such as technology, business processes, data, and personnel, and distribute it to employees in different departments within the company. Use the questionnaire to collect their views and opinions on the current status of the company's digitalization.

[0005] 2Interviews: Conduct face-to-face or online interviews with senior management, department heads, and key employees of the company to gain a deeper understanding of the digital issues they encounter at work and their expectations for digital transformation.

[0006] 3. Document review: Review the company’s technical documents, business process manuals, data reports, etc. to obtain objective information about the company’s digital infrastructure, business processes, and data management.

[0007] 4. Data analysis: Collect the company's system logs, business data, etc., and use data analysis tools and techniques to conduct quantitative analysis of the company's digital operations, such as system usage frequency, data growth trends, etc.

[0008] Finally, expert consultants are needed to conduct demand research in the enterprise, communicate with the company's senior executives and heads of various departments, and even sort out the company's existing operating processes. It will take weeks or months to complete digital diagnosis and system selection. The manpower and material resources consumed are very large and the cycle is long. Therefore, it is necessary to develop a more intelligent and modern diagnosis and selection method to improve the existing technical means and help companies quickly complete digital diagnosis and selection in the manufacturing industry. Summary of the invention

[0009] The purpose of the present invention is to provide a digital diagnosis and selection method for manufacturing industry, which overcomes the shortcomings of the prior art. By collecting monthly active product model Y, product structure sub-components C at each level, planning responsible personnel R and adjustment factor N, and then bringing them into the formula Quickly calculate the monthly operation data flux T. Referring to the comparison table in the attachment, it only takes a few minutes to complete the enterprise digital diagnosis and selection work.

[0010] To solve the above problems, the technical solutions adopted by the present invention are as follows:

[0011] A digital diagnosis and selection method for the manufacturing industry, characterized by including:

[0012] S100. Collect the quantity of product model numbers of sales orders generated by the enterprise in the current month or on a monthly average, define it as the monthly active product model, and mark it as Y;

[0013] S200. Collect the quantity of sub-components at each level after the product BOM is expanded, define it as the sub-components at each level of the product structure, and mark it as C;

[0014] S300. Collect the planning staff in the entire value stream link during the enterprise operation process, define it as the planning responsibility staff, and mark it as R;

[0015] S400. Collect the management elements related to industry customization and product circulation, count according to the quantity of element types, define it as the adjustment factor, and mark it as N;

[0016] S500. Substitute the collected Y, C, R, and N into the formula to obtain the monthly operation data flux, where T is the monthly operation data flux;

[0017] S600. Compare the value of T through big data analysis or manual definition to obtain that the digital demand type is simple type or basic type or functional type or intelligent type.

[0018] Furthermore, the monthly active product model generally refers to the products that have not reached the delisting stage in the product life cycle, including the R & D prototypes and engineering machine sales orders after project establishment.

[0019] Furthermore, the sub-components at each level of the product structure include semi-finished products, raw materials, main packaging and auxiliary materials, and core process links.

[0020] Furthermore, the planning responsibility staff generally refers to the personnel in the planning-related responsibility positions such as sales planning, production planning, material control planning, and procurement planning.

[0021] Furthermore, the adjustment factors include industry production equipment, molds, testing equipment, non-standard production, etc.

[0022] Furthermore, when T≤150, the digital demand type is simple type; when 150<T≤250, the digital demand type is basic type; when 250<T≤400, the digital demand type is functional type; when 400<T, the digital demand type is intelligent type.

[0023] Furthermore, the simplified type recommends that the enterprise digital system adopt financial management and simple purchase, sales and inventory management. Offline report management status: achieve operational health through offline form management; comprehensive digital management status is determined as: start-ups or small category manufacturers, overcapacity.

[0024] Furthermore, the basic type recommends the use of ERP, simple MES, WMS and simple Kanban for enterprise digital systems. Offline report management status: relying on offline management with reports, generally facing abnormal value stream problems such as manufacturing continuity and inventory abnormalities; comprehensive digital management status is determined as: small batch and multi-variety flexible manufacturing, production based on sales, active connection between R&D, production and sales, and approaching or occasionally exceeding the capacity bottleneck during peak hours.

[0025] Furthermore, the functional type recommends the enterprise digital system to adopt ERP, MES, WMS, CRM, SRM, 0A, and BI dashboards. Offline report management status: enterprise operation is chaotic, with various operational anomalies; comprehensive digital management status is determined as: flexible manufacturing scale enterprise, lean concept organization operation, small fluctuations in production and sales, and stable production capacity.

[0026] Furthermore, the aforementioned smart type recommends that the enterprise digital system adopt comprehensive digitalization and automation + AI applications. Offline report management status: serious chaos, the throughput seriously exceeds the scope of human management, and the sales plan peak occurs and lasts for a long time; the comprehensive digital management status is judged as: smooth value flow, smart factory type.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The digital diagnosis and selection method for manufacturing industry described in the present invention collects monthly active product model Y, product structure sub-components C at each level, planning responsible personnel R and adjustment factor N, and then puts them into the formula Quickly calculate the monthly operational data flux T and refer to the comparison table in the attachment. It only takes a few minutes to complete the enterprise's digital diagnosis and selection work, saving a lot of manpower and material resources. At the same time, as the amount of data collected by big data increases, the comparison table can be further refined to upgrade to a smarter selection, which has very good intelligence and growth potential. DETAILED DESCRIPTION

[0029] The following will be combined with the tables in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] As shown in the table, the digital diagnosis and selection method for manufacturing industry described in the present invention is:

[0031] S100. Collect the number of product models and article numbers of sales orders generated by the enterprise in the current month or the monthly average, which is defined as the monthly active product model, marked as Y; the monthly active product model refers to products that have not reached the delisting stage in the product life cycle, including R&D prototypes and engineering machine sales orders after project approval.

[0032] S200. Collect the number of sub-components at each level after the product BOM is expanded, and define them as sub-components at each level of the product structure, marked as C; the sub-components at each level of the product structure include semi-finished products, raw materials, main packaging and auxiliary materials, and core process links

[0033] S300, collect the planning staff of the entire value stream during the enterprise operation process, defined as planning responsible personnel, marked as R; the planning responsible personnel generally refers to the personnel in the sales plan, production plan, material control plan, procurement plan and other planning-related responsibilities

[0034] S400, collect management elements related to industry customization and product circulation, count by element type, define as adjustment factors, marked as N; the adjustment factors include industry production equipment, molds, testing equipment, non-standard production, etc.

[0035] S500, put the collected Y, C, R, N into the formula Get the monthly operational data flux, where T is the monthly operational data flux;

[0036] S600. Compare the values ​​of T through big data analysis or manual definition to determine the type of digital demand as simple type, basic type, functional type or intelligent type.

[0037] A reference indicator for measuring the digitalization needs of manufacturing companies. The indicator value represents the scale of information managed by each planning manager within the company, and measures the difficulty of operational planning.

[0038] If the value stream is not pulled through, it will exceed the critical range and encounter related problems such as abnormal manufacturing continuity and abnormal inventory. The larger the flux value after the value stream is pulled through, the stronger the company's flexible manufacturing capabilities.

[0039] 1. Annex Reference Table

[0040] Operational data throughput and digitalization selection reference table

[0041]

[0042] Operational data throughput and management status reference table

[0043]

[0044] Example

[0045] An assembly manufacturing company M has 12 monthly active product models, 165 sub-components per product model, and 3 on-the-job planners (planning positions include production planning, material control planning, procurement planning, etc.)

[0046] Operational data throughput = (12*165) / 3 = 660

[0047] A precision manufacturing company Y, custom processing according to drawings or molds, has an average of 200 active product models per month (peak 300+), 4 sub-components for each product model (process route semi-finished products), and 2 on-the-job planners (workshop planning)

[0048] Operational data throughput = (200*4) / 2 = 400

[0049] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations within the meaning and range of equivalent elements of the claims be included in the invention. Any tabular marking in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A digital diagnosis and selection method for manufacturing industry, characterized by: Include S100. Collect the quantity of product model numbers of the sales orders generated by the enterprise in the current month or on a monthly average, and define it as the monthly active product model, marked as Y. S200. Collect the quantity of sub-components at each level after the product BOM is expanded, and define it as the sub-components at each level of the product structure, marked as C. S300. Collect the planning staff in all value stream links during the enterprise operation, and define it as the personnel with planning responsibilities, marked as R. S400. Collect the management elements related to industry customization and product circulation, count them according to the quantity of element types, and define it as the adjustment factor, marked as N. S500, put the collected Y, C, R, N into the formula Get the monthly operational data flux, where T is the monthly operational data flux; S600. Compare the value of T through big data analysis or manual definition to obtain the digital demand type as simple type, basic type, functional type or intelligent type.

2. A manufacturing digital diagnosis and selection method according to claim 1, characterized in that: The monthly active product model generally refers to the products that have not reached the delisting stage in the product life cycle, including the R & D prototypes and engineering machine sales orders after project approval.

3. A manufacturing digital diagnosis and selection method according to claim 1, characterized in that: The sub-components at each level of the product structure include semi-finished products, raw materials, main packaging and auxiliary materials, and core process links.

4. A manufacturing digital diagnosis and selection method according to claim 1, characterized in that: The personnel with planning responsibilities generally refer to the personnel in planning-related positions such as sales planning, production planning, material control planning, and procurement planning.

5. The method for digital diagnosis and selection of manufacturing industry according to claim 1, characterized in that: The adjustment factor includes industry production equipment, molds, testing equipment, non-standard production, etc.

6. A manufacturing digital diagnosis and selection method according to claim 1, characterized in that: When T≤150, the digital demand type is simple type; when 150<T≤250, the digital demand type is basic type; when 250<T≤400, the digital demand type is functional type; when 400<T, the digital demand type is intelligent type.

7. A manufacturing digital diagnosis and selection method according to any one of claims 1 or 6, characterized in that: For the simple type, it is recommended for the enterprise digital system to adopt financial management and simple purchase and sales inventory. Status of offline report management: Achieve operational health through offline form management; The determination of the comprehensive digital management status is: start-up enterprises or small-category manufacturers with overcapacity.

8. A manufacturing digital diagnosis and selection method according to any one of claims 1 or 6, characterized in that: For the basic type, it is recommended for the enterprise digital system to adopt ERP, simple MES, WMS and simple kanban. Status of offline report management: Rely on offline report management and generally face value stream anomalies such as manufacturing continuity and inventory anomalies. The determination of the comprehensive digital management status is: small-batch, multi-variety flexible manufacturing, production based on sales, active connection of R & D, production and sales, and the peak time is close to or occasionally exceeds the production capacity bottleneck.

9. A manufacturing digital diagnosis and selection method according to any one of claims 1 or 6, characterized in that: For the functional type, it is recommended for the enterprise digital system to adopt ERP, MES, WMS, CRM, SRM, 0A, BI kanban. Status of offline report management: The enterprise operation is chaotic and various business anomalies occur; The determination of the comprehensive digital management status is: flexible manufacturing scale enterprises, lean concept organizational operation, small production and sales fluctuations, and stable production capacity.

10. A manufacturing digital diagnosis and selection method according to any one of claims 1 or 6, characterized in that: For the intelligent type, it is recommended for the enterprise digital system to adopt comprehensive digitalization and automation + AI applications. Status of offline report management: Seriously chaotic, the throughput seriously exceeds the scope of human management, and the sales plan appears and lasts at the peak time; The determination of the comprehensive digital management status is: value stream smooth, intelligent factory type.

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