A multi-dimensional work time statistics and analysis method based on processes, types of work and equipment

Abstract

The application belongs to the technical field of mechanical manufacturing production, and discloses a multi-dimension work hour statistics and analysis method based on working procedures, working types and equipment. The target product is first decomposed according to assembly relations to form a hierarchical product structure tree, and then a process route library of binding working procedures, working types, equipment and standard work hours is established for each component. Through a statistics engine, work hour data is aggregated from bottom to top according to a selected range, and finally a high-dimension analysis report of working type dimension and equipment dimension is generated, so that fine, multi-dimension and strongly associated management of work hour data is realized. The application can accurately calculate the work hour demand of each working type and the equipment load time, effectively locate the production bottleneck, provide data support for personnel allocation, equipment scheduling, cost accounting and production plan optimization, and solve the problems of coarse statistics granularity, single dimension, weak data association and insufficient decision support in traditional work hour statistics.

Description

Technical Field

[0001] This invention belongs to the field of mechanical manufacturing technology, and in particular relates to a multi-dimensional method for time statistics and analysis based on processes, types of work and equipment. Background Technology

[0002] In the machining industry, a product typically consists of hundreds or even thousands of parts, involving long production processes, numerous trades, and highly specialized equipment. For example, a large mechanical product might contain over 1600 parts, each requiring multiple processes (such as turning, milling, grinding, drilling, heat treatment, and surface treatment), each completed by a specific trade (such as a milling machine operator) on specific equipment (such as a vertical machining center). The product structure is complex, with numerous parts and rigorous machining processes. Accurate time tracking is fundamental for cost accounting, production scheduling, personnel performance evaluation, and equipment utilization analysis. However, traditional time tracking methods have the following drawbacks: 1. Coarse statistical granularity: Statistics are typically only collected at the part level, or even the product level, failing to penetrate to the process level. This prevents managers from understanding which specific processing step is the time bottleneck.

[0003] 2. Limited Dimensions: Traditional statistical results often only show a total working hours, which cannot effectively separate the working hours consumed by different types of work (such as turning, milling, fitting, grinding) or calculate the actual load (machine hours) of each key piece of equipment.

[0004] 3. Poor data correlation: Data on working hours, personnel (job types), and equipment exist in isolation, lacking effective structured correlation. When process adjustments or problem analysis are needed, it is difficult to quickly trace and analyze the impact.

[0005] 4. Weak management decision support: Due to the lack of multi-dimensional and granular working hour data, managers often rely on experience rather than data when making production staff allocation, job ratio adjustments, and equipment procurement and maintenance plans, resulting in insufficient scientific decision-making.

[0006] Therefore, there is an urgent need in this field for a refined statistical and analytical method that can decompose working hours into processes and strongly correlate them with job types and equipment. Summary of the Invention

[0007] The technical problem to be solved by this invention is to provide a statistical method that enables refined, structured management and flexible multi-dimensional analysis of work time data, thereby providing accurate data insights for production management.

[0008] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows: A multi-dimensional method for time statistics and analysis based on process, job type, and equipment includes the following steps: S1. Product Structure Decomposition: Decompose the target product into components and parts according to the assembly relationship to form a hierarchical product structure tree; S2. Construction of Process Route Library: Create a corresponding process route for each component in the product structure tree. Each process route record includes the process number, process name, process content, type of work to be performed, required equipment and standard working hours, and establish a data model relating working hours, type of work and equipment.

[0009] S3, Working Hour Data Aggregation: By selecting the statistical range through the statistical engine, the working hour data of parts is aggregated from bottom to top, completing the transfer of working hours from micro to macro. S4. Multidimensional Report Generation: Based on the aggregated working hour data, generate analysis views in the dimensions of job type and equipment. The job type dimension counts the total working hours of each job type, and the equipment dimension counts the total machine hours of each piece of equipment.

[0010] Furthermore, in step S2, the process route is recorded sequentially according to the part processing order, and each process is uniquely bound to the corresponding execution type and processing equipment, forming a one-to-one correspondence between process, type of work, equipment and time.

[0011] Furthermore, in step S3, the statistical range supports flexible selection of individual parts, individual components, and the entire product. During aggregation, the total working hours are calculated by weighting the number of parts installed.

[0012] Furthermore, in step S4, job-specific analysis is used to calculate production personnel requirements, and equipment-specific analysis is used to assess equipment load and utilization.

[0013] Furthermore, it also includes a time calculation model: the time for a single part is the sum of the time for all processes of that part; the total time for the product is the sum of the product of the time for each part and the corresponding number of units installed.

[0014] Furthermore, by using index models and indicator functions, the working hours records of target parts, target jobs, or target equipment can be filtered and matched to achieve accurate working hour statistics in specified dimensions.

[0015] The present invention has the following advantages: 1) Refinement: Time management is deepened from the part level to the process level, realizing refined management of time data, which helps to identify production bottlenecks.

[0016] 2) Multi-dimensional: It breaks away from the traditional single-dimensional concept of working hours and realizes cross-analysis based on two key production factors: "job type" and "equipment", which multiplies the value of data.

[0017] 3) Strong correlation: By linking working hours, job types, and equipment through "processes", a complete data chain is formed, and changes in any one party can be quickly assessed for their impact on other parties.

[0018] 4) Strong management decision support capabilities: Staffing: Managers can clearly see the total working hours required for each job, thus enabling them to scientifically arrange and adjust the number and proportion of staff for each job.

[0019] Equipment utilization rate: It can accurately measure the load of each piece of equipment, providing a direct basis for equipment maintenance, upgrades, and procurement plans, and avoiding equipment idleness or overload.

[0020] Cost control: Detailed working hour data is the foundation for accurate cost accounting and helps to calculate product costs more accurately.

[0021] Production planning optimization: Provides accurate basic data support for advanced planning and scheduling systems, making scheduling more reasonable and efficient. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the overall method of an embodiment of the present invention. Figure 2 Example diagram of product structure tree (hierarchical relationship of product-component-part); Figure 3 Example diagram of part manufacturing process (relationship between process, type of work, equipment, and time); Figure 4 This is a schematic diagram of time data aggregation and multidimensional analysis in an embodiment of the present invention. Detailed Implementation

[0023] To better understand the purpose, structure, and function of this invention, the invention will be described in further detail below with reference to the accompanying drawings.

[0024] This embodiment takes a certain model of product as an example (containing 1600 parts).

[0025] Step 1: Establishing the basic database 1. Establish a product structure tree for this product in the system (corresponding to...) Figure 2 This clarifies the product's assembly relationships and the number of units that can be installed. For example, product XXXX has n subordinate components, each component consists of multiple parts, and each part corresponds to a different number of units that can be installed.

[0026] 2. Create a process route for each component and assign a process name, process content, working hours, and equipment information to each process: For example, the 19-6 long pin shaft belongs to assembly 19-01, and its process route is: Modulation—Carving—Carving—Grinding—Carving, as detailed below: Process 1: Process Name – Box-type tempering, Process Content – ​​Quenching, Job Type – Heat Treatment Worker, Duration – 6 minutes, Equipment – ​​RJ2.4-3202-HD Operation 2: Operation Name – Turning; Operation Content – ​​Drill φ10, depth greater than 100, chamfer the hole opening C0.4, place the center point, turn φ40f9 into φ40.5h13, groove turning, chamfer turning; Occupation – Turning Operator; Time – 30 minutes; Equipment – ​​CK61125 Operation 3: Operation Name – Turning; Operation Content – ​​Turning and Aligning, Turning End Face, Turning Outer Diameter φ43, Drilling Hole φ10, Chamfering C2, Chamfering Hole Opening C0.4; Occupation – Turning Operator; Time – 15 minutes; Equipment – ​​CK61125 Process 4: Process Name – Grinding; Process Content – ​​Grinding the outer diameter φ40.5h13 to φ40f9; Job Type – Grinding Operator; Duration – 36 minutes; Equipment – ​​SRA600 Operation 5: Operation Name – Lathe, Operation Content – ​​Cleaning, Job Type – Lathe Operator, Working Hours – 24 minutes, Equipment – ​​CK61125 By analogy, a database will be established for the 1,600 components of the product.

[0027] Step 2: Establishing the Mathematical Model Single part time model: Suppose that the machining of a part requires m operations, and the machining time for a single part is equal to the sum of the machining times for all operations: , T i This is the time required for the i-th process of the part. For example, the time required for a single long pin shaft of part 19-6 is 6+30+15+36+24=111 (minutes).

[0028] Product total working hours model: Suppose the product is composed of n different parts. Represents the total working hours of the product. Representing the The number of such parts installed (i.e., the number of times such part is used in the product). For the first The sum of the time required for all processes of a certain type of part.

[0029] Total product working hours Therefore, the working hours for this model of product are 7500 hours.

[0030] 3. Index Model: Set up product time record In China, there are a total of records, index Represents the first in the set 1 record .

[0031] For each record Define the following properties: : indicates the first The number of working hours recorded; : indicates the first The part number of the record; : indicates the first The type of work (or equipment) recorded.

[0032] Define query conditions: : The target part number that needs to be counted; The target job type (or equipment) that needs to be statistically analyzed, e.g.: =“Lathe worker” Define indicator functions: A function is needed to determine the validity of a specific record. To determine whether both conditions are met simultaneously, use an indicator function. If the condition is true, return 1; if it is false, return 0.

[0033] Indicator functions for part number conditions: Indicator functions for job conditions: Establish mathematical model formula The working hours for a certain job are those that simultaneously meet all the requirements in all records. and Recorded working hours The sum of .

[0034] Working hours for a certain job Step 3: Work Hour Statistics and Analysis 1. When managers need to understand the total working hours and resource requirements of the entire product, they can select the product in the system interface and execute: Statistics.

[0035] 2. The system's backend time aggregation engine starts working, automatically accumulating all the time for all 1600 parts to obtain the total time for the product.

[0036] 3. Generate multidimensional reports (corresponding to...) Figure 4 ) 1) Job type time report: Automatically categorizes and summarizes the time for each job type.

[0037] 2) Equipment Hourly Report: Automatically categorizes and summarizes the hourly data of each piece of equipment.

[0038] 3) Managers can use these reports to make precise arrangements, such as how many lathe operators and milling machine operators are needed to meet the production schedule, and can accurately identify bottleneck equipment that needs to be prioritized or have additional shifts added.

[0039] The above embodiments fully demonstrate the practicality, effectiveness, and innovation of the method of the present invention.

[0040] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art will be able to make various modifications and improvements without departing from the principles of the present invention, and these modifications and improvements should also be considered to fall within the scope of protection of the present invention.

Claims

1. A multi-dimensional time statistics and analysis method based on process, job type, and equipment, characterized in that, Includes the following steps: S1. Product Structure Decomposition: Decompose the target product into components and parts according to the assembly relationship to form a hierarchical product structure tree; S2. Construction of Process Route Library: Create a corresponding process route for each component in the product structure tree. Each process route record includes the process number, process name, process content, type of work to be performed, required equipment and standard working hours, and establish a data model relating working hours, type of work and equipment. S3, Working Hour Data Aggregation: By selecting the statistical range through the statistical engine, the working hour data of parts is aggregated from bottom to top, completing the transfer of working hours from micro to macro. S4. Multidimensional Report Generation: Based on the aggregated working hour data, generate analysis views in the dimensions of job type and equipment. The job type dimension counts the total working hours of each job type, and the equipment dimension counts the total machine hours of each piece of equipment.

2. The multi-dimensional time statistics and analysis method based on process, job type and equipment as described in claim 1, characterized in that, In step S2, the process route is recorded sequentially according to the part processing order. Each process is uniquely bound to the corresponding execution type and processing equipment, forming a one-to-one correspondence between process, type of work, equipment and time.

3. The multi-dimensional time statistics and analysis method based on process, job type and equipment as described in claim 1, characterized in that, In step S3, the statistical range supports flexible selection of individual parts, individual components, and the entire product. When aggregating, the total working hours are calculated by weighting the number of parts installed.

4. The multi-dimensional time statistics and analysis method based on process, job type and equipment as described in claim 1, characterized in that, In step S4, job-specific analysis is used to calculate production personnel requirements, and equipment-specific analysis is used to assess equipment load and utilization.

5. The multi-dimensional time statistics and analysis method based on process, job type and equipment as described in claim 1, characterized in that, It also includes a time calculation model: the time for a single part is the sum of the time for all processes of that part; the total time for the product is the sum of the product of the time for each part and the corresponding number of units installed.

6. The multi-dimensional time statistics and analysis method based on process, job type and equipment as described in claim 5, is characterized in that, By using index models and indicator functions, the time records of target parts, target jobs, or target equipment can be filtered and matched to achieve accurate time statistics in a specified dimension.

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