Standard man-hour setting method for print-oriented smart factory

Abstract

The application discloses a standard man-hour setting method for a printing intelligent factory, and specifically comprises the following steps: step 1, constructing a product type database, and dividing products into typical standardized products and non-standardized products; step 2, obtaining actual production data of the typical standardized products; step 3, calculating the benchmark man-hour of the typical standardized products according to the production data obtained in step 2; step 4, determining a work procedure difficulty degree evaluation coefficient alpha and a worker skill operation level evaluation coefficient beta; and step 5, calculating the standard man-hour of the non-standardized products according to the results obtained in steps 3 and 4. The application can simultaneously set the man-hour of the standardized products and the non-standardized products.

Description

Technical Field

[0001] This invention belongs to the technical field of standard working time setting in the packaging and printing industry, and relates to a method for setting standard working times for intelligent printing factories. Background Technology

[0002] Standard working hours are an important basis for enterprises to formulate production plans, evaluate worker performance, and calculate and improve factory efficiency. Due to the differences in production models, product types, production processes, equipment types, and other factors among different types of industries, the methods for formulating standard working hours also vary, exhibiting typical industry attributes.

[0003] Currently, the commonly used methods for setting standard working hours in the industry mainly include manual measurement, empirical estimation, analogy comparison, mathematical modeling, and MTM methods. However, these methods lack scientific rigor, accuracy, time consumption, and efficiency. Therefore, it is necessary to design a standard working hour setting method suitable for the packaging and printing industry and geared towards intelligent printing factories. Summary of the Invention

[0004] The purpose of this invention is to provide a standard working time determination method for intelligent printing factories, which can simultaneously determine the working time for both standardized and non-standardized products.

[0005] The technical solution adopted in this invention is a standard working time determination method for intelligent printing factories, which specifically includes the following steps:

[0006] Step 1: Build a product type database and classify products into typical standardized products and non-standardized products;

[0007] Step 2: Obtain actual production data for typical standardized products;

[0008] Step 3: Calculate the baseline working hours for a typical standardized product based on the production data obtained in Step 2;

[0009] Step 4: Determine the difficulty level assessment coefficient α and the worker skill level assessment coefficient β;

[0010] Step 5: Calculate the standard working hours for non-standard products based on the results obtained in Steps 3 and 4.

[0011] The invention is further characterized by:

[0012] The production data obtained in step 2 includes the actual production quantity N, registration accuracy δ, and actual processing time T in the printing process. m Processing waiting time T n Time conversion index H.

[0013] The specific process of step 3 is as follows: Set the relaxation rate in the printing process to θ, and calculate the baseline working time for a typical standardized product using the following formula (1):

[0014]

[0015] The specific process of step 4 is as follows:

[0016] Step 4.1: Establish a process difficulty assessment model and determine the assessment coefficient α;

[0017] Step 4.2: Establish a worker skill operation level assessment model and determine the worker skill operation level assessment coefficient β.

[0018] The specific process of step 5 is as follows:

[0019] By multiplying the base working hours by the corresponding evaluation coefficients α and β, i.e., by formula (2), we can obtain:

[0020] ST′(A i )=BT′(A i )×α×β (2).

[0021] The beneficial effects of this invention are as follows:

[0022] 1. This invention takes into account the impact of factors such as the difficulty of the process, the differences in equipment and workers on the standard working hours, making them more accurate and refined, so as to ensure the accurate execution of the production scheduling plan.

[0023] 2. According to the present invention, the baseline working hours for standardized products and the standard working hours for new products with similar process characteristics can be formulated, which solves the problem that due to the wide variety of packaging and printing products and the frequent updates of layouts, the order update frequency is high, requiring a large amount of actual measurement data to formulate standard working hours, which is time-consuming, costly, labor-intensive, and has a long cycle. Detailed Implementation

[0024] The present invention will now be described in detail with reference to specific embodiments.

[0025] This invention provides a method for setting standard working hours in intelligent printing factories, which specifically includes the following steps:

[0026] Step 1: Build a product type database. The product types in the database are divided into: typical standardized products and non-standardized products.

[0027] From typical standardized products, randomly select one typical standardized product A. The process route of product A is: slitting - cutting - printing - varnishing - corrugating - die-cutting - box gluing. The characteristics of the printing process are: 6 colors, registration accuracy, layout complexity, and color difference Lab value. From non-standardized products, randomly select one non-standardized product A′ with similar process or procedure characteristics to typical standardized product A. The process route of product A′ is: slitting - cutting - printing - varnishing - corrugating - die-cutting - box gluing. The characteristics of the printing process are: 6 colors, registration accuracy, layout complexity, and color difference Lab value.

[0028] Based on the process routes of products A and A′, the processes are broken down and matched to determine the specific processes for which standard working hours need to be established (taking the printing process as an example). Based on these specific processes, the corresponding production equipment (taking a printing press as an example) is determined. The difference in standard working hours between the two types of products can be adjusted using two adjustment coefficients: the difficulty of the process and the skill level of the operators. This reduces the time and resources required to establish standard working hours for each product based on extensive experimental data.

[0029] Step 2: Collect data in real time from the enterprise's MES system and obtain production data for each process of product A through the enterprise's ERP system (taking the printing process as an example).

[0030] Obtain relevant information such as process route, process requirements, BOM composition, production quantity, and loss rate for the production order of the printing process of product A from the enterprise ERP system;

[0031] The actual production quantity N = 14,000 sheets for the printing process of product A is obtained from the enterprise MES system. The number of printing colors for this order is 6, and the production loss rate for this printing process is δ = 0.45% / color. The actual processing time T on the 3# full-size printing press 6C+1 equipment is also obtained. m =85min, material preparation time T1=6min, car wash and color change time T2=8min, printing plate loading time T3=6min, machine setup time T4=20min, time conversion index H=60 data.

[0032] Step 3, Calculation of the baseline working hours for typical standardized products. Based on the data obtained in Step 2, the enterprise can customize the corresponding allowance rate θ according to the actual situation of the workshop workers and the production difficulty of the order. Here, the allowance rate is taken as θ = 5%. The baseline working hours BT′(A) for typical standardized products are calculated by the baseline working hour formula (1). i ):

[0033] Substituting the corresponding data from step 2 into formula (1) yields the following result:

[0034]

[0035] Step 4: Establish the process and worker evaluation level model and determine the corresponding evaluation coefficients α and β (taking the printing process as an example).

[0036] Step 4.1, the method for establishing the process difficulty assessment model is as follows:

[0037] Step 4.1.1: Construct a process difficulty assessment index system. Based on the analysis of the printing production process flow and process characteristics, the factors affecting the difficulty of each process are divided into first-level indicators U. i Second-level indicator U ij This allows for the construction of an evaluation factor system for the difficulty of a process.

[0038] The first-level indicator is: U i ={U1,U2,U3,...,U n}

[0039] The second-level indicator is: U ij ={U i1 U i2 U i3 ,...,U ns}

[0040] Where i = 1, 2, ..., n, j = 1, 2, ..., s, n represents the number of primary evaluation indicators and s represents the number of secondary evaluation indicators.

[0041] Based on the analysis of the packaging and printing production process and its characteristics, the factors affecting the difficulty of an order are categorized into seven primary evaluation indicators for each process: slitting (U1), cutting (U2), printing (U3), varnishing (U4), corrugating (U5), die-cutting (U6), and box gluing (U7). Each process has numerous influencing factors; therefore, based on the analysis of these factors for each type of process, the factors affecting the difficulty of each process are further subdivided into: roll width (U...). 11 Number of cutting blades U 21 , printing color number U 31 The 20 evaluation indicators are a subset of secondary evaluation indicators. Here, i represents the sequence number of the primary evaluation indicator, and j represents the sequence number of the secondary indicator.

[0042] Step 4.1.2: Construct the assessment level of the assessment object.

[0043] The different assessment levels affecting the assessment object are divided into: V U ={v1,v2,v3,…,v m} where u = 1, 2, ..., m, and m represents the number of output levels for the evaluation grades. In this example, the difficulty levels of production orders and processes for packaging and printing companies are divided into five evaluation grades: "A, B, C, D, E," which correspond to the following meanings: "A grade - Very easy, B grade - Relatively easy, C grade - Moderate difficulty, D grade - Relatively difficult, E grade - Very difficult," as shown in Table 1.

[0044] Table 1. Order and Process Difficulty Level Assessment

[0045] Comments Level Grade A Grade B Grade C Grade D Grade E Meaning of level Very easy Relatively easy Normal Difficulty Relatively difficult Very difficult

[0046] Step 4.1.3: Construct the judgment matrix of the evaluation indicators.

[0047] The judgment matrix is ​​an n-order matrix composed of comparisons between evaluation indicators. It uses AHP importance scale values ​​from 1 to 9, or their reciprocals, to represent the relative importance of each indicator. The meanings of 1 to 9 are shown in Table 2 below. The judgment matrix model is established as follows:

[0048]

[0049] In the formula, a ij This indicates the degree of importance of indicator i relative to indicator j.

[0050] Table 2. Meaning of Importance Scale Values ​​1-9

[0051]

[0052] Therefore, we can obtain the second-level judgment matrix A3 for the printing process in the order.

[0053]

[0054] Step 4.1.4: Determine the weights of each factor.

[0055] The weights of each factor are determined by the eigenvector corresponding to the largest eigenvalue of the judgment matrix. The eigenvector corresponding to the largest eigenvalue of the judgment matrix is ​​generally calculated using the square root method, and the calculation steps are as follows:

[0056] (1) Calculate the product of the elements in each row of the judgment matrix.

[0057]

[0058] (2) Calculate M i nth root

[0059]

[0060] (3) For vectors Normalization, i.e.

[0061]

[0062] but This is the desired feature vector, which is the weight vector of the n factors.

[0063] (4) Calculate the largest eigenvalue of the judgment matrix.

[0064]

[0065] In formula (5), (Aw T ) i Let represent the i-th element of vector Aw.

[0066] Normalization yields the index weight matrix W U as follows:

[0067]

[0068] In the formula, W i (i = 1, 2, ..., n) and W iu (i = 1, 2, ..., n; u = 1, 2, ..., m) are the weight matrices corresponding to the first and second level evaluation indicators, respectively; n is the number of evaluation indicators, that is, one evaluation indicator corresponds to one weight.

[0069] Therefore, the maximum characteristic value λ of the printing process in the order can be obtained. max and indicator weights W i As shown in Table 3 below:

[0070] Table 3 Results of Hierarchical Single Sort Calculation

[0071]

[0072] Step 4.1.5, Fuzzy comprehensive evaluation of the difficulty of the process.

[0073] The expert evaluation method is used to process the data appropriately, and the membership degree of each indicator relative to the evaluation level is obtained, thereby constructing the fuzzy matrix R of each indicator. U as follows:

[0074]

[0075] In the formula, matrix element r ij U represents the weight of the i-th evaluation indicator to the j-th evaluation level. i For v i The membership relationship is given by i = 1, 2, ..., n; j = 1, 2, ..., m.

[0076] Based on the statistical results of expert scoring, seven fuzzy relation matrices are established for the first-level indicator factors according to equation (2-7), where r ij The membership value is determined by equation (2-8):

[0077]

[0078] In the formula, n ij This indicates that the indicator U i The rating is v. j The number of people, the resulting orders, and the difficulty assessment of the processes are represented by a two-level fuzzy matrix.

[0079] The first-level fuzzy matrix is ​​obtained using the second-level evaluation vector operation, where the membership degree of the second-level indicators is calculated as follows:

[0080]

[0081] The membership degree of the primary indicators is calculated as follows:

[0082]

[0083] In the formula, R U =[B1,B2,B3,…,B n ] T b i (i = 1, 2, ..., n) represents the evaluation object's attitude towards the fuzzy evaluation level v. i The degree of membership. Based on the principle of maximum membership, determine which evaluation level the overall evaluation result of the evaluated object belongs to.

[0084] The comprehensive evaluation results of the printing process difficulty levels in the order are shown in Table 4 below:

[0085] Table 4. Comparison of Comprehensive Evaluation Results of Order and Process Difficulty Levels

[0086]

[0087] The difficulty level assessment results of the printing process in the order calculated according to the above formula are shown in Table 4. The maximum membership degree of the comprehensive assessment result of the production order is 0.3259, which corresponds to the level "D level - relatively difficult".

[0088] Step 4.1.6, determine the rating coefficient α.

[0089] Enterprises can determine the difficulty rating coefficient α for each process based on its product type and characteristics, process complexity, and historical data analysis, within the range of [1.2, 0.8]. The difficulty level of each process is rated using an expert scoring method to determine the coefficient α. The specific procedure is as follows:

[0090] 1) Organize r experts to estimate the rating coefficient α corresponding to each evaluation level, and obtain the estimated value ω of the rating coefficient α corresponding to each evaluation level. k1 ω k2 ω k3 ω k4 ω k5 (k = 1, 2, 3, ..., r).

[0091] 2) Calculate the average of the rating coefficients α given by r experts, using the following formula:

[0092]

[0093] 3) Calculate the deviation between the estimated value and the average value.

[0094]

[0095] 4) For the deviation Δ kj If the j-th rating coefficient estimate is larger, then k experts should be asked to re-evaluate ω. kj Until the deviation is less than 0.05, the average estimated correction value of a set of rating coefficients α is finally obtained.

[0096] The final rating coefficient α, determined by the expert scoring method, is shown in Table 5.

[0097] Table 5. Evaluation level of difficulty for each process and its corresponding evaluation coefficient α

[0098]

[0099] As we know from step 4.1.6, the order corresponds to the level "D level - relatively difficult".

[0100] Therefore, the difficulty rating coefficient α of the printing process of a certain example order of a new product A′ with similar process or procedure characteristics to the typical standardized product A is 0.9;

[0101] Step 4.2, the method for establishing the worker skill operation level assessment model is as follows:

[0102] Step 4.2.1: Construct an evaluation index system for workers' skill operation level. Based on a survey and analysis of production operation workers in a packaging and printing company, this paper mainly divides the influencing factors of workers' skill operation level into: initial skill U1, learning ability U2, number of times the work is repeated U3, and task difficulty U4, thereby constructing an evaluation index system for the difficulty of the process.

[0103] The first-level indicator is: U i ={U1,U2,U3,...,U n}

[0104] Where i = 1, 2, ..., n, j = 1, 2, ..., s, and n represents the number of primary evaluation indicators input.

[0105] Step 4.2.2: Construct the evaluation level of the evaluation object.

[0106] The different assessment levels affecting the assessment object are divided into: V U ={v1,v2,v3,…,v m}, where i = 1, 2, ..., m, and m represents the number of output evaluation levels. The evaluation levels are mainly divided into five levels: "A, B, C, D, E", which correspond to the following meanings: "A - Excellent, B - Good, C - Average, D - Poor, E - Poor". See Table 6 below:

[0107] Table 6 Worker Skill Level Assessment Grades

[0108] Comments Level Grade A Grade B Grade C Grade D Grade E Meaning of level Very easy Relatively easy Normal Difficulty Relatively difficult Very difficult

[0109] Step 4.2.3: Construct the judgment matrix of the evaluation indicators and calculate their weights.

[0110] The judgment matrix is ​​an n-order matrix composed of comparisons between evaluation indicators. It uses AHP importance scale values ​​from 1 to 9, or their reciprocals, to represent the relative importance of each indicator. The meanings of 1 to 9 are shown in Table 2-1 above. The judgment matrix model is established as follows:

[0111]

[0112] In the formula, a ij This indicates the degree of importance of indicator i relative to indicator j.

[0113] Therefore, the judgment matrix for the worker's operational skill level can be obtained as follows:

[0114]

[0115] Step 4.2.4: Determine the weights of each factor.

[0116] The weight matrix W is obtained by normalization using the square root method employed in step 4.1.4. u as follows

[0117]

[0118] In the formula, W i (i = 1, 2, ..., n) is the weight matrix corresponding to the first-level indicators; n is the number of evaluation indicators, that is, one evaluation indicator corresponds to one weight.

[0119] The worker's weight vector is: W = (0.0679, 0.3899, 0.3899, 0.1524)

[0120] Step 4.2.5, Fuzzy comprehensive evaluation of the difficulty of the process.

[0121] The expert evaluation method is used to process the data appropriately, and the membership degree of each indicator relative to the evaluation level is obtained, thereby constructing the fuzzy matrix R of each indicator. U as follows:

[0122]

[0123] In the formula, matrix element r ij U represents the weight of the i-th evaluation indicator to the j-th evaluation level. i For v i The membership relationship is given by i = 1, 2, ..., n; j = 1, 2, ..., m.

[0124] The membership degree of the primary indicators is calculated as follows:

[0125] B U =W U ×R U ={b1,b2,…,b n} (16)

[0126] The calculation is based on the above formula:

[0127] The fuzzy relation matrix is ​​as follows:

[0128]

[0129] The fuzzy comprehensive evaluation is: B = A × R = (0.2017, 0.3306, 0.2593, 0.1542, 0.0542)

[0130] The comprehensive evaluation results of worker operational levels in the order are shown in Table 7 below:

[0131] Table 7 Comparison Table of Workers' Skill Level Comprehensive Assessment Results

[0132]

[0133] Based on the principle of maximum membership, the worker's overall evaluation result has a maximum membership degree of 0.3306, and their overall skill level is rated as "Grade B - Good".

[0134] Step 4.2.6, determine the rating coefficient β.

[0135] Enterprises can analyze workers' historical work reports and performance appraisal data to determine the worker's operational skill rating coefficient β, which falls within the range of [1.2, 0.8]. Workers are then divided into five levels, and an expert scoring method is used to determine the coefficient β for each worker's operational skill level. The specific procedures are as follows:

[0136] 5) Organize r experts to estimate the rating coefficient β corresponding to each evaluation level, and obtain the estimated value ω of the rating coefficient β corresponding to each evaluation level. k1 ω k2 ,ωk3,ωk4,ω k5 (k = 1, 2, 3, ..., r).

[0137] 6) Calculate the average of the rating coefficients β given by r experts, using the following formula:

[0138]

[0139] 7) Calculate the deviation between the estimated value and the average value.

[0140]

[0141] 8) For the deviation Δ kj If the j-th rating coefficient estimate is larger, then k experts should be asked to re-evaluate ω. kj Until the deviation is less than 0.05, the average estimated correction value of a set of rating coefficients β is finally obtained. The final rating coefficient β, determined by the expert scoring method, is shown in Table 8.

[0142] Table 8. Assessment Levels of Workers' Skill Operation Levels and Corresponding Rating Coefficients β

[0143]

[0144] Therefore, the skill level assessment coefficient β = 1.10 for the worker operating the printing process in a certain instance order of a new product A′ that has similar process or procedure characteristics to the typical standardized product A.

[0145] Step 5: Calculate the standard working hours for non-standard products based on the results obtained in Steps 3 and 4. The standard working hours can be obtained by multiplying the benchmark working hours by the corresponding evaluation coefficients α and β, i.e., by formula (1-2).

[0146] ST′(A i )=BT′(A i )×α×β=6572.80×0.9×1.1=6507.07 (19);

[0147] Step 6: Updating and adjusting standard working hours. Based on historical standard working hour data analysis and expert opinions, adjustments are made. There are two main methods for updating and adjusting: 1. Store the baseline and standard working hour data corresponding to the product in Step 5 into the standard working hour database to provide historical data for the formulation of standard working hours for other products; 2. Update and adjust the value comparison table for the two adjustment parameters: the difficulty level assessment coefficient α of the process and the worker's skill level assessment coefficient β.

Claims

1. A method for setting standard working hours for intelligent printing factories, characterized by: Specifically, the steps include the following: Step 1: Build a product type database and classify products into typical standardized products and non-standardized products; Step 2: Obtain actual production data for typical standardized products; the production data obtained in Step 2 includes the actual production quantity N and registration accuracy in the printing process. Actual processing time Processing waiting time Time conversion index H; Step 3: Calculate the baseline working hours for a typical standardized product based on the production data obtained in Step 2; the specific process of Step 3 is as follows: Set the allowance rate in the printing process. The baseline working hours for a typical standardized product are calculated using the following formula (1): （1） Step 4: Determine the difficulty level assessment coefficient of the process. Worker skill operation level assessment coefficient The specific process of step 4 is as follows: Step 4.1: Establish a process difficulty assessment model and determine the evaluation coefficients. ; Step 4.1.1: Construct an evaluation index system for the difficulty of each process, and classify the factors affecting the difficulty of each process into first-level indicators. Second-level indicators This allows for the construction of an evaluation factor system for the difficulty of a process: The first-level indicator is: The second-level indicator is: in, , , This indicates the number of primary evaluation indicators input. This indicates the number of secondary evaluation indicators input; Step 4.1.2, Construct the evaluation level of the evaluation object: The different evaluation levels affecting the evaluation object are divided as follows: ,in , The number of outputs indicating the evaluation level; Step 4.1.3, construct the judgment matrix of the evaluation indicators: The judgment matrix is ​​an n-order matrix composed of the comparisons between the evaluation indicators. Using an AHP importance scale value of integers between 1 and 9 or the reciprocal of that integer, the judgment matrix model is established as follows: In the formula, Indicators relative to indicators The degree of importance; Step 4.1.4, Determine the weights of each factor: The weights of each factor are determined by the eigenvector corresponding to the largest eigenvalue of the judgment matrix. The calculation steps are as follows: (1) Calculate the product of the elements in each row of the judgment matrix. (2) Calculate M i nth root (3) For vectors Normalization, i.e. but This is the desired feature vector, i.e., the weight vector of the n factors; (4) Calculate the largest eigenvalue of the judgment matrix. : In formula (5), (Aw T ) i The term represents the i-th element of vector Aw; Normalization yields the index weight matrix W U as follows: In the formula, and These are the weight matrices corresponding to the first and second level evaluation indicators, respectively; The number of evaluation indicators, i.e., one evaluation indicator corresponds to one weight; Step 4.1.5, Fuzzy Comprehensive Evaluation of Process Difficulty, specifically as follows: Calculate the membership degree of each indicator relative to the evaluation level, thereby constructing a fuzzy matrix for each indicator. as follows: In the formula, matrix elements Indicates the first The evaluation index is for the first The weight of each evaluation level, i.e. right The subordinate relationship, among which, ; Based on the expert scoring statistics, seven fuzzy relation matrices were established for the first-level indicator factors according to equation (7), where... The membership value is determined by equation (8): In the formula, Indicates the indicators The rating is graded The number of people, the resulting orders, and the assessment of the difficulty of the processes are represented by a second-level fuzzy matrix. The first-level fuzzy matrix is ​​obtained using the second-level evaluation vector operation, where the membership degree of the second-level indicators is calculated as follows: The membership degree of the primary indicators is calculated as follows: In the formula, , Indicates the fuzzy evaluation level of the evaluated object. The degree of membership is used to determine which evaluation level the comprehensive evaluation result of the evaluated object belongs to, based on the principle of maximum membership. Step 4.1.6, determine the evaluation coefficient α, as follows: 1) Organize r experts to estimate the rating coefficient α corresponding to each evaluation level, and obtain the estimated value of the rating coefficient α for each evaluation level. , ; 2) Calculate the average of the rating coefficients α given by r experts, using the following formula: 3) Calculate the deviation between the estimated value and the average value. For deviation If the estimated value of the j-th rating coefficient is relatively large, then ask k experts to re-evaluate it. Until the deviation is less than 0.05, the average estimated correction value of a set of rating coefficients α is finally obtained. ; Step 4.2: Establish a worker skill operation level assessment model and determine the worker skill operation level assessment coefficient. ; Step 5: Calculate the standard working hours for non-standardized products based on the results obtained in Steps 3 and 4. The specific process of Step 5 is as follows: multiply the baseline working hours by the corresponding evaluation coefficient. and That is, the result can be obtained by formula (13): （13）。

Images