Product production management and control method, device and equipment and storage medium

Abstract

The invention discloses a product production management and control method, device and equipment and a storage medium, and relates to the technical field of informatization management and optimization in the manufacturing industry, and the method comprises the steps: obtaining the quality parameters of a to-be-detected product batch, and calculating the sample size of the to-be-detected product batch based on the quality parameters, the quality parameters comprise a nominal defective rate, a maximum error and a confidence level; sampling products with the number corresponding to the sample size are extracted from the to-be-detected product batch for quality detection, so that a sampling quality detection result of the to-be-detected product batch is obtained; and outputting a production decision of the batch of products to be inspected based on the sampling quality inspection result, and updating the quality parameters based on the production decision. According to the method, the needed sample size is calculated through the quality parameters, and it is ensured that expected detection precision can be achieved with the minimum sample size in each time of detection. Therefore, not only is the unnecessary detection times reduced, but also the detection cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of information management and optimization technology for manufacturing industry, and in particular to a product production control method, device, equipment and storage medium. Background Art

[0002] In modern industrial production, quality control has always been a key link to ensure product performance and customer satisfaction. With the advancement of science and technology, especially the increasing complexity and diversity of electronic products, traditional quality control methods have been unable to meet the requirements of high efficiency and low cost. From early manual inspections to the later application of automated testing equipment, and then to the introduction of statistical process control (SPC) theory, each advancement is aimed at improving detection accuracy and efficiency while reducing detection costs. In recent years, with the development of advanced technologies such as big data analysis and machine learning, people have begun to explore how to use these new technologies to further optimize decision-making in the production process to achieve more scientific quality management and cost control.

[0003] At present, in the field of electronic product manufacturing, enterprises generally adopt the method of full inspection or random inspection combined with manual experience judgment to carry out quality control. Among them, although a comprehensive inspection of all parts can ensure product quality, it is costly and time-consuming; while randomly selecting some samples for testing is relatively economical and fast, but there is a certain risk of missed detection, especially when the sample selection is not scientific enough, potential problem points may be missed; and manual experience: relying on the experience of the operator to judge whether further processing is needed, this method lacks standardization and systematic support, and is easily affected by subjective factors.

[0004] It can be seen that although the current quality control methods have guaranteed product quality to a certain extent, there are still some shortcomings. For example, whether it is full inspection or simple sampling inspection, it is accompanied by high operating costs and low work efficiency. Especially in the case of large-scale production and fast delivery, this contradiction is more prominent. Therefore, a more intelligent and efficient technical solution is urgently needed to make up for the above shortcomings. Summary of the invention

[0005] The main purpose of this application is to provide a product production control method, device, equipment and storage medium, aiming to solve the technical problem of how to balance efficiency and cost in the production process.

[0006] To achieve the above objectives, the present application provides a product production control method, the product production control method comprising:

[0007] Obtaining quality parameters of a batch of products to be inspected, and calculating the sample size of the batch of products to be inspected based on the quality parameters, wherein the quality parameters include a nominal defective rate, a maximum error, and a confidence level;

[0008] Extracting a number of sampled products corresponding to the sample size from the batch of products to be inspected for quality inspection to obtain a sampling quality inspection result of the batch of products to be inspected;

[0009] Based on the sampling quality inspection result, the production decision of the batch of products to be inspected is output, and based on the production decision, the quality parameter is updated.

[0010] In one embodiment, the step of calculating the sample size of the batch of products to be inspected based on the quality parameters includes:

[0011] Finding a critical value corresponding to the confidence level;

[0012] Use the following formula to calculate the sample size for the product batch to be tested:

[0013]

[0014] Where n is the sample size, Z is the critical value, ρ0 is the nominal defective rate, and E is the maximum allowable error.

[0015] In one embodiment, the step of outputting a production decision for the batch of products to be inspected based on the sampling quality inspection result includes:

[0016] The sampling quality inspection results include the sample defective rate;

[0017] Calculate the test statistic corresponding to the batch of products to be tested based on the sample defective rate, the nominal defective rate and the sample size;

[0018] Based on the inspection statistic, a production decision for the batch of product to be inspected is output.

[0019] In one embodiment, the step of calculating the inspection statistic corresponding to the batch of products to be inspected based on the sample defective rate, the nominal defective rate and the sample size includes:

[0020] The test statistic is calculated using the following formula:

[0021]

[0022] Where W is the test statistic, ρ0 is the nominal defective rate, ρ is the sample defective rate, and n is the sample size.

[0023] In one embodiment, the step of outputting a production decision of the batch of products to be inspected based on the inspection statistic includes:

[0024] Finding a critical value corresponding to the confidence level;

[0025] Based on a first size relationship between the sample defective rate and the nominal defective rate, and a second size relationship between the test statistic and the critical value, a production decision for the batch of products to be tested is output.

[0026] In one embodiment, the step of outputting a production decision of the batch of products to be inspected based on the sampling quality inspection result, and updating the quality parameter based on the production decision includes:

[0027] Based on the sampling quality inspection results, determine the product defective rate of the product corresponding to the batch of products to be inspected, and obtain the cost parameters of the product;

[0028] Defining decision variables of the product, and calculating the cost-benefit value of the batch of products to be inspected based on the decision variables, the product defect rate and the cost parameters;

[0029] Based on the cost-benefit value, output a production decision for the batch of products to be inspected, wherein the production decision includes a testing decision;

[0030] The actual defective rate of the batch of products to be inspected obtained based on the inspection decision is counted, and the quality parameter is updated based on the actual defective rate.

[0031] In one embodiment, the step of determining the product defect rate of the product corresponding to the batch of products to be inspected based on the sampling quality inspection result and obtaining the cost parameter of the product includes:

[0032] Based on the quality inspection results, the defective rate of parts in the batch of products to be inspected and the defective rate of finished products corresponding to the parts are counted;

[0033] Obtaining a first cost parameter of each of the parts and a second cost parameter of each of the finished products in each production stage corresponding to the batch of products to be inspected, wherein the products include parts and finished products;

[0034] The step of defining the decision variables of the product and calculating the cost-benefit value of the batch of products to be inspected based on the decision variables, the product defective rate and the cost parameters comprises:

[0035] Defining decision variables corresponding to the parts and the finished products;

[0036] Calculate the cost-benefit value of each decision combination based on the decision variables, the defective rate of parts, the defective rate of finished products, the first cost parameter and the second cost parameter;

[0037] The step of outputting the production decision of the batch of products to be inspected based on the cost-benefit value comprises:

[0038] Select the decision combination with the largest cost-benefit value as the production decision.

[0039] In addition, to achieve the above-mentioned purpose, the present application also provides a product production control device, the product production control device comprising:

[0040] A calculation module, used for obtaining quality parameters of a batch of products to be inspected, and calculating the sample size of the batch of products to be inspected based on the quality parameters, wherein the quality parameters include a nominal defective rate, a maximum error, and a confidence level;

[0041] A sampling module, used for extracting a number of sampled products corresponding to the sample size from the batch of products to be inspected for quality inspection, so as to obtain the sampling quality inspection results of the batch of products to be inspected;

[0042] The decision module is used to output the production decision of the batch of products to be inspected based on the sampling quality inspection result, and update the quality parameter based on the production decision.

[0043] In addition, to achieve the above-mentioned purpose, the present application also provides a product production control device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the product production control method as described above.

[0044] In addition, to achieve the above-mentioned purpose, the present application also provides a storage medium, which is a computer-readable storage medium, and a program for implementing the product production control method is stored on the computer-readable storage medium. The program for implementing the product production control method is executed by a processor to implement the steps of the product production control method as described above.

[0045] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, including a computer program, which implements the steps of the product production control method as described above when executed by a processor.

[0046] The present application provides a product production control method, which specifically obtains the quality parameters of a batch of products to be inspected, and based on the quality parameters, calculates the sample size of the batch of products to be inspected, wherein the quality parameters include the nominal defective rate, the maximum error, and the confidence level; extracts a number of sampled products corresponding to the sample size from the batch of products to be inspected for quality inspection to obtain the sampling quality inspection results of the batch of products to be inspected; based on the sampling quality inspection results, outputs the production decision of the batch of products to be inspected, and based on the production decision, updates the quality parameters. The present application calculates the required sample size through quality parameters to ensure that each test can achieve the expected detection accuracy with the minimum sample size. This not only reduces the number of unnecessary tests, but also reduces the cost of testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0048] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0049] Figure 1 A flow chart of the first embodiment of the method for controlling the production of products of this application;

[0050] Figure 2 A flow chart of the second embodiment of the method for controlling the production of products of this application;

[0051] Figure 3 This is a schematic diagram of the module structure of the product production control device in the embodiment of the present application;

[0052] Figure 4 This is a schematic diagram of the structure of the product production control equipment in the embodiment of the present application.

[0053] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0055] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0056] In the current production process of electronic products, quality control mainly relies on full inspection or simple random inspection combined with manual experience judgment. This traditional method has the following problems: First, the cost is high: full inspection leads to high inspection costs. Second, the efficiency is low: full inspection and traditional random inspection are inefficient, delaying delivery time. Third, the risk of missed inspection is high: traditional random inspection is prone to missed inspection, affecting product quality and corporate reputation. Fourth, there is a lack of basis for decision-making: the existing methods lack a scientific decision-making model, and it is difficult to achieve the best balance between cost and quality.

[0057] These problems are particularly prominent in complex production environments, especially when involving multiple processes and a variety of parts and components. Traditional quality control methods are difficult to cope with, resulting in inaccurate decision-making and inability to effectively reduce costs and improve efficiency.

[0058] In order to solve the above problems, the main solution of this application is to propose an electronic product production process optimization method based on sampling inspection and multi-stage decision-making. On the one hand, the sample size of sampling inspection is dynamically adjusted according to different confidence levels and allowable errors to improve inspection efficiency and accuracy; on the other hand, multiple factors such as spare parts defective rate, inspection cost, discard cost, disassembly cost, etc. are considered to provide a more comprehensive cost analysis model. In general, this application provides a systematic sampling inspection and production decision-making method to help companies optimize production processes, reduce cost losses, and improve overall production efficiency.

[0059] Based on this, this application proposes a product production control method of the first embodiment, please refer to Figure 1 The product production control method includes steps S10 to S30:

[0060] Step S10, obtaining quality parameters of the batch of products to be inspected, and calculating the sample size of the batch of products to be inspected based on the quality parameters, wherein the quality parameters include a nominal defective rate, a maximum error, and a confidence level;

[0061] First, it is necessary to obtain the relevant quality parameters of the product batch to be inspected, which include the nominal defective rate, maximum error and confidence level.

[0062] Among them, the nominal defective rate is determined based on historical data or the factory labels provided by the supplier, which indicates the expected proportion of defective products. For example, based on past production records, through long-term trend analysis, the changing trend of the defective rate can be identified, and the predicted value can be used as the nominal defective rate.

[0063] The maximum error is a threshold set by the enterprise, indicating the acceptable error range of the defective rate estimate.

[0064] The confidence level refers to the possibility that the sample statistic falls within a certain interval under a given probability. Common confidence levels are 90% and 95%, which can be set according to the actual situation of the product, such as selecting according to industry standards and specifications. For example, for medical equipment, due to patient safety, a higher confidence level (such as 99%) is usually required; for general consumer electronics, a 95% confidence level is a common choice because it provides a reasonable balance between accuracy and cost; for automobile manufacturing, because the quality control of key components may require a higher confidence level (such as 99%) to ensure safety and reliability, etc. In addition, it can also be selected based on risk assessment and cost-benefit analysis, which is not limited here.

[0065] In one embodiment, the selection can be made based on the product type of the product batch to be inspected, that is, the product type of the product batch to be inspected is determined. If it is a critical component, a high confidence level is selected; if it is a non-critical component, a relatively low confidence level is selected.

[0066] Next, use these parameters to calculate the sample size of the product batch to be inspected, which is the number of samples to be taken.

[0067] In one embodiment, step S10 includes:

[0068] Step S11, finding the critical value corresponding to the confidence level;

[0069] Step S12, using the following formula to calculate the sample size of the product batch to be tested:

[0070]

[0071] Where n is the sample size, Z is the critical value, ρ0 is the nominal defective rate, and E is the maximum allowable error.

[0072] In specific implementation, there is a mapping relationship between the confidence level and the critical value, and a mapping table can be made based on empirical values ​​or statistics for search. For example, the critical value corresponding to the 95% confidence level is Z0.95=1.645, and the critical value corresponding to the 90% confidence level is Z0.90=1.28. These critical values ​​can be found based on the standard normal distribution table to ensure the probability of the sample statistic falling within a certain interval at a given confidence level.

[0073] It should be noted that in actual implementation, the volatility of actual product quality parameters needs to be considered. Therefore, weighted averaging and other methods can be used for processing. That is, when running the above formula, weights can also be configured for the parameters in the formula.

[0074] In this embodiment, compared with the traditional use of statistical methods (for example, hypothesis testing based on the central limit theorem), a reasonable sample size and acceptance criteria are determined, the products are sampled and tested, and the qualified rate of the entire batch of parts is evaluated based on the test results. The improvement is that the sample size can be dynamically adjusted according to different confidence levels and allowable errors to improve the detection efficiency and accuracy. Of course, the calculated sample size is a theoretical value, and the enterprise can also flexibly adjust it according to specific circumstances, such as according to the number of product batches to be tested.

[0075] Step S20, extracting a number of sampled products corresponding to the sample size from the batch of products to be inspected for quality inspection, so as to obtain a sampling quality inspection result of the batch of products to be inspected;

[0076] Next, according to the calculated sample size n, a corresponding number of products are randomly selected for quality inspection. The inspection process includes measuring the key indicators of the product (such as size, function, etc.) and recording the test results to obtain the sampling quality inspection results, that is, full inspection is not required, but sampling inspection is carried out in a more scientific way.

[0077] Step S30: outputting a production decision for the batch of products to be inspected based on the sampling quality inspection result, and updating the quality parameters based on the production decision.

[0078] Finally, according to the sampling quality inspection results, the corresponding production decision is output. Among them, the production decision includes accepting or not accepting the products corresponding to the batch of products to be inspected, that is, the sampling quality inspection results are used to evaluate whether the products in this batch are qualified. If it is judged to be unqualified, further measures are taken (such as rejecting the batch or increasing the sampling inspection ratio, etc.). In addition, based on the test results, quality parameters such as the nominal defective rate, maximum error and confidence level are updated to make the inspection of subsequent batches more accurate.

[0079] In one embodiment, the number of qualified products in the sampling quality inspection results can be used as a basis for production decision-making.

[0080] In order to make production decisions more scientific and avoid misjudgment caused by the instability of a single result, in another embodiment, the sampling quality inspection result includes the sample defective rate, and step S30 includes:

[0081] Step a, calculating the test statistic corresponding to the batch of products to be tested based on the sample defective rate, the nominal defective rate and the sample size;

[0082] Step b: outputting a production decision for the batch of products to be inspected based on the inspection statistic.

[0083] Among them, the number of defective products X in the sample and the sampling quantity n can be obtained from the sampling quality inspection results, and the sample defective rate can be calculated: ρ = X / n.

[0084] Then, the test statistic corresponding to the batch of products to be tested can be calculated based on the sample defective rate, nominal defective rate and sample size. Finally, the production decision of the current batch of products to be tested is output based on the test statistic.

[0085] It should be noted that the main purpose of calculating the test statistic is to quantify the degree of deviation between the actual sampling test results and the pre-set nominal defective rate. For example, if the calculated value is large, it means that the actual defective rate is far from the nominal defective rate, which may mean that the quality of the products in the batch to be tested is far from what we expected; on the contrary, if the value is small, it means that the actual defective rate is close to the nominal defective rate, and the product quality may be more in line with expectations. At the same time, it also provides an objective reference for enterprises to decide whether to accept this batch of products. For example, if the value of the test statistic is less than the preset critical value, and the defective rate in the sample also meets the requirements, then the enterprise can accept this batch of products with more confidence, because this shows that the actual test results are within an acceptable range and the product quality is relatively reliable. If the value of the test statistic is greater than the preset critical value, or the defective rate in the sample exceeds expectations, the enterprise needs to carefully consider whether to accept this batch of parts, and may need to further investigate or take other measures, such as increasing the number of test samples or communicating with suppliers.

[0086] By calculating the test statistic, the difference between the actual defective rate and the nominal defective rate can be converted into a relative value, which makes it easier for companies to evaluate the quality of different batches of parts on the same scale and make more scientific and consistent decisions.

[0087] In the specific calculation process, the following formula can be used to calculate the test statistic:

[0088]

[0089] Where W is the test statistic, ρ0 is the nominal defective rate, ρ is the sample defective rate, and n is the sample size.

[0090] The formula compares the difference between the sample defective rate and the nominal defective rate and takes into account the impact of sample size to derive a standardized statistic for evaluating the quality of the current batch.

[0091] In one embodiment, the step of outputting a production decision for the batch of products to be inspected based on the sampling quality inspection result includes:

[0092] Finding a critical value corresponding to the confidence level;

[0093] Based on a first size relationship between the sample defective rate and the nominal defective rate, and a second size relationship between the test statistic and the critical value, a production decision for the batch of products to be tested is output.

[0094] That is, in one embodiment, the calculated test statistic W can be compared with a preset critical value to determine whether the batch is qualified. The defective rate can also be taken into account, such as ρ≤ρ0, W<1.28 (critical value), then the batch of spare parts is determined to be qualified.

[0095] It should be noted that the sample defective rate ρ is compared with the nominal defective rate ρ0 to determine the size relationship between the two. This step is used to preliminarily evaluate the quality status of the current batch, such as:

[0096] If ρ≤ρ0, it means that the sample defective rate is not higher than expected, and it is preliminarily judged that the batch may be qualified.

[0097] If ρ>ρ0, it means that the sample defective rate is higher than expected, and it is preliminarily judged that the batch may have quality problems.

[0098] The calculated test statistic W is compared with the preset critical value to further confirm the quality status of the batch, such as:

[0099] If W < 1.28 (critical value), the batch of spare parts is judged to be qualified.

[0100] If W>1.28 (critical value), the batch of spare parts is judged to be unqualified.

[0101] For ease of understanding, the following is an example of a practical application scenario:

[0102] Suppose a certain electronic product manufacturer has a batch of new spare parts arriving. The factory label of this batch of spare parts shows that the nominal defective rate is 10%, that is, ρ0=0.1. The maximum error set by the company is 2%, that is, E=0.02, and a confidence level of 95% is selected.

[0103] Substitute the above parameters into the formula to calculate the sample size:

[0104]

[0105] Therefore, approximately 370 parts will be sampled from the batch for testing.

[0106] If the test results show that there are 40 defective samples out of 370, then the sample defect rate ρ is 40 / 370≈0.108. Record the test results as the basis for subsequent analysis.

[0107] The test statistic is then calculated using the following formula:

[0108]

[0109] Assuming that the preset critical value is 1.28, since W<1.28, the batch is judged to be qualified and a production decision to accept it is made.

[0110] In addition, quality parameters such as the nominal defective rate are updated, for example by weighted averaging:

[0111] ρ new=ω·ρ+(1-ω)·ρ0

[0112] Where ω is the weight coefficient. Assuming the weight coefficient ω=0.2, then:

[0113] ρ new =ω·ρ+(1-ω)·ρ0≈0.1016

[0114] Therefore, the new nominal defective rate ρ0 is updated to 0.1016 for the inspection of subsequent batches.

[0115] This embodiment provides a product production control method, which ensures that each test can achieve the expected detection accuracy with the minimum sample size by calculating the required sample size based on the nominal defective rate, maximum error and confidence level. This not only reduces the number of unnecessary tests, but also reduces the cost of testing. Compared with the traditional full inspection method, sampling inspection greatly shortens the inspection time and improves production efficiency, especially in a large-scale production environment, the advantages are more obvious. And according to the results of each sampling quality inspection, the quality parameters such as the nominal defective rate, maximum error and confidence level are dynamically adjusted to ensure that the inspection strategy of subsequent batches can adapt to the latest quality conditions, thereby further improving the accuracy and stability of the detection.

[0116] In summary, by optimizing testing strategies and production decisions, enterprises have minimized testing costs and production costs while ensuring product quality, thereby improving overall economic benefits.

[0117] Based on the above embodiment of the present application, another embodiment of the present application is proposed. For the same or similar contents as the above embodiment, please refer to the above introduction and will not be repeated in the following. Figure 2 , step S30 comprises:

[0118] Step S31, based on the sampling quality inspection result, determine the product defective rate of the product corresponding to the batch of products to be inspected, and obtain the cost parameters of the product.

[0119] According to the sampling quality inspection results, the defective rate of the products corresponding to the batch of products to be inspected is counted. At the same time, the cost parameters of the product are obtained, where the cost parameters are parameters related to cost or price from the production stage to the sales stage, such as testing cost: the cost of testing each product; assembly cost: the cost of assembling the product into a finished product; disassembly cost: the cost when unqualified products need to be disassembled; market price: the price of qualified products sold in the market, etc. These cost parameters are used for subsequent cost-benefit analysis to ensure the economic efficiency of decision-making.

[0120] Step S32, defining the decision variables of the product, and calculating the cost-benefit value of the batch of products to be inspected based on the decision variables, the product defect rate and the cost parameters;

[0121] Define a decision variable for each product, indicating whether the product is tested, with a value of 0 or 1. Then, based on the decision variable, defective rate, and cost parameters, calculate the cost-benefit value of the batch.

[0122] It is understandable that the batch of products to be inspected contains several products. The production and sales of each product involve cost and benefit issues. In order to make subsequent production decisions for the current batch of products to be inspected more scientific and objective, and to meet the development needs of the enterprise, it is necessary to correctly estimate the cost and benefit of the current batch of products to be inspected.

[0123] The cost-benefit value can be calculated, in one embodiment, by the following formula:

[0124] C sum =V sell +V recycle -C det -C assembly -C disassemble

[0125] Among them, C sum is the cost-benefit value, V sell is the sales proceeds, V recycle is the recycling value, C det is the detection cost, C assembly is the assembly cost, C disassemble It is the dismantling cost.

[0126] It is understandable that in addition to the above situations, more targeted cost parameters can be used to assist decision-making based on actual production possibilities. For example, if the actual products involved do not have recycling value, then the recycling value parameters can be omitted.

[0127] Step S33, outputting a production decision of the batch of products to be inspected based on the cost-benefit value, wherein the production decision includes a testing decision;

[0128] In one embodiment, taking the detection decision as an example, the cost-benefit values ​​of the two situations of detection and no detection are calculated and compared through the cost parameters related to the detection decision, and the decision with the largest cost-benefit value is selected as the final production decision.

[0129] Step S34, counting the actual defective rate of the batch of products to be inspected obtained based on the inspection decision, and updating the quality parameter based on the actual defective rate.

[0130] Based on the final inspection decision, the actual defective rate is calculated and relevant quality parameters are updated to optimize the inspection strategy for subsequent batches.

[0131] For ease of understanding, the following example shows the application scenario:

[0132] Assume that a company has sampled 370 parts from a batch of spare parts (batch of products to be inspected) for inspection and found 40 defective products. The defective rate ρ is 40 / 370≈0.108. Then obtain the cost parameters involved in the current product, such as: inspection cost C det =2 yuan / piece, assembly cost C assembly = 5 yuan / piece, disassembly cost C disassemble = 10 yuan / piece, sales revenue V sell =100 Yuan / item.

[0133] Define the decision variable x to indicate whether to inspect the part, where x = 1 indicates inspection and x = 0 indicates no inspection. Calculate the cost-benefit value of the two cases:

[0134] For the detection case (x=1):

[0135] C sum，det =(370×100)-(370×2)-(40×10)=35860

[0136] For the non-detection case (x=0):

[0137] C sum，nodet =(370×100)-(40×10)=36600

[0138] The cost-benefit of not testing is greater than the cost-benefit value of testing, so not testing is chosen as the final production decision.

[0139] Assume that after the decision not to test, the actual test results show that the defective rate is 12%. Based on this result, update the nominal defective rate and other quality parameters, for example, by weighted average method:

[0140] ρ new =ω·ρ+(1-ω)·ρ0

[0141] Where ω is the weight coefficient. Assuming the weight coefficient ω=0.2, then:

[0142] ρ new =ω·ρ+(1-ω)·ρ0≈0.104

[0143] Therefore, the new nominal defective rate ρ0 is updated to 0.104 for the inspection of subsequent batches.

[0144] However, in the actual production process, in addition to single-type component products, there may also be situations where multiple components and multiple processes are required. In order to make the production decision more universally applicable, in another embodiment, step S31 includes:

[0145] Based on the quality inspection results, the defective rate of parts in the batch of products to be inspected and the defective rate of finished products corresponding to the parts are counted;

[0146] The first cost parameter of each of the parts and the second cost parameter of each of the finished products in each production stage corresponding to the batch of products to be inspected are obtained, wherein the products include parts and finished products.

[0147] That is, if the production environment is complex with multiple parts and multiple processes, the defective rate of each part in the batch of products to be inspected and the defective rate of the finished product should be counted separately according to the sampling quality inspection results. Among them, the part is one of the components that make up the finished product, and the finished product includes semi-finished products and finished products.

[0148] It is understandable that a substandard part, when assembled into a product, may or may not affect the finished product. In other words, the quality of a part is not only related to itself, but also to the corresponding finished product. Therefore, when evaluating production, it is necessary to fully understand all quality issues in the entire production process.

[0149] It can be understood that the first cost parameter of a part refers to the cost-benefit parameters involved in each stage of the production of the part, such as testing cost, assembly cost, etc., while the second cost parameter of a finished product refers to the cost-benefit parameters involved in each stage of the production of the finished product, such as testing cost, disassembly cost, sales revenue, etc.

[0150] Further, step S32 includes:

[0151] Defining decision variables corresponding to the parts and the finished products;

[0152] The cost-benefit value of each decision combination is calculated based on the decision variables, the part defective rate, the finished product defective rate, the first cost parameter and the second cost parameter.

[0153] Define the decision variables x1, x2, x3, ..., xn for each part and finished product. Each variable represents a specific decision (such as whether to test a part or finished product). The value of each variable is 0 or 1. This step is used to build a multi-stage decision model covering multiple parts and multiple processes.

[0154] x1 indicates whether to inspect part 1;

[0155] x2 indicates whether to inspect part 2;

[0156] x3 indicates whether the finished product is tested;

[0157] x4 indicates whether the defective products are disassembled; ......

[0159] Then, based on the defined decision variables, defective rate and cost parameters, the cost-benefit value C of each decision combination is calculated. sum The specific formula is as follows:

[0160] C sum =V sell +V recycle -C ling -C cheng -C ci

[0161] Among them, V sell is the proceeds from sale;

[0162] V recycle It is the recycling value;

[0163] C ling1 is the cost of part 1;

[0164] C ling2 is the cost of part 2;

[0165] C cheng is the cost of the finished product;

[0166] C ci Is the cost of dismantling or replacing

[0167] Further, step S33 includes:

[0168] Select the decision combination with the largest cost-benefit value as the production decision.

[0169] Finally, compare the cost-benefit values ​​of all possible decision combinations, and select the decision combination with the largest cost-benefit value as the final production decision.

[0170] For ease of understanding, the following example illustrates an application scenario:

[0171] Suppose a company selects 1357 parts from a batch of spare parts for testing and finds 150 defective parts (parts defect rate ρ part =150 / 1357≈0.11). After the batch was assembled into finished products, 500 finished products were sampled for testing again, and 60 unqualified products were found (the finished product defective rate ρ finished 60 / 500=0.12), and the part inspection cost C det，part ==3 Yuan / piece

[0172] Finished product testing cost C det，finished =8 yuan / piece

[0173] Dismantling cost C disassemble =15 yuan / item

[0174] Sales Profit V sell =150 yuan / item

[0175] Define decision variables and calculate the cost-benefit values ​​of different decision combinations, and select the optimal decision combination. Finally, assume that both parts and finished products are tested and the defective products are disassembled. Count the actual defective rate and update the quality parameters to optimize the inspection strategy for subsequent batches.

[0176] This embodiment provides a product production control method. Through reasonable sample size calculation and multi-stage decision-making models, enterprises can better allocate testing resources, avoid over-testing or under-testing, and thus effectively reduce costs. In actual implementation, it can significantly improve testing efficiency, enhance the accuracy of quality control, reduce testing costs, support data-driven decision-making, enhance robustness and reliability, and ultimately improve overall economic benefits.

[0177] In summary, by introducing statistical methods and multi-stage decision-making models, enterprises can make more scientific and reasonable production decisions based on actual data, avoiding the uncertainty brought about by relying on experience and intuition.

[0178] In the above examples, we have described in detail how to output production decisions and update quality parameters based on the sampling quality inspection results. In order to ensure the robustness and reliability of these decisions, sensitivity analysis is an indispensable part. Sensitivity analysis evaluates the responsiveness of the solution to different conditions by changing key parameters (such as defective rate, confidence level, cost parameters, etc.), thereby verifying the stability and adaptability of the solution in practical applications.

[0179] In this embodiment, the sensitivity analysis method includes Monte Carlo simulation and parameter change analysis, and the specific steps are as follows:

[0180] Determine the key parameters first: Identify the key parameters that affect production decisions, such as the nominal defective rate ρ0, maximum error E, confidence level Z, and various cost parameters (inspection cost, assembly cost, disassembly cost, market price, etc.).

[0181] Set parameter ranges: Set a reasonable fluctuation range for each key parameter. For example, the defective rate can fluctuate 10% above or below the expected value, the confidence level can vary between 90% and 95%, and the cost parameter can be adjusted according to market conditions.

[0182] Next, perform simulations: Use Monte Carlo simulation or other random sampling techniques to generate a large number of different parameter combinations and calculate the cost-benefit values ​​and production decisions for each combination.

[0183] Final evaluation results: Statistics of decision results under different parameter combinations, and analysis of their distribution and stability. Focus on the following indicators:

[0184] Decision consistency: whether the same optimal decision is chosen under different parameter combinations;

[0185] Cost sensitivity: the magnitude of change in cost-benefit values;

[0186] Impact of defective rate fluctuations: the impact of changes in defective rate on the final decision, etc.

[0187] For ease of understanding, the following example illustrates an application scenario:

[0188] Impact on the fluctuation of defective rate:

[0189] Suppose a company takes 370 parts from a batch of spare parts for testing and finds 40 defective parts (i.e., the initial defect rate ρ = 0.108). Now let's consider the fluctuation of the defect rate within the range of ρ ± 0.02, that is, the defect rate may vary from 0.088 to 0.128. Generate 10,000 sets of different defect rates through Monte Carlo simulation and calculate the corresponding cost-benefit value and production decision in each case.

[0190] The results show that within the defective rate fluctuation range, non-detection is still the optimal decision in most cases. However, when the defective rate is close to 0.128, the cost-benefit value of detection gradually approaches that of non-detection, indicating that the detection strategy needs to be re-evaluated at this time.

[0191] Impact of changes in cost parameters:

[0192] Assume that the parts inspection cost of a company is C det，part =2 yuan / piece, finished product testing cost C det，finished = 5 yuan / piece, disassembly cost C disassemble = 10 yuan / piece, market price V sell = 100 yuan / piece. Now let's consider that these cost parameters fluctuate by 20% on their respective bases, that is, the inspection cost varies between 1.6 and 2.4 yuan / piece, and the disassembly cost varies between 8 and 12 yuan / piece.

[0193] Through Monte Carlo simulation, 10,000 different cost parameter combinations were generated, and the corresponding cost-benefit value and production decision were calculated in each case. The results show that when the cost of detection increases significantly, the cost-benefit value of not detecting is significantly better than that of detecting; and when the cost of disassembly decreases, the economic efficiency of detection is improved.

[0194] Through sensitivity analysis, we can comprehensively evaluate the performance of product quality control methods under different parameter changes to ensure that the method is sufficiently robust and reliable. This not only helps companies make more robust decisions in uncertain environments, but also provides strong support for companies to optimize production and quality control strategies.

[0195] That is, integrating sensitivity analysis into the entire product production control method can further enhance the scientificity and practicality of the method and ensure that the expected technical effects are achieved in actual applications.

[0196] The present application also provides a product production control device, please refer to Figure 3 , the product production control device includes:

[0197] A calculation module 10, used to obtain quality parameters of the batch of products to be inspected, and calculate the sample size of the batch of products to be inspected based on the quality parameters, wherein the quality parameters include a nominal defective rate, a maximum error, and a confidence level;

[0198] The sampling module 20 is used to extract a number of sampled products corresponding to the sample size from the batch of products to be inspected for quality inspection, so as to obtain the sampling quality inspection results of the batch of products to be inspected;

[0199] The decision module 30 is used to output the production decision of the batch of products to be inspected based on the sampling quality inspection result, and update the quality parameter based on the production decision.

[0200] The product production control device provided in the embodiment of the present application adopts the product production control method in the above embodiment, which can solve the technical problem of how to balance high efficiency and cost in the production process. Compared with the prior art, the beneficial effects of the product production control device provided in the embodiment of the present application are the same as the beneficial effects of the product production control method provided in the above embodiment, and the other technical features in the product production control device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0201] The present application also provides a product production control device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the product production control method in the above-mentioned embodiment one.

[0202] Reference below Figure 4, which shows a schematic diagram of the structure of a product production control device suitable for implementing an embodiment of the present application. The product production control device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The product production control device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0203] like Figure 4 As shown, the product production control device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the product production control device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the product production control device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a product production control device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0204] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0205] The product production control equipment provided by this application adopts the product production control method in the above embodiment, which can solve the technical problem of how to balance efficiency and cost in the production process. Compared with the prior art, the beneficial effects of the product production control equipment provided by this application are the same as the beneficial effects of the product production control method provided by the above embodiment, and the other technical features in the product production control equipment are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0206] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0207] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field 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. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0208] The present application also provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, the computer-readable program instructions being used to execute the product production control method in the above-mentioned embodiment.

[0209] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0210] The above-mentioned computer-readable storage medium may be included in the product production control device; or it may exist independently without being assembled into the product production control device.

[0211] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the product production control device, the product production control device: obtains the quality parameters of the product batch to be inspected, and based on the quality parameters, calculates the sample size of the product batch to be inspected, wherein the quality parameters include the nominal defective rate, the maximum error and the confidence level; extracts a number of sampled products corresponding to the sample size from the product batch to be inspected for quality inspection to obtain the sampling quality inspection results of the product batch to be inspected; based on the sampling quality inspection results, outputs the production decision of the product batch to be inspected, and based on the production decision, updates the quality parameters.

[0212] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0213] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0214] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0215] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned product production control method, and can solve the technical problem of how to balance efficiency and cost in the production process. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the product production control method provided in the above-mentioned embodiment, and will not be repeated here.

[0216] An embodiment of the present application provides a computer program product, including a computer program, which implements the steps of the product production control method as described above when executed by a processor.

[0217] The computer program product provided in this application can solve the technical problem of how to balance efficiency and cost in the production process. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiment of this application are the same as the beneficial effects of the product production control method provided in the above embodiment, which will not be repeated here.

[0218] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

Claims

1. A product production control method, characterized in that: The product production control method includes: Obtaining quality parameters of a batch of products to be inspected, and calculating the sample size of the batch of products to be inspected based on the quality parameters, wherein the quality parameters include a nominal defective rate, a maximum error, and a confidence level; Extracting a number of sampled products corresponding to the sample size from the batch of products to be inspected for quality inspection to obtain a sampling quality inspection result of the batch of products to be inspected; Based on the sampling quality inspection result, the production decision of the batch of products to be inspected is output, and based on the production decision, the quality parameter is updated.

2. The product production control method according to claim 1, characterized in that: The step of calculating the sample size of the batch of products to be inspected based on the quality parameters comprises: Finding a critical value corresponding to the confidence level; Use the following formula to calculate the sample size for the product batch to be tested: Where n is the sample size, Z is the critical value, ρ0 is the nominal defective rate, and E is the maximum allowable error.

3. The product production control method according to claim 1, characterized in that: The step of outputting the production decision of the batch of products to be inspected based on the sampling quality inspection result comprises: The sampling quality inspection results include the sample defective rate; Calculate the test statistic corresponding to the batch of products to be tested based on the sample defective rate, the nominal defective rate and the sample size; Based on the inspection statistic, a production decision for the batch of product to be inspected is output.

4. The product production control method according to claim 3, characterized in that: The step of calculating the inspection statistic corresponding to the batch of products to be inspected based on the sample defective rate, the nominal defective rate and the sample size comprises: The test statistic is calculated using the following formula: Where W is the test statistic, ρ0 is the nominal defective rate, ρ is the sample defective rate, and n is the sample size.

5. The product production control method according to claim 3, characterized in that: The step of outputting the production decision of the batch of products to be inspected based on the inspection statistic comprises: Finding a critical value corresponding to the confidence level; Based on a first size relationship between the sample defective rate and the nominal defective rate, and a second size relationship between the test statistic and the critical value, a production decision for the batch of products to be inspected is output.

6. The product production control method according to claim 1, characterized in that: The step of outputting a production decision of the batch of products to be inspected based on the sampling quality inspection result, and updating the quality parameter based on the production decision comprises: Based on the sampling quality inspection results, determine the product defective rate of the product corresponding to the batch of products to be inspected, and obtain the cost parameters of the product; Defining decision variables of the product, and calculating the cost-benefit value of the batch of products to be inspected based on the decision variables, the product defect rate and the cost parameters; Based on the cost-benefit value, output a production decision for the batch of products to be inspected, wherein the production decision includes a testing decision; The actual defective rate of the batch of products to be inspected obtained based on the inspection decision is counted, and the quality parameter is updated based on the actual defective rate.

7. The product production control method according to claim 6, characterized in that: The step of determining the product defect rate of the product corresponding to the batch of products to be inspected based on the sampling quality inspection result and obtaining the cost parameter of the product includes: Based on the quality inspection results, the defective rate of parts in the batch of products to be inspected and the defective rate of finished products corresponding to the parts are counted; Obtaining a first cost parameter of each of the parts and a second cost parameter of each of the finished products in each production stage corresponding to the batch of products to be inspected, wherein the products include parts and finished products; The step of defining the decision variables of the product and calculating the cost-benefit value of the batch of products to be inspected based on the decision variables, the product defective rate and the cost parameters comprises: Defining decision variables corresponding to the parts and the finished products; Calculate the cost-benefit value of each decision combination based on the decision variables, the defective rate of parts, the defective rate of finished products, the first cost parameter and the second cost parameter; The step of outputting the production decision of the batch of products to be inspected based on the cost-benefit value comprises: Select the decision combination with the largest cost-benefit value as the production decision.

8. A product production control device, characterized in that: The product production control device comprises: A calculation module, used for obtaining quality parameters of a batch of products to be inspected, and calculating the sample size of the batch of products to be inspected based on the quality parameters, wherein the quality parameters include a nominal defective rate, a maximum error, and a confidence level; A sampling module, used for extracting a number of sampled products corresponding to the sample size from the batch of products to be inspected for quality inspection, so as to obtain the sampling quality inspection results of the batch of products to be inspected; The decision module is used to output the production decision of the batch of products to be inspected based on the sampling quality inspection result, and update the quality parameter based on the production decision.

9. A product production control device, characterized in that: The product production control device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the product production control method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps of the product production control method according to any one of claims 1 to 7 are implemented.

Images