What are the seven methods of quality QC and quality inspection standard?

List (counting sheet)

Checklist is a tool for sorting out data and analyzing preliminary reasons by using statistical tables, and its format can be varied. Although this method is simple, it is practical and effective, and it is mainly used for recording or spot checking.

Data layering method

The data stratification method, also known as stratification method, is to summarize the data with the same nature collected under the same conditions for comparative analysis. Because in actual production, there are many factors that affect the quality change. If we don't distinguish these factors, it is difficult to get the law of change. Data layering can be done in many ways according to the actual situation. For example, according to different times and shifts, according to the type of equipment used, according to the feeding time of raw materials, according to the composition of raw materials, according to inspection methods, according to the conditions of use, according to different defective items and so on. The data stratification method is usually used in combination with the above statistical analysis table.

The application of data layering method is mainly a systematic concept, that is, to deal with quite complex data, we must know how to classify and summarize these data systematically and purposefully.

Scientific management emphasizes management technology to make up for the shortcomings of previous management based on experience and visual judgment. This management technology requires not only the establishment of correct ideas, but also the application of data, so as to analyze the work and take correct measures.

How to establish raw data and collect these data according to the required purpose is also the most basic work of many quality control methods. For example, the aviation market in China has become more and more fierce with the opening up in recent years. In order to win the market, airlines have not only strengthened various measures, but also made efforts in service quality. We can also often see customer satisfaction surveys on the plane. The survey was conducted through a questionnaire. The design of questionnaire is usually divided into ground service quality and aircraft service quality. The ground is divided into reservation and waiting; Planes are divided into flight attitude, catering, hygiene and so on. Through these surveys, we can collect these data and understand how to strengthen the quality of service.

pareto chart

Pareto chart, also known as Plato, Critical Analysis Diagram and ABC Analysis Diagram, is named after Plato, the inventor of this diagram and an Italian economist in the19th century. Plato first used pareto chart to analyze the distribution of social wealth. He found that 80% of Italy's wealth was concentrated in the hands of 20% people. Later, people found that this law was observed in many occasions, so it was called Pareto Law. Later, Dr. Zhu Lan, an American quality management expert, expanded Plato's statistical chart and applied it to quality management. Pareto diagram is a tool to analyze and find the main factors affecting quality. Its form is a double rectangular coordinate diagram, and the left ordinate indicates the frequency (such as the number of chess pieces, etc.). ), and the ordinate on the right represents the frequency (such as percentage). The dotted line represents the cumulative percentage, and the abscissa represents various factors affecting the quality, which are arranged from left to right according to the degree of influence (that is, the frequency of occurrence). By observing and analyzing pareto chart, we can grasp the main and original factors that affect the quality. In fact, this method is very useful not only in quality management, but also in many other management work, such as inventory management.

In the process of quality management, there are many problems to be solved, but we often don't know where to start. But in fact, as long as we can find a few influential reasons, we can solve more than 80% of the problems. Plato systematically classified the projects (levels) according to the collected data, and calculated the data (such as the defective rate and the loss amount) and the proportion of each project, and then arranged them in order of size, plus a chart of accumulated value.

In factories or offices, losses such as inefficiency, defects and defective products can also be converted into losses of more than 80% according to their causes or phenomena, which is called Plato's analysis.

Plato uses a hierarchical method based on item classification (phenomenon classification), and Plato can adjust the order according to the statistical table.

Steps of Plato's analysis:

(1) The things that need to be handled should be classified according to the situation (phenomenon) or reason.

(2) Although the vertical axis can represent the number of pieces, it is best to strongly express it by quantity.

(3) Determine the period of data collection, from when to when to end, as the basis of Plato's data, and the period should be as regular as possible.

(4) Items are arranged on the horizontal axis from left to right at half the size.

⑤ Draw a histogram.

(6) Connect the cumulative curve.

histogram

How to predict and monitor product quality in quality management? How to analyze quality fluctuation? Histogram is a tool that can deal with these problems graphically at a glance. It reflects the distribution of product quality by processing the seemingly disorderly data collected, and judges and predicts the product quality and unqualified rate.

Histogram, also known as mass distribution map and histogram, is the main tool to display data changes. By using histogram, we can analyze the regularity of data, intuitively see the distribution of product quality characteristics, and see the distribution of data at a glance, which is convenient to judge its overall quality distribution. When making histogram, it involves scientific concepts. First, the data should be grouped, and how to group them reasonably is the key problem. According to the principle of equal group spacing, the two key figures are the number of groups and the group spacing. It is a geometric figure, which is drawn into a series of connected rectangular figures with high frequency based on the distribution of quality data collected from the production process.

The purpose of making histogram is to judge whether the production process is stable and predict the quality of the production process by observing the shape of the graph. Specifically, the purpose of the histogram is:

(1) to judge a batch of processed products;

② Verify the stability of the working procedure;

(3) Collect relevant data of calculation process capability.

Histogram classifies data according to differences, which is characterized by distinguishing differences.

Function of histogram

(1) displays the status of quality fluctuation;

(2) Information about process quality is conveyed intuitively;

(3) After studying the fluctuation of quality, we can grasp the state of the process, so as to determine where to concentrate on quality improvement.

Common mistakes and preventive measures in the application of histogram method

A. If the number of samples is too small, it will lead to large error and low reliability, thus losing statistical significance. Therefore, the number of samples should not be less than 50.

B. improper selection of group number k, too large or too small will lead to wrong judgment of distribution state.

C. Histograms are usually suitable for measuring data, but in some cases they are also suitable for counting data, depending on the purpose of drawing histograms.

D. The graph is incomplete and the labeling is incomplete, and the histogram should be labeled: the position of the tolerance range line and the average value (indicated by the dotted line) should not be confused with the tolerance center m; Mark in the upper right corner of the diagram: N, S, C, P or CPK.

Causality diagram (causality diagram)

Causal analysis chart is characterized by results, with reasons as factors, and the relationship between them is represented by arrows. Causal analysis chart is a good way to fully mobilize employees' brains, find out the reasons and brainstorm, especially suitable for quality democratic management in working groups. When there is a certain quality problem and the reason is not clear, we can mobilize everyone to find the possible reasons for the problem, let everyone speak freely and list all the possible reasons.

The so-called causal analysis diagram is to explain many reasons for a certain result in a systematic way, that is, to express the relationship between the result (characteristic) and the cause (factor) in a diagram. Its shape is like fishbone, also known as fishbone map.

There must be a reason for the formation of a certain result, and we should try to find out the reason by graphic method. Dr. Kaoru Ishikawa, a Japanese quality control authority, first put forward this concept, so the characteristic cause diagram is also called [Ishikawa diagram]. Causal analysis chart can be used in all stages of general management and work improvement, especially in the early stage of establishing consciousness. It is easy to make the cause of the problem clear and design the steps to solve the problem.

Analysis chart using steps:

Step 1: gather experienced personnel related to this problem, preferably 4- 10.

Step 2: Hang a big piece of white paper and prepare 2-3 colored pens.

Step 3: The members of the assembly speak on the reasons that affect the problem, and the content of the speech is recorded on the map, and no criticism or questioning is allowed in the middle (brainstorming method).

Step 4: The time is about 1 hour, and it will be over after collecting 20-30 reasons.

Step 5: As for the collected reasons, which one has the greatest influence, and then everyone takes turns to speak. After consultation, people who think the greatest influence will be circled.

Step 6: As in Step 5, people who have been circled in red can be circled in two or three circles if they think this is the most important. Step 7: redraw a reason map and remove the ones that are not circled. Columns with more circles are the first.

Causal analysis chart provides a tool to capture important reasons, so participants should include those who have experience in this work to be effective. Histogram Histogram, also known as histogram, is the main tool to display data changes. Histogram can be used to analyze the regularity of chaotic data, visually see the distribution of product quality characteristics, and see the central value or distribution of data at a glance, which is convenient for judging its overall quality distribution. When making histogram, some statistical concepts will be involved. First of all, data should be grouped, and how to group reasonably is the key problem. Grouping is usually carried out according to the principle of equal group spacing. The two key numbers are the number of groups and the group distance.

Scatter diagram ().

Scatter chart, also known as correlation chart, is to point out two variable data that may be related on the coordinate chart to show whether there is correlation between a pair of data. This pairing data may be the relationship between feature-cause, feature-feature and cause-cause. Through observation and analysis, we can judge the correlation between two variables. This problem is also very common in practical production, such as the relationship between quenching temperature and workpiece hardness during heat treatment, and the relationship between the content of certain elements in materials and the strength of materials. Although this relationship exists, it is difficult to express it with accurate formulas or functional relationships. In this case, it is very convenient to use correlation diagram for analysis. Assuming that there are a pair of variables X and Y, X represents a certain influencing factor and Y represents a certain quality characteristic value, the data of X and Y collected through experiments can be represented by points on the coordinate map, and X and Y can be judged according to the distribution characteristics of points.

Relevant information.

In our life and work, many phenomena and causes are regularly related, while others are irregularly related. If we want to know about it, we can judge the correlation between them by scatter plot statistics.

Control chart (control chart)

Control chart is also called control chart. The control chart was first proposed in 1924 by Dr. W.A.Shewhart of Bell Telephone Laboratory in the United States. Since then, the control chart has become an important tool for scientific management, especially quality management. It is a graph with control boundary, which is used to distinguish whether the cause of quality fluctuation is accidental or systematic, and can provide information about the existence of systematic causes, so as to judge whether the production process is under control. According to the purpose, control charts can be divided into two categories. One is the control chart for analysis, which is used to analyze the change of quality characteristic value in the production process to see whether the process is in a stable and controlled state; The other is the control chart for management, which is mainly used to find out whether there is any abnormal situation in the production process to prevent unqualified products.

Statistical management method is an effective tool for quality control, but the following problems must be paid attention to in application, otherwise the due effect will not be achieved. These problems are mainly: 1) data errors. There may be two reasons for data errors, one is that the wrong data is used artificially, and the other is that the statistical method is not really mastered; 2

) The data collection method is incorrect. If the sampling method itself is wrong, then the subsequent analysis method is useless no matter how correct; 3) The data record is copied by mistake; four

) handling of abnormal values. Usually, the data obtained in the production process always contains some abnormal values, which will lead to incorrect analysis results.

Quality inspection standards usually specify standard methods for testing parameters.