The emergence and role of simple quality control tools. Seven new quality control tools 7 quality management tools

• quality control tools;
• quality management tools;
• quality analysis tools;
• quality design tools.

- we are talking here about control tools that allow you to make managerial decisions, and not about technical means control. Most of the tools used for control are based on the methods of mathematical statistics. Modern statistical methods and the mathematical apparatus used in these methods require good training from the employees of the organization, which not every organization can provide. However, without quality control, it is impossible to manage quality, much less improve quality.

Of all the variety of statistical methods for control, the simplest statistical quality tools are most often used. They are also called the seven quality instruments or the seven quality control instruments. These tools have been selected from a variety of statistical methods. Union of Japanese Scientists and Engineers (JUSE). The peculiarity of these tools lies in their simplicity, clarity and accessibility for understanding the results obtained.

Quality control tools include - histogram, Pareto chart, control chart, scatter chart, stratification, control sheet, Ishikawa (Ishikawa) chart.

The use of these tools does not require deep knowledge of mathematical statistics, and therefore, employees easily master quality control tools in a short and simple training.

Not always information characterizing an object can be presented in the form of parameters that have quantitative indicators. In this case, to analyze the object and make management decisions, it is necessary to use qualitative indicators.

Quality management tools- these are methods that basically use qualitative indicators about an object (product, process, system). They allow you to organize such information, structure it in accordance with some logical rules and apply it to make informed management decisions. Most often, quality management tools are used to solve problems that arise at the design stage, although they can be applied at other stages. life cycle.

Quality management tools contain such methods as affinity diagram, link diagram, tree diagram, matrix diagram, network diagram (Gantt chart), decision chart (PDPC), priority matrix. These tools are also called the seven new quality control tools. These quality tools were developed by a union of Japanese scientists and engineers in 1979. All of them have a graphical representation and therefore are easily perceived and understood.

Quality Analysis Tools is a group of methods used in quality management to optimize and improve products, processes, systems. The most famous and commonly used quality analysis tools are functional physical analysis, functional cost analysis, failure cause and effect analysis (FMEA analysis). These quality tools require more training from the organization's employees than quality control and management tools. Some of the quality analysis tools are formalized in the form of standards and are mandatory for use in some industries (in the event that an organization implements a quality system).

Quality Design Tools- it's relatively a new group methods used in quality management in order to create products and processes that maximize value for the consumer. From the name of these quality tools it is clear that they are applied at the design stage. Some of them require deep engineering and mathematical training, some can be mastered in a fairly short period of time. Quality design tools include, for example, quality function deployment (QFD), inventive problem solving theory, benchmarking, heuristic techniques.

Quality control is one of the main functions in the quality management process. The value of control lies in the fact that it allows you to detect errors in time, so that you can quickly correct them with minimal losses.

Quality control is carried out by comparing the planned quality indicator with its actual value. Actually quality control consists in the fact that by checking the quality indicators to detect their deviation from the planned values. If such a deviation is detected, it is necessary to find the reason for its occurrence, and after adjusting the process, re-check the compliance of the adjusted quality indicators with their planned values. It is on this continuous cycle that the management and provision of the required quality, and its further improvement, are carried out.

Quality requirements are established and fixed in normative and normative-technical documents: state, industry, company standards, product specifications, etc.

The deviation of product quality from the specified parameters occurs, as a rule, for the worse and has general and particular manifestations.

Common ones include obsolescence, physical and moral aging of products, that is, the loss of original properties during operation and aging.

Private quality deviations from the established requirements are extremely diverse and are no longer due to economic and technological nature, but to external conditions: violations of operating rules, errors of developers and manufacturers, violations of production discipline, defects in equipment with which products are manufactured and used, etc.

Therefore, it can be argued that the quality of products is in constant motion. Consequently, quality defines itself as a chronically unstable object that requires control.

The scientific basis of modern technical control is mathematical and statistical methods. Product quality control can be achieved in two ways: by sorting out products and by increasing technological accuracy. Since ancient times, control methods have been reduced, as a rule, to the analysis of defects through a complete check of the output products. In mass production, such control is very expensive: the control apparatus must be five to six times the number of production workers, and even then there is no complete guarantee against defects. Therefore, from continuous control, they pass to selective with the use of statistical methods for processing the results.

One of the founders of the use of statistical methods in serial production, the American specialist W.A. Shewhart wrote: “For a long time the effectiveness of statistics will depend on lesser degree from the existence of a detachment of statisticians with excellent training than from the training of a whole generation brought up in the spirit of statistics, with physicists, chemists, engineers and many other specialists who will be responsible in one way or another for the preparation and management of new production processes.

What statistical methods should be used? The answer largely depends on specialists, but there is a principle that the importance of a statistical method is equal to its mathematical potential, multiplied by the probability of its application. Therefore, when it comes to the wide application of statistical methods, only those that are understood and can be easily applied by non-statisticians should be considered.

Japanese experts have collected seven methods from the total set. Their merit lies in the fact that they provided simplicity, visibility, visualization of these methods, turning them into effective quality control tools:

Checklist - a tool for data collection and automatic ordering to facilitate further use of the collected information;

Stratification (stratification) is a tool that allows you to select data in accordance with various factors.

A histogram is a tool that allows you to visually evaluate the distribution of statistical data grouped by the frequency of data falling into a certain (preset) interval.

Pareto analysis is a tool that allows you to objectively present and identify the main factors influencing the problem under study and distribute efforts to solve it.

Ishikawa cause-and-effect diagram is a tool that allows you to identify the most significant factors (causes) that affect the final result (consequence);

The scatter diagram is a tool that allows you to determine the type and closeness of the relationship between the two considered process parameters;

A control chart is a tool that allows you to track the progress of a process and influence it (using appropriate feedback), preventing it from deviating from the requirements for the process.

These methods can be viewed both as separate tools and as a system of methods. The sequence of application of the seven methods may be different depending on the goal.

Prof. K. Ishikawa, a well-known Japanese quality specialist, said: Based on my experience, I can say that 95% of all company problems can be solved using these seven techniques. Therefore, statistical methods are the tool that needs to be studied in order to implement quality management. They are the most important component of the comprehensive control system of Total Quality Management.

block diagram

block diagram is a schematic representation of the steps involved in the execution of a process. It reflects the order in which the individual operations follow.

There are rules for drawing up flowcharts.

Rice. 4.1 Flow charting rules

Related documents can be shown with dotted arrows.

Control sheets

British Standard BS 7850 considers checklists as a form of information gathering.

Consider the use of a checklist using the example of defect logging. Pay attention to the following:

This particular form does not report the total number of copies (or the number of good copies), and therefore the percentage of each type of defect remains unclear.

The checklist can be used to collect both attribute and parametric (variable) data.

Rice. 4.2 Checklist

bar graph

Represents a visual representation or generalization of information characterizing the distribution of variables.

Rice. 4.3 Histogram

Pareto Analysis (Pareto Principle)

There is a method of choosing a solution preference, commonly known as the Pareto principle.

Statistical data can be presented in two ways - in the form of a histogram and in the form of a cumulative distribution, with the latter representation being used by Lorenz.

Describing the problem in this way, it is easy to establish which factors have the greatest effect and at what point, going down the list, a possible improvement becomes unproductive.

As an economist, he deduced that 20% of people usually own 80% of wealth.

In our case, 20% of the factors determine 80% of the defects. Our task is to find these 20%.

The Pareto principle is represented graphically.

Rice. 4.4 Pareto chart

Ishikawa analysis (fish skeleton)

The method was developed to identify the causes of observed failures by Professor Ishikawa.

The analysis usually begins with a brainstorm where everyone involved tries to identify all possible causes.

In fact, the result may be due to a combination of several causes, and the elimination of only one of them may not solve the problem at all or reduce the likelihood of its occurrence. This is the essence of Ishikawa analysis, it forces the user to check all possible explanations.

At the starting point, the types of possible sources of the observed result were determined, namely: machines, methods, materials, work force and etc.

Rice. 4.5 Cause and effect diagram

Brain attack

The purpose of this method is not to allow possible solutions to the problem to be excluded from view. To do this, follow the rules for organizing brainstorming:

Create a group of people (about six people) who are familiar with the area where the problem arose;

Don't be too specific about the issue to be discussed;

Let people write down whatever comes to their mind for five to ten minutes;

Consider all of the above considerations. Allow no discussion or criticism;

Group ideas, eliminating duplication;

Form a “fish skeleton” and start discussions.

Scatterplot

It is used in cases where it is necessary to build a relationship between two factors or variables. Looking at the diagram, we can talk about positive, weak, strong negative correlation (the degree of dependence between the factor and variables).

Rice. 4.6 Scatterplot

Evaluation of product quality involves the compliance of product quality indicators with the requirements of the consumer and the choice, if necessary, of the direction of improving its quality.

Quality is determined by the measure of compliance of goods, works, services with the conditions and requirements of consumer requests, standards, agreements, contracts.

Quality indicators can be characterized by continuous or discrete values. They can be absolute or relative. The value of the quantities depends on the conditions and methods for their determination. Product quality indicators are established by objective methods, as well as by expert means.

Product quality analysis includes characterization of quality according to established indicators or plans, study of the main factors affecting quality, calculation of the impact of quality on the volume of production in monetary terms. How the PEP analysis can be done is shown in Fig. 4.9.

Quality control- this is an activity that includes carrying out measurements, examinations, tests or evaluations of the parameters of an object and comparing the obtained values ​​\u200b\u200bwith the established requirements for these parameters (quality indicators) .

Rice. 4.9.

Modern quality control tools are methods that are used to solve the problem of quantifying quality parameters. Such an assessment is necessary for an objective choice and management decision-making when standardizing and certifying products, planning to improve its quality, etc.

The role of control in the quality management process. Modern approaches to quality management involve the introduction of a system for monitoring product quality indicators at all stages of its life cycle, from design to after-sales service. The main task of quality control is to prevent the occurrence of marriage. Therefore, during the control, a constant analysis of the specified deviations of the product parameters from the established requirements is carried out. In the event that the parameters of the products do not meet the specified quality indicators, the quality control system will help to quickly identify the most likely causes of the discrepancy and eliminate them.

Is it necessary to control all the products that the company produces? The answer depends on the specifics of the production. If it is of a single or small-scale nature, the product should be subjected to continuous, i.e. 100% control. Continuous control, as a rule, is quite laborious and expensive, therefore, in large-scale and mass production, the so-called selective control is usually used, exposing only a part of a batch of products (sample) to the test. If the quality of the products in the sample meets established requirements, then the entire batch is considered to be of high quality, if not, the entire batch is rejected. However, with this method of control, the probability of erroneous rejection (supplier's risk) or, conversely, recognition of a batch of products as suitable (customer's risk) remains. Therefore, during selective control, concluding a contract for the supply of their products, the participants in the transaction must stipulate both possible mistakes expressed as a percentage.

There are various product quality control tools, among which a special place is occupied by statistical methods .

Many of the modern methods of mathematical statistics are quite difficult to understand, and even more so for widespread use by all participants in the quality management process. Therefore, Japanese scientists have selected seven methods from the whole set, which are most applicable in quality control processes. The merit of the Japanese is that they provided simplicity, visibility, visualization of these methods, turning them into quality control tools that can be understood and effectively used without special mathematical training. At the same time, for all their simplicity, these methods allow you to maintain a connection with statistics and allow professionals to improve them if necessary.

So, the seven main quality control tools include the following statistical methods (Fig. 4.10):

• control sheet;
• bar graph;
• scatter diagram;
• Pareto chart;
• stratification (stratification);
• Ishikawa diagram (cause and effect diagram);
• control card.

Rice. 4.10.

The listed quality control tools can be considered both as separate methods and as a system of methods that provides a comprehensive control of quality indicators. They are the most important component of the integrated control system of total quality management.

Features of the use of quality control tools in practice. The introduction of the seven quality control tools should begin with the training of all participants in the process in these methods. For example, the successful introduction of quality control tools in Japan has been facilitated by the training of company management and employees in quality control techniques. A major role in teaching statistical methods in Japan was played by "quality circles" (see Chapter 1), which trained workers and engineers in most Japanese companies.

Speaking of seven simple statistical quality control methods, it should be emphasized that their main purpose is to control the ongoing process and provide the participant in the process with facts to correct and improve the process. Knowledge and application in practice of seven quality control tools underlie one of the most important requirements TQM– constant self-control.

Statistical quality control methods are currently used not only in production, but also in planning, design, marketing, logistics, etc. The sequence of applying the seven methods may be different depending on the goal that is set for the system. Similarly, the applied quality control system does not need to include all seven methods. There may be fewer, or there may be more, as there are other statistical methods.

However, we can say with full confidence that the seven quality control tools are necessary and sufficient statistical methods, the use of which helps to solve 95% of all problems that arise in production.

Whatever the task facing the system, combining the sequence of application of statistical methods, they always begin with the collection of initial data, on the basis of which this or that tool is then used.

Control sheet is a tool for collecting data and automatically organizing it to facilitate further use of the collected information.

Typically, the control sheet is a paper form on which controlled parameters are pre-printed, according to which data can be entered on the sheet using marks or simple symbols. It allows you to automatically arrange the data without their subsequent rewriting. So the checklist is good remedy data registration.

There are hundreds of different checklists, for each specific purpose a sheet can be developed. But the principle of their design remains unchanged. For example, a patient temperature chart is one possible type of checklist. Another example is the checklist used to fix failed parts in cathode ray devices (Fig. 4.11).

Based on the data collected using these checklists, it is not difficult to compile a table of total failures (Table 4.3).

Table 4.3

Checklist Disclaimer Table

Rice. 4.11.

When compiling the checklists, care should be taken to indicate who, at what stage of the process and for how long the data was collected, and also that the form of the sheet is simple and understandable without additional explanations. It is also important that all data is recorded in good faith and that the information collected in the checklist can be used to analyze the process.

For a visual representation of the trend in observed values, apply graphic image statistical material. The most common plot used when analyzing the distribution of a random variable in quality control is the histogram.

bar graph is a tool that allows you to visually evaluate the law of distribution of statistical data.

The distribution histogram is usually built for the interval change of the parameter value. To do this, on the intervals plotted on the x-axis, rectangles (columns) are built, the heights of which are proportional to the frequencies of the intervals. Lay along the y-axis absolute values frequencies (Fig. 4.12). A similar form of the histogram can be obtained if the corresponding values ​​of the relative frequencies are plotted along the y-axis. In this case, the sum of the areas of all columns will be equal to one. The histogram is very useful for visually evaluating where statistics are within tolerance. To assess the adequacy of the process to the requirements of the consumer, we must compare the quality of the process with the tolerance field set by the user. If there is a tolerance, then the upper ( S U) and bottom ( S L) its boundaries in the form of lines perpendicular to the x-axis, in order to compare the distribution of the process quality parameter with these boundaries. Then you can see if the histogram is well located within these boundaries.

On fig. Figure 4.12 shows a histogram of gain values ​​for 120 tested amplifiers as an example. The specifications for these amplifiers indicate the nominal value of the coefficient S N for this type of amplifier, equal to 10 dB. Specifications admissible values ​​of the amplification factor are also set: the lower limit of the tolerance S L = 7.75 dB and the top S v = 12.25 dB. In this case, the width of the tolerance field T equal to the difference between the values ​​of the upper and lower tolerance limits: T=S U/- S L.

Rice. 4.12.

If you arrange all the gain values ​​in a ranked series, they will all be within the tolerance zone, which will create the illusion that there are no problems. When constructing a histogram, it immediately becomes obvious that although the distribution of gain factors is within the tolerance, it is clearly shifted towards the lower limit, and for most amplifiers the value of this quality parameter is less than the nominal value. This, in turn, provides additional information for further problem analysis.

The next quality control tool is the scatterplot.

- a tool that allows you to determine the type and closeness of the relationship between pairs of relevant variables.

These two variables may refer to:

• - to the quality characteristic and the factor influencing it;
• – two different quality characteristics;
• – two factors affecting one quality characteristic.

To identify the relationship between them, a scatter diagram is used, which is also called a correlation field.

The use of a scatterplot in the quality control process is not limited to identifying the type and closeness of the relationship between pairs of variables. The scatter diagram is also used to identify cause-and-effect relationships of quality indicators and factors influencing them (Fig. 4.13).

Rice. 4.13.

The construction of a scatter diagram is performed in the following sequence.

Step 1. Collect paired data ( x, at) between which you want to explore the relationship, and place them in a table. Preferably ns less than 25–30 data pairs.

Step 2: Find the maximum and minimum values ​​for X and y. Select the scales on the horizontal and vertical axes so that both lengths of the working parts are approximately the same, then the diagram will be easier to read. Take from 3 to 10 gradations on each axis and use round numbers for easier reading. If one variable is a factor, and the second is a quality characteristic, then choose a horizontal axis for the factor X, and to characterize the quality - the vertical axis y.

Ethan 3. On a separate sheet of paper, draw a graph and plot the data on it. If in different observations one obtains same values, show these points either by drawing concentric circles or by drawing a second point next to the first.

Stage 4. Make all the necessary designations: the name of the diagram; time interval; number of data pairs; names and units of measurement for each axis; the name (and other details) of the person who made this diagram.

Make sure that the following data reflected in the diagram is understandable to anyone, and not just the one who made the diagram.

EXAMPLE 4.2

It is required to find out the effect of heat treatment of integrated circuits at T= 120°C for time t = 24 h to reduce reverse current p-n-transition ( I arr). For the experiment, 25 integrated circuits (k = 25) were taken and the values ​​were measured I arr, which are given in table. 4.4.

Table 4.4

Measurement data /rev|, integrated circuits

• 1. According to the table, find the maximum and minimum values X and at: maximum values X = 92, at= 88; minimum values x = 60, at = 57.
• 2. On the graph, the values ​​are plotted along the x-axis X, along the y-axis - values y. In this case, the length of the axes is made almost equal

the difference between their maximum and minimum values ​​​​and put on the axis of division of the scale. In appearance, the graph approaches a square. Indeed, in the case under consideration, the difference between the maximum and minimum values ​​is 32 (92-60) for X and 31 (88-57) for y, therefore, the intervals between scale divisions can be made the same.

• 3. Plot the data in the order of measurements and scatter plot points on the graph.
• 4. The graph indicates the number of data, purpose, product name, process name, artist name, schedule date, etc. It is also desirable that when recording data during measurements, accompanying information is also provided that is necessary for further research and analysis: the name of the measurement object, characteristics, sampling method, date, measurement time, temperature, humidity, measurement method, type of measuring instrument, operator's name, who carried out the measurements (for this sample), etc.

An example of constructing and analyzing a scatter diagram is shown in fig. 4.14.

Rice. 4.14.

The scatter diagram allows you to visually show the nature of the change in the quality parameter over time. To do this, draw a bisector from the origin of coordinates. If all points lie on the bisector, this means that the values ​​of this parameter have not changed during the experiment. Therefore, the factor (or factors) under consideration does not affect the quality parameter. If the bulk of the points lies under the bisector, then this means that the values ​​of the quality parameters have decreased over the past time. If the points lie above the bisector, then the values ​​of the parameter have increased over the considered time. Having drawn the rays from the origin, corresponding to the decrease/increase of the parameter by 10, 20, 30, 50%, it is possible to find out the frequency of the parameter values ​​in the intervals by counting the points between the straight lines.

Pareto chart- a tool that allows you to distribute efforts to resolve emerging problems and identify their main causes, with the elimination of which you need to start acting.

In 1897, the Italian economist V. Pareto proposed a formula showing that public goods are unevenly distributed. The same theory was illustrated in a diagram by the American economist M. Lorenz. Scientists have shown that in most cases the largest share of income or wealth (80%) belongs to a small number of people (20%).

Dr. D. Juran used the M. Lorenz diagram to classify quality problems into few, but essential, and numerous, but insignificant. He called this method Pareto analysis and pointed out that in most cases the vast majority of defects and associated losses are due to a relatively small number of causes. At the same time, he illustrated his conclusions with the help of a diagram, which was called the Pareto diagram.

In the daily activities of quality control and management, various problems constantly arise, associated, for example, with the appearance of marriage, equipment malfunctions, an increase in the time from the release of a batch of products to its sale, the presence of unsold products in the warehouse, and complaints. The Pareto chart allows you to distribute efforts to resolve emerging problems and establish the main factors from which you need to start acting in order to overcome emerging problems.

There are two types of Pareto charts.

• 1. Pareto chart based on performance. This diagram is intended to identify the main problem and reflects the following undesirable results of activity:
  • quality: defects, breakdowns, errors, failures, complaints, repairs, product returns;
  • prime cost: volume of losses, expenses;
  • delivery times: stock shortages, billing errors, delivery delays;
  • safety: accidents, tragic mistakes, accidents.
• 2. Pareto chart for reasons. This diagram reflects the causes of problems that occur during production and is used to identify the main one:
  • performer of the work: shift, team, age, work experience, qualifications, individual characteristics;
  • equipment: machine tools, units, tools, equipment, organization of use, models, stamps;
  • raw materials: manufacturer, type of raw materials, supplier plant, batch;
  • method of work: production conditions, work orders, work methods, sequence of operations;
  • measurements: accuracy (indications, readings, instrumental), fidelity and repeatability (the ability to give the same indication in subsequent measurements of the same value), stability (repeatability over a long period), joint accuracy, i.e. together with the instrument accuracy and calibration of the instrument, the type of instrument (analogue or digital).

The construction of the Pareto chart consists of the following steps (Fig. 4.15).

Step 1: Decide what problems to investigate and how to collect data.

• 1. What type of problem do you want to investigate? For example, defective products, loss of money, accidents.
• 2. What data should be collected and how should they be classified? For example, by types of defects, by the place of their occurrence, by processes, by machines, by workers, by technological reasons, by equipment, by measurement methods and measuring instruments used.

Note. Summarize the remaining infrequent signs under the general heading "other".

3. Set the data collection method and period.

Note. If appropriate, use a special form.

Step 2: Develop a data recording checklist listing the types of information collected. It must provide a place for graphic recording of these checks.

Step 3. Complete the data entry sheet and calculate the totals.

Step 4. Develop a blank table for data checks, providing in it columns for the totals for each checked feature separately, the accumulated sum of the number of defects, percentages of the total and accumulated percentages.

Step 5. Arrange the data obtained for each test feature in order of importance and fill in the table.

Note. The "other" group should be placed in the last line, regardless of how large the number turned out to be, since it is made up of a set of features, the numerical result for each of which is less than the smallest value obtained for the feature selected in a separate line.

Step 6. Draw one horizontal and two vertical axes.

• 1. Vertical axes. Put a scale on the left axis at intervals from 0 to the number corresponding to the grand total. A scale is applied to the right axis at intervals from 0 to 100%.
• 2. Horizontal axis. Divide this axis into intervals according to the number of features to control.

Step 7. Build a bar chart.

Step 8. Draw a Pareto curve. To do this, on the verticals corresponding to the right ends of each interval on the horizontal axis, mark the points of the accumulated amounts (results or percentages) and connect them with straight line segments.

Step 9. Put all symbols and inscriptions on the diagram.

• 1. Inscriptions relating to the diagram (title, marking of numerical values ​​on the axes, name of the controlled product, name of the compiler of the diagram).
• 2. Data captions (data collection period, research object and location, total number of control objects).

Using the Pareto chart, you can analyze the quality problems that arise in the enterprise. When using it, the most common method of analysis is the so-called ABC-analysis, the essence of which we will consider on an example.

EXAMPLE 4.3

Suppose a large number of finished products of various types have accumulated in the warehouse of an enterprise. At the same time, all products, regardless of their type and cost, are subject to continuous final control. Due to the long time of control, the sale of products is delayed, and the company incurs losses due to the delay in deliveries.

We will divide all finished products stored in the warehouse into groups depending on the cost of each product (Table 4.5).

Table 4.5

Data on the availability of products in stock

To build a Pareto chart and conduct DVS analysis, we will build a table with an accumulation of up to 100% (Table 4.6).

Table 4.6

Table of cumulative frequencies

The cumulative frequency table is constructed as follows.

First, the total cost of products is found as the sum of the products for the values ​​of the centers of classes and the number of samples, multiplying the values ​​of columns 1 and 2, i.e. the total cost is

95 × 200 + 85 × 300 + 75 × 500 + ... + 15 × 5000 + 5 × 12,500 = 465.0 million rubles

Then the data of column 3 is compiled. For example, the value from the first row is determined as follows: 95 × 200 = 19 million rubles. The value from the second line is determined as follows: 95 × × 200 + 85 × 300 = 44.5 million rubles. etc.

Then the value of column 4 is found, which shows what percentage of the total cost is the data of each row.

Column 6 data is formed as follows. The value of 0.8 from the first row is the number of percentages attributable to the accumulated stock of products (200) of the total number of samples (25,000). The value 2.0 from the second row represents the percentage of the accumulated stock of products (200 + 300) of the total amount.

After this preparatory work It's easy to build a Pareto chart. In a rectangular coordinate system, along the abscissa, we plot the relative frequency of the product n i /Ν, % (data of column 6), and along the ordinate axis - the relative cost of these products Сi/St,% (data of column 4). By connecting the obtained points with straight lines, we get the Pareto curve (or Pareto diagram), as shown in Fig. 4.15.

The Pareto curve turned out to be relatively smooth as a result of a large number of classes. As the number of classes decreases, it becomes more broken.

Rice. 4.15.

From the analysis of the Pareto chart, it can be seen that the share of the most expensive products (the first 7 rows of the table), which is 20% of the total number of samples stored in the warehouse, accounts for more than 50% of the total cost of all finished products, and the share of the cheapest products located in the last line of the table and accounting for 50% of the total number of products in stock, accounts for only 13.3% of the total value.

Let's call the group "expensive" products the group BUT, a group of cheap products (up to 10 thousand rubles) - a group WITH, intermediate group - group AT. Let's build a table ABC- analysis of the obtained results (Table 4.7).

Table 4.7

ABC-analysis of the results obtained from the Pareto diagram

Now it is clear that the control of products in the warehouse will be more effective if the control of samples of the group BUT will be the most stringent (continuous), and the group sample control With- selective.

One of the most effective statistical methods widely used in the quality management system is the method stratification or delamination. In accordance with this method, the stratification of statistical data is introduced, i.e. group data depending on the conditions of their receipt and process each group of data separately. Data divided into groups according to their characteristics is called layers (strata), and the process of division into layers (strata) is called stratification (stratification).

Method stratification of the statistical data under study is a tool that allows you to make a selection of data that reflects the required information about the process.

There are various delamination methods, the application of which depends on specific tasks. For example, data related to a product manufactured in a shop at a workplace may vary somewhat depending on the contractor, equipment used, work methods, temperature conditions, etc. All of these differences can be delamination factors. Often used in manufacturing processes method 5M , which takes into account factors depending on the person ( man), cars ( machine), material (material), method ( method), measurements ( measurement).

Delamination can be carried out according to the following criteria:

• stratification by performers - by qualification, gender, work experience, etc.
• stratification by machines and equipment - by new and old equipment, brand, design, manufacturing company, etc.
• delamination according to the material - according to the place of production, manufacturer, batch, quality of raw materials, etc.
• delamination according to the method of production - according to temperature, technological method, place of production, etc.
• stratification by measurement - according to the method of measurement, the type of measuring instruments or their accuracy, etc.

However, this method is not always easy to use. Sometimes delamination by a seemingly obvious parameter does not give the expected result. In this case, you need to continue analyzing the data for other possible parameters in search of a solution to the problem.

Ishikawa Diagram (Cause and Effect Diagram)- a tool that allows you to identify the most significant factors (causes) that affect the final result (consequence).

The result of the process depends on many factors, between which there are relations of the type: cause - effect (result). The cause and effect diagram is a means of expressing these relationships in a simple and accessible way.

In 1953, a professor at the University of Tokyo, Kaoru Ishikawa, while discussing a quality problem in a factory, summarized the opinions of engineers in the form of a cause-and-effect diagram. When the diagram was put into practice, it proved to be very useful and soon became widely used in many companies in Japan, becoming known as the Ishikawa diagram. It has been included in the Japanese Industrial Standard ( JIS) on the terminology in the field of quality control and is defined in it as follows: cause and effect diagram - a diagram that shows the relationship between a quality indicator and factors influencing it.

If, as a result of the process, the quality of the product turned out to be unsatisfactory, then in the system of causes, i.e. at some point in the process, there was a deviation from the specified conditions. If this cause can be found and eliminated, then only high quality products will be produced. Moreover, if you constantly maintain the specified process conditions, you can ensure the formation of high quality products.

It is also important that the result obtained - quality indicators (dimensional accuracy, degree of purity, the value of electrical quantities, etc.) - is expressed by specific data. Using these data, statistical methods are used to control the process, i.e. check the system of causal factors. Thus, the process is controlled by the quality factor.

The scheme of the cause-and-effect diagram (Ishikawa diagram) is shown in fig. 4.16.

Quality score information for charting is collected from all available sources; the operation log, the current control data log, the messages of the production site workers, etc. are used. When constructing a diagram, the most important factors from a technical point of view are selected. For this purpose, peer review is widely used. It is very important to trace the correlation between causal factors (process parameters) and quality indicators. In this case, the parameters are easily correlated. To do this, when analyzing product defects, they should be divided into random and systematic, Special attention on the possibility of identifying and subsequently eliminating the causes of systematic defects in the first place.

Rice. 4.16.

1 - a system of causal factors: 2 - the main factors of production; 3 - materials; 4 - operators; 5 - equipment; 6 - methods of operations; 7 - measurements; 8 - process; 9 - consequence; 10 - quality parameters; 11 - quality indicators; 12 - process control by quality factor

It is important to remember that the quality indicators that result from the process are bound to vary. The search for factors that have a particular big influence on the spread of product quality indicators (i.e., on the result), is called research into the causes.

Currently, the cause-and-effect diagram, being one of the seven quality control tools, is used all over the world in relation not only to product quality indicators, but also to other areas of diagrams. The procedure for its construction consists of the following main steps.

Step 1. Determine the quality score, i.e. the result you would like to achieve.

Step 2. Write your chosen Quality Score in the middle of the right edge of a blank piece of paper. From left to right, draw a straight line ("ridge"), and enclose the recorded indicator in a rectangle. Next, write down the main reasons that affect the quality score, enclose them in rectangles and connect them to the "backbone" with arrows in the form of "big bones of the ridge" (the main reasons).

Step 3. Write the secondary causes that affect the main causes ("big bones") and arrange them in the form of "middle bones" adjacent to the "large". Write the tertiary causes that affect the secondary causes and arrange them as "small bones" adjacent to the "middle" ones.

Step 4. Rank the causes (factors) according to their significance using the Pareto chart for this, and highlight the most important ones that are supposed to have greatest influence on the quality score.

Stage 5. Put all the necessary information on the diagram: its name; name of the product, process or group of processes; names of process participants; date, etc.

On fig. 4.17 shows a diagram built to identify possible causes consumer dissatisfaction.

Rice. 4.17.

After you have completed the diagramming, the next step is to rank the causes according to their importance. Not all of the reasons included in the diagram will necessarily have a strong impact on Quality Score. List only those that you think have the most impact.

All of the above statistical methods make it possible to fix the state of the process at a certain point in time. In contrast, the control chart method allows you to track the state of the process over time and, moreover, to influence the process before it gets out of control.

- a tool that allows you to track the course of the process and influence it (using appropriate feedback), preventing its deviations from the requirements for the process.

The use of control charts has the following goals:

• keep under control the value of a certain characteristic;
• check process stability;
• take immediate corrective action;
• check the effectiveness of the measures taken.

However, it should be noted that the listed goals are specific to the current process. During the start of the process, control charts are used to check the capabilities of the process, i.e. its ability to consistently maintain the established tolerances.

Typical example control chart shown in fig. 4.18.

Rice. 4.18.

When constructing control charts, the values ​​of the controlled parameter are plotted on the ordinate axis, and the time is plotted on the abscissa axis. t sampling (or its number).

Any control chart usually consists of three lines. The central line represents the required average value of the characteristic of the controlled quality parameter. So, in case ( X – R)-maps, these will be the nominal (given) values X and R, applied corresponding cards.

Two other lines, one of which is above the center - the upper control limit (Kv or UCL – Upper Control Level), and the other one is the lower control limit (Kn or LCL – Lower Control Level), are the maximum allowable limits for changing the values ​​of the controlled characteristic (quality indicator) in order to consider the process as satisfying the requirements for it.

If all points correspond to the sample mean values ​​of the controlled parameter and its variability and are within the control limits without showing any trends, then the process is considered to be in a controlled state. If, on the contrary, they fall outside the control limits or take some unusual form of location, then the process is considered out of control.

The process is considered controlled if the systematic components of its errors are regularly identified and eliminated, and only random components of the errors remain, which, as a rule, are distributed in accordance with the normal (Gaussian) distribution law.

For the successful implementation of control charts in practice, it is important not only to master the technique of compiling and maintaining them, but it is much more important to learn how to “read” the chart correctly.

Location of checkpoints on x-map indicates an increase in the sample mean over time. And the meaning X in the fourth sample was outside the control limit, which indicates that at the time when the fourth sample was taken, the process no longer met the requirements. However, this could have been avoided if, on the basis of the results of the first three samples, when the process was still within the established limits, the trend of its change was already visible, indicating a clear influence of systematic errors, and appropriate measures were taken to eliminate them. good example such a systematic error can be the state of the cutter, the movement of which during automatic processing of a part on a lathe does not take into account its blunting.

Thus, the control chart helps not only to identify the non-compliance of the process with the requirements of the consumer, but also to anticipate the possibility of its occurrence in the future.

The tools we have analyzed are used in various methods for assessing product quality, which we now consider.

• Efimov V. V., Bart T. V. Mazur I. I., Shapiro V. D. Fedyukin V.K. Quality management of production processes: textbook. allowance. M.: KnoRus, 2011.
• Gorbashko E. A. Mazur I. I., Shapiro V. D. Quality management: textbook. allowance. Moscow: Omega-L, 2011; Shestopal Yu. T., Dorofeev V. D. Quality management: textbook. allowance. Moscow: INFRA-M, 2011; Salimova T. A. Quality management: textbook. Moscow: Omega-L, 2010.
• Tebekin A.V. Quality management: textbook., 2011 Shestopal Yu. T., Dorofeev V.D. Quality management: textbook. allowance. M. INFRA-M, 2011; Salimova T. A. Quality management: textbook. M. Omega-L, 2010.
• Gorbashko E. A. Quality management: textbook., 2012; Efimov V. V., Bart T. V. Statistical methods in product quality management: textbook. allowance. Moscow: KnoRus, 2012; Fedyunin V.K. Quality management of production processes: textbook. allowance. Moscow: KnoRus, 2011; Tebekin A. V. Agarkov A. P. Quality management: textbook. allowance. Moscow: Dashkov i K°, 2009; Gerasimov V.I. Quality management: textbook. allowance. Moscow: Forum, 2009; Feigenbaum A.V. Product quality control. M.: Logos, 2004.
• Magomedov Sh. III., Bespalova G. E. Product quality management: textbook. Moscow: Dashkov i K°, 2012; Freidipa E.V. Quality management: textbook. allowance. Moscow: Omega-L, 2012; Gorbashko E. A. Quality management: textbook., 2012; Efimov V. V., Bart T. V. Statistical methods in product quality management: textbook. allowance. M.: KnoRus, 2012.
• Efimov V.V. Means and methods of quality management: textbook. allowance. Moscow: KnoRus, 2012; Gorbashko E. A. Quality management: textbook., 2012; Mazur I. I., Shapiro V. D. Quality control; textbook allowance. Moscow: Omega-L, 2011.

Any production process necessarily includes product quality control, the important goals of which are to determine the defect and verify the process. There are different techniques for doing this, such as tests, trials, comparisons, and so on.

Quality control - what is it?

This term refers to the verification of quality indicators for compliance with existing requirements, which are defined normative documents: standards, norms, rules and so on. The organization of quality control implies the process of obtaining information about the object in order to determine the parameters that must be within specified limits. It consists of input, production and systematic control, as well as accounting for models, prototypes and finished products.

Quality control methods

In order to determine the quality of products, various techniques are used, which, when applied, ensure the achievement of the desired quality indicators. There are different types of quality control, for example, related to the identification of characteristics software, stimulation of his work, identification of violations and so on. In most cases, several methods are used in production at once, which is important for obtaining a high-quality result.

Statistical quality control methods

In order to obtain high-quality products as a result, statistical methods are often used, the purpose of which is to eliminate the causes that cause random changes in quality indicators. Statistical quality control is divided into several groups, which have their own advantages and disadvantages:

• selective control by changing characteristics during reception;
• quality control on an alternative sign at the time of admission;
• methods of regulation of the technological process;
• acceptance control standards;
• continuous sampling plans.

Technical quality control of products

To understand whether a product or process meets existing requirements, technical control is carried out. Different types product quality controls are used at different stages of production, for example, during development, they check whether a prototype fits the technical specifications or documentation. Technical control includes three main stages:

1. Collection of primary information about the object and its specific indicators.
2. Secondary information shows possible deviations from the required parameters specified when compiling primary information, taking into account the planned criteria, norms and requirements.
3. Drawing up a report that includes the conclusions necessary for the development of control actions on the object that was under control.

Intralaboratory quality control

This controlling method is understood as a set of measures aimed at conducting high-quality clinical trials in the laboratory and improving their characteristics. Product quality control is done in order to evaluate whether the result of the experiment meets the existing criteria. It applies to all types of research.

The presented methodology is aimed at identifying problems that are solved first. To do this, the process is controlled, the collection, processing and analysis of the information received. The selected seven quality control tools are self-explanatory and can be used by a variety of professionals. Thanks to them, you can quickly identify the problem and think about ways to fix it. Statistics show that up to 95% of failures are solved with their help. Quality control is carried out with the following seven tools:

1. The checklist is used to collect data and organize it for ease of further use.
2. The histogram helps to visually evaluate the distribution of statistical data that has been distributed according to the frequency of falling into a specific interval.
3. The Pareto diagram objectively represents and identifies the main factor influencing the problem under study, and distributes efforts to eradicate it.
4. The stratification method divides data into subgroups according to a specific attribute.
5. A scatterplot defines the type and relationship between variables.
6. The Ishikawa diagram reveals the most important causes that affect the final result.
7. The control chart helps to track the progress of the process and the impact on it. Thanks to this, it can be prevented from deviating from the requirements put forward.

Organization of quality control at the enterprise

In order for the production of products to fully comply with the requirements specified in the documents, the enterprise uses a system of technical and administrative measures. The quality control system at the enterprise is based on the following conditions:

1. Careful processing and modification technical documentation which is important for the production of high quality products.
2. Development and mastering of technical processes that are important for the production of products that will fully comply with the design documentation.
3. The quality control system includes the development and inclusion in the work of accompanying documentation. It must contain data on the conduct of control measurements.
4. Periodic accuracy check measuring instruments and other devices used in the work.
5. Purchase of high-quality materials and components specified in the technical documentation.
6. For quality control, it is important that the qualifications of the working personnel correspond to the requirements put forward for the position held.

Quality control department

The organization that coordinates the quality control work in the enterprise is called the quality control department (QC). The structure and staff of this organization is developed taking into account the nature and volume of production. The quality control service in most cases includes laboratories that carry out analytical, microbiological and pharmacological control. The OCC performs the following functions:

• conducts control operations provided for by the technical process;
• carries out input control the quality of materials coming from outside;
• draws up documents confirming the compliance of the finished product with the requirements;
• takes part in product testing;
• analyzes and records marriage;
• participates in the preparation of products for certification;
• contributes to the development of the technical control system and so on.

Quality Control Engineer

One of the key positions in the enterprise is the product quality control engineer, since it depends on his correct work whether the product will be accepted by the consumer. The quality control specialist must have a professional technical or higher education in the industry. His main responsibilities are: control of the work of the company's divisions, compliance with safety regulations, ensuring the compliance of products / services with existing requirements. In addition, he analyzes claims to quality coming from the side.

Seven quality management tools (simple quality tools, seven new quality management tools) were identified in 1979 by the Union of Japanese Scientists and Engineers (JUSE) as a complement to the seven simple statistical methods. They are logical tools that allow you to analyze any events, problems, etc. in a visual, graphical form.

The seven quality management tools are:

• affinity diagram;
• link diagram;
• tree diagram (decision tree);
• matrix chart or quality chart;
• arrow (network) diagram;
• process diagram (PDPC);
• priority matrix (analysis of matrix data).

I What is the key difference between this group of tools and the seven simple statistical tools?

An affinity diagram is a tool that allows you to organize a lot of verbal data (ideas, consumer desires, group opinions, etc.) according to the principle of affinity. This diagram is often called the KJ-method, after the name of its founder - Jiro Kawakita. An affinity diagram serves to group many similar or related ideas, rather illustrates associations than logical connections. It is used when it is necessary to organize a large amount of data, as well as stimulate the collective creative process.

Rice. 6.18

library

Procedure for compiling an affinity diagram:

• 1. definition of the object for data collection;
• 2. data collection through brainstorming. Data on receipts are recorded on stickers and pasted on a large sheet or board;
• 3. grouping of related data by directions. According to the principle of kinship, the data are combined into groups by adding stickers.

A link diagram (link graph) allows you to identify logical relationships between the main idea, problem and various data. Unlike an affinity diagram, which requires creative, associative thinking to construct, a link diagram is logical tool.

The link diagram is used in cases where:

• the topic is so complex that links between different ideas cannot be established by ordinary discussion;
• the sequence of steps in time is critical;
• there is a suspicion that the problem under study is part of a more fundamental one, not touched upon in this case Problems.

There are two types of link diagrams:

• qualitative connection graph;
• quantitative graph of connections.

Rice. 6.19

A qualitative link graph establishes a relationship between different factors. A quantitative relationship graph is designed to determine the influence of several factors on each other. It is often used to determine the role of a factor (cause or effect): if a factor has more outgoing arrows than incoming arrows, then this is a cause, otherwise it is a consequence.

Rice. 6.20

A tree diagram (goal tree, systematic diagram) is a tool that provides a systematic way to resolve a significant problem or central idea presented at various levels. Unlike the affinity diagram and the link diagram, this tool is more targeted.

The goal tree is built in the form of a multi-stage hierarchical structure whose elements are, for example, various means and ways to solve the problem. The procedure for its construction is similar to that described above for the affinity diagram. But in this case, the object under study must be precisely defined and recognized.

A variant of constructing a tree diagram to solve a problem is called root cause analysis (Five Whys method). A tree diagram can also be built to identify customer requirements, to compile a list of activities to improve performance, etc.

Rice. 6.21

The matrix chart allows you to identify the importance of various relationships. This tool serves to organize large amounts of data and allows you to graphically display the logical relationships between various elements.

The purpose of a matrix chart is to depict the relationships between tasks, functions, and characteristics, and to identify their relative importance. Therefore, the matrix diagram in the final form reflects the correspondence of certain factors and phenomena to the various causes of their occurrence and the means of eliminating their consequences, and also shows the degree of dependence of these factors on the causes of their occurrence and measures to eliminate them. Such matrix charts are called connection matrices.

Rice. 6.22

In practice, various forms of the relationship matrix are used depending on the number of groups of variables under study:

• L-forms (variables - 2, direct connections - 1, indirect - no);
• T-forms (variables - 3, direct connections - 2, indirect -
• Y-forms (variables - 3, direct connections - 3, indirect - no);
• X-forms (variables - 4, direct connections - 4, indirect -
• "roof" (variables - 1, direct connections - no, indirect - no).

The most common is the L-shape matrix chart, which is often referred to as the "quality table". In this case, two interrelated groups of components are presented in the rows and columns of the matrix, respectively, with the help of which it is necessary to establish a connection between the individual components.

2 What quality management tools that you already know use matrix charts?

An arrow (network) diagram is used to plan the optimal timing of all necessary work to achieve the set goal. The use of this tool is possible only after identifying problems, determining the necessary measures, terms and stages of their implementation.

The arrow diagram is a diagram of the progress of work with an indication of the sequence and timing of their implementation and serves to solve optimization problems. This tool is borrowed from operations research methods and is widely used not only in planning, but also in the subsequent monitoring of the progress of work.

There are several methods for constructing a network diagram, depending on the orientation to processes or events:

• CPM method (Critical Path Method);
• PERT method (Program Evaluation and Review Technique);
• MPM method (Metra Potential Method).

The most common is the critical path method (CPM method), which can be graphically represented as Gantt charts or network graph. A network graph is preferred, as it more clearly reflects the sequence of actions and the impact of a particular operation on the execution of subsequent ones.

Rice. 6.23

Rice. 6.24

A process diagram (Process Decision Program Chart - PDPC, process flow diagram, Dznro Kondo method) is a diagram that reflects the sequence of actions and decisions necessary to obtain the desired result.

The most effective application of the process diagram:

• when developing new programs. In this case, the process diagram allows you to plan and follow the sequence of actions, analyzing the occurrence possible problems;
• with the possibility of major errors in the planning of the process. The process diagram allows you to analyze all actions, predict undesirable results and carry out appropriate actions in advance.

adjustments.

Rice. 6.25

1 What is the difference between the situations of using a process diagram and an arrow diagram?

Priority matrix (analysis of matrix data)

is intended for processing large numerical arrays obtained during the construction of matrix diagrams. Using multivariate statistical analysis, priority data are identified. This method is used in cases where it is necessary to present numerical data from matrix charts in a more visual form. An example of using matrix data analysis is to identify

importance specifications in quality function deployment (QFD) technology.

Rice. 6.26

• What other quality management tool uses a similar data presentation principle? What is the essential difference between these tools?