Seven Basic Quality Tools |Simple But Effective

The Seven Basic Tools of Quality are a fixed set of graphical techniques identified as being most helpful in troubleshooting issues related to quality. They are called basic because they are suitable for people with little formal training in statistics and because they can be used to solve the vast majority of quality-related issues.

Kaoru Ishikawa, a professor of engineering at Tokyo University, first emphasized these seven basic tools of quality. They were named the Seven QC Tools after the famous seven weapons of the Japanese Kamakura-era warrior-priest Benkei which enabled Benkei to triumph in battle; so too, the Seven QC Tools, if used skilfully, will enable 95% of workplace problems to be solved. In other words, intermediate and advanced statistical tools are needed in about only 5% of cases.”

Let’s understand all one by one:

1.Cause-and-Effect Diagrams

Figure 1

Cause-and-effect diagrams are charts that identify potential causes for particular quality problems. They are often called fishbone diagrams because they look like the bones of a fish. A general cause-and-effect diagram is shown in Figure 1. The “head” of the fish is the quality problem, such as discharge delay in a hospital. The diagram is drawn so that the “spine” of the fish connects the “head” to the possible cause of the problem. Each of these possible causes can then have smaller “bones” that address specific issues that relate to each cause. Cause-and-effect diagrams are problem-solving tools commonly used by quality control teams. Specific causes of problems can be explored through brainstorming. The development of a cause-and-effect diagram requires the team to think through all the possible causes of poor quality (Discharge Delay in Figure 1).

2.Flowcharts

Figure 2

 A flowchart is a schematic diagram of the sequence of steps involved in an operation or process. It provides a visual tool that is easy to use and understand. By seeing the steps involved in an operation or process, everyone develops a clear picture of how the operation works and where problems could arise.

3.Checklists

Figure 3

A checklist is a list of common defects and the number of observed occurrences of these defects. It is a simple yet effective fact-finding tool that allows the worker to collect specific information regarding the defects observed. The checklist in Figure 3 shows ten defects and the number of times they have been observed. It is clear that the biggest problem is Supplied parts rusted . This means that the plant needs to focus on this specific problem. A checklist can also be used to focus on other dimensions, such as location or time. For example, if a defect is being observed frequently, a checklist can be developed that measures the number of occurrences per shift, per machine, or per operator. In this fashion we can isolate the location of the particular defect and then focus on correcting the problem.

4.Control Charts

Figure 4

Control charts are a very important quality control tool. These charts are used to evaluate whether a process is operating within expectations relative to some measured value such as weight, width, or volume. For example, we could measure the weight of a sack of flour, the width of a tire, or the volume of a bottle of soft drink. When the production process is operating within expectations, we say that it is “in control.”

To evaluate whether or not a process is in control, we regularly measure the variable of interest and plot it on a control chart. The chart has a line down the center representing the average value of the variable we are measuring. Above and below the center line are two lines, called the upper control limit (UCL) and the lower control limit (LCL). As long as the observed values fall within the upper and lower control limits, the process is in control and there is no problem with quality. When a measured observation falls outside of these limits, there is a problem.

5.Scatter Diagrams

Figure 5

Scatter diagrams are graphs that show how two variables are related to one another. They are particularly useful in detecting the amount of correlation, or the degree of linear relationship, between two variables. For example, increased production speed and number of defects could be correlated positively; as production speed increases, so does the number of defects. Two variables could also be correlated negatively, so that an increase in one of the variables is associated with a decrease in the other. For example, increased worker training might be associated with a decrease in the number of defects observed.

The greater the degree of correlation, the more linear is the observations in the scatter diagram. On the other hand, the more scattered the observations in the diagram, the less correlation exists between the variables. Of course, other types of relationships can also be observed on a scatter diagram, such as an inverted U. This maybe the case when one is observing the relationship between two variables such as oven temperature and number of defects, since temperatures below and above the ideal could lead to defects.

6.Pareto Analysis

Figure 6

Pareto analysis is a technique used to identify quality problems based on their degree of importance. The logic behind Pareto analysis is that only a few quality problems are important, whereas many others are not critical. The technique was named after Vilfredo Pareto, a nineteenth-century Italian economist who determined that only a small percentage of people controlled most of the wealth. This concept has often been called the 80–20 rule and has been extended to many areas. In quality management the logic behind Pareto’s principle is that most quality problems are a result of only a few causes. The trick is to identify these causes. One way to use Pareto analysis is to develop a chart that ranks the causes of poor quality in decreasing order based on the percentage of defects each has caused. For example,  a tally can be made Causes of Capacitor Failure,such as Contamination, Oxide Defect , Silicon Defect or Corrosion.70 Percent of Capacitor Failures are just because of Contamination & Oxide Defects and probably the best areas to start our quality improvement journey. Percentages of defects can be computed from the tally and placed in a chart like those shown in Figure 6.We generally tend to find that a few causes account for most of the defects.

 7.Histograms

Figure 7

 A histogram is a chart that shows the frequency distribution of observed values of a variable. We can see from the plot what type of distribution a particular variable displays, such as whether it has a normal distribution and whether the distribution is symmetrical.

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