The 7 QC Tools
The Seven Quality Control tools (7QC tools) are graphical and statistical tools which are most often used in QC for continuous improvement. Since they are so widely utilized by almost every level of the company, they have been nicknamed the Magnificent Seven. They are applicable to improvements in all dimensions of the process performance triangle: variation of quality, cycle time and yield of productivity.
Each one of the 7QC tools had been used separately before 1960. However, in the early 1960s, they were gathered together by a small group of Japanese scientists lead by Kaoru Ishikawa, with the aim of providing the QC Circles with effective and easy-to-use tools. They are, in alphabetical order, cause-and-effect diagram, check sheet, control chart, histogram, Pareto chart, scatter diagram and stratification. In Six Sigma, they are extensively used in all phases of the improvement methodology – define, measure, analyze, improve and control.
(1) Cause-and-effect diagram
An effective tool as part of a problem-solving process is the cause-and-effect diagram, also known as the Ishikawa diagram (after its originator) or fishbone diagram. This technique is useful to trigger ideas and promote a balanced approach in group brainstorming sessions where individuals list the perceived sources (causes) with respect to outcomes (effect).
When constructing a cause-and-effect diagram, it is often appropriate to consider six main causes that can contribute to an outcome response (effect): so-called 5M1E (man, machine, material, method, measurement, and environment).
When preparing a cause-and-effect diagram, the first step is to agree on the specific wording of the effect and then to identify the main causes that can possibly produce the effect. The main causes can often be identified as any of 5M1E, which helps us to get started, but these are by no means exhaustive.
Using brainstorming techniques, each main cause is analyzed. The aim is to refine the list of causes in greater detail until the root causes of that particular main cause are established. The same procedure is then followed for each of the other main causes. The method is a main cause, the pressure and the temperature are the causes, and “the pressure is low” and “the temperature is too high” are the root causes.
(2) Check sheet
The check sheet is used for the specific data collection of any desired characteristics of a process or product that is to be improved. It is frequently used in the measure phase of the Six Sigma improvement methodology, DMAIC. For practical purposes, the check sheet is commonly formatted as a table. It is important that the check sheet is kept simple and that its design is aligned to the characteristics that are measured. Consideration should be given as to who should gather the data and what measurement intervals to apply. For example, Figure 4.2 shows a check sheet for defect items in an assembly process of automobile ratios.
(3) Control chart
The control chart is a very important tool in the “analyze, improve and control” phases of the Six Sigma improvement methodology. In the “analyze” phase, control charts are applied to judge if the process is predictable; in the “improve” phase, to identify evidence of special causes of variation so that they can be acted on; in the “control” phase, to verify that the performance of the process is under control.
The original concept of the control chart was proposed by Walter A. Shewhart in 1924 and the tool has been used extensively in industry since the Second World War, especially in Japan and the USA after about 1980. Control charts offer the study of variation and its source. They can give process monitoring and control, and can also give direction for improvements. They can separate special from common cause issues of a process. They can give early identification of special causes so that there can be timely resolution before many poor quality products are produced. Shewhart control charts track processes by plotting data over time in the form shown in Figure 4.3. This chart can track either variables or attribute process parameters. The types of variable charts are process mean (x), range (R), standard deviation (s), individual value (x) and moving range (Rs). The attribute types are fraction nonconforming (p), number of nonconforming items (np), number of nonconformities (c), and nonconformities per unit (u).
It is meaningful to present data in a form that visually illustrates the frequency of occurrence of values. In the analysis phase of the Six Sigma improvement methodology, histograms are commonly applied to learn about the distribution of the data within the results Ys and the causes Xs collected in the measure phase and they are also used to obtain an understanding of the potential for improvements.
(5) Pareto chart
The Pareto chart was introduced in the 1940s by Joseph M.Juran, who named it after the Italian economist and statistician Vilfredo Pareto, 1848–1923. It is applied to distinguish the “vital few from the trivial many” as Juran formulated the purpose of the Pareto chart. It is closely related to the so-called 80/20 rule – “80% of the problems stem from 20% of the causes,” or in Six Sigma terms “80% of the poor values in Y stem from 20% of the Xs.”
In the Six Sigma improvement methodology, the Pareto chart has two primary applications. One is for selecting appropriate improvement projects in the define phase. Here it offers a very objective basis for selection, based on, for example, frequency of occurrence, cost saving and improvement potential in process performance.
The other primary application is in the analyze phase for identifying the vital few causes (Xs) that will constitute the greatest improvement in Y if appropriate measures are taken.
A procedure to construct a Pareto chart is as follows:
1) Define the problem and process characteristics to use in the diagram.
2) Define the period of time for the diagram – for example, weekly, daily, or shift.
Quality improvements over time can later be made from the information determined within this step.
3) Obtain the total number of times each characteristic occurred.
4) Rank the characteristics according to the totals from
(6) Scatter diagram
The scatter plot is a useful way to discover the relationship between two factors, X and Y, i.e., the correlation. An important feature of the scatter plot is its visualization of the correlation pattern, through which the relationship can be determined. In the improve phase of the Six Sigma improvement methodology, one often searches the collected data for Xs that have a special influence on Y. Knowing the existence of such relationships, it is possible to identify input variables that
cause special variation of the result variable. It can then be determined how to set the input variables, if they are controllable, so that the process is improved. When several Xs may influence the values of Y, one scatter plot should be drawn for each combination of the Xs and Y.
Stratification is a tool used to split collected data into subgroups in order to determine if any of them contain special cause variation. Hence, data from different sources in a process can be separated and analyzed individually. Stratification is mainly used in the analyze phase to stratify data in the
search for special cause variation in the Six Sigma improvement methodology.
The most important decision in using stratification is to determine the criteria by which to stratify. Examples can be machines, material, suppliers, shifts, day and night, age groups and so on. It is common to stratify into two groups. If the number of observations is large enough, more detailed stratification is also possible.