The seven quality tools (Q7) go back to the father of Japanese quality control Prof. Ishikawa Kaoru. He compiled them for dealing with quality problems in business processes.
The quality tools have two uses: issue recording and issue analysis. For issue recording, issue summary lists, histograms and control charts are used. Pareto diagram, flow chart, correlation diagram and cause-effect diagram are used for issue analysis.
Q1 – The Error Collection List.
The issue tally card or list is a commonly used method for evaluating counting tests. Tally sheets are a tool for recording data. In this case, the issue summary list does not contain any measured values, but only the number of issues. The number is entered by tally list into a table, which contains the issue designation and the number of issue possibilities.
In many cases it is possible to make statements about error frequencies after only a short time. Causes of errors can be derived from this. A issue summary list is therefore the basis for statistical evaluations and analyses. An evaluation of the information in a issue summary list is usually carried out using the Pareto diagram.
- Simple setup
- Data can be visualized easily
- Error trends can be identified at an early stage
- Error lists are quickly understood by employees
- Focal points and causes of issues are quickly identified
Disadvantages of a issue summary list:
- No analysis of error causes possible
- Clarity decreases with increasing number of recorded issue types
As a prerequisite for the format of the error list must/must:
- Problem must be determined
- Known error types must be listed
- Space must be left for “other errors
- Place and time of recording must be defined
- Sample size must be defined
Q2 – the histogram.
A histogram is a graphical representation of a frequency distribution of measurement data over a period of time. It is a bar chart that shows a frequency distribution of metrically scaled characteristics. It is based on the data ordered by size and divides the whole range of the sample into k ranges, these are called classes.
The numerical value separating two classes is called the class boundary. Class boundaries are particularly important because a poor choice of these boundaries can severely consume the facts and thus the statements of the measurement data are lost.
The results displayed with the help of the histogram allow a comparison to be made between the actual values and the target values. The classification and the visualization of the frequency distribution make it possible, for example, to positively influence the future process behavior with regard to the target value. It is especially this visual information that creates added value for the viewer with this tool, because it cannot be derived from pure measurement data. Difficulties are possibly to be expected in case of an unfavorably selected class division.
- Large amounts of data can be summarized in one figure
- It is easy to understand
- It is limited to metric data
- No root cause analysis possible
Q3 – The quality control chart.
A quality control chart is a form for the graphical representation of statistical characteristics for a series of samples with intervention limits (upper and/or lower) and often also with warning limits and a center line. The control charts make visible whether the changing individual values of a repetitive process are within tolerated limits. If defined limits are exceeded, control action must be taken in the process.
In the case of quality control charts for counting characteristics, a certain number of issues is taken into account in advance, since they do not warn of negative process changes until they have occurred. Thus, they only show the percentage of products that are out of order. This is a major disadvantage compared to quality control charts for continuous characteristics. These charts also show the direction and magnitude of the variation.
- Temporal recording of the course of the process
- Statistically sound
- No error analysis
- Training is required
- No short-term results
Q4 – The Pareto Diagram.
The Pareto principle states that a large part of the impact of a problem, about 80%, is often due to a small number, about 20%, of causes. The Pareto diagram and subsequent Pareto analysis is used to order existing data, e.g. in a tally sheet, according to their importance and to derive priorities from them. The characteristics are ordered either according to impact or frequency, e.g. costs, falling according to size.
The aim of this analysis is to identify the cause as well as weighting and to initiate measures to solve or improve the problem according to their priorities.
It makes sense to address the largest, most important, or most costly problem first. The application of the Pareto chart is relatively simple to understand and perform. Difficulties can arise from many different types of errors that require classification.
- Important errors are highlighted
- Can use data from different sources
Q5 – The Correlation Diagram.
In the correlation diagram (scatter diagram), the relationship between two statistical characteristics is represented graphically. A pair of values forms a point. After plotting the values in a Cartesian coordinate system, an accumulation of values is obtained, which in turn provide conclusions about statistical relationships. There are different types of correlation depending on the dispersion and tendency.
With the help of the correlation diagram, a statement about the strength and direction of an interaction can be made in a simple way. Linear relationships can be examined most quickly and easily. Another positive aspect is that a suspected cause-effect relationship determined by a cause-effect diagram can be checked and thus refuted or confirmed.
- The chart can be created from 30 pairs of data
- Examines the direction and strength of a relationship
- Exact calculation is mathematically complex
- Bogus correlations are possible
Q6 – The flow chart.
The flowchart is often used for the representation of processes and procedures. First and foremost, the flowchart is used to get a first visual impression of processes. This shows where there are superfluous or missing process steps that can be adjusted in further steps.
- Understandable visualization through simple symbolism
- High level of detail
- No previous knowledge necessary
- A lot of room for interpretation, as a lot of freedom in the design
Invented by the father of Japanese quality control Prof. Ishikawa Kaoru and named after him, the Ishikawa diagram is a cause-effect diagram. Other common names are fishbone diagram or fishbone diagram.
It is a tool for visualizing a problem-solving process and represents causality relationships by looking for primary causes / influences of a problem / effect. It is suitable for the analysis of any problem.
The Ishikawa diagram is divided into main groups. These can be expanded depending on the problem in order to more easily identify the dependencies between the individual causes. The analysis is most effective in a multidisciplinary team. In this way, different aspects are taken into account when finding the causes.
Due to the systematic nature of the Ishikawa diagram and teamwork, different causes for the emergence of the problem become visible. Thus, one-sided approaches or the consideration of interests can be avoided. Further advantages are the number of cause suggestions as well as the recognition of the affiliations and dependencies of the individual causes. Cross-departmental influences can also be identified through the cooperation of the cross-departmental team members.
A disadvantage when using the method for the first time is the attitude of the employees who see it as a “gimmick”. With the help of a knowledgeable moderator, this difficulty is quickly overcome. Another disadvantage is a rather confusing presentation of complex problems. Dividing the problem into individual problems can be used here as a solution approach. The biggest weakness is that a time dependency cannot be represented.
- Systematic search for the “true causes
- Visualizes correlations and enables further statements by looking at them
- there is no link between the cause found and the time of occurrence