7 Quality Control tools: A Necessity for all Industries.
By: Darshan Kulkarni
Tags: Statistics, Industry, Management

The seven basic tools of quality is a designation given to a hard and fast set of graphical techniques identified as being most helpful in troubleshooting issues associated with quality. They are called basic because they’re suitable for people with little formal training in statistics and since they cant solve the overwhelming majority of quality-related issues.

What are the 7 QC tools.

I. Cause and Effect diagram (Fishbone Diagram)

Fishbone Diagram

A cause and effect diagram examines why something happened or might happen by organizing potential causes into smaller categories. It can also be useful for showing relationships between contributing factors. One of the Seven Basic Tools of Quality, it is often referred to as a fishbone diagram or Ishikawa diagram.

One of the reasons cause & effect diagrams are also called fishbone diagrams is because the completed diagram ends up looking like a fish’s skeleton with the fish head to the right of the diagram and the bones branching off behind it to the left.

2)Check Sheet

A Check Sheet is normally a table with defined rows and columns where the data collected is usually 1 check mark within each category. The Check Sheet is not just one of the seven QC Tools, but it’s also one of the primary tools used during the Measure Phase of a Six Sigma DMAIC Cycle to collect data. A typical check sheet is divided into regions, and marks made in different regions have different significance. Data is read by observing the location and number of marks on the sheet.

3)Control Chart

A Control chart is one of many process improvement techniques. It is not the answer to all your problems. Nor should a control chart be used alone. There are always other process improvement tools that should be used along with control charts.

A control chart is used to monitor a process variable over time. That variable can be in any type of company or organization — service, manufacturing, non-profit and, yes, healthcare. It provides a picture of the process variable over time and tells you the type of variation you are dealing with as you move forward with continuous improvement. This understanding of variation is the key to using control charts effectively.

4)Histogram

A histogram is a graphical representation that organizes a group of data points into user-specified ranges. It is similar in appearance to a bar graph. The histogram condenses a data series into an easily interpreted visual by taking many data points and grouping them into logical ranges or bins.

The purpose of a histogram is to graphically summarize the distribution of a univariate data set.

The histogram graphically shows the following:

center (i.e., the location) of the data; spread (i.e., the scale) of the data; skewness of the data; presence of outliers; and presence of multiple modes in the data.

These features provide strong indications of the proper distributional model for the data. The probability chart or a good fit test can be used to verify the distributional model.

5)Pareto chart

A Pareto diagram is a simple bar chart that ranks related measures in decreasing order of occurrence. The principle was developed by Vilfredo Pareto, an Italian economist and sociologist who conducted a study in Europe in the early 1900s on wealth and poverty. He found that wealth was concentrated in the hands of the few and poverty in the hands of the many. The principle is based on the unequal distribution of things in the universe. It is the law of the “significant few versus the trivial many.” The significant few things will generally make up 80% of the whole, while the trivial many will make up about 20%.

The purpose of a Pareto diagram is to separate the significant aspects of a problem from the trivial ones. By graphically separating the aspects of a problem, a team will know where to direct its improvement efforts. Reducing the largest bars identified in the diagram will do more for overall improvement than reducing the smaller ones.

6)Scatter Diagram

A scatter plot, scatter graph, and correlation chart are other names for a scatter diagram.

We draw this graph with two variables. The first variable is independent and the second variable depends on the first.

This diagram is used to find the correlation between these two variables, how they are related. After determining the correlation, you can then predict the behavior of the dependent variable based on the measure of the independent variable.

A scatter chart is useful when one variable is measurable and the other is not.

According to the PMBOK Guide 6th edition, a scatter diagram is, “a graph that shows the relationship between two variables. Scatter diagrams can show a relationship between any element of a process, environment, or activity on one axis and a quality defect on the other axis.”

7)Stratification

Stratification is defined as the act of sorting data, people, and objects into distinct groups or layers. It is a technique used in combination with other data analysis tools. When data from a variety of sources or categories have been lumped together, the meaning of the data can be difficult to see. This data collection and analysis technique separates the data so that patterns can be seen and is considered one of the 7 QC tools.

Stratification Diagram

Why we use the 7 QC tools

1)The 7 QC tools help us visualise the problems faced in our process and hence reduce the complexity of troubleshooting.

2)It can be used to observe product quality fluctuation and fix them before any defects happen.

3) They are relatively easy to understand and do not require complex mathematics knowledge.

NOTE: In real life situations using all 7 tools may not be required or certain tools may be used multiple times.

Case Study(Rejection of lathe beds)

Lathe Bed

Problem :

The problem investigated in this case study is about the rejections of lathe beds, because of the issues/defects that occurred after the heat treatment process using flame hardening, which was used to provide the required hardness.

· How the 7QC will help in this situation step by step

1)The first step is to collect data about samples collected and their hardness.

2)The collected data needs to be arranged in a tabular form in excel.

The data from this table will be used to plot certain graphs to find out the issue.

3)The engineers present on the sites have certain data like what is the ideal hardness of the material and what range of hardness is acceptable in the industry. Using this information we can plot a control chart to understand which of the samples do not match the standards expected and we can also find the batch of products that have declining quality.

From preliminary observations we can see that six of the 32 samples have excessive hardness, four have permissible but very high hardness and one sample has the lowest permissible hardness.

4)From the data we saw in the control table it can be concluded that the data does not show a very regular pattern so it becomes necessary that we check of the data is following anormal distribution or not. To do this we will have to plot a histogram to show frequency distribution of hardness values.

From preliminary observation we can see that the samples don’t seem to be following a normal distribution. We can conclude that 26 out of 32 lathe beds follow the permissible range (till 42–48) hence 6 lathe beds are rejected from this study.

By doing this analysis we found out that there are 6 batches of units that are very likely to have underwhelming products. Doing this study helped prevent bad name for the company and financial losses as well.

5) After finding out the defects in the data it is important to find out what could be the reason that these products were not up to standards and for this we use cause and effect diagrams.

Cause and Effect Diagram

The entire production team needs to brainstorm reasons from their respective departments that could have caused such a problem.

After shortlisting the most likely reason a cause and effect diagram is made.

The seven causes were further elaborated to form tables so that the controllable causes wont happen in the future.

6)Investigation in the causes of defects

The next step is to ascertain the product quality by plotting control charts to fid out whether the process is under statistical control. For this the given 32 samples are divided into 8 subgroups to find the average mean.

From the charts it is clear that the process is under statistical control but unable to stop defects from happening. This calls for assessment of quality assessment of product.

Conclusion and final actions.

The team of engineers discussed these results with the chief of heat treatment. After discussion it is concluded that the gap between the base of flame and nozzle that disperses the gas may be responsible for inaccuracies and hence causing defects in certain products. To counter this another variable of distance between base of flame and tip of nozzle was added to rectify the mistakes.