Histogram is a bar graph which shows the distribution of data. It shows the snap shot of the data taken from a process.
The Purpose of Histogram is to
The Data spread in the process is the result of the variation that exists in the process.
Histogram helps us to understand that with the prevailing variation the process is capable to produce within the specifications. This is done by just superimposing the Specification Limits with the Histogram drawn
The above example indicates that the process is well capable as the variability of the process data is well within the specification
There is always possibilities of a capable process to produce defects if the setting parameters is not in line with the specification Mean. Histogram also help to understand the setting of a process is line with the specification.
The above example indicates that the variability in the process is very less compared to the specification limits indicating that the process is a capable process. However a pile of data fall outside the specification limits producing defects. This is due to the poor setting parameters.
In statistical terms histogram Helps us to
Histogram is a powerful tool helps to understand the process in just a glimpse. It helps in decision making during Problem solving.
Few books recommended to read on Problem solving and Histogram are given below
As in any tool chest, you can have dozens, if not hundreds, of different tools for all types of specialized tasks. Such is the case with Six Sigma. There are many tools available for your use depending on what you want to accomplish. However, like your home tool chest, there are a small set of basic tools that are your go-to tools you will use most often and on most projects.
Let’s review the 7 QC tools that are most commonly used in Six Sigma, the benefits of those tools, and some best practices for using them.
It is believed that the 7 QC tools were introduced by Kaoru Ishikawa in postwar Japan, inspired by the seven famous weapons of Benkei. Benkei was a Japanese warrior monk who armed himself with seven weapons and was on a personal quest to take 1,000 swords from samurai warriors who he believed were arrogant and unworthy.
Ishikawa was influenced by a series of lectures on statistical quality control given by Dr. W. Edwards Deming in 1950 to a group of Japanese scientists and engineers. Unfortunately, the complexity of the subject intimidated most workers, so Ishikawa focused primarily on a reduced set of tools that would suffice for most quality-related issues.
The 7 QC tools are:
Let’s explore each in a little more detail.
Check sheet: A form to collect and tally data for further analysis.
Image source: techqualitypedia.com.
Fishbone diagram: Fishbone diagrams are used to drill down to find the root cause of a problem. As the name implies, the diagram looks like the bones of a fish, where each main bone represents a specific category of possible root cause, and the subsequent drilling down is shown as smaller and smaller bones.
Image source: asq.org.
Histogram: This is a bar graph showing the frequency of a set of data, usually continuous data. The histogram allows you to see the center of the data, the range of the data, and the distribution of the data. It is a very useful snapshot. The downside is that you can’t see the sequence or order of the data.
Image source: statisticsbyjim.com.
Pareto chart: This chart is based on the 80/20 principle that says 80% of your effect is caused by 20% of your causes. For example, 80% of your sales comes from 20% of your customers. Dr. Joseph Juran, who developed this chart, often referred to this principle as the vital few and trivial many. He later revised that to the vital few and useful many. The Pareto chart lists the causes in descending order of frequency or magnitude. It is used to prioritize what you should look at first to improve your process.
Image source: www.automateexcel.com.
Control chart: A control chart is a statistical tool that looks at your process data over time for the purpose of distinguishing between special cause and common cause variation.
Image source: www.spcforexcel.com.
Scatter diagram: These are also known as scatter plots. They’re used to show a graphical correlation between a set of paired data on an X and Y axis. It is the graphical representation of what you would use for regression analysis.
Image source: www.spcforexcel.com.
Stratification: This is a graph that shows data that has been stratified when the data comes from different sources. It is useful to view the data by certain strata such as shift, gender, geographic location, machines, or suppliers.
Image source: www.systems2win.com.
These seven tools are easy to understand and apply and will help you understand what is going on in your process.
1. Easy
These 7 QC tools are easy to understand and implement yet powerful in identifying root causes, in discriminating between types of variation, and as a visual description of your data. A picture is truly worth 10,000 words (or statistical calculations).
2. Software-driven
Gone are the days when you had to draw all of your graphs by hand. There are many simple and cost-effective software packages that will take your data and quickly produce graphs.
3. 80/20
The Pareto principle applies to the 7 QC tools as well. 80% of your quality issues can be addressed by using 20% of the most common tools.
The key thing to understand is when to use each tool — which one is appropriate for your specific situation?
The more familiar you are with these common tools, the quicker you’ll be able to select the right one to help you solve your problem or answer your question. The Fishbone diagram is used to search for root causes of your problem. A control chart is used to distinguish between common and special cause variation. A scatter diagram is used to look for correlation or relationship between an X and Y variable.
Graphs don’t tell the whole story
Graphs and diagrams are useful for providing an overview and directional indicator of your process, but statistical analysis will provide greater confidence than a graph alone.
Flexibility
These seven tools can be used for different types of data and across any type of function. Their flexibility makes them useful in myriad situations and industries, so becoming familiar with them can be a wise investment.
Use these tools for as many applications as is feasible. Keep it simple, and only use the more sophisticated and complex tools if you need the additional information and analysis.
1. Have a clear idea of what question you’re trying to answer
Since each of the tools can be used to answer different data and process questions, be sure you’ve clearly defined the question you’re trying to answer.
2. Use them as your primary presentation
Use the 7 QC tools and their accompanying graphs and diagrams as your primary presentation format. Reserve the statistical analysis for questions that go beyond what’s answered in the graphs.
3. Make sure they’re self-explanatory
Be sure your graphs are succinct and self-explanatory so people can understand what you’re trying to tell them without the need for a long-winded explanation.
What is meant by stratification?
If you collected production data throughout the day across all three shifts and five machines, you might want to stratify or separate your data and look at it by shift and by machine. This would allow you to understand whether there were any differences between the strata. This might indicate the source of a root cause or an opportunity to improve the other shifts if one is found to be doing better than the others.
Scatter diagrams, Pareto charts, control charts, histograms, stratification, fishbone diagrams and check sheets.
With the use of current software and computer technology, you will rarely be required to create the graphs by hand. Still, it might be interesting to do it by hand once to fully appreciate the tools and software available to us.
Let’s review what’s in your tool belt
The 7 QC tools are basic graphical representations of your data. They can be used to answer a wide variety of questions about your data and your process. Use them as your primary presentation format when talking about what your data is telling you. While they are not a complete list of tools, they should be robust enough to address many of your improvement issues.
The 7 QC tools, while basic, are foundational to the Six Sigma methodology and have stood the test of time. Their simplicity and versatility make them indispensable for professionals across industries. As businesses evolve and data becomes more integral to decision-making, the importance of these tools only grows. They bridge the gap between raw data and actionable insights, allowing teams to make informed decisions. Moreover, in today’s digital age, with the integration of AI and machine learning, these tools can be further enhanced to provide even deeper insights. However, the essence remains the same: understanding and improving processes through data visualization.
Origin and Influence: Introduced by Kaoru Ishikawa, inspired by Benkei’s seven weapons and influenced by Dr. W. Edwards Deming’s lectures on statistical quality control.
List of 7 QC Tools: Check sheet, Fishbone diagram, Histogram, Pareto chart, Control chart, Scatter diagram, and Stratification.
Benefits: These tools are easy to understand, software-driven, and adhere to the 80/20 principle, addressing 80% of quality issues with 20% of the most common tools.
Importance: They address different issues, provide an overview of processes, and offer flexibility across data types and functions.
Best Practices: Clearly define the question, use the tools as the primary presentation format, and ensure graphs are self-explanatory.
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