Stratification, also known as flow or run chart, is one of the 7 Basic Tools of Quality in data analysis. It is relevant to the field of manufacturing as it allows business leaders to identify patterns within multiple data sets.
One of the 7 Tools of Quality
Sometimes called flow chart, run chart, or run-sequence plot
Type of line graph displaying various data sources
Used for analyzing quality control in manufacturing
Stratification is a mode of data analysis where data is grouped into homogenous groups – called strata – for visual graphical representation.
Each stratum is from a different data source, and is represented differently on the stratification chart according to a visual legend.
The purpose of a stratification chart is to allow someone to see patterns between and different sets of homogenous data. It is not as in-depth, statistically or mathematically-speaking, as a control chart.
Here is an example of a stratification graph:
Stratification and control charts are both basic tools of quality control, but there are a couple significant differences:
A control chart should be used when attempting to see if a process is stable, or with control limits. A stratification chart should be used when investigating the possibility of trends across multiple data sources.
Note that this is also different from a check sheet, one of the other 7 tools of quality.
Earlier, we referred to stratification by other names: flow charts or run charts. For our purposes in explaining basic core concepts, these are all the same thing. However, there is a caveat that there are critical differences between types of stratification analyses.
For example, a run chart is a subtype of stratification chart that includes a time sequence. This is often shown along the X-axis.
When using a run chart in particular, the focus in this style of data analysis is measuring a variable against increments of time. An example of this would be measuring units produced on a single production line over the course of a day.
A common use of stratification is in organizing survey data. In this example, data points are visually separated by color or by drawing trend lines.
Because stratification is used for detecting patterns, they can include all sorts of data. Here are some examples of data sources that can be used, even within the same chart:
You may have noticed that this list is incredibly broad. This is because stratification is a very applicable tool by design.
Putting stratification into its simplest terms, it is a way to visually see patterns in data. This means that you can use pretty much any type of data you’d like, as long as you set it up properly by ensuring each stratum is fairly represented according to your specifications.
For example, a shop floor manager would be able to see several groups or “buckets” of data points at once, such as number of workers present each day, the number of defects produced per shift, and number of final products shipped.
These 3 separate strata – workers, defects, and final products – may have statistically significant patterns between them or within each of them, depending on how the data shows up visually.
It’s important to choose the right application for your stratification chart, and that includes following some basic guidelines.
Stratification charts are quite simple analysis tools, but there are definitely elements that must be included. These are:
It is important to remember that stratification charts only show correlation, not necessarily causation. For example, just because similar patterns between strata may emerge within your stratification chart, doesn’t mean that one action causes the other. They may in fact just be correlative. This is why it is helpful to combine stratification with one or many other tools of quality. Many perspectives of the same data sets can expose insights and data collection biases alike.
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