SPC Data Display

This blog post will explain the details of histogram, pareto chart and scattered diagram along with some examples.


In SPC application, histogram is applied for the following purposes:

    • Observe variation and frequency of occurrence within the data.
    • Interpret data distribution and predict potential trend of process performance.
    • Help to indicate if there are any changes within the process
    • Determine if process is capable to meet customer requirement.

Histogram can be interpreted based on process data distribution (data points per group) based on the specification median, upper spec limit (USL) and lower spec limit (LSL). The following charts are demonstration with respect to the process distribution along with countermeasures for each situation.

The following below are displayed SPC scenarios and respective countermeasure methods to bring process back to idea state.

In addition, there are processes which have naturally skewed distribution, therefore normal distribution might not be the distribution for process results.

Pareto Chart

Pareto Chart is used to identify root cause’s relative frequency or sample size, and plot them into descending bar graph. Pareto chart is generally used for the following purposes:

    • Identify and focus on root cause with greatest impact.
    • Visualize the critical level of root causes.
    • Measures progress and indicate potential improvement.

The following illustration gives the chemical IQC laboratory’s out of spec pareto chart.

Based on the above summary, the top 3 issues occurred for raw material could be Cobalt %, Sulfur % and Mooney Viscosity.

In this case, Pareto Chart can accurately prioritize the issues which needs to be investigated first for stakeholders.

Scattered Diagram

Scattered diagram indicates correlation between independent variable (typically on the x-axis) and dependent variable (also known as a function of x, typically on the y-axis). It is applied for the following purposes:

    • Identify and verify correlation between independent variable and dependent variable (x versus y).
    • Provide both visual and statistical means for the potential relation between x and y.  

The table below summarizes the correlation justification.

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