Pareto Analysis: Also known as 80/20 Analysis, this technique is versatile, powerful, and easy to use with spreadsheet software applications. Charts are a graphical tool for ranking causes from most to least significant. Data can be divided into classes with varying levels of impact on processes or results.
Cause-and-Effect Diagrams:The “fishbone” diagram assists a group in locating the cause(s) of error. Normally, the relevant quality characteristic appears at the head of the “fish,” with the spine acting as the link upon which causes can be hung. As a
starting point, four “bones” are usually labelled “manpower”, “methods”, “machinery”, and“materials”, but these are frequently changed as the investigation progresses.
A graphic or symbolic representation, often using ASME Standard Symbols, of work performed on a product as it passes through the stages of a process.
Control Charts (XBar and R):
Particularly useful in a manufacturing environment, the development of Process Control Charts requires some education in statistics. They are an effective way to determine whether or not a process is in control. Upper and Lower Control Limits are calculated, and process outputs are plotted in a run chart relative to these limits.
Run Charts: A simple yet effective graphical representation of performance over time. Trends, cycles, and exceptions can be easily identified.
Scatter Diagrams: This technique is used to plot the relationship between two variables, for example, a worker’s hours spent in training and the number of defective parts produced.
Histograms: A graphical representation to measure frequency of occurrence. Observations are gathered over time and plotted onto the histogram to identify the most common output as well as the least common.