A standard toolkit used by quality project management professionals who are responsible for planning, monitoring, and controlling the issues related to quality in an organization is known as the seven basic quality tools. It is also known in the industry as &QC Tools, are used within the context of the PDCA Cycle to solve quality-related problems.

The seven basic quality tools are:

Cause-and-effect diagrams, which are also known as fishbone diagrams or as Ishikawa diagrams. The problem statement placed at the head of the fishbone is used as a starting point to trace the problem’s source back to its actionable root cause. It is proven useful in linking the undesirable effects seen as special variation to the assignable cause upon which project management teams should implement corrective actions to eliminate the special variation detected in a control chart.
Flowcharts, which are referred to as process maps because they display the sequence of steps and the branching possibilities that exist for a process that transforms one or more inputs into one or more outputs.
Checksheets, which are also known as tally sheets and used as a checklist when gathering data. It is used to organize facts in a manner that will facilitate the effective collection of useful data about a potential quality problem.
Pareto diagrams, exist as a special form of the vertical bar chart and are used to identify the vital few sources that are responsible for causing most of the problem’s effects.
Histograms, are a special form of the bar chart and are used to describe the central tendency, dispersion, and shape of a statistical distribution. Unlike the control chart, the histogram does not consider the influence of time on the variation that exists within a distribution
Control Charts, are used to determine whether or not a process is stable or has predictable performance.
Scatter diagrams, plot ordered pairs (X,Y) and are sometimes called correlation charts because they seek to explain a change in the dependent variable, Y, in relationship to a change observed in the corresponding independent variable, X. If the correlation can be established, a regression line can be calculated and used to estimate how a change in the independent variable will influence the value of the dependent variable.