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The SPC or Statistical process control is a control tool by which you could have full
control of your process. The data that is
collected is used to plot a graph that gives
valuable inferences about its Quality and feed back. SPC is an integral part of any Quality Improvement program. It is basically a tool for controlling the variables that add value to a process. We at problemsolvinginstitute try to make the statistical procedures and theorems
simple and application driven so that you will find learning and applying SPC to be fun.

SPC provides real time analysis to establish controllable process baselines; learn, set, and dynamically improve the capability of the process.

SPC is an important component of any Six Sigma program and the first choice in
improving and controlling the processes. Learn the power of SPC ,statistical charts
and output. Learn how to identify shift due to causes that you are supposed to
control and those that are due to chance.

SPC Course Overview

Monitors and provides feedback for
keeping processes in control.

Triggers when a problem occurs

Differentiates between problems that
are correctable and those that are due
to chance.

Gives you better control of your
process.

Reduces need for inspection

Provides valuable knowledge in the
working of the process.

You need SPC in the following situations.

When controlling ongoing processes by finding and correcting problems as they occur.

When predicting the expected range of outcomes from a process.

When determining whether a process is stable (in statistical control).

When analyzing patterns of process variation from special causes (non-routine events) or common causes (built into the process).

When determining whether your quality improvement project should aim to prevent specific problems or to make fundamental changes to the process.

Determine if a controlled process is
Yielding an adequate degree of control.

Improve Quality of the product or
service.

Be used as feedback for redesigning
the product or service.

Supplement the Design robustness of
a product.

Solve the problem and control the
variables that cause deviation.

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