Process Statistical Control ( PSC ) is a method of quality control that employs statistical methods to control a process and provide business agility. It helps ensure that the process runs efficiently, producing more products that meet specifications with less. PSC can be applied to any process in which the output of “conforming product” (product that meets specifications) is measured. Key tools used in PSC include run charts, control charts or dashboards, a focus on continuous improvement, and design of experiments.
The philosophy of total quality management is based on the constant improvement of the process, in order to prevent defective products or services. Therefore a fundamental element in this philosophy is the control of the process. This control is indispensable, because in every process the phenomenon of variability is latent. It can also be applied to business management, with full applicability has the statistical control of business processes and financial processes such as the exchange rate or bank interest.
Statistical control variability
The factors that cause this phenomenon are among others:
– The machinery or tool used, which does not always work in the same way.
– The raw material, which does not have the same characteristics all the time
– The human factor, whose work depends on many external and internal circumstances
The aim of process control is not to eliminate variability but to reduce it.
Any source of variation at any point in time in a process will fall into one of two classes.
1) “Common causes”: sometimes referred to as normal non-assignable sources of variation. It is through many sources of variation that acts n.o in the process. This type of causes produce a stable and repeatable distribution over time.
2) “Special causes” – sometimes referred to as assignable sources of variation. Refers to any factor that causes variation that affects only some of the process outputs. They are often intermittent and unpredictable.
The application of statistical process control involves three main phases of activity:
- Understand the process and specification limits.
- Eliminating assignable (special) sources of variation, so that the process is stable.
- Monitor the ongoing production process, assisted by the use of control charts, to detect significant changes in mean or variance.
Measurement data at points on the process map are monitored on control charts. The control charts attempt to differentiate “assignable” sources of variation from “common” sources. The “common” sources, because they are an action part of the process, are much less important to the manufacturer than the “assignable” sources. The use of control charts is an ongoing activity, continuous over time.
When the process does not trigger any of the “detection rules” in the control box for the monitoring panel, it is said to be “stable”. A process capability analysis can be performed on a stable process with Statistical Control for view the status of the process’ ability to produce “conforming product” in the future.
A stable process can be demonstrated by a process signature that is free of variations outside the capability index. A process signature is the points plotted against the capability index.
When the process any of the “detection detection rules” of the control chart (or, alternatively, the process capability is low), other activities can be performed for the source of the excessive variation. Tools used in these additional activities include:
- Ishikawa diagram ,
- experiments of serves and
- Pareto diagrams .
Our solution allows a better statistical control of all processes in a digital way.