Quality Control And Six Sigma

Introduction: Quality control system shows the applications of the statistical method and identifies unusual circumstances that might merit attention.

Statistical quality control:   

• The phrase “statistical quality control” (SQC) refers to the application of statistical methods to monitor and evaluate systems and to determine whether changing key input variable (KIV) settings is appropriate.
• Some of these methods do not relate to monitoring or controlling processes and do not involve complicated statistical theory. In many places, SQC has become associated with all of the statistics and optimization methods that professionals use in quality improvement projects and in their other job functions.
• This includes methods for design of experiments (DOE) and optimization.

Method Names as Buzzwords:

1.       The names of problem-solving methods have become “buzzwords” in the corporate world.

2.       Some of the activities associated with performing these methods can be accomplished by a single person working alone, and others require multidisciplinary teams.

The following is an abbreviated list of the methods to illustrate the breadth and purposes of these methods:

Acceptance Sampling:

1.       It  Involves collecting and analyzing a relatively small number of KIV measurements to make “accept or reject” decisions about a relatively large number of units.

2.       Statistical evidence is generated about the fraction of the units in the lot that are acceptable.

Control Planning:

Control planningis an activity performed by the “owners” of a process to assure that all process KOV variables are being measured in a way that assures a high degree of quality. This effort can involve application of multiple methods.

Design of Experiments (DOE):

1.       DOE methods are structured approaches for collecting response data from varying multiple KIVs to a system.

2.       After the experimental tests yield the response outputs, specific methods for analyzing the data are performed to establish approximate models for predicting outputs as a function of inputs.

Failure Mode & Effects Analyze (FMEA):

FMEA is a method for prioritizing response measurements and subsystems addressed with highest priority.

Formal Optimization:

Formal optimizationis itself a diverse set of methods for writing technical problems in a precise way and for developing recommended settings to improve a specific system or product, using input-output models as a starting point.

Gauge Repeatability and Reproducibility (R&R)

R&R involves collecting repeated measurements on an engineering system and performing complicated calculations to assess the acceptability of a specific measurement system. (“Gage” is an alternative spelling.)

Process Mapping:

It  involves creating a diagram of the steps involved with an engineering system. The exercise can be an important part of waste reduction efforts and lean engineering and can aid in identifying key input variables.

Regression

1.       It is a curve-fitting method for developing approximate predictions of system KOVs (usually averages) as they depend on key input variable settings.

2.       It can also be associated with proving statistically that changes in KIVs affect changes in KOVs if used as part of a DOE method.

 

Statistical Process Control (SPC) charting includes several methods to assess visually and statistically the quality and consistency of process KOVs and to identify unusual occurrences. Therefore, SPC charting is useful for initially establishing the value and accuracy of current settings and confirming whether recommended changes will consistently improve quality.