Process Capabilities

Introduction

This software-based course presents the two disciplines that cover the subject of process capability: the total capability of a stable process such as is captured by popular capability indexes (Cpk, six sigma, z-score); and the design and analysis of nested experiments, the purpose of which is to break the total capability into its component parts (test repeatability, reproducibility, part-to-part, and various short-term and long-term sources).
NOTE: MINITAB Software is used for class simulation.

Benefits Of Attending The Course

The course enables the delegates to:

	Understand various expressions for process capability and when each is appropriate.
	Understand the various assumptions that go into making a capability statement useful and how to derive meaningful statements when assumptions are not met.
	Design and interpret a sources of variation study to "peel the onion" of process capability in order to point improvement efforts in the best direction.
	Be able to express capability measures to others.

Who Should Attend?

This course is ideal for manufacturing engineers, quality engineers, SPC coordinators, quality managers, process engineers, and product engineers. For maximum benefit, attendees should have a working knowledge of means, standard deviations, and control charts.

Brief Course Outline

Day 1 (AM)

Day 1 (PM)

Introduction

	Motivation
	Process
	Process control
	Process capability
	Process capability versus designed experiments.

Total Process Capability

Objectives & Assumptions

Verifying Control

Verifying Normality

Statistical measures

Goodness-of-fit tests

Histograms

Normal probability plots

Estimating the standard deviation

Expressing capability

± 3 sigma

Non-normal distributions

Capability indexes: Cp, Cpk

The usefulness of capability indexes

Percent out of specifications

"Sigma" indexes.

Motivating a component of variance study

Day 2 (AM)

Day 2 (PM)

Components of process capability

Conjecture step

Design step

	Crossed vs. nested arrangements of factors
	Balanced nested designs
	Unbalanced nested designs

Analysis Step

	The ANOVA
	Unbalanced designs
	Whither variation
	R&R studies
	Allocating resources between samples and tests

Diagnostics

	Assumptions
	Multi-vari plots
	s-charts

Examples

Mixed models

Fixed vs. random effects

Mixed models defined

Highlights

	Conjecture
	Design
	Observations
	Analysis

Review the experimental framework for components of variance studies

[ Home ] [ Up ]

Process Capabilities

Introduction

Benefits Of Attending The Course

Who Should Attend?

Brief Course Outline

Introduction

Total Process Capability

Components of process capability

Diagnostics

Examples

Mixed models

Kindly e-mail questions or comments about this web site directed to dominic@ict-m.com Copyright © 2003 Belmah Strategies (M) Sdn. Bhd. Last modified: 06/18/04

Kindly e-mail questions or comments about this web site directed to dominic@ict-m.com
Copyright © 2003 Belmah Strategies (M) Sdn. Bhd.
Last modified: 06/18/04