Course overview
The use of statistical techniques and other proven methods will result in improved quality and manufacturing maturity. This course will help organizations select appropriate control strategies and demonstrate their effectiveness through statistical analysis to the benefit of themselves and their customers.
The course objective is to qualify practitioners who will be ‘subject experts’ capable of implementing applicable methods and training operators.
The course meets in full the requirements of the AS13006 and AS13100 and augments the guidance of RM13006
TEC’s 2-day course uses Case Studies, Exercises and Quizzes – using ‘real’ data – and the practical use of Minitab®.
Course details
The requirements and best practices required for deploying effective Process Control Methods are covered in a systematic and practical manner using Case Studies, Exercises and Quizzes – using ‘real’ data – and the practical use of Minitab® –
Preamble
Process Control Methods – Standard, Reference Manual, Syllabus
Preamble – Process control ‘gameplan’
Understanding process variability and its causes
Process variation – the irrefutable facts!
Process variation – product characteristics
Common causes of variation
Exercise: Examples of common causes
Special causes of variation
Exercise: Examples of special causes
Walter A. Shewhart’s quotations on variation and causes
W. Edwards Deming’s quotations on variation and causes
Donald J. Wheeler’s quotations on variation and causes
Introduction to basic statistical analysis
Basic statistics – the measures
Exercise: Machining shaft diameters (Case Study)
Advisement: Run Chart
Advisement: Dot-Plot (Histogram)
Other useful visual representations of data
Words of wisdom – Dr W Edwards Deming
Quiz: Analysis of the Case Study data
The ‘giants’ of the statistics we routinely use
Understanding ‘patterns’ of variation
Why ‘bell shaped’ and ‘normally distributed’?
Check for ‘normality’ – methodologies
Quiz: Does the Case Study data look normal?
Putting ‘properties’ into words
Quiz: Which quadrant are we in?
Calculating the mean (Xbar)
Calculating the standard deviation (σ)
Exercise: Calculating Mean (Xbar) and StDev (σ)
Advisement: Comparative calculations of Mean and StDev – Excel and Minitab®
The importance of process control
Examples: Recent process control failures!
Quiz: Reputational impact and effect on aerospace industry
Benefits of proactive process control
Benefits of achieving design nominal within tolerances
Taguchi’s Loss Function
Example: The Taguchi Loss Function
Exercise: Manufacture of tyres
Advisement: Manufacture of tyres
Understanding and importance of a closed loop control system
Operating a closed loop control system
Effectiveness of process control over end-of-line (EOL) inspection
Exercise: Pros & Cons of process control
Process Control in Context of Quality Planning
Product Development Process (PDP)
Advanced Product Quality Planning (APQP)
PDP synchronized with APQP phases
AESQ – Key Quality Tools for Zero Defects
Quiz: Advance Product Quality Planning (APQP)
Design-to-Manufacturing links and milestones
Concurrent engineering (DfM&A)
Exercise: DFA, DFM and DFM&A features and differences
Process risk identification and management
Parameter Diagram (P-Diagram)
Design Failure Mode & Effects Analysis (DFMEA)
DFMEA sequence of steps
Foundational Activities
Process Flow Diagram (PFD)
Process Failure Mode & Effects Analysis (PFMEA)
PFMEA sequence of steps
Quiz: Pros & Cons of FMEAs
Control Plan (CP)
Generic example: Control Plan (CP)
Products within families: Control Plan
The development of the control plan
Quiz: Advantages of Control Plans
Work Instructions & related documents
Designed to be used by operators!
Quiz: Workstation documentation
Error Proofing and Automated Control systems
Error/Mistake Proofing (Poka-Yoke)
Examples: Popular error/mistake proofing devices
How to implement error/mistake proofing
Jidoka (Autonomation)
Exercise: Brief explanations of Poka-yoke and Jidoka
TPM (Total Productive Maintenance)
TPM – the seven ‘pillars’
Projected augmented reality (AR))
Exercise: Projected augmented reality (AR
Visual Process Check + First Piece Check + Test Piece evaluation
Visual Process Check – Pre-operation process checklist
First piece check (aka ISIR)
Test Piece evaluation
First Article Inspection (FAI)
Quiz: First piece check, Pre-operation process checklist and First Article Inspection
Data Types, Collection & Sample Size
Data (characteristic) ‘types’
Matching characteristics to gauges
Determining sampling frequency
Quiz: Data types/forms/sheets, Gauges, Sample sizes & frequencies
Importance of reliable measurement systems
MSA (Measurement Systems Analysis) plan
Components of measurement variation
Gauge selection – resolution (discrimination)
Visualizing measurement system errors
Type 1 Gauge Study – gauge variation study
Exercise: Type-1 Gauge Study
Advisement: Basic test for normality – recommend first check
Advisement: Type 1 Gauge Study – Minitab® graphs & analysis
Type 2 Gauge Study – GR&R Study
Steps in an effective GR&R Study
Understanding GR&R acceptability criteria
Typical ‘team’ for undertaking a GR&R study
Exercise: Diameter measurement Gauge R & R study
Advisement: Variance components
Advisement: Gauge Evaluation
Pre-Control Charts Control charts, the CLT & Populations and Samples
Pre-Control Charts – purpose and set-up
Pre-Control Charts – operation
Quiz: Case Study – Pre-Control Chart
Control charts – the basic principles
Understanding ‘populations’ and ‘samples’
The ‘CLT’ (Central Limit Theorem)
Case Study (Original): Normality check and p-Value
Exercise: Estimating population μ and σ from samples
Advisement: Estimating population μ and σ from samples
The ‘SEM’ (Standard Error of the Mean)
Exercise: CLT and SEM
Advisement: CLT and SEM
Process Capability Analysis
Characteristics of processes and products
Stable processes and Capable processes
Reminder – Properties of normally distributed data
Quantifying process capability
A ‘non-capable’ process (centred)
A ‘capable’ process (centred)
Process capability – Cp (best case)
Process capability – Cpk (worse case)
Exercise: Cp and Cpk calculation (Case Study)
Illustration: Case Study Cp and Cpk using Minitab®
9145 requirements – Process improvement strategies
Benefits of centring a process (Cpk ~ Cp)
Exercise: Targets for process improvement
Advisement: Common & Special causes of variation
Summary report for shaft diameters – Improved
Control Charts for Variable and Attribute Data
I-MR (XmR) Control Charts
I-MR (XmR) Control Charts – Formulae
Constants for I-MR charts – Table
Exercise: I-MR (XmR) Control Chart – data
I-MR (XmR) Control Chart – Analysis and Actions
I-MR (XmR) Control Charts – with VoC
Xbar & R Control Charts
Determining Control Limits (UCL & LCL)
Xbar & R Control Charts – Table & Formulas
Exercise: Xbar & R Control Chart (Case Study – Optimized)
Control charts monitor stability and variation
Control charts presuppose capability!
Manually maintained Xbar & R chart
Monitoring charts using the 8 industry standard tests
I-MR-R/S Charts
I-MR-R/S Charts vs. I-MR (XmR) Control Charts
Attribute Control Charts (P-Charts)
Workmanship and Visual Inspection ‘difficulties’
Attribute Agreement Analysis (aka Visual Inspection)
Charts for Rare Events
Charts for Rare Events (G Charts)
Charts for Rare Events (T Charts)
G Chart ~ T Chart ~ I-MR (XmR) Chart
Short run (SPC) Control Charts
Short-run SPC ~ Notes
Quiz: Benefits of ‘proactive’ process control (SPC)
Advisement: Benefits of ‘proactive’ process control (SPC)
Advisement: Excuses for not implementing proactive process control
Basic Root Cause Analysis and Process Improvement
WARNING – VoC, Cpk and Control Charts
Root cause analysis – Guidance and Standards
Quiz: Problem-solving & CI tools
Problem solving/documenting using the 8D method
Outline of the 8D steps
Quiz: Identify the 8D steps
8D comparisons with other methodologies
A3 Problem-Solving – an alternative methodology
Examples: A3 reports (Contents and Quality Tools)
Examples: A3 report (Principles and Handwritten)
Quiz: Understanding the three types of special causes
Fixing special causes of problems – permanently!
Fixing special causes of problems – different ‘sources’
Determining countermeasures
Mistake-proofing strategies
Continuous improvement – Statistically demonstrating process improvements
Continual improvement – centre and reduce variation
Continual improvement – monitoring and evaluating
Example: Test scores from two classes
Calculations and visual representations
Hypothesis testing
Statistical significances of differences
Statistical evaluation of results
Quick visual confirmation of results
Exercise: Case Study (Original vs. Optimized)
Exercise: Checks for normality
Exercise: Checks for process capability
Exercise: Statistical evaluation of improvement results
Exercise: Quick visual confirmation of results
Exercise: Tukey’s Quick Test (means)
Exercise: One-way ANOVA
Exercise: Quick visual confirmation of results
Two more ‘giants’ of statistics to add to our roster!
Exercise: Case Study (Xbar-R Charts)
Roll-up session
Final warning – Voice of the Customer (VoC)
An apocryphal tale: Boosting Cpk index!
Results: BEFORE/CURRENT data and Cpk indices
Monitoring: BEFORE/CURRENT I-MR Control Charts
Analysis: What was going on?
Reminder – Process control ‘gameplan’
Extended questions & answers session
Who should attend
This course is designed for Quality Engineers, Continual Improvement Practitioners, Six Sigma ‘Belts’, QMS Internal Auditors and others involved in process control deployment.
It will also be applicable for AAs/AEAs conducting third-party audits involving process control selection and usage.
Deliverables & benefits
Participants attending this course, and successfully completing the Case Studies, Exercises and Quizzes will receive a training certificate containing the TEC logo.
TEC’s course has been designed to meet in full the AEQG syllabus and will qualify and empower personnel to implement and use powerful process control tools to the benefit of their organizations and customers.
The course includes capability analysis, setting up/operating control charts, problem solving, process enhancement, error/mistake proofing and statistical analysis of process improvements.