Quality Management Systems & Statistical Basics Flashcards
Unambiguous Terms are the Basis for a Successful QMS
The success of a systematic quality management system depends on a uniform
terminology that is as unambiguous as possible and quality-related.
Why are Unambiguous Terms so Important for a Successful QMS?
- Quality cannot be absolute, especially not “goodness par excellence”
- Quality is influenced by a high degree of multidimensionality
- Quality is always a determined or thought result, never a demand
Most Essential Terms of Quality Management
- Product
- Process
- Requirements
- Quality
7 Principles of Quality Management Systems according to ISO 9000
- Customer focus
- Leadership
- Personal commitment
- Improvement
- Fact-based decision making
- Relationship management
- Process-oriented approach
Five Main Tasks/Goals can be Derived from ISO 9001
- Quality Policies & Goals
- Quality Planning
- Quality Control
- Quality Assurance
- Quality Improvement
7 Quality Tools
- Error Collection Card
- Histogram
- Pareto Diagram
- Control Chart
- Correlation Diagram
- Ishikawa-Diagram
- Flow Chart
7 Management Tools
- Affinity Diagram
- Relation Diagram
- Tree Diagram
- Matrix Diagram
- Portfolio Analysis
- Precedence Diagram
- Decision Plan
7 Intelligence Tools
- Statistics
- Evolutionary Computation
- Classifiers
- Monte Carlo Simulation
- Graph Theory
- Bayes
- Decision Trees
Main Take-Aways from Today’s Lecture
An entrepreneurial quality management system (QMS) is the basis to
process information and data
▪ Definition of terms is unambiguous for a proper quality management system
▪ Product, Process, Requirements and Quality are important terms within
quality management
▪ ISO 9001 gives a structure for quality management systems
▪ Quality management has 5 main goals:
− Quality Politics & Goals
− Quality Planning
− Quality Control
− Quality Assurance
− Quality Improvement
▪ Main quality management tools consist of Q7and M7 Tools
Essential Visualization Forms
- histogram
- pareto diagram
- correlation diagram
- regression analysis
- confidence interval
Characteristics of histogram
- good overview
- frequencies of the individual classes are viewed and visualized
Characteristics of pareto diagram
- Pareto diagram is based on the Pareto principle, according to which most of the effects of a problem (80%) are often due to a small number of causes (20%)
- Bar chart that ranks causes according to their meaning
- The importance of a cause can be read directly from the diagram
Weibull Distribution
Name the Motivation. Advantage and Parameters
Motivation
statistical analysis of the failure behaviour of parts
▪ failure probability
▪ failure mechanism
Advantage
▪ no representative samples are required as input data
▪ the first failures in a total population are sufficient
(the manufacturer can usually access this
information easily)
Parameters
▪ failure rate λ
▪ changes in the failure rate b
Hypothesis Test
Application:
Elementary method for the verification of statistically
significant features, quality, attributes, properties and
functions.
null hypothesis H0
Initial assumption, assumed to be true until evidence
indicates otherwise.
alternative hypothesis H1
Is valid if null hypothesis is rejected.
The objective is the verification of characteristics of the chosen sample.
Type I and Type II Errors
Type I error: α error
▪ The α error describes an inaccurate decision in favor of the alternative
hypothesis.
▪ By convention, α equals 5% (in many cases).
Type II error: β error
▪ The β error describes an inaccurate decision in favor of the null hypothesis.
▪ The power (1-β) describes the probability with which a hypothesis test
decides in favor of the alternative hypothesis.
▪ Influencing factors on power
− Sample size: increasing power with increasing size.
− Difference (μ0-μ1): decreasing power with decreasing difference.
− Variance σ: decreasing power with increasing variance.
