1. Introduction to Statistical Quality Control Douglas C. Montgomery
Chapters 1, 2 and 3
Introduction to Statistical Quality Control
Douglas C. Montgomery
Presentation by Mikey Awbrey

2. Chapter 1: Introduction
KEY POINTS
Quality
Deming’s 14 points
PDCA

3. Chapter 1 - Quality Quality is a factor of Multiple dimensions
Performance
Reliability
Durability
Serviceability
Aesthetics
Features
Perceived Quality
Conformance to Standards

4. Chapter 1 – Deming’s 14 points
W. Edwards Deming was an engineer who wanted to improve business practices. He studied in the USA and Japan to create his philosophy based around 14 key points.
Create a constancy of purpose focused on the improvement of products and services
Adopt a new philosophy that recognizes we are in a different economic era
Do not rely on mass inspection to “control” quality
Do not award business to suppliers on the basis of price alone, but also consider quality
Focus on continuous improvement

5. Deming’s 14 points cont’d
Practice modern training methods and invest in on-the-job training for all employees
Improve leadership, and practice modern supervision methods
Drive out fear
Break down the barriers between functional areas of the business
Eliminate targets, slogans, and numerical goals for the workforce
Eliminate numerical quotas and work standards
Remove barriers that discourage employees from doing their jobs
Institute an ongoing program of education for all employees
Create a structure in top management that will vigorously advocate the first 13 points

6. Chapter 1 - PDCA
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7. Chapter 2 The DMAIC Process
Define
Clearly identify opportunity and validate its potential
Measure
Evaluate and understand the current status of the process
Analyze
Determine cause and effect relationships and understand variables
Improve
Problem solve to create changes to improve process performance
Control
Create a control plan to maintain the improvements and set them in motion

8. Chapter 3: Modeling Process Quality
Key points
Variation
Discrete vs Continuous
Distribution Methods
Probability Plots

9. Chapter 3- Variation
Variation - a change or difference in condition, amount, or level, typically with certain limits. (Taken from Google dictionary) Variation can be described in many ways and is the focus of statistical analysis.

10. Describing Variation Stem and Leaf Histogram
Group data together numerically
Raw data is exposed
Group data together graphically
Data is sorted into sets
Commonly has bell curve overlay (not required)

11. Describing Variation Cont’d
Stem and Leaf
Histogram
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12. Describing Variation Cont’d
Box Plot
Used to represent a number of important features about the data.
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13. Discrete Vs Continuous Distribution
The data can only be in certain values, such as integers i.e. 1,2,3,4
The variables can be expressed on a continuous scale. i.e , 4.325, 0.2

14. Discrete Distributions
Bernoulli Trials
When a part is pulled form a lot and it either passes or fails. The data collected from this type of trial is a very typical type of discrete data
Hypergeometric Distribution
Used to predict the probability of selecting a defective part form a sample within the assumptions of pulling a select number from the whole and the parts pulled are not replaced and the number defective are known.
Binomial Distribution
Similar to hypergeometric, however this assumes the lot from which samples are taken is virtually infinite and uses a percentage for number of defective parts in the lot.

15. Discrete Distributions Cont’d
Poisson Distribution
Used to predict the number of defects any given part pulled from a lot might have.
Negative Binomial
This represents the number of Bernoulli trials to achieve a certain number of successes, rather than the predicted number of failures as it relates to the number of Bernoulli trials performed.
Geometric Distribution
The number of Bernoulli trials until the first success is acheived

16. Continuous Distribution
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Normal Distribution
A symmetrical, unimodal, bell-shaped curve
Lognormal Distribution
The variables follow an exponential pattern. This describes ln (x) = w which means the natural log of x is normally distributed.

17. Continuous Distribution cont’d
Exponential Distribution
The probability distribution of an exponential variable. Used in reliability engineering to model time to failure of a component.
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Gamma Distribution
This reduces the exponential distribution it terms of λ, the result of the exponential distribution. This gives the curve a larger variety of potential shapes.
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18. Continuous Distribution cont’d
Weibull Distribution
This is the most flexible distribution with the widest variety of applications in reliability engineering. The exponential distribution is reduced with 1/θ where θ>0 is the scale parameter.
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19. Probability Plots
Probability plotting is a graphical method to check if the data is suitable for probability modeling. The data is placed on a special graph and plotted. This can then be evaluated with a subjective visual examination to determine if the data fits the hypothesized model.
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20. Final Notes of Importance
It is very important to remember that all of these calculations are approximations. As was apparent in the previous slide, the calculated lines do not fit the data perfectly.
Chapter 3 is about using collected data and creating usable predictions from it.