1. Statistical Process Control Module 2
Dr. Salih Duffuaa & Dr. Mohamed Ben Daya
Systems Engineering Department
King Fahd University of Petroleum & Minerals

2. Training Objectives
The overall objective of this program is to build a strong learning base in the area of Statistical Process Control (SPC) in order to sustain the implementation of SPC and contribute to the growth of the man-power in Quality Assurance Laboratories and production.
This require knowledge in basic statistics and SPC tools and their interpretation.

3. Objectives (cont’d) Create a culture of continuous improvement.
Improve the skills of the man-power in data analysis and the interpretation of SPC results.

4. Training Program Outcomes of Module 1
Summarize and present data in meaningful format.
Analyze data.
Assess variability in data.
Construct confidence interval using excel.
Develop regression models and understand their use in calibration of instruments.

5. Training Program Outcomes of Module 2
Understand the relation between variability and quality.
Construct control charts for plant key processes.
Assess whether a process is in control or out of control.
Utilize SPC tools to identify major causes of poor quality.
Initiate process improvement based on information from SPC analysis
Assess process capability.
Suggest action plans to improve plant's process capability

6. Training Modules Module 1 Data collection and presentation
Descriptive statistics.
Probability
Probability distribution.
Regression
Estimation
Concept of variation
SPC tools
All with real data and realistic examples.
Duration: 4 months:
March – June, 2005
Module 2
Improvement using SPC tools
Fundamentals of control charts
Control charts for variables
Control charts for attributes
Process capability
Process improvements.
SPC implementation and case studies
Duration: 1 months:
November, 2005

7. Typical Schedule 8:30– 9:30 : First Presentation 9:30 – 9:45 : Break
9:45– 10:30 : Exercises
10:30 – 10:45 : Break
10:45 – 11:45 : Second Presentation
11:45 – 1:00 : Lunch Break
1:00 – 3:00 : Exercises and cases

8. Assessment
Final Exam
Small project
Two homeworks.

9. Team Formation and Project
Teams of 2 to apply SPC on a process
Identify a process (latter)
Project requirements
Define process
Choose appropriate measures
Develop control charts
Assess process capability
Suggest action plan to improve capability.

10. Week 1 Schedule 8:30 – 9:30 : Module 2 Introduction
Process improvement using SPC tools
9:15 – 9:30 : Break
9:30 – 10:30 : Basics of Control charts
10:30 – 10:45 : Break
10:45 – 11:45 : Basics of Control charts
11:45 – 1:00 : Lunch Break
1:00 – 3:00 : Cases and Examples
Next week’s assignment

11. Learning Outcomes Define quality and quality improvement.
Define Statistical Process Control.
Define quality and quality improvement.
Describe the role of variability and statistical methods in controlling and improving quality.
Explain the link between quality and productivity and
Define quality costs.

12. Learning Outcomes Distinguish between random and assignable causes
Use SPC tools other than control charts.
Define a control chart.
Explain the statistical basis for control charts.
Explain essential factors in control chart design
State the steps to implement SPC

13. Definition of SPC
S: for statistical: means based on the science of data collection and analysis.
P: for process: A process is A process is no more than the steps and decisions involved in the way work is accomplished. Everything we do in our lives involves processes and lots of them. Here are some examples:
writing a work order, shooting a weapon, getting out of bed
repairing a valve , ordering a part, performing a test,
conducting an UNREP, preparing a message, loading a missile
allocating a budget , mooring a ship , conducting a drill.
A sequence of activities (steps) that takes an input and produces an output.
C: for control : stability and predictability.

14. Definitions and Meaning of Quality
The Eight Dimensions of Quality
Performance
Reliability
Durability
Serviceability
Aesthetics
Features
Perceived Quality
Conformance to Standards

15. This is a traditional definition
Quality of design
Quality of conformance

16. This is a modern definition of quality
How do we measure variability ?

17. The Transmission Example

18. The transmission example illustrates the utility of this definition
An equivalent definition is that quality improvement is the elimination of waste. This is useful in service or transactional businesses.

19. Terminology

20. Terminology cont’d Specifications Defective or nonconforming product
Lower specification limit
Upper specification limit
Target or nominal values
Defective or nonconforming product
Defect or nonconformity
Not all products containing a defect are necessarily defective

21. 1-2. History of Quality Improvement

25. Statistical Methods for Quality Improvement

26. Statistical Methods Statistical process control (SPC)
Control charts, plus other problem-solving tools
Useful in monitoring processes, reducing variability through elimination of assignable causes
On-line technique
Designed experiments (DOX)
Discovering the key factors that influence process performance
Process optimization
Off-line technique
Acceptance Sampling

27. Walter A. Shewart (1891-1967) Trained in engineering and physics
Long career at Bell Labs
Developed the first control chart about 1924

28. A factorial experiment with three factors

30. Quality Philosophies and Management Strategies
W. Edwards Deming
Taught engineering, physics in the 1920s, finished PhD in 1928
Met Walter Shewhart at Western Electric
Long career in government statistics, USDA, Bureau of the Census
During WWII, he worked with US defense contractors, deploying statistical methods
Sent to Japan after WWII to work on the census

31. Deming
Deming was asked by JUSE to lecture on statistical quality control to management
Japanese adopted many aspects of Deming’s management philosophy
Deming stressed “continual never-ending improvement”
Deming lectured widely in North America during the 1980s; he died 24 December 1993

32. Deming’s 14 Points 1. Create constancy of purpose toward improvement
2. Adopt a new philosophy, recognize that we are in a time of change, a new economic age
3. Cease reliance on mass inspection to improve quality
4. End the practice of awarding business on the basis of price alone
5. Improve constantly and forever the system of production and service
6. Institute training
7. Improve leadership, recognize that the aim of supervision is help people and equipment to do a better job
8. Drive out fear
9. Break down barriers between departments

33. Note that the 14 points are about change
14 Points cont’d
10. Eliminate slogans and targets for the workforce such as zero defects
11. Eliminate work standards
12. Remove barriers that rob workers of the right to pride in the quality of their work
13. Institute a vigorous program of education and self-improvement
14. Put everyone to work to accomplish the transformation
Note that the 14 points are about change

34. Deming’s Deadly Diseases
Lack of constancy of purpose
Emphasis on short-term profits
Performance evaluation, merit rating, annual reviews
Mobility of management
Running a company on visible figures alone
Excessive medical costs for employee health care
Excessive costs of warrantees

35. Joseph M. Juran Born in Romania (1904), immigrated to the US
Worked at Western Electric, influenced by Walter Shewhart
Emphasizes a more strategic and planning oriented approach to quality than does Deming
Juran Institute is still an active organization promoting the Juran philosophy and quality improvement practices

36. The Juran Trilogy Planning Control Improvement
These three processes are interrelated
Control versus breakthrough
Project-by-project improvement

37. Some of the Other “Gurus”
Kaoru Ishikawa
Son of the founder of JUSE, promoted widespread use of basic tools
Armand Feigenbaum
Author of Total Quality Control, promoted overall organizational involvement in quality,
Three-step approach emphasized quality leadership, quality technology, and organizational commitment
Lesser gods, false prophets

38. Quality Systems and Standards

39. The ISO certification process focuses heavily on quality assurance, without sufficient weight given to quality planning and quality control and improvement

42. Quality Costs

43. Legal Aspects of Quality
Product liability exposure
Concept of strict liability
Responsibility of both manufacturer and seller/distributor
Advertising must be supported by valid data

44. Quality and Productivity
Example: Suppose a worker produces 100 units and 20% are defective. Which is better option to improve quality by 20% or productivity by 20%.
Does improving quality improves productivity?

45. Seven Quality Tools

46. Seven Quality Control Tools
Pareto Chart
Histogram
Process flow diagram
Check sheet
Scatter diagram
Control chart
Run Chart
Cause and Effect Diagram

47. Pareto Principle Vilfredo Pareto (1848-1923) Italian economist
20% of the population has 80% of the wealth
Juran used the term “vital few, trivial many”. He noted that 20% of the quality problems caused 80% of the dollar loss.
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48. Pareto chart
% Complaints
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49. Percent from each cause
Pareto Chart 10 20 30 40 50 60 70
(64)
Percent from each cause
(13)
(10)
(6)
(3)
Pareto analysis uses an ordered histogram to highlight the major causes of quality problems.
(2)
(2)
Poor Design
Defective parts
Machine calibrations
Operator errors
Wrong dimensions
Defective materials
Surface abrasions
Causes of poor quality

50. Histogram
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51. Histogram 5 10 15 20 25 30 35 40
Histograms are graphical frequency tables that visually capture and display the variation in a set of data.

52. Flowcharts Flowcharts Graphical description of how work is done.
Used to describe processes that are to be improved.
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53. Flow Diagrams
" Draw a flowchart for whatever you do. Until you do, you do not know what you are doing, you just have a job.”
-- Dr. W. Edwards Deming.

54. Flowchart
Activity
Decision
Yes No
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55. Flowchart
A flowchart diagrams the steps in a process. Flowcharts help problem solvers better understand the process so they can highlight quality problems.

56. Flow Diagrams

57. Process Chart Symbols Operations Inspection Transportation Delay
Storage

58. Check Sheet 7 Quality Tools                   
Shifts
  
    
  
 
  
Defect Type
  
   
  
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59. Cause-and-Effect Diagrams
Show the relationships between a problem and its possible causes.
Developed by Kaoru Ishikawa (1953)
Also known as …
Fishbone diagrams
Ishikawa diagrams
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60. Cause and Effect “Skeleton”
Materials
Procedures
Quality
Problem
People
Equipment
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61. Fishbone Diagram Quality Problem Machines Measurement Human Process
Environment
Materials
Faulty testing equipment
Incorrect specifications
Improper methods
Poor supervision
Lack of concentration
Inadequate training
Out of adjustment
Tooling problems
Old / worn
Defective from vendor
Not to specifications
Material-
handling problems
Deficiencies
in product design
Ineffective quality
management
Poor process design
Inaccurate
temperature
control
Dust and Dirt
A cause-and-effect diagram, or fishbone diagram, is a chart showing the different categories of problem causes.

62. Cause and effect diagrams
Advantages
making the diagram is educational in itself
diagram demonstrates knowledge of problem solving team
diagram results in active searches for causes
diagram is a guide for data collection

63. Cause and effect diagrams
To construct the skeleton, remember:
For manufacturing - the 4 M’s
man, method, machine, material
For service applications
equipment, policies, procedures, people

64. Scatter Diagram .
Scatter diagrams and tightness of points plotted on the graph gives an indication of the strength of the relationship. A cluster of points resembling a straight line indicates the strongest correlation between the variables. In this graph, there is a strong positive correlation between x and y.

65. Run Charts Run Charts (time series plot)
Examine the behavior of a variable over time.
Basis for Control Charts

66. Control Chart Number of defects Sample number 27 24 UCL = 23.35 2118 15
Number of defects 12 9
Process control involves monitoring a production process and charting the results on a control chart. If any of the points plotted falls outside the control limits, the process is out-of-control. 6
LCL = 1.99 3 2 4 6 8 10 12 14 16
Sample number

67. Control Charts
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68. A process is operating with only chance causes of variation present is said to be in statistical control.
A process that is operating in the presence of assignable causes is said to be out of control.

69. A control chart contains
A center line
An upper control limit
A lower control limit
A point that plots within the control limits indicates the process is in control
No action is necessary
A point that plots outside the control limits is evidence that the process is out of control
Investigation and corrective action are required to find and eliminate assignable cause(s)
There is a close connection between control charts and hypothesis testing

70. Photolithography Example
Important quality characteristic in hard bake is resist flow width
Process is monitored by average flow width
Sample of 5 wafers
Process mean is 1.5 microns
Process standard deviation is 0.15 microns
Note that all plotted points fall inside the control limits
Process is considered to be in statistical control

72. Shewhart Control Chart Model

76. More Basic Principles
Charts may be used to estimate process parameters, which are used to determine capability
Two general types of control charts
Variables Continuous scale of measurement
Quality characteristic described by central tendency and a measure of variability
Attributes
Conforming/nonconforming
Counts
Control chart design encompasses selection of sample size, control limits, and sampling frequency

77. Types of Process Variability
Stationary and uncorrelated  data vary around a fixed mean in a stable or predictable manner
Stationary and autocorrelated  successive observations are dependent with tendency to move in long runs on either side of mean
Nonstationary  process drifts without any sense of a stable or fixed mean

78. Reasons for Popularity of Control Charts
Control charts are a proven technique for improving productivity.
Control charts are effective in defect prevention.
Control charts prevent unnecessary process adjustment.
Control charts provide diagnostic information.
Control charts provide information about process capability.

79. 3-Sigma Control Limits Probability Limits Warning Limits
Probability of type I error is
Probability Limits
Type I error probability is chosen directly
For example, gives 3.09-sigma control limits
Warning Limits
Typically selected as 2-sigma limits

87. Pattern is very nonrandom in appearance
19 of 25 points plot below the center line, while only 6 plot above
Following 4th point, 5 points in a row increase in magnitude, a run up
There is also an unusually long run down beginning with 18th point

91. In phase II, the control chart is used to monitor the process
Phase I is a retrospective analysis of process data to construct trial control limits
Charts are effective at detecting large, sustained shifts in process parameters, outliers, measurement errors, data entry errors, etc.
Facilitates identification and removal of assignable causes
In phase II, the control chart is used to monitor the process
Process is assumed to be reasonably stable
Emphasis is on process monitoring, not on bringing an unruly process into control

92. SPC Implementation Issues
Define process
Chose Quality characteristic and measurement system
Focus on trends and shifts
Calculate control charts limits.
Investigate and act
SPC training

94. Nonmanufacturing applications do not differ substantially from industrial applications, but sometimes require ingenuity
Most nonmanufacturing operations do not have a natural measurement system
The observability of the process may be fairly low
Flow charts and operation process charts are particularly useful in developing process definition and process understanding. This is sometimes called process mapping.
Used to identify value-added versus nonvalue-added activity