1. Measure : SPC
Dedy Sugiarto

2. Statistical Process Control ≈
Variation or Variability

3. No two units of product produced by a manufacturing process are identical.
Some variation is inevitable. Simple case: make signature two times!
Statistics is the science of analyzing data and drawing conclusions, taking variation in the data into account.

4. Less Variation = Higher Quality What is Variation?
Major Point Quality improvement relies on reducing variation rather than inspect-rework-scrap based on spec limits. This theme forms the basis for the remainder of the module.
Discussion Open the class for any discussion, or question and answer that has not taken place yet. 32

5.
SPC = The application of statistical techniques to control a process, reducing variation so that performance remains within boundaries, or specification limits.
SQC = The application of statistical techniques to control quality; includes acceptance sampling (inspection of a sample from a lot to decide whether to accept that lot) as well as SPC.

6. The seven major tools are
Histogram
Control Chart
Flow Chart (With Microsoft Visio)
Check Sheet (With Microsoft Excel)
Scatter Diagram
Pareto Diagram
Cause-and-Effect Diagram

7. Seven Tools of Quality Improvement
Histogram - A graphic summary of variation in a set of data. The pictorial nature of the histogram lets people see patterns that are difficult to detect in a simple table of numbers.
Control chart - A chart with upper and lower control limits on which values of some statistical measure for a series of samples or subgroups are plotted. The chart frequently shows a central line to help detect a trend of plotted values toward either control limit.
Flowchart/process map - Graphical tools for process understanding. A flowchart creates a graphical representation of the steps in a process. A process map adds lists of inputs and outputs for each step.
Check sheet - A simple data-recording device. The check sheet is custom-designed by the user, which allows him or her to interpret the results easily.

8. Seven Tools of Quality Improvement
Scatter diagrams - A graphical technique to analyze the relationship between two variables. Two sets of data are plotted on a graph, with the y-axis being used for the variable to be predicted and the x-axis being used for the variable to make the prediction. The graph will show possible relationships among variables: those who know most about the variables must evaluate whether they are actually related or only appear to be related.
Pareto chart - A graphical tool for ranking causes from most significant to least significant. It is based on the Pareto principle, which was first defined by J. M. Juran in The principle, named after nineteenth-century economist Vilfredo Pareto, suggests that most effects come from relatively few causes; that is, 80% of the effects come from 20% of the possible causes
Cause-effect diagram - A tool for analyzing process dispersion. It is also referred to as the "Ishikawa diagram," because Kaoru Ishikawa developed it, and the "fishbone diagram," because the complete diagram resembles a fish skeleton. The diagram illustrates the main causes and subcauses leading to an effect (symptom). .

9. Types of variation Common Causes of variaton
Special Causes of variaton

10. Special Causes
Common Causes
* Change in raw material
* Badly maintained machines
* Change in machine setting
* Poor lighting
* Broken tool or die or pattern
* Poor workstation layout
* Failure to clean equipment
* Poor instructions
* Equipment malfunction
* Poor supervision
* Keying in incorrect data
* Materials and equipment no suited to the requirements

11. Examining Variation
Common Causes
The cause of variations in a stable process is called a Common Cause.
A common cause is a natural cause of variation in the system.
Major Point To introduce the definition of common causes
Example Make sure the funnel has been reset and the funnel is centered. Drop a couple of balls through the funnel.
Ask What causes the variation in this process?
The pins.
Do the pins always cause variation?
Yes
Explain The cause of variation that always acts the same way in a process is called a common cause variation.
Variations in a stable process are always caused by common causes.
Define Common causes of variation are ever-present sources of variation in a stable process.
Exercise Use the following idea to get people thinking about common cause variation
Process = Driving from home to work
Measurement = time to get from home to work 44

12. Normal distribution at all times
Examining Variation
Stable Process
Normal distribution at all times
Prediction
Major Point A stable process has the same center and spread at all times
Explain For a stable process to have a normal distribution it must have the same bell-shaped curve (i.e., the same spread, and same center) at all times.
Discuss The general definition of stability just says the same distribution at all times. Some distributions, e.g., defects, will NOT be normal.
For simplicity, in this class we will assume a stable process has the same normal distribution at all times.
Poster Keep the poster of the definition of stability in front of the class throughout the remainder of this session.
Time 43

13. Examining Variation Activity: Comparing stable processes
Which process has better quality?
150
150
140
140
130
130
120
120
110
110
Thickness
100
Thickness
100 90 90 80 80 70 70 60 60 50
Major Point The less the variation from the target, the better the quality
Ask For each A-B pair which is better?
Why?
What is meant by on-target?
For pair #1 which is on target?
Trick question. Both are on target
For pair #2 which has less variation from its mean?
Vocabulary On target refers to the average, long term behavior 50
Sequence 5 10 15 20 25
Sequence 5 10 15 20 25 B A 49

14. Reducing Variation Improving a Stable Process
Two strategies for improving a stable process
Centering at Target
Reducing Common Cause Variation
Major Point There are two strategies for improving a stable process: centering at target, reducing common cause variation.
Ask How can you tell which is most appropriate?
Examine trend charts 79

15. Reducing Variation Centering at Target Time Thickness 200 200 180 180
160
160
140
140
120
120
Thickness
100
100 80 80 60
Major Point If the process is off-target and stable, then you must make the necessary adjustments to move toward the target. 60 40 40 20 20
Time 80

16. Reducing Common Cause Variation
Reducing Variation
Reducing Common Cause Variation
200
200
180
180
160
160
140
140
120
120
Thickness
100
100 80 80 60 60 40 40
Major Point If the process is on-target and stable, but has large variation, then you must identify and work to reduce common cause variation. 20 20
Time 81

17. Reducing Variation Reducing Variation in a Stable Process
Make Permanent Changes
Changes are based on the scientific approach
Structured problem solving
Planned experiments
Major Point To improve a stable process, the process must be permanently changed
Discuss There are two key concepts on this slide
It is important that changes be permanent rather than temporary “let’s see if this will work” changes.
Changes must be scientific rather than “seat-of-the-pants” or intuitive
Examples New Equipment
Upgraded Equipment
New Procedure
New Machine Settings
Higher Quality Materials
Examples: new equipment, equipment upgrade, new procedure, new machine settings, better raw material 82

18. Examining Variation Special Causes
Anything that causes variations that are not part of the stable process is called a special cause, assignable cause, or unnatural cause.
Major Point To introduce the definition of special cause.
Do Display the results of the driving to work example on the flipchart.
Ask What are the special causes of variation?
accidents, breakdown, road repair, etc. 58

19. Examining Variation Unstable Process
Any process that is not stable is called an unstable or out-of-control process. ? ?
Prediction
Major Point To introduce the definition of an unstable process.
Ask What is an unstable process?
this one is just stupid enough to be fun
Any process that is NOT stable is unstable!
Time 51

20. Reducing Variation in an Unstable Process
Do not ignore special causes.
Do quickly detect special cause variations.
Do stop production until the process is fixed. (Reactive)
Do identify and permanently eliminate special causes. (Preventive)
Major Point Since unstable processes are characterized by special causes of variation, they can be improved by removing these special causes.
Ask How can we avoid tampering?
Never make adjustments
Discuss Tampering is adjusting a stable process, reacting to a common cause as if it were a special cause.
The opposite of this is ignoring special cause variation in unstable processes. This is equally bad.
There are two modes of dealing with special cause variation:
Reactive (local): Quick detection and response to special cause variation.
Preventive (systemic): Identification and permanent elimination of special cause. 85

21. Improving an Unstable Process
Reducing Variation
Improving an Unstable Process
Four Step Process
Detect the special cause variation.
Identify the special cause.
Fix the process
Remove the special cause, or
Compensate for the special cause.
Prevent the special cause from occurring again
Major Point We must react carefully and appropriately to special cause variation. The four step model is one way to apply a systematic approach.
Discuss Point out that fixing is reactive, while preventing is preventive. Both are appropriate.
Note the options under fix
Remove
Compensate
Two ways to deal with idiots: idiot proof the process, or fire the idiots. 86

22. Improving an Unstable Process
Reducing Variation
Improving an Unstable Process
Reactive
200
180
160
140
120
100 80 60 40 20
Thickness
Time
Not Here
Detect Here
Detect Here
Major Point Both local and system efforts can improve an unstable process.
Discuss The term reactive is not intended to carry a negative connotation. Rather, it denotes reaction (response) to special causes that occur locally.
A trend chart makes timely response possible. 87

23. Detecting Variation
How can we decide if variation is the result of common or special cause?
By using control chart
Major Point To motivate the need for a control chart
Ask How can we avoid tampering?
Never adjust
What is wrong with this strategy?
It ignores special cause variation.
What is the key to taking correct action (not tampering vs. corrective action)
Recognize the type of variation
If stable, do not adjust; work on reducing common cause variation
If unstable, respond to special cause variation
How can we tell if a process is stable or unstable?
There is subjective judgment involved 95

24. Control Chart (The Concept)
Process that not in a state of statistical control will show excessive variations and exhibit variations that change with time.
Process in a state of statistical control is called statistically stable.
Control chart is used to detect whether a process is statistically stable.

25. Control Chart (Assumption on the Statistic)
It is independent, i.e. value is not influenced by its past value and will not affect future values.
It is normally distributed, i.e. the data has a normal probability density function.

26. Control Chart (Types of Chart)
Different charts is used depending on the nature of the charted data.
For continuous (variables) data:
Shewhart sample mean and range charts.
Shewhart sample mean and standard deviation charts.
Shewhart sample and moving range charts.

27. Control Chart (Types of Chart)
For discrete (attributes) data:
Sample proportion defective chart.
Sample number of defectives.
Sample number of defects.
Sample number of defects per unit.

28. Control Chart Selection
Quality Characteristic
variable
attribute
defective
defect no
n>1?
x and MR
constant
sampling
unit?
yes
constant
sample
size?
yes
p or np no
n>=10 or
computer?
x and R
yes no no
yes
p-chart with
variable sample
size
c u
x and s

30. Interpreting x-bar charts : one point outside control limit

31. Interpreting R charts : one point outside control limit

32. Interpreting x-bar and R charts : one point outside control limit