1. Quality management: SPC - I
Presented by:
Dr. Husam Arman

2. Quality Control (QC)
Control – the activity of ensuring conformance to requirements and taking corrective action when necessary to correct problems
Importance
Daily management of processes
Prerequisite to longer-term improvements

3. Designing the QC System
Quality Policy and Quality Manual
Contract management, design control and purchasing
Process control, inspection and testing
Corrective action and continual improvement
Controlling inspection, measuring and test equipment (metrology, measurement system analysis and calibration)
Records, documentation and audits

4. Inspection/Testing Points
Receiving inspection
In-process inspection
Final inspection

5. Receiving Inspection Spot check procedures 100 percent inspection
Acceptance sampling

6. Acceptance Sampling Lot received for inspection
Sample selected and analyzed
Results compared with acceptance criteria
Accept the lot
Send to production
or to customer
Reject the lot
Decide on disposition

7. Pros and Cons of Acceptance Sampling
Arguments for:
Provides an assessment of risk
Inexpensive and suited for destructive testing
Requires less time than other approaches
Requires less handling
Reduces inspector fatigue
Arguments against:
Does not make sense for stable processes
Only detects poor quality; does not help to prevent it
Is non-value-added
Does not help suppliers improve

8. In-Process Inspection
What to inspect?
Key quality characteristics that are related to cost or quality (customer requirements)
Where to inspect?
Key processes, especially high-cost and value-added
How much to inspect?
All, nothing, or a sample

9. Human Factors in Inspection
Inspection should never be means of assuring quality. The purpose of inspection should be to gather information to understand and improve the processes that produce products and services.

10. Measurement system components
Equipment or gage
Type of gage
Attribute: go-no go, vision systems (part present or not present)
Variable: calipers, probe, coordinate measurement machines
Unit of measurement
Operator and operating instructions

11. Measurement error
Measurement error is considered to be the difference between a value measured and the true value.

12. Examples of Gauges

13. Metrology - Science of Measurement
Accuracy - closeness of agreement between an observed value and a standard
Precision - closeness of agreement between randomly selected individual measurements

14. Measurement accuracy and precision

15. Calibration
Calibration - comparing a measurement device or system to one having a known relationship to national standards

16. Types of measurement variation
Accuracy
Stability
Reproducibility
Repeatability

17. Accuracy Difference between the true average and the observed average.
(True average may be obtained by using a more precise measuring tool)
Accuracy
True average
Observed
average

18. Stability
The difference in the average of at least 2 sets of measurements obtained with a gage over time.
Stability
Time 1
Time 2

19. Reproducibility
Variation in average of measurements made by different operators using the same gage measuring the same part.
True Average
Operator C
Operator B
Operator A

20. Repeatability
Repeatability is the variability of the measurements obtained by one person while measuring the same item repeatedly. This is also known as the inherent precision of the measurement equipment.
Observed Average
True Average
Repeatability

21. Which one is more repeatable?

22. How do we improve gage capability?
Reproducibility
operator training, or
more clearly define measurement scale available to the operator
Repeatability
gage maintenance
gage redesign to better fit application

23. “We best manage what we can measure”
Quality Metrics
“We best manage what we can measure”

24. Metrics
A strategy without metrics is just a wish. And metrics that are not aligned with strategic objectives are a waste of time.
Emery Powell
If you don’t keep score, you’re only practicing.
You get what you inspect, not what you expect

25. Metric A metric is a verifiable measure that
captures performance in terms of how something is being done relative to a standard,
allows and encourages comparison,
supports business strategy.
A metric is a verifiable measure stated in either quantitative or qualitative terms.
“95 percent inventory accuracy”
“as evaluated by our customers, we are providing above-average service”

26. Metric
In quality management, we use metrics to translate customer needs into producer performance measures.
Internal quality metrics
scrap and rework
process capability (Cp or Cpk)
first time through quality (FTTQ)

27. Customer quality measures
Customers typically relate quality to:
Feature based measures; “have” or “have not” - determined by design
Performance measures - “range of values” - conformance to design or ideal value

28. True versus substitute performance measures
Customers - use “true” performance measures.
example: a true measure of a car door may be “easy to close”.
true performance measures typically vary by each individual customer.
Unfortunately, producers cannot measure performance as each individual customer does.
Producers - use “substitute” performance measures
these measures are quantifiable (measurable units).
Substitute measure for a car door: door closing effort (foot-pounds).
Other example: light bulb
true performance measure -- brightens the room
substitute performance measure – wattage or lumens

29. Educating Consumers
Sometimes, producers educate consumers on their substitute performance measures.
What are substitute performance measures for the following customer desires:
Good Gas Mileage
Powerful Computer
What is the effect of educating consumers on performance measures?

30. Identifying effective metrics
Effective metrics satisfy the following conditions:
performance is clearly defined in a measurable entity (quantifiable).
a capable system exists to measure the entity (e.g., a gage).
Effective metrics allow for actionable responses if the performance is unacceptable.
There is little value in a metric which identifies nonperformance if nothing can or will be done to remedy it.
Example: Is net sales a good metric to measure the performance of a manufacturing department?

31. Acceptable ranges
In practice, identifying effective metrics is often difficult.
Main reason: non-performance of a metric does not always lead to customer dissatisfaction.
Consider the car door example again, if door closing effort is the metric, will a customer be dissatisfied if the actual effort is 50 foot-pounds versus 55 foot-pounds.
Producers typically identify ranges of acceptable performance for a metric.
(a) For services, ranges often referred to as break points.
(b) In manufacturing, these ranges are known as targets, tolerances, or specifications.

32. Break points
Break points are levels where improved performance will likely change customer behavior.
Example: waiting in line
Suppose the average customer will only wait for 5 minutes
Wait longer than 5 minutes -- customer is dissatisfied.
1-5 minutes -- customer is satisfied.
less than 1 minute -- customer is extremely satisfied
Should a company try to reduce average wait time from 4 to 2 minutes.?

33. Targets, tolerances and specifications
Target (nominal) - desired value of a characteristic.
A tolerance specifies an allowable deviation from a target value where a characteristic is still acceptable.
Lower specification
limit (LSL)
Upper specification
limit (USL)
TARGET -1 +1

34. The Use of Statistics in Quality
Chapter Four

35. Statistical Process Control (SPC)
A methodology for monitoring a process to identify special causes of variation and signal the need to take corrective action when appropriate
SPC relies on control charts

36. A few notes on SPC’s historical background
Walter Shewhart (Bell Labs 1920s) - suggested that every process exhibits some degree of variation and therefore is expected.
identified two types of variation (chance cause) and (assignable cause)
proposed first control chart to separate these two types of variation.
SPC was successfully applied during World War II as a means of insuring interchangeability of parts for weapons/ equipment.
Resurgence of SPC in the 1980s in response to Japanese manufacturing success.

37. The basics “Don’t inspect the product, inspect the process.”
“If you can’t measure it, you can’t manage it.”

38. Barriers to process control
Tendency to focus on volume of output rather than quality of output.
Tendency to measure products against a set of internal conformance specifications that may or may not relate to customer expectations.

39. The SPC approach
The SPC approach is designed to identify underlying cause of problems which cause process variations that are outside predetermined tolerances and to implement controls to fix the problem.

40. The SPC steps Basic approach: Awareness that a problem exists.
Determine the specific problem to be solved.
Diagnose the causes of the problem.
Determine and implement remedies.
Implement controls to hold the gains achieved by solving the problem.

41. SPC requires the use of statistics
Quality improvement efforts have their foundation in statistics.
Statistical process control involves the
collection
tabulation
analysis
interpretation
presentation
of numerical data.

42. Statistic types Deductive statistics describe a complete data set
Inductive statistics deal with a limited amount of data

43. Statistics Parameters: 2 POPULATION Inferential Statistics
Deductive
SAMPLE
Statistics: x, s, s2
Inductive

44. Types of data
Variables data - quality characteristics that are measurable values.
Measurable and normally continuous; may take on any value.
Attribute data - quality characteristics that are observed to be either present or absent, conforming or nonconforming.
Countable and normally discrete; integer

45. Descriptive statistics
Measures of Central Tendency
Describes the center position of the data
Mean Median Mode
Measures of Dispersion
Describes the spread of the data
Range Variance Standard deviation

46. Measures of central tendency: Mean
Arithmetic mean x =
where xi is one observation and N is the number of observations
So, for example, if the data are : 0,2,5,9,12 the mean is ( )/5 = 28/5 = 5.6

47. Measures of central tendency: Median - mode
Median = the observation in the ‘middle’ of sorted data
Mode = the most frequently occurring value

48. Median and mode
Mode
Median
Mean = 79.22

49. Measures of dispersion: range
The range is calculated by taking the maximum value and subtracting the minimum value.
Range = = 12

50. Measures of dispersion: variance
Calculate the deviation from the mean for every observation.
Square each deviation
Add them up and divide by the number of observations

51. Measures of dispersion: standard deviation
The standard deviation is the square root of the variance. The variance is in “square units” so the standard deviation is in the same units as x.

52. Standard deviation and curve shape
If  is small, there is a high probability for getting a value close to the mean.
If  is large, there is a correspondingly higher probability for getting values further away from the mean.

53. The normal curve A normal curve is symmetrical about 
The mean, mode, and median are equal
The curve is uni-modal and bell-shaped
Data values concentrate around the mean
Area under the normal curve equals 1

54. The normal curve 1 standard deviation of the mean is 68%
If x follows a bell-shaped (normal) distribution, then the probability that x is within
1 standard deviation of the mean is 68%
2 standard deviations of the mean is 95 %
3 standard deviations of the mean is 99.7%

55. One standard deviation 
68.3%

56. Two standard deviations2 2
95.5%

57. Three standard deviations3 3
99.73%

58. The standardized normal
 = 0
 = 1
x scale
-3
-2
- 
+
+2
+3
z scale -3 -2 -1 +1 +2 +3