1. QUALITY CONTROL
UNIT III

2. Meaning of quality Excellence of a product or service
Fitness for use - Juran (1974)
conformance to requirements or specifications – Crosby (1979)
Grade
Degree of preference
“The quality of a product or service is the fitness of that product or service for meeting or exceeding its intended use as required by the customer”.

3. Quality Characteristic
A physical or chemical property/element(s) that are used to define the nature of the product or service (which contributes to fitness for use) is a quality characteristic. Can be classified as: Technological/Structural – length, Weight, Strength, dia., frequency, etc. Psychological (Sensory) – taste, odour, beauty etc. Time-oriented – Reliability, maintainability, Warranty etc. Ethical – Honesty, Integrity etc.

4. Quality characteristic
Quality characteristic may be 1. Directly measurable - weight, shear strength, Sp. Gravity, length, dia. Etc. 2. Non-measurable - rejections due to flaws, cracks, breakages etc.

5. Quality characteristic
Fall in to two broad classes – Variables - Measurable, Expressed on a numerical scale and Attributes - if classified as either conforming or nonconforming to a stipulated specification

6. Nonconformity and Nonconforming Unit
A nonconformity is a quality characteristic that does not meet its stipulated specification.
Example
A nonconforming unit has one or more nonconformities such that the unit is unable to meet the intended standards and is unable to function as required.
Example

7. Defect and Defective Nonconformity - Defect and
Nonconforming Unit - Defective

8. Quality of Product/Service
Quality of Design Quality of Conformance Quality of performance

9. Quality of Design
“ The product or service must be designed to meet at least minimally the needs of the consumer”. Design should be simplest and least expensive while still meeting the customer’s expectations. Yield strength (customer requirement) = 100kg/cm2 “Tightness of the specifications for manufacture of the product” Tolerance : ± mm and ± 0.01mm

10. Quality of Design Factors controlling Quality of Design
Type of customers in the market
Profit policy of the firm
Environmental conditions
Special requirements of the product

11. Cost or Value Vs. Designed quality level (Quality of Design)

12. Quality of Conformance
“ The manufactured product or the service rendered must meet the standards selected in the design phase”.
Defect prevention, finding, analysis and rectification
Factors controlling Quality of conformance
Adequate quality of incoming raw material
Adequate machines tools and measuring instruments
Proper selection of process
Trained operators
Proper storage and shipment
Feedback from internal inspection and customers
Use of S.Q.C. techniques

13. Quality of performance
“ How well the manufactured product functions or service performs when put to use”. Measures the degree to which the product or service satisfies the customer. Function of Quality of design Quality of conformance

14. Quality of service Service Industries Retailing Distribution
Financial services – banking, insurance
Hotels and tourism
Leisure, recreation, entertainment
Professional and business services like accountancy, marketing, law, consulting
Healthcare
Software etc.

15. Services are perishable
Continue…
Service is intangible - it cannot be easily measured, tested and verified in advance of sales to assure quality
Services are perishable
Precise standards like manufacturing can rarely be set
It is created and consumed simultaneously or near simultaneously
Service Production and Consumption are often inseparable i.e. Customer is in service factory.

16. Continue…
Services offered can vary from “Producer to Producer” e.g. outlet to outlet
Outcome of Service is as important as process of delivery. assessment of quality is made during the service delivery process.
Service Quality is more difficult for customer, to evaluate, than product quality
Customer cannot retain the actual service. The effect of the service can be retained.

17. Cost of Quality
The cost of carrying out the company’s quality functions (meeting the quality needs of the customers) are known as costs of quality. Four categories: As per ASQC Cost of Prevention Cost of Appraisal Cost of Internal failures Cost of external failures

18. Cost of Prevention
Costs incurred in planning, implementing and maintaining a quality system.
Includes Salaries.
Incurred to keep failure and appraisal costs to a minimum.
Cost of quality planning – market research, product development, inspection plan
Cost of documenting
Process control cost
Cost of training
Costs associated with preventing recurring defects
Cost of investigation, analysis and correction of causes of defects by QC and Engg. Deptt.
Cost of cost consciousness programmes

19. Cost of Appraisal
Costs associated with the measuring, evaluating or auditing products, components and purchased materials to assure conformance with specified standards.
Cost of verification- incoming , in-process, after production
Cost of quality audits (appraisal of the quality system)
Cost of inspection equipments, maintenance, Calibration
Cost of vendor rating

20. Cost of internal Failures
Costs associated with defective products, components and materials that fail to meet quality requirements and result in manufacturing losses.
Scrap
Rework or rectification
Re-inspection
Downgrading
Waste
Failure analysis
Lost production time

21. Cost of external failures
Costs which are generated because of defective products being shipped to customers.
When product does not perform satisfactory after ownership is transferred
Repair and servicing
Customer Complaints
Returns
Loss of goodwill

22. Economics of quality of conformance

23. Value of quality
“ Return direct or indirect gained by the manufacturer due to mission of quality control”.
It is composed of
Value inherent in the design
Value inherent in the conformance to that design

24. Difficult to assess the value of quality. Factor for assessment:
Continue…
Difficult to assess the value of quality.
Factor for assessment:
Saving due to increased production
Reduction in scrap and rework cost
Increased sales of good quality product
Indirect factors such as
a. Reputation of manufacturer and goodwill of the customer
b. Psychological stability in the enterprise

25. Guru’s of TQM Walter. A. Shewhart - TQC & PDSA
W. Edwards Deming Points & PDCA
Joseph. M. Juran - Juran’s Trilogy
A. Feiganbaum Customer requirement, CWQC, Employee
Involvement, TQC.

26. Guru’s of TQM
Kaoru Ishikawa - Disciple of Juran & Feigenbaum TQC in Japan, SPC, Cause & Effect Diagram
Philips. B. Crosby - Four Absolutes-Quality-Req, Prevention of NC, Zero Defects & Measure of NC
Taguchi. G Loss Function

27. W. Edwards Deming
Focus on bringing about improvements in product and service quality by reducing uncertainty and variability in goods and services design and associated processes (the beginning of his ideas in 1920s and 1930s).
Higher quality leads to higher productivity and lower costs.
“14 Points” management philosophy.
Deming Cycle – Plan, Do, Study, and Act.

28. Deming’s 14 points for management
Point 1: Create and publish Vision and Demonstrate Commitment
Point 2: Learn the new Philosophy
Point 3: Understand purpose of Inspection
Point 4: Stop Making Decisions Purely on the Basis of Cost
Point 5: Improve Constantly and Forever the system
Point 6: Institute Training
Point 7: Teach and Institute Leadership

29. Deming’s 14 points for management
Point 8: Drive Out Fear. Create trust. Create a climate for innovation
Point 9: Optimize the Efforts of Teams , Groups and staff areas
Point 10: Eliminate Exhortations for the workforce
Point 11: a) Eliminate Numerical Quotas for the work force
b) Eliminate management by objectives
Point 12: Remove Barriers to Pride in Work
Point 13: Encourage Education and Self-Improvement
Point 14: Take Action to accomplish the transformation.

30. The Deming Cycle Plan: Recognize the opportunity for improvement
Do: Test the theory on a small scale
Check: Analyze results of the test phase
Act: Implement the plan to improve customer satisfaction

31. Joseph Juran
Wrote Quality Control Handbook in 1951, a comprehensive quality manual.
Defined quality as “fitness for use.”
Advocated use of quality cost measurement.
Quality Trilogy: quality planning,
quality control, and
quality improvement

32. Philip B. Crosby
Wrote Quality is Free in 1979, which brought quality to the attention of top corporate managers in the U.S.
Crosby’s Absolutes of Quality Management include:
Quality means conformance to requirements, not elegance.
Quality comes from prevention.
The only performance standard is Zero Defects (ZD).
The only performance measurement is the expense of nonconformance.

33. Inspection and Quality Control
Inspection: Checking of material, product or components of product at various stages, with reference to certain pre-determined factors and detecting and sorting out the faulty or defective items. Quality Control: Effective system for integrating quality development, quality maintenance and quality improvement efforts of various groups in an organization to enable the production to be carried out at most economical level

34. Difference between inspection amd QC
S. N.
Parameters
Inspection
Quality control 1
Scope
Part of QC
Broad term, Inspection at particular stage 2
Definition
Checking of material, product or components of product at various stages, with reference to certain pre-determined factors and detecting and sorting out the faulty or defective items.
Effective system for integrating quality development, quality maintenance and quality improvement efforts of various groups in an organization to enable the production to be carried out at most economical level 3
Devices used
Precision measuring instruments such as vernier calipers, micrometers, profile projectors etc.
Statistics, control charts, acceptance sampling, process capability study, V. R., quality audits etc. 4
Applicability
Concerned with quality of past production
Concerned with quality of future production 5
Concern
Mainly the responsibility of inspectors
Everybody working in the organization is responsible

35. Quality Control
A system that is used to maintain a desired level of quality in a product or service
Can be achieved through
1. Planning
2. Design
3. Use of proper equipment and procedures
4. Inspection and taking corrective actions in case deviation is observed

36. Quality Control Offline quality control Statistical process control
Acceptance sampling plans

37. Process Control
Control: Process by means of which we observe the actual performance and compare it with some standard. If there is a deviation between the observed performance and the standard performance then it is necessary to take corrective action. Quality control: The process through which we measure the actual quality performance, compare it with the standards and take corrective action if there is a deviation.

38. Statistical Quality control
When statistical techniques are employed to control, improve and maintain quality or to solve quality problems, it is called S. Q. C. Statistics: is the collection, organization, analysis, interpretation and presentation of the data. It is based on law of large numbers and mathematical theory of probability.

39. Basic Statistical concept
The concept of variation:
No two items will be perfectly identical even if extreme care is taken to make them identical in some respect.
Types of variation:
Within the part itself
Among the parts produced during the same period of time
Among parts produced at different periods of time

40. Reasons for variation Tool wear Machine vibrations Loose bearings
Play between nut and screw of the machine table
Faulty jigs/fixtures
Poor quality of raw material
Carelessness of operators
Fatigue to the operator
Change in working condition
Poor maintenance
Measuring errors

41. Reasons for variation
Causes of variation can be subdivided in to two groups –
1. Assignable or special causes
2. Chance or random or common causes

42. Statistical Process Control using control charts
Control charts: Graphical tool for monitoring the activity of an ongoing process
Shewhart control charts
Proposed by Walter A. Shewhart
Control chart description

43. Universe or Population
Sample

44. Variable Data (continuous data)
When a record is made of an actual measured quality characteristics, the quality is said to be expressed in variables. Examples: Dimension in mm Hardness in rockwell unit Temp. in 0c Tensile strength in Kg/cm2 Weight in Kg.

45. Attribute Data (Discrete data)
When the record shows only the no. of articles conforming and the number of articles failing to conform to any specified requirements, it is said to be recorded by attributes.
This method simply notes the presence or absence of a quality characteristic in the parts examined
Example: Number of defective pieces found in a sample
Surface finish of furniture
Cracks in sheet by spot welds
(Defective – Parts which do not possess the characteristics of conformity)

46. Control Charts For Variable
Normal Curve

47. Control Charts For Variable
Variations
Variations due to Assignable causes
- Greater magnitude
- because of difference among M/cs, workers, material etc.
Variations due to chance causes
- Random
- example – little play between nut and screw of table

48. Control Charts For Variable
If many random samples of any given size are taken from universe, the averages of the samples will themselves form a F.D. having its own central tendency and dispersion or spread.
If the universe is normal, statistical theory tells that expected F.D. of the average values will also be normal.
In long run
1. Average of samples = Average of the universe
2. S. D. of the expected F.D. of the average
= S. D. Of the universe / √ sample size

49. Control chart : Procedure
Making and recording measurement
Calculate the average characteristic value and Range for each subgroup
Calculate the grand average and average range
Calculation of control limits on control chart for X-chart
Calculation of control limits on R-chart
Plot the X and R chart
Draw conclusions from control chart

50. Some control chart patterns
Chance pattern

51. Some control chart patterns
Chance pattern characteristics
1. Most of the points will lie near the central line
2. Very few points will be near the control limits
3. None of the points (except 3 in 1000) fall outside the control limits

52. Some control chart patterns
Extreme variations

53. Some control chart patterns
Extreme variations Causes
Error in measurement and calculations.
Sample chosen at peak position of pressure, temp. etc.
Wrong setting of machine, tool etc.
Sample chosen at the commencement or end of an operation

54. Some control chart patterns
Trend

55. Some control chart patterns
Causes of Trend
Tool wear
Wear of threads on clamping device
Effects of temperature and humidity
Accumulation of dirt and clogging of fixtures and holes
On R chart
Increasing trend – gradual wearing of operating machine part
Decreasing trend – Improvement in operations, better maintenance, improved control on back process

56. Some control chart patterns
Shift

57. Some control chart patterns
Causes of Shift
Change in material
Change in operator, inspector, inspection equipment
Change in machine setting
New operator carelessness of operator
Loose fixture etc.

58. Some control chart patterns
Erratic fluctuations

59. Some control chart patterns
Causes of Erratic fluctuations
Frequent adjustment of machine
Different types of material being processed
Change in operator. Machine, test equipment etc.

60. Process capability
Minimum spread of a specific measurement variation which will include 99.7% of the measurements from the given process.
Measure of a process spread (6σ) – Natural tolerance
Process capability analysis consists of
Finding whether the process is inherently capable of meeting the specified tolerance limit.
Discovering why a process capable is failing to meet specification

61. Specifications and process capability
Case I: 6σ < ( Xmax - Xmin ) or (USL – LSL)

62. Conclusion
In a, b, c practically all the products manufactured will meet specification as long as the process stays in control
X¯ may be permitted to go out of control or the distribution may be allowed to move between b and c. cost of frequent machine setup and cost and time of hunting causes will reduce.
If ( Xmax - Xmin )/6σ is considerably large, frequency of control chart may be reduced.
For economical advantages the specification limits may be tightened.

63. Case II: 6σ > ( Xmax - Xmin ) or (USL – LSL)

64. Conclusion Defective parts will always be there. The remedy will be
Increase the tolerance
Reduce the dispersion
Suffer and sort out the defectives
Important to maintain the centering of the process.

65. Case III: 6σ = ( Xmax - Xmin ) or (USL – LSL)

66. Conclusion
A little change in centering will cause defective parts. Calls for continuous use of control charts.
To increase tolerance if they are tighter than necessary.
Reduce dispersion if economical

67. Control Charts for Attributes
Practical limitations of the control charts for Variables:
Can be used for quality characteristics that can be measured and expressed in numbers. However many quality characteristics can be observed only as attributes.
Can be used only for one measurable quality characteristic at a time.
For the reason of economy even in some cases , where the direct measurement of variable quality characteristics is possible, it is common practice to classify them as good or bad on the basis of inspection by GO-No-GO gauges.

68. P-Chart (control chart for fraction defectives)
The day’s production or other lot of any manufactured article can be thought of as a sample from a larger quantity with some unknown fraction defective. This unknown universe fraction defective depends upon a complex set of causes influencing the production and inspection operations. Fraction defective in the sample may vary considerably. But as long as the universe fraction defective remains unchanged, relative frequencies of various sample fraction defectives may be expected to follow the binomial law.

69. Inspection results of magnets for nineteen observations
Week No.
No. of magnets inspected
No. of defective magnets
Fraction defectives 1
724 48
0.066 2
763 83
0.109 3
748 70
0.094 4 85
0.114 5 45
0.062 6
727 56
0.077 7
726 8
719 67
0.093 9
759 37
0.049 10
745 52
0.07 11
736 47
0.064 12
739 50
0.068 13
723
0.065 14 57
0.076 15
770 51 16
756 71 17 53
0.074 18
757 34
0.045 19
760 29
0.038
Total
14091
1030

70. Calculate the average fraction defective and 3 sigma control limits, construct the control chart and state whether the process is in statistical control.
The avg. sample size = 14091/19 = n = 742 (say) Total defectives in all samples The avg. Fraction defectives = Total inspected in all samples 1030 = =

71. Calculation of 3 sigma limits
UCLp =
0.1018
LCLp =
0.0444

72. P-Chart

73. Conclusion
From the resulting control chart Sample numbers 2nd and 4th go above the upper control limit and the sample number 19th goes below the lower control limit. Therefore the process does not exhibit statistical control.

74. Problem : A certain product is given 100% inspection as it is manufactured and resultant data are summarized by the hour. In the following table, 16 hours of data are recorded. Calculate the 3 sigma limits, construct the control chart.
Hour
No. of units inspected
No. of defective units
Fraction defective 1 48 5
0.104 2 36
0.139 3 50 4 47
0.106 6 54
0.056 7 8 42
0.024 9 32
0.156 10 40
0.05 11
0.043 12
0.085 13 46
0.022 14 15
0.063 16 39
Total
720

75. C-Chart (Control chart for Number of defects)
Control limits on C chart based on Poisson distribution.
Two conditions:
The area of opportunity for occurrence of defects should be fairly constant from period to period.
Opportunities for defects should be large, while the chances of a defect occurring in any one spot should be small.

76. C-Chart (Control chart for Number of defects)
Area of application:
Number of surface defects in a roll of coated paper
Number of defective rivets in an aircraft wing
Number of small air holes in glass bottle
Number of imperfections observed in a cloth of unit area
Number of defects such as blow holes, cracks, undercuts etc in a casting or welded piece.

77. Problem : The following table gives the numbers of missing rivets noted at aircraft final inspection. Find C¯ compute trial control limits, and plot control chart for C’ would you suggest for the subsequent period?
Air plane no.
no. of missing rivets 1 8 10 12 19 11 2 16 23 20 9 3 14 21 4 13 22 5 25 7 6 15 24 28 17 18

78. C-Chart

79. Comparison between Attribute charts and variable charts
Attirbute charts
Example
X¯, R, σ charts
P, np, C, u charts
2. Data required
Variable data ( Measured values
of characteristics)
Attribute data ( Using Go-No-Go gauges)
3. Field of applications
Control of individual characteristics
Control of proportion of defectives or number of defects
4. Advantage
provides detailed information on
process average and variation
Provides overall picture of quality history
5. Disadvantage
Not easily understood
Do not recognize different degree of defectiveness

80. Acceptance sampling
Process of evaluating a portion of the product/material in a lot for the purpose of accepting or rejecting the lot as either conforming or not conforming to a quality specifications.
Inspection is carried out in two ways
100% inspection
Sampling inspection

81. The main purpose of acceptance sampling is to distinguish between good lots and bad lots, and to classify the lots according to their acceptability and non acceptability.
Advantages:
Items subjected to destructive testing must be inspected by sampling inspection.
Cost and time required is quite less.
Inspection fatigue is eliminated.
Smaller inspection staff.
Less damage to products as only few to be handled.
Problem of monotony and inspector error is minimized.
Exerts more effective pressure on quality improvement.

82. Limitations
Risk of making wrong decisions.
Sample usually provides less information about the product.
Extra planning and documentation is necessary.

83. The operating characteristic (OC) curve
Measures the performance of a sampling plan
Probability of acceptance of lot
Vs.
Proportion nonconforming of the lot
Discriminatory power of sampling plan

84. Ideal OC curve
A sampling plan with such an OC curve is Totally Discriminatory

85. The operating characteristic (OC) curve
In practice the shape of OC curve is not ideal
Let N = Lot size
n = Sample Size
c = Acceptance number
If sample is chosen from an isolated lot of finite size. The probability of acceptance (Pa) of lot is calculated based on Hypergeometric distribution.

86. The operating characteristic (OC) curve
If a stream of lots is produced by the process and the lot size is large (at least 10 times ) compared to the sample size. The probability of acceptance (Pa) of lot is calculated based on Binomial distribution.
If the lot size is large and the probability of nonconforming item is small. The probability of acceptance (Pa) of lot is calculated based on Poisson distribution.

89. The operating characteristic (OC) curve

90. OC Curve
Producer’s risk Consumer’s risk Acceptable Quality Level (AQL) Rejectable Quality Level (RQL) Indifferent Quality level (IQL)

91. Effect of the sample size (n) and the acceptance number (c)
Effect of sample size on the shape of the OC curve

92. Effect of the sample size (n) and the acceptance number (c)
Effect of Acceptance number on the shape of the OC curve

93. Evaluating Sampling Plans
OC curve – measure of the performance of a sampling plan Average Outgoing Quality (AOQ) Average Total Inspection (ATI) Average Sample Number (ASN)

94. Average Outgoing Quality (AOQ)
Screening and Rectifying Inspection
AOQ is the average outgoing level of a series of batches that leave the inspection station, assuming rectifying inspection, after coming in for inspection at a certain quality level.
Pa P (N – n)
AOQ = N

95. Average Outgoing Quality (AOQ)
n = items in the sample having no non conforming items
If the lot is rejected, (N-n) items will go through screening, So No non conforming items
If the lot is accepted, (N-n) items in the lot will leave the inspection station with p(N-n) non conforming items
The probability is Pa .
So Pa p(N-n) = number of nonconforming items per lot expected to leave the inspection station.
Hence the average proportion non conforming or
Pa P (N – n)
AOQ = N

96. Average Outgoing Quality (AOQ)

97. Average Total Inspection (ATI)
Average number of items inspected per lot. For a single sampling plan ATI = n + (1 – Pa) (N- n) For double sampling Plan ATI = n1 Pa1 + (n1 + n2) Pa2 + N (1- Pa1 – Pa2)

98. Average Total Inspection (ATI)

99. Average Sample Number (ASN)
The average number of items inspected for a series of lots with a given incoming lot quality in order to make a decision. for a single sampling plan if inspection is not curtailed N = 800, n = 60, c =2 ASN = n = 60 For Double sampling plan ASN = n1 P1 + (n1 + n2) ( 1- P1) P1 = Probability of making a decision on the first sample

100. Average Sample Number (ASN)

101. Types of sampling plan Single sampling Plan Double sampling plan
Multiple sampling plan

102. Types of sampling plan Single sampling Plan
Decision is made on the basis of only one sample
N = Lot size N = 50
n = Sample Size n = 5
c = Acceptance number c = 1
Inspect a sample of n pieces
If number of defectives:
Does not exceed ‘c’ Accept the lot
Exceeds ‘c’ Reject the lot

103. Double sampling plan

104. Multiple sampling plan

105. Advantages and Disadvantages
All types of Sampling plans are equivalent if they have the same Pa for batches of a given quality.
Simplicity – Single >Double >Multiple
Administrative cost – Single <Double <Multiple
No. of items inspected (Inspection cost)
- Single >Double >Multiple
Amount of information – Single >Double >Multiple