1. Quality Control Prof. R. S. Rengasamy Department of Textile Technolgoy
Indian Institute of Technology Delhi

2. Quality & Standard
Quality is a measure of how closely a good or service conforms to specified standard.
Quality Standard may be any one or combination of attributes/ variables of product.

3. Attributes
Attributes (discrete data, binary) (Accept or reject; using Go/NoGo gauges)
Performance
Reliability
Appearance
Commitment to delivery time etc

4. Variables Variables (continuous data; measurable)
Some measurement like
Length, Width, Height, Diameter
Surface roughness, Count, Strength
Work of rupture, Elongation, Tear strength , Rate of moisture vapour transport, Rate of wicking
Air permeability, Bursting strength, etc.

5. Results of ‘No quality control’
No yardstick for comparing quality of goods/ services
Difficulty in maintaining consistency in quality
Dissatisfied customers due to increased maintenance & operating costs
Increased rework cost
Reduced life time of products/services
Reduced flexibility for use of standard spares

6. Strategic areas of QC program in manufacturing
Supplier quality
Incoming raw material quality
Process quality
Final inspection
Customer quality

7. Basic components of product quality
Careful consideration of product design specifications
Adequate inspection procedures for products
Acceptance procedures for purchased materials & parts
Control practices to maintain quality levels in process-stage
Commitment from top to bottom of enterprise towards quality
Formulation of quality assurance procedures are necessary to integrate & coordinate these functions

8. Quality control techniques
Control Charts (for in-process quality)
Acceptance Sampling (for quality of materials, semi-finished, finished products)
For Variables
For Attributes
For
Variables
Attributes
1) X Chart
1) P chart
1) Plan with α
1) Single sampling plan
2) R Chart
2) C chart
2) Plan with α and β
2) Double sampling plan
3) Multiple sampling plan

9. Process capability

10. Process capability

11. Control Charts

12. Control Charts for variable
Notations
=Mean of a sample
= Mean of sample means
R = Range of a sample observation
= mean range of samples
A, B, & C are factors for different sample size (n) obtained from published table
K = number of samples

13. Control Charts for variable
Control limits for chart Upper control limit, Lower control limit, Control limits for R chart

14. S. No
K = 10
Observations (n = 5) R 1 2 3 4 5 10 12 13 8 9
10.4 7 11
9.0
10.8
10.2
8.8 6
9.6
11.4
11.2
11.6
Total
103.2 39
n = 5
A = 0.58
B = 2.11
C = 0
Mean
3.9

15. Chart R Chart

16. Control Charts for attributes (good or bad etc.)
Percent defective chart (P-chart)
Based on normal distribution
The no. of defectives per sample (C-chart)
Based on Poisson distribution

17. Purpose of P-chart
To discover average proportion of non-conforming parts/articles submitted for inspection over a period of time
To bring to the management attention, if there is any change in average quality level

18. P-chart p = percentage defective in a sample
= Process mean percent defective
n = Sample size
k = No. of samples
= Standard deviation of percent defective

19. P-chart Sample no (k = 5) No. Of defective, n = 50
% of defective rolls 1 10
0.20 2 3 9
0.18 4 5
0.08 6
0.12 7
0.04 8
0.06 11
0.16 12
0.22 13 14 15

20. P-chart

21. C-chart Applies to the no. of nonconformities in each sample Purpose
To control the no. of defects in final assemblies
Sample No. 1 2 3 4 5 6 7 8 9 10
Mean
Missing items (c ) 14 13 26 20 25 15 11 16

22. C-chart

23. Why acceptance sampling?
100% inspection on lots is not possible due to
High cost of inspection
Destructive method of test results in spoilage
Long time for testing
Impossible & impracticable, if population is too large

24. Possibilities Product Accept Reject Good product Desired Not desired.
Rejecting a good product.
Producer’s risk
(Error I)
Bad product
Accepting a bad product.
Consumer’s risk
(Error II)
Not Desired.

25. Operative Characteristic (OC-Curve
Sample size = n Percentage defective = p (5% means 0.05) Acceptance number = c AQL = Acceptance Quality Level = percentage defective, p1 LTPD = Lot Tolerance Percent Defectives = p2 p2 > p1 (n) and (c ) must be decided based on AQL and LTPD

26. O. C. Curve

27. O. C. Curve Probability of acceptance of lot is pa
Solution is based on Chi-square distribution,
where (c+1) is degrees of freedom & c can be found out

28. O. C. Curve for different n and c

29. Six Sigma
Origin
Management standard in product variation (later service) coined by Motorola Engineer Bill Smith during 1920’s
Standards such as defects in thousands of opportunities did not provide depth of information
Decided to measure defects per million opportunities

30. What is Six Sigma?
It is a disciplined data driven approach and methodology for eliminating defects which amounts to driving towards six-standard deviation between the mean and the nearest specification
Six Sigma level indicates that we are % confident that the product/service delivered by us is defect free
3.4 defects per million opportunities (DPMO)
A process is said to be at 6-Sigma level provided that the process is not producing more than 3.4 defects per million opportunities

31. Objectives & concepts in 6-Sigma
Process improvement & reduction of variation
Concepts
Attributes which are most important to customers are critical
Focus on process more specifically what it can deliver
Consistent & predictable processes to improve quality is utmost expectation of customer
Employing systematic methodology, utilizing tools, training & measurements to produce at Six Sigma level

32. Approaches for Six Sigma
DMAIC: Define, Measure, Analyze, Improve, & Control
For improving existing process falling below specification and looking for incremental improvements
DMADV: Define, Measure, Analyze, Define, & Verify
To develop new processes/products at Six Sigma level

33. Execution of Six Sigma
DMAIC & DMADA are executed by Six Sigma Green Belts and Six Sigma Black Belts and these are overseen by Six Sigma Master Black Belts.
Six Sigma Green Belt Team
Team leaders capable of forming & facilitating Six Sigma teams and managing Six Sigma projects from concept to completion
5-days of classroom & practice in Six Sigma projects

34. Execution of Six Sigma Six Sigma Black Belt Team
Team leaders responsible for measuring, analyzing & improving & controlling key processes that influence customer satisfaction/or productivity growth
Full time positions
They focus on 1 to 3 projects

35. Benefits of Sig Sigma It ensures enhanced product quality
It enables predictable delivery of products
It helps to achieve productivity improvement
It helps to have rapid response to changing needs of customers
It facilitates the development & introduction of new products