1. Quality Management
N.K.Agarwal

2. Quality Management
• Product and service quality can be defined
as
– The total composite product & service
characteristics of marketing, engineering,
manufacturing and maintenance
– Through which the product and service in use
• Will meet the expectations of the customer
• Quality to industry means best for
satisfying customer conditions
• Important among these conditions are
– The actual end use
– The selling price of product/service

3. Quality features of a Product
• Product features
– Performance
– Reliability
– Durability
– Ease of use
– Serviceability

4. Quality features of a Product
– Esthetics
– Availability of options and expandability
– Reputation
• Freedom from deficiencies
– Free of defects and errors at delivery,
during use and during servicing
– Sales, billing and other business processes
free of errors

5. Quality features of Service
• Quality of a service judged by
– Reliability
– Availability
– Credibility
– Security
– Competence of staff
– Understanding of customer needs
– Responsiveness to customers

6. Quality features of Service
– Courtesy of staff
– Comfort of surroundings
– Communication between participants
– Associated goods provided with the service
• Freedom from deficiencies
– Service free of errors during original and
future service transactions
– Sales, billing and other business processes
free of errors

7. Quality Control (Q.C.)
• Procedures for meeting the goals
• Generally 4 steps
– Setting standards
– Appraising conformance
– Acting when necessary
– Planning for improvement

8. Statistical Quality Control (SQC)
• Application of the statistical techniques
to accept or reject products already
produced or to control the process, and
therefore product quality while the part
is being made.
– Former is named as acceptance sampling
– The later process called process control
• These are two prominent techniques of
QC

9. SQC for Process Control
• Based on probability theory
• During manufacturing of identical parts, some are a little
large, some a little small but the average will be most
frequent
• The smaller and bigger sizes are extremes from the
average
• Bell or normal shaped curve obtained when frequency or
counts of items by size plotted with size on the
horizontal scale and count on the vertical scale
• In practice, SQC for process control done through
control charts
• First developed by Dr. Walter A. Shewart of Bell
Telephone Labs during 1930s
• Horizontal extensions of the bell shaped curve

10. NO. OF CASES
Bell Shaped Curve
SIZE

11. QUALITY VARIATION
Control Chart
UCL
CL AVERAGE QUALITY
LCL
TIME

12. Control Charts
• Types
– Control charts for variables
– Control charts for attributes
• Variables
– Quality characteristics that can be measured
on a continuous scale ex: diameter of a shaft
• Attributes
– Quality characteristics which can be classified
into one of the categories namely good or bad,
defective or non- defective ex: an ammunition
bullet

13. Control Charts
• Center line (CL): average quality
• Upper and Lower control limits (UCL & LCL): also
called tolerance limits
• Process is said to be stable or under control if the
quality of samples checked and variations plotted
on the charts show the values within UCL and LCL

14. Control Charts
• Usually two types of information available from
the charts
– Whether the process is running under stable
condition or not i.e. Whether the process is
under state of statistical control or not
– Whether the process is meeting the desired
quality standards or not.
• If statistical control does not exist, it has to
be established through technical control

15. X - R CHART
• X-R (MEAN-RANGE) CHART FOR VARIABLE CHARACTERISTICS
– X -R CHARTS HAVE TO EXIST AS A PAIR. INTERPRETATION OF
QUALITY OF THE ON-GOING PROCESS HAS TO BE DONE
ANALYSING BOTH THE CHARTS TOGETHER
• PROCEDURE
– CHOICE OF VARIABLE(X)
– SELECTION OF RATIONAL SUB-GROUPS
– CHOICE OF FREQUENCY
– COLLECT K NUMBERS SUB-GROUPS ( USUALLY K=25) EACH OF
COVENIENT SAMPLE SIZE ‘n’ ( SAY 4-10)
X
– FOR EACH SUB-GROUP, CALCULATE MEAN AND RANGE R
– CALCULATE AVERAGE OF THE DIFFERENCE RANGES FOR ‘K’
SUB-GROUPS
R
R
– COMPUTE CENTRAL LINE ( ), UCLR=D4* AND LCLR=D3*R , WHERE
D3 AND D4 ARE SAMPLE SIZE DEPENDENT CONSTANTS

16. X - R CHART…contd
• TEST FOR HOMOGENITY
• IF SUB-GROUPS NOT HOMOGENOUS, REMOVE OUT OF
RANGE LIMIT SAMPLES AND COMPUTE MODIFIED R, UCLR
AND LCLR TILL HOMOGENITY IS OBTAINED
• FOR HOMOGENOUS SUB-GROUPS( SAY K1), CALCULATE
X=∑X/K1 , COMPUTE UCLX = X+A2*R AND LCLX=X-A2R,
WHERE A2 IS SAMPLE SIZE DEPENDENT CONSTANT
• TEST FOR HOMOGENITY FOR ALL INDIVIDUAL VALUES
OF X FOR K1 SUB-GROUP WITHIN THE VALUES OF UCLX
AND LCLX
• IF NON-HOMOGENOUS, REMOVE EXTREMES OUTSIDE
UCLX AND LCLX AND RECALCULATE X, UCLX AND LCLX
• CONSTRUCT X AND R CHART FOR RATIONAL SUBGROUPS OBTAINED AFTER TESTING FOR HOMOGENITY
MEAN CHART

17. MEAN CHART
SAMPLE MEAN X
X
UCL X
X
LCL X
1
2
3
4
5
6
7
8
NO. OF SUBGROUPS
K

18. SAMPLE RANGE R
RANGE CHART
R
UCL R
R
LCL R
1
2
3
4
5
6
7
8
NO. OF SUBGROUPS
K

19. Acceptance Sampling Techniques
• Best alternative of estimating quality of incoming/out
going lots when 100% inspection not practical
• Sampling inspection necessary because of high cost
of 100% inspection or destructive nature of
inspection or testing
• Based on the premise that a sample represents the
whole lot from which the sample is drawn
• Random sampling provides each element an equal
chance of being selected and permit logical
inferences to be made about the lot quality based on
sample evidence

20. Acceptance Sampling
• Lot accepted or rejected based on the
number of defects found in the sample
• No need to inspect the entire lot
• Risks of accepting bad lots or rejecting good
lots always associated while making
decisions based on sample evidence

21. Errors in Sampling
• Type-I error
– An error when a sample from the output of a
process may lead to conclusion that the
process is out of control, when in fact, it is
operating as intended: Producer’s risk (α)
• Type-II error
– An error when sample leads to conclusion
that the process is satisfactory , when in fact,
the process is not working as intended:
Consumer’s risk (β)

22. Total Quality Management
• A philosophy that involves everyone in the
organisation in a continual effort to improve
quality and achieve customer satisfaction
• With TQM, the whole organisation works
together to guarantee product quality
• The aim is to make products of perfect
quality- with Zero Defect

23. TQM- Principles
• Three important principles
– Customer satisfaction
– Employee involvement
– Continuous improvement in quality

24. Deming Philosophy
• Deming's 14 points to serve as
guidelines for quality management
– Create constancy of purpose for continual
improvement of product & service
– Adopt the new philosophy for economic
stability
– Cease dependency on inspection to
achieve quality

25. Deming Philosophy
– End the practice of awarding business on
price tag alone
– Improve constantly & for ever the system
of production & service
– Institute training on the job
– Adopt & institute modern methods of
supervision & leadership
– Drive out fear
– Break down barriers between departments
& individuals

26. Deming Philosophy
– Eliminate the use of slogans, posters &
exhortations
– Eliminate work standard & numerical
quotas
– Remove barriers that rob the hourly worker
of the right to pride in workmanship
– Institute a vigorous program of education &
retraining
– Define top management’s permanent
commitment to ever-improving quality &
productivity

27. Quality Management
• Quality of a Product or Service must meet or
exceed the customers expectation Quality in
the organisation has to be built into the
organisation in stages
• Statistical Quality Control can be applied to
the products under processing as well as
manufactured lots in large quantities
• Total Quality Management aims at
– Customer satisfaction
– Employees involvement, and
– Continuous improvement in quality

28. Thank You

29. References
• TOTAL QUALITY CONTROL :
ARMAND V. FEIGERBAUM
• PRODUCTION AND OPERARTIONS
MANAGEMENT : ASWATHAPPA
• PRODUCTIVITY TECHNIQUES :
GONDHALEKAR/SALUNKHE
• OPERATIONS MANAGEMENT : DONALD
WALTERS
• TOTAL QUALITY MANAGEMENT :
K.SRIDHARA BHAT

31. ASSIGNMENT VII-QUALITY
MANAGEMENT
• Discuss “Quality is what the customer wants”.
How is this implemented ?

32. QUALITY CONTROL
• Total Quality Control (TQC)
– An effective system for integrating the
quality development, quality
maintenance and quality improvement
efforts of the various groups in an
organisation so as to enable marketing,
engineering, production and services at
the most economical levels which allow
for full customer satisfaction

33. EVOLUTION OF TQC
• Quality Assurance (QA)
– QA includes QC and also refers to emphasis on the
quality in the design of products, processes and jobs, in
personnel selection and training
• Inspection
– The act of determining conformance or otherwise of the
expected performance
– Basis of inspection is usually a specification which is
called inspection standard
– Inspection is made by comparing the quality of the
product to its standard

34. CONTROL CHART FOR ATTRIBUTES
•
CONSTRUCTION OF ‘np’ (NUMBER OF DEFECTIVES CHART) CHART FOR
CONSTANT SAMPLE SIZE ‘n’
SAMPLE SIZE=n,
NUMBER OF SUB-GROUPS=K,
NUMBER OF DEFECTIVES PER SUB-GROUP=C
FRACTION DEFECTIVE p=C / n
CALCULATED FOR EACH SUB-GROUP i.e.p1=C1/n, p2=C2/n, …….
pk=Cn/K
AVERAGE FRACTION DEFECTIVE p=∑p/K = (p1+p2….pk)/K
OR p= (C1/n+C2/n……CK/n)/K
np
= (C1+C2……..CK)/(n*K) =∑C/(n*K)
CENTRAL LINE
=n*∑C/(n*K)
np
np
UCL= n p+3√{ p (1-p)}
n
LCL= -3√{ (1-p)} =ZERO, IF NEGATIVE
TEST FOR HOMOGENITY DONE AS IN (X-R) CHART.

35. np OR c
np OR c
CHART
UCL
CL= P OR C
LCL

36. ‘p’ CHARTS
•
‘ p ‘ CHARTS(FRACTION DEFECTIVE CHART) FOR VARYING SAMPLE SIZE
– DATA COLLECTED GIVE SAMPLE SIZE(n1,n2,…….nk) FOR K SUBGROUPS AND VALUES OF NUMBER OF DEFECTIVES(C1,C2,C3…..Ck)
– FRACTION DEFECTIVES FOR EACH GROUP CALCULATED AS:
p1=C1/n1, p2=C2/n2 ….pk=Ck/nk
CENTRE LINE p=∑p/k=(p1+p2….pk)/K
UCL FOR EACH SUB-GROUP = p+3√[p(1-p)]/ SAMPLE SIZE
LCL FOR EACH SUB-GROUP = p-3√[p(1-p)]/ SAMPLE SIZE=ZERO,IF
NEGATIVE
– AS SAMPLE SIZE VARIES FOR EACH SUB-GROUP, THERE WILL BE AS MANY
VALUES OF LCLs AND UCLs AS THE NUMBER OF VALUES OF SAMPLES ARE (i.e.
n1, n2……..nk)
np OR c
CHART

37. ‘ C ‘ CHARTS
• ‘ C ‘ CHARTS( NUMBER OF DEFECTS CHART) FOR
CONSTANT SAMPLE SIZE
SAMPLE SIZE= n
NUMBER OF DEFECTS EXISTING IN ALL THE SAMPLES IN
EACH SUB-GROUP FOR K SUB-GROUPS ARE SAY C1, C2
….Ck
CENTRE LINE(CL) C=∑C/K
=(C1+C2+……CK)/K
UCL FOR EACH SUB-GROUP=C+3√C
LCL FOR EACH SUB-GROUP=C- 3√C =0, IF NEGATIVE

38. ‘U’ CHARTS
• ‘U’ CHARTS( NUMBER OF DEFECTS/UNIT) FOR
VARYING SAMPLE SIZE
– DATA COLLECTED REGARDING SAMPLE SIZE AND NUMBER
OF DEFECTS IN ALL THE SAMPLES FOR EACH OF K SUBGROUPS
– SAMPLE SIZE n1,n2….nk
– NUMBER OF DEFECTS PER SUB-GROUP
=U=C/n=C/SAMPLE SIZE
i.e. U1=C1/n1, U2=C2/n2…….Uk=Ck/nk
CENTRE LINE CL=U=∑U/K=(U1+U2+…UK)/K
UCL FOR EACH SUB-GROUP=U+3√U/SAMPLE SIZE
LCL FOR EACH SUB-GROUP=U-3√U/SAMPLE SIZE=ZERO, IF
NEGATIVE

39. Total Quality Management
• Two key philosophies in TQM
– A never ending push to improve ( i.e.
continuous improvement or Kaizen in
Japanese) , and
– A goal of customer satisfaction which
involves meeting or exceeding customer
expectation

40. Implementation of TQM
•
•
•
•
•
•
•
Get top management commitment
Find out what the customer wants
Design products with quality in mind
Design the process with quality in mind
Build teams of empowered employees
Keep track of results
Extend these ideas to suppliers & distributors

41. TQM - Failure
• Lack of commitment from top management
• Focusing on specific techniques rather than
on the system
• Not obtaining employee buy-in & participation
• Program stops with training
• Expecting immediate results, not a long term
pay-off
• Forcing the organisation to adopt methods
that are not productive or compatible with its
production system & personnel