This PPT is thoroughly explained the Statistical Process Control , types and examples.

1. SUBMITTED TO :- Dr. Indu Uprety
SUBMITTED BY :-
MAYANK DUBEY-
COLLEGE – GAUTAM BUDDHA UNIVERSITY

2. Contents :-
Meaning and overview
History
Importance
Steps of Statistical Process Control
Variation And it’s types
Tools Used in Statistical Process Control
Control Chart & it’s features
Graph of Control Chart and it’s Analysis
Types Of Control Chart
S.P.S used In T.C.S Company
Conclusion

3. Statistical Process Control (SPC) is a statistical method to
measure, monitor, and control a process. It is a method of
quality control which employs statistical method.
Meaning of S.P.C
 Statistics:-Statistics is a science which deals with a collection,
summarization, analysis, and drawing information from the data.
Process:- It converts input resources into the desired output (goods or
services) with a combination of people, materials, methods, machines, and
measurements.
Control:- It is System, policies, and procedures in place the overall output
meets the requirement.

4. Walter A Shewhart developed the control chart and
the concept that a process could be in statistical
control in 1924 at Bell Telephone Laboratories,
USA.
The SPC concepts are included in the management
philosophy by Dr. W.E. Deming just before World
War II.
SPC became famous after Japanese industries
implement the concepts and compete with western
industries.

5. Reduce scrap and rework
Increase productivity
Improve overall quality
Match process capability to product requirement
Continuously monitor process to maintaining control
Provide data to support decision making
Streamline the process
Increase in product reliability
Opportunity for company-wide improvements

6. 1.Identify the processes
2.Determine measurable
attributes of the process
3.Determine measurement
method and perform
Gage R&R
4.Develop a subgroup
strategy & sampling plan.
5.Collect the data &
plot SPC chart
6.Describe natural
variation of attributes
7. Monitor process variation.

7. The basic assumption of SPC is that all processes are subject to
variation. Variation measures how data are spread around the
central tendency.
There are two types of Variation :-
1. Natural or Common Cause Variation:-It consists of
the variation inherent in the process as it is designed.
It may include variations in temperature, properties of
raw materials, strength of an electrical current etc.
2. Special Cause Variation or Assignable cause
Variation:- The variation in a process that is not due to
chance therefore can be identified and eliminated.
Process under influence of special cause will not be
stable and predictable.

8. 1.Run Chart - A Run chart is a line chart of data plotted overtime,
but they do not display statistical control limits.
2.Control Chart - A control chart also plots a single line of data
over time. It include upper and lower control limit lines with a
centerline.
Simple graphical tools that enable process performance
monitoring.
Designed to identify which type of variation exists within
the process.
Designed to highlight areas that may require further
investigation.
Easy to construct and interpret.

9. 1) Data Points:
• Either averages of subgroup measurements or individual
measurements plotted on the x/y axis and joined
by a line. Time is always on the x-axis.
2)The Average or Center Line
• The average or mean of the data points and is drawn
across the middle section of the graph, usually as a heavy
or solid line.
3)The Upper Control Limit (UCL)
• Drawn above the centerline and denoted as "UCL". This is
often called the “+ 3 sigma” line.
4)The Lower Control Limit (LCL)
• Drawn below the centerline and denoted as "LCL". This is
called the “- 3 sigma” line.

11. Control limits define the zone where the observed data for a
stable and consistent process occurs virtually all of the time
(99.7%).
Any fluctuations within these limits come from common causes
inherent to the system, such as choice of equipment, scheduled
maintenance or the precision of the operation that results from
the design.
An outcome beyond the control limits results from a special
cause.
The automatic control limits have been set at 3-sigma limits.

12. Three-sigma limits is a statistical calculation where the
data are within three standard deviations from a mean. The term
"three-sigma" points to three standard deviations.
It refers to processes that operate efficiently and produce items of the
highest quality.
It is used to set the upper and lower control limits in statistical quality
control charts.
It is used to check data from a process and if it is within statistical control.
It minimize the economic loss.
For efficient position, around 99.73% of a controlled process should occur
within plus or minus three sigma.
Three-sigma limits set a range for the process parameter at 0.27% control
limits.

13. •The area between each control limit and the centerline is divided
into thirds.
1) Zone A - "1-sigmazone“
2) Zone B - "2-sigma zone"
3) Zone C - " 3-sigma zone “

14. 1.Variable Control Chart - Variables control charts plot continuous
measurement process data, such as length or pressure, in a time-
ordered sequence. It plot count data, such as the number of
defects or defective units.
2.Attribute Control Chart - Attribute Control Charts are a set of
control charts specifically designed for tracking defects .
An Attribute is a quality characteristics for which a numerical
value is not specified.
It is measured on a nominal scale.
For Example-The taste of a certain dish is labeled as acceptable
or unacceptable, or is categorized as exceptional ,good,fair,or
poor.

15. Variables charts:
• Variable data are
measured on a
continuous scale
• Ex: time, weight,
distance or
temperature can be
measured in fractions
or decimals.
• Applied to data with
continuous
distribution.
• Attribute charts:
• Attribute data are counted and
cannot have fractions or
decimals.
• Attribute data arise when you are
determining only the presence
or absence of something:
success or failure, accept or
reject, correct or not correct.
• Ex: A report can have four or five
errors but it cannot have four
and half errors.
• Applied to data following discrete
distribution.

16.  P Chart - Proportion of nonconforming items
 Np Chart - Number of nonconforming items
 C Chart - Total number of nonconformities
 U Chart - Nonconformities per unit

17. • It tracks the proportion or percent of nonconforming units or percent
defective in each sample over time.
• Ex: Count defective chairs ( X ) & divided by total chairs inspected ( n ) i.e
P=X/n.
• Chair is either defective or not defective.
• C – Chart is an attribute chart which is used to track the total no. of
nonconformities in sample of Constant Size.
• It shows the number of nonconformities i.e defects in a unit
• It follow Poisson Distribution to count nonconformities.
• Ex: Count defects (scratches, injury etc.) in chair of a sample of 100 chairs.

18. • When the sample size varies, a U-Chart is used to monitor the number of
nonconformities per unit.
•U-charts show how the process, measured by the number of nonconformities
per item or group of item changes over time.
• For example, a scratch, dent, bubble, blemish, missing button, and a tear are
all nonconformities.
•An np-chart when the data collected in subgroups that are the same size. np-
charts show how the process, measured by the number of nonconforming
items (defectives) it produces, changes over time. In other words, the process
describes pass or fail, yes or no.
•For example:-the number of incomplete accident reports in a constant
daily sample of five would be plotted on an NP-CHART.

19. BASIS P-CHART NP-CHART C-CHART U-CHART
DEFINE
The fraction
defective is the
no. of defective
items in a sample
divided by total
no.of items in the
sample.
Chart shows the
no.
of defective
items in samples
rather than the
fraction of
defective items.
It is a plot of the
number of defects
in items.
Chart is for the
number of defects
in a sample.
SYMBOL P-proportion
NP-
No.Proportion C- Count U- Unit
DISTRIBUTION Binomial Binomial Poisson Poisson
SUBGROUP
SIZE Varying Constant Contant Varying
DISPLAY Percentage Raw count Raw count Percentage

20. It was established in 1968 as part of the Tata
group of companies.
It has grown to be the largest software and management
consultancy organization in southern Asia.
TCS first implemented quality control procedures in the early
70s.
TCS introduced structured systems analysis and design
processes in 1982
In 1992, TCS decided to adopt ISO 9000 as a quality norm

21. Between 19s TCS had to make more stuggle for enhancement.
18
16
14
12
10
8
6
4
2
Percentage
of
total
effort
1997 1998 1999
Figure. The reduction in rework. Each data
point indicates the rework effort as a
percentage of the total effort, for one
project.

22. In 1996 TCS decided to adopt the Carnegie Mellon University
Software Engineering Institute’s Software Capability Maturity Model
( C.M.M ).
It got much success in the software field after advancement of
C.M.M.
The objective of achieving CMM Level 4 at TCS-Seepz required
enhancing the metrics program through statistical process control.
The SEPG created control charts to monitor schedule slippage, in-
process defects per component, and maintenance field errors.
The project teams and the SEPG used run charts to track review
effectiveness, measured as the percentage of defects detected in
peer reviews before testing.
After applying statistical control chart, company got much
success in the field of software and technology.

23. Figure. Continuous improvement in estimation accuracy for the
Calcutta center. The x-axis indicates the instance numbers.

24. Figure . A U-chart for module defect density

25. • Leadership system
• Strategic planning
• Customer focus
• Information and analysis
• Human resource focus
• Process management
• Business results

26. Today companies are facing increasing competition and also
operational costs, including raw material continuously
increasing. So for the organizations, it is beneficial if they
have control over their operation.
Organizations must make an effort for continuous
improvement in quality, efficiency, and cost reduction.
Many organizations still follow inspection after the
production for quality related issues.
SPC helps companies to move towards prevention-based
quality control instead of detection based quality controls.
By monitoring SPC graphs, organizations can easily predict
the behavior of the process.