Statistical Process Control in Oeration and Production Mnanagement

1. PRESENTED BY
GROUP 11
P. ASHWIN AYAPPADAS-231250092
UTTKARSH YADAV-231250160
VIVEK KUMAR-231250165
TARUN KUMAR-231250151
TUSHAR SINGH-231250154
STATISTICAL
PROCESS
CONTROL
Submitted to:- Proof. Suvendu Naskar

2. INTRODUCTION
SPC is method used in quality control and management to monitor, control, and improve processes. It involves the
use of statistical techniques to analyze and understand process variability and to ensure that processes operates
efficiently and consistently, producing products and services that meet predefined quality standards.
Statistical Process Control
It is caused by factors that are inherent to the processes
and affect all outcomes to some extent. Common cause
variations are predictable and can be characterized by
statistical distribution. Examples of common cause include
minor fluctuations in temperatures, variations in raw
material quality, or small change in operator’s technique.
It is caused by specific, identifiable factors that are not
inherent to the process under normal operating
conditions. Special causes are often sporadic and
unpredictable. Examples of special cause include
equipment malfunctions, material defects or
environmental disturbances
Natural cause Special cause
V
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3. Steps involved in statistical process
Data Collection: Data is
collected regularly from
the production process.
This data could include
measurements of
dimensions, weights, or
other relevant variables.
Data Analytics:
Statistical tools and
techniques are applied to
analyze the collected data.
This involves calculating
measures of central tendency
(median or mode) and
measures of dispersion (such
as standard deviation).
Control Charts: Control charts
are graphical tools used in SPC
to monitor the stability and
performance of a process over
time. Common types of control
charts include the X-bar chart
for monitoring the process
mean and the R-chart for
monitoring the process
variation.
Process Capability
Analysis: SPC also involves
evaluating the capability of
a process to meet
customer requirements.
This is done by comparing
the process variability to
the tolerance limits
specified by the
customers.

4. Steps for implementing Statistical Process Control (SPC)
Define the
Process and
its
Objectives
Select
Appropriate
Control Charts
and Data
Collection
Methods
Analyze
Data Using
Control
Charts
Collect Data
and Establish
Baseline
Performance
Take Action
Based on
the Analysis
Continuousl
y Monitor
and Adjust
the Process

5. Understanding Central Tendency and Dispersion in Statistical Process
Control (SPC)
Central
tendency
refers to
the
tendency
of data
points to
cluster
around a
central
value.
Dispersion
measures
the spread
or
variability
of data
points
around the
central
tendency.
Central
Tendency Dispersion Central tendency
and dispersion are
crucial for
• Monitoring process
variability
• Identifying sources
of variation
• Ensuring quality and
process stability
Central tendency
and dispersion are
key metrics
monitored in SPC to
• Detect shifts or
trends in process
performance
• Distinguish between
common cause and
special cause
variation

6. The control chart is a graph used
to study how processes changes
over time. Data are plotted in time
order. A control chart always has a
central line for average, upper line
for the upper control limit, and a
lower line for the lower control
limit. These lines are determined
from the historical data. By
comparing current data to these
lines, you can draw conclusions
about whether the process
variation is due to natural cause or
due to special cause.
CONTROL CHARTS

7. Process Control Chart
Plots the process mean over time. Use to
track the process level and detect the
presence of special causes affecting the
mean.
Plots the process range over time. Use to
track process variation and detect
unexpected variation.
Plots the process standard deviation
over time. Use to track the process
variation and detect unexpected
variation.
Plots the cumulative scores based on
"zones" at 1, 2, and 3 standard deviations
from the center line. Use to detect
unexpected variation.
Variable
Control
Chart
Variables control charts plot continuous
measurement process data, such as length or
pressure, in a time-ordered sequence. There
are two main types of variables control charts:
charts for data collected in subgroups and
charts for individual measurements. Variables
control charts for subgroups include Xbar, R,
S, and Zone.

8. Process Control Chart
P chart is one of the quality control charts used to assess
trends and patterns in counts of binary events (e.g., pass,
fail) over time. p charts are used when the subgroups are
not equal in size and compute control limits based on the
binomial distribution.
C chart is also known as the control chart for defects (i.e.,
counting the number of defects). It generally monitors the
number of defects in consistently sized units.
Attribute
Control
Chart
In contrast, attribute control charts plot count data, such as the number of defects or
defective units. If your process can be measured in attribute data, then attribute charts
can show you exactly where in the process you’re having problems, if any. Attribute
Control Charts include p-charts and c-charts.

9. Process Variation
Process variation (also known as process variance or process variability) can be defined as the numerical value
indicating how far processes vary from their expected performance. This is a leading cause of quality issues in
production and transactional processes. Many times, product quality issues are not identified until it has turned
into a large-scale disaster.
two types of process variation
Common
cause
variation
Special
cause
variation

10. Common cause variation,
also referred to as “noise”
results from things that
may or may not be known.
The causes for this
variance are usually
quantifiable and natural in
the system. Common
cause variation usually lies
within three standard
deviations from the mean
where 99.73% of values
are expected to be found.
If you are plotting on a
control chart this variation
is indicated by a few
random points that are
within the control limit.
The impact common cause
variation has on the
process outputs is
controllable and
predictable resulting in a
statistically stable process.
Special cause variation
refers to unexpected
things that affect a
process. These variations
were not observed, are
unusual and non-
quantifiable. These
sporadic causes are the
result of a change
introduced in the process
resulting in a problem.
Things can get more
challenging when special
cause variation is
introduced because they
occur out of the blue.
This failure can be
corrected by making
changes in the methods,
material, or processes.
Process Variation

11. Enhancing Process Stability and Performance with SPC
Early problem
detection by
Real-time
monitoring
Improved product
quality,Consistent
processes within
specifications
ensure higher
quality products
Reduction of
variation Identify
and eliminate
sources of
unwanted
variation
Waste and
rework
minimization By
preventing
defects upfront
Data-driven
decision making
by objective,
quantifiable data
that facilitates
informed process
improvement
choices.

12. Cost of
implementation
Complexity
Data collection
Misinterpretation of
results
Sustaining usage
Challenges
of SPC
Implementati
on