This document provides an overview of statistical process control (SPC). It defines SPC as the application of statistical methods to measure and analyze variation in a process. The document discusses the importance of SPC in reducing waste and costs while improving quality and uniformity. It also describes key SPC tools like control charts and process capability analysis. Control charts help monitor processes for common and special causes of variation, while process capability analysis compares process performance to product specifications to ensure quality.

1. TOPIC :- STATISTICAL PROCESS
CONTROL
PRESENTED BY :-
M.PHARM {QUALITY ASSURANCE}
UNIVERSITY OF INSTITUTE
PHARMACEUTICAL SCIENCES
PUNJAB, CHANDIGARH
SUBMITTED TO :- Prof. Gurpal sir
REFRENCE :-Pv publications and Internet

2. Contents:-
Definition
 Importance of SPC
 Quality measurement in manufacturing
 Statistical control charts
• Introduction
• Types of variation
• Control charts
Process capability
• Basic Definition.
• Use of Process capability information.
• Standardized formula.
• Relationship to product specification.
• The capability index.

3. STATISTICAL PROCESS CONTROL ?
D E F I N I T I O N ; -
• A q u a l i t y c o n t r o l s y s t e m p e r f o r m s i n s p e c t i o n , t e s t i n g a n d
a n a l y s i s t o c o n c l u d e w h e t h e r t h e q u a l i t y o f e a c h p r o d u c t i s a s
p e r l a i d q u a l i t y s t a n d a r d s o r n o t . I t i s c a l l e d s t a t i s t i c a l q u a l i t y
c o n t r o l w h e n s t a t i s t i c a l t e c h n i q u e s a r e e m p l o y e d t o c o n t r o l
q u a l i t y o r t o s o l v e q u a l i t y c o n t r o l p r o b l e m s .
S P c m a k e s i n s p e c t i o n m o r e r e l i a b l e a n d a t t h e s a m e t i m e l e s s
c o s t l y.
S t a t i s t i c a l p r o c e s s c o n t r o l a s t h e a p p l i c a t i o n o f s t a t i s t i c a l
m e t h o d t o t h e m e a s u r e m e n t a n d a n a l y s i s o f v a r i a t i o n i n a
p r o c e s s .
• T h i s t e c h n i q u e s a p p l i e s t o b o t h i n - p r o c e s s p a r a m e t e r a n d e n d -
o f - p r o c e s s p a r a m e t e r s .
• A p r o c e s s i s a c o l l e c t i o n o f a c t i v i t i e s t h a t c o n v e r t s i n p u t s i n t o

4. IMPORTANCE OF SPC
1. Reduces waste.
2. Reduction in the time which is required to produce the product.
3. Detecting error at inspection.
4. Reduces inspection time.
5. Saves cost of material by reducing number of rejects.
6. More uniform quality of production.
7. Customer satisfaction.
8. It provides direction for long term reduction in process variability.
9. It is stable process and operates with less variability.

5. •Quality measurement is central to the process of quality control: "what
gets measured, gets done.“
• Measurement is basic for all three operational quality process and for
strategic management
1. Quality control measurement - provides feedback and early warnings
of problems.
2. Operational quality planning measurement -quantifies customer
needs and product and process capabilities.
3. Quality improvement measurements - can motivate people,
prioritize improvement opportunities. and help in diagnosing causes.
QUALITY MEASUREMENT IN MANUFACTURING

6. • A statistical control chart compares process performance
data to computed "statistical control limits' drawn as limit lines
on the chart.
• Prime objective of control chart is detecting special causes of
variation in a process by analysing data from both the past
and the future
• Process variations have two kinds of causes
1. Common (random or chance)
2. Special (assignable)
STATISTICAL CONTROL CHARTS

7. • Two kinds of variation occur in all manufacturing processes
1 • Common Cause Variation or Random Cause Variation
• Consists of the variation inherent in the process as it is designed,
• May include variations in temperature, properties of raw materials, strength
of an electrical current etc.
• Common cause is the only type of variation that exist in the process and
process is said to be in control" and stable
2 • Special Cause Variation or Assignable-cause Variation
• With sufficient investigation, a specific cause, such as abnormal raw
material or incorrect set-up parameters, can be found for special cause
variations.
• Special cause variation exist within the process and process is said to be
'out of control’ and unstable.
TYPES OF VARIATION

8. • SPC control chart is one method of identifying the type of variation
present.
Statistical Process Control (SPC) Charts are essentially:
 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.
2 most popular SPC tools
 Run Chart
 Control Chart.
• SPC charts can be applied to both dynamic processes and static
processes.

9.  Show the variation in a measurement during the time period that
the process is observed.
 Monitor processes to show how the process is performing and
how the process and capabilities are affected by changes to the
process. This information is then used to make quality
improvements.
 A time ordered sequence of data, with a centre line calculated by
the mean
.
 Used to determine the capability of the process.
 Help to identify special or assignable causes for factors that
impede peak performance.
CONTROL CHARTS

10. 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.
Control charts have four key features:

12. 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.

14. TYPE OF CONTROL CHART
CONTROL
CHARTS
Variables
charts
R chart X chart
Attributes
charts
P chart C chart

15. TYPE OF CONTROL CHART

16. TYPE OF VARIABLES CHART

17. TYPE OF ATTRIBUTES CHART

18. • Provides means of detecting error at inspection.
• Leads to more uniform quality of production.
• Improves the relationship with the customer.
• It reduces cost.
• It reduces the number of rejects and saves the cost of material.
• It determines the capability of the manufacturing process
• It provides direction for long term reduction in process variability.
• It is stable process and operates with less variability.
ADVANTAGES OF STATISTICAL CONTROL

19. Process Capability
Process capability is defined as a statistical measure of the inherent process variability of a
given characteristic. We can use a process-capability study to assess the ability of a process
to meet specifications.
Product Specifications :
*Preset product or service dimensions, tolerances.
•e.g. tablet weight might be 16 gm ±.2 gm (15.8 gm.- 16.2 gm)
•Based on how product is to be used or what the customer expects.
•. During a quality improvement initiative, such as Six Sigma, a capability estimate is
typically obtained at the start and end of the study to reflect the level of improvement that
occurred.

20. BASIC DEFINITIONS
• Process
• Capability
• Process capability
• Measured capability
• Inherent capability
some machine tools, methods & people engaged in production.
an ability based on tested performance to achieve
measurable result.
performance of the process when it is operating in
control.
the fact that process capability is quantified
from data
the product uniformity resulting from process.
• Product is measure because product variation is end result
• Process Capability provide a quantified prediction of process adequacy.

21. 1) Predicting the extent of variability that process will exhibit
2) Choose most appropriate process to meet the tolerance.
3) Planning the inter-relationship of sequential process.
4) Assign the machines to work for which they are best suited.
5) Testing causing of defect during quality improvement
programs.
USE OF PROCESS CAPABILITY INFORMATION

26. The major reason for quantifying Process Capability is to compute the
ability of the process to hold the specification.
 Planner try to select process within the 60 Process Capability well within
the specification width.
A measure of this relationship is the capability ratio
Cp= Upper Specification - Lower Specification
6
α = standard deviation of the process
That means
C{p} = (USL - LSL)
(6 α )
Where, USL= Upper specification limit
LSL=Lower specification limit
RELATIONSHIP TO PRODUCT SPECIFICATION
α

27. FOUR EXAMPLES OF PROCESS VARIABILITY

31. CAPABILITY INDEX Cpk
• If actual avg. = mid point of the specification range
Cpk = CpO
•Higher the Cp lower the amount of product outside specification limit.
• A capability index can also be calculated around a target value rather
than actual avg.
• This index called as Taguchi index (Cpm).
• Krishnamoorti & Khatwani (2000) propose capability index for handling
normal and non normal characteristic.

32. TYPES OF PROCESS CAPABILITY STUDIES
1. Study of process potential
An estimation is obtained of what
the process can do under certain condition.
The Cp index estimate potential
process capability
2: Stucky of pose's performance
An estimation of capability provides a picture of
what the process is doing over an extended
period of time.
The Cpk index estimate performance
process capability

33. ASSUMPTION OF STATISTICAL CONTROL & ITS
EFFECT ON PROCESS CAPABILITY
There are five key assumption
1. Process Stability:-statistical validity requires a state of statistical control with no drift
or oscillation.
2. Normality of the characteristic being measured :-Normality is needed to draw
statistical interference about the population.
3. Sufficient Data :-It is necessary to minimize the sampling error for the capability
index.
4. Representativeness of samples:- must include random sample.
5. Independence of measurements:- Consecutive measurement cannot be correlated .
• Are not theoretical refinements they are important condition for applying capability
index.

34. THANK YOU