Statistical process control (SPC) techniques apply statistical methods to measure and analyze variation in manufacturing processes. SPC uses control charts to distinguish between common cause variation inherent to the process and special cause variation that can be assigned to a specific reason. Control charts monitor process data over time against statistical control limits. Process capability analysis compares process variation to product specifications to determine if the process is capable of meeting specifications. Key metrics like Cp, Cpk and Cpm indices quantify a process's capability relative to the specifications. For a process to have a valid capability analysis, it must meet assumptions of statistical control, normality, sufficient representative data, and independence of measurements.

1. STATISTICAL PROCESS CONTROL
Prepared by:
Miss.Gorhe Ankita A
F.Y M pharm (PQA)
Guided by:
Dr. A.D Kulkarni
HOD of PQA
Sanjivani College Of Pharmaceutical Education
and
Research, Kopargaon
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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.
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3. DEFINITION:
• Statistical process control as the application of statistical method to the measurement and analysis of
variation in a process.
• This techniques applies to both in-process parameter and end-of-process parameters.
• A process is a collection of activities that converts inputs into outputs or result.
• More specifically a process is a unique combination of machine, tools, methods, materials and
people that attain an output in goods, software or services.
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4. IMPORTANCE OF SPC
• Reduces waste
• Reduction in the time which is required to produce the product.
• Detecting error at inspection.
• Reduces inspection cost.
• Saves cost of material by reducing number of rejects.
• More uniform quality of production.
• Customer satisfaction.
• It provides direction for long term reduction in process variability.
• It is stable process and operates with less variability.
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5. QUALITY MEASUREMENT IN MANUFACTURING
• 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.
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6. STATISTICAL CONTROL CHARTS
• 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)
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7. TYPES OF VARIATION
• 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 asit 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
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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 requirefurther 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.
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9. CONTROL CHARTS
 Show the variation in a measurement during the time periodthat 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 factorsthat impede peak performance.
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10. • Control charts have four key features:
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 solidline.
3) The Upper Control Limit (UCL)
• Drawn above the centerline and denoted as "UCL". Thisis often called the “+ 3 sigma” line.
4) The Lower Control Limit (LCL)
• Drawn below the centerline and denoted as "LCL". Thisis called the “- 3 sigma” line.
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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.
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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 “
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14. TYPES OF CONTROL CHART
Variables
charts
Attributes
charts
R chart x chart P chart C chart
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15. TYPES OF CONTROL CHARTS
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
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16. TYPES OF VARIABLES CHARTS
R-Chart:
• It controls the dispersion of the process
• R is the range or difference between the highest
and lowest values in sample
• It measures gain or loss of uniformity within a
sample which represents the variability in the
response variable over time.
• Ex: Weigh samples of coffee and computes ranges
of samples;Plot
x-Chart:
• It controls the central tendency of the process
• Shows sample means over time
• Monitors process average
• Example: Weigh samples of coffee and compute
means of samples; Plot
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17. TYPES OF ATTRIBUTES CHARTS
P-Chart:
• It tracks the proportion or percent of
nonconforming units or percent defective in each
sample over time.
• Ex: Count defective chairs & divided by total
chairs inspected
Chair is either defective or not defective
C-Chart:
• It shows the number of nonconformities i.e defects
in a unit
Unit may be chair , steel sheet , car etc.
Size of unit must be constant
• Ex: Count defects (scratches .chips etc.) in chair of
a sample of 100 chairs
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18. ADVANTAGES OF STATISTICAL CONTROL
• 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.
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19.  Process capability studies distinguish between conformance tocontrol limits and conformance to
specification limits (also called tolerance limits)
◦ if the process mean is in control, then virtually all points willremain within control limits
◦ staying within control limits does not necessarily mean that specification limits are satisfied
◦ specification limits are usually dictated by customers
PROCESS CAPABILITY
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20. BASIC DEFINITIONS
 Process some machine ,tools, methods & people engaged in production.
 Capability an ability based on tested performance to achieve measurable result.
 Process capability performance of the process when it is operating in control.
 Measured capability the fact that process capability is quantified from data
 Inherent capability the product uniformity resulting from process.
 Product is measure because product variation is end result
 Process Capability provide a quantified prediction of process adequacy.
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21. USE OF PROCESS CAPABILITY INFORMATION
 Predicting the extent of variability that process will exhibit.
 Choose most appropriate process to meet the tolerance.
 Planning the inter-relationship of sequential process.
 Assign the machines to work for which they are best suited.
 Testing causing of defect during quality improvement programs.
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22. STANDARDIZED FORMULA
 The most widely used formula for process capability is
Process capability = ± 3σ
Where,
σ = Standard deviation of the process.
o If the process is centered and follows normal probability 99.37% product will fall
within ± 3σ of the normal specification.
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23. RELATIONSHIP TO PRODUCT SPECIFICATION
 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 6σ Process Capability well within the specification width.
 A measure of this relationship is the capability ratio
6σ
Upper Specification – Lower Specification
  standard deviation of the process
pC
 That means
Cp = USL – LSL
6σ
Where,
USL= Upper specification limit
LSL= Lower specification limit
=
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24. FOUR EXAMPLES OF PROCESS VARIABILITY
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25. CAPABILITY INDEX (CPK )
 Cp index measures potential capability, assuming that the process avg. is equal to the mid point of the
specification limit and the process is operating in statistical control because the avg. often not at the mid point it
is useful to have capability index that reflects both variation and the location of the process avg. Such index is the
capability index (Cpk) .
Where,
x process mean
  standard deviation of the process population
Cpk = [ Upper Specification limit – x ] or
3
[ x - Lower Specification Limit ]
3
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26. CAPABILITY INDEX CPK
 If actual avg. = mid point of the specification range
Cpk = Cp
 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.
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27. 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. Study of process 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
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28. 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 .
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29. THANK YOU
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