Statstical process control

STATSTICAL PROCESS CONTROL
1
Quality management system

Content
• Definition
• Introduction
• Objectives
• Quality measurement in manufacturing of different types of formulation
• Significance
• Statistical control charts
• Advantages
• Application
• Conclusion
• Reference 2

Definition
Statistical process control (SPC) is a method of quality control which employs statistical
methods to monitor and control a process. This helps ensure the process operates
efficiently, producing more specification-conforming product with less waste (rework
or scrap). SPC can be applied to any process where the "conforming product" (product
meeting specifications) output can be measured. Key tools used in SPC include run
charts, control charts, a focus on continuous improvement, and the design of
experiments.
3

Introduction
• The term statistical means collecting the data, tabulating and summarizing using
prescribed statistical tools for purpose of analysis and reporting.
• SQC is important for improving the quality.
• Identifies any decline in quality during initial stages of production and taking immediate
corrective steps instead of identifying defectives after the damage has been done.
• One of the methods used for identifying defects is “SAMPLING”.
• Sampling always shows defects as well as 100%inspection.
4

Statistical quality control
• Statistical quality control (SQC) is the term used to describe the set of statistical tools
used by quality professionals. Statistical quality control can be divided into three broad
categories:
1. Descriptive statistics are used to describe quality characteristics and relationships.
Included are statistics such as the mean, standard deviation, the range, and a measure of
the distribution of data.
2. Statistical process control (SPC) involves inspecting a random sample of the output
from a process and deciding whether the process is producing products with
characteristics that fall within a predetermined range. SPC answers the question of
whether the process is functioning properly or not.
3. Acceptance sampling is the process of randomly inspecting a sample of goods and
deciding whether to accept the entire lot based on the results. Acceptance sampling
determines whether a batch of goods should be accepted or rejected.
5

General terms used in statistical analysis
• Mean: A statistic that measures the central tendency of a set of data. (average)
• Range:The difference between the largest and smallest observations in a set of data.
• Standard deviation: A statistic that measures the amount of data dispersion around the
mean.
6

Causes of variation
• Common causes: Random causes that cannot be identified.
• Assignable causes: Causes that can be identified and eliminated.
7

Objectives
• Increases consumer satisfaction by producing a more trouble-free product.
• Decreases scrap, rework, and inspection costs by controlling the process.
• Decreases operating costs by increasing the frequency of process adjustments and
changes.
• Improves productivity by identifying and eliminating the causes of out-of-control
conditions.
• Sets a predictable and consistent level of quality.
• Reduces the need for receiving inspection by the purchaser.
8

• Provides management with an effective and impersonal basis for making decisions.
• Increases the effectiveness of experimental studies.
• Helps in selecting equipment and processes.
• Helps people to work together to solve problems.
9

Quality measurement in manufacturing of
different types of formulation
• The in-process checking during manufacturing plays an important role in the auditing of
the quality of the product at various stages of production. duties of the control inspector
consisting of checking, enforcing and reviewing procedures and suggesting the change
for upgrading the procedures when necessary.
• The aim of in process quality control system is to monitor all the features of a product
that may affect its quality and to prevent errors during processing .
10

Process controls involved in the manufacturing
process of parental
Step 4 sterility
indicator
Step 3
Physical
examination
Step 2
checking the
filled volume /
sterile powder
weight
Step 1
checking the
bulk solution
11

Process controls involved in the manufacturing
process of solid dosage forms
Step 1
determination
of drug
content
Step 2
checking of
weight
variation
Step 3
determination
of post
formulation
tests
12

Process controls involved in the manufacturing of
the semisolid dosage forms
Step 4 testing for filling and leakage
Step 3 checking the appearance
Step 2 determination of particle size
Step 1 checking the drug content uniformity
13

Significance
• Control charts are an essential tool of continuous quality control.
• Control charts monitor processes to show how the process is performing and how
the process and capabilities are affected by changes to the process.
• Significant quality improvements.
• Determination of the capability of the process
• Identification of special or assignable causes for factors that impede peak
performance.
15

Statistical control charts
• A control chart is a statistical tool used to check the stability of a process over time. Its
basic functions are:
 To describe what control there is.
 To help get control.
 To help judge whether control has been attained.
 To detect change in process performance.
 To estimate the process capability.
 To signal when corrective action is needed.
16

Control charts
• The control chart is a graphical display of a quality characteristic that has been
measured or computed from a sample versus the sample number or time.
• The chart contains a centre line that represents the average value of the quality
characteristic corresponding to the in-control state.
• Two other horizontal lines, called the upper control limit (UCL) and the lower control
limit (LCL), are also shown on the chart.
17

Fig; a typical control chart
18

Title
Legend
Measuring
Parameter
Centre line
&
Control line
Data
collection
section
ELEMENTS OF
CONTROL
CHARTS
19

FUNCTION OF CONTROL CHARTS
• It provides statistical ease for detecting and monitoring process variation over
time.
• It provides a tool for ongoing control of a process.
• It differentiates special from common causes of variation in order to be a guide
for local or management action.
• It helps to improve a process to perform consistently and predictably to achieve
higher quality, lower cost, and higher effective capacity.
• It serves as a common language for discussing process performance.
20

TYPES OF CONTROL CHARTS
• Control charts are one of the most commonly used tools in statistical process
control.
• The different characteristics that can be measured by control charts can be
divided into two groups:
1. Variables
2. Attributes
21

Variables control charts
• It is used when measurements are quantitative (for example, height, weight, or
thickness).
• e.g.: Syrup solution bottling operation is an example of a variable measure,
since the amount of syrup solution in the bottles is measured and can take on a
number of different values.
• Other examples are the weight of a bag of paracetamol powder, the
temperature of a Hot air oven, or the diameter of plastic tubing.
• Two of the most commonly used control charts for variables monitor both the
central tendency of the data (the mean) and the variability of the data (either
the standard deviation or the range).
 Mean (x-Bar) Charts
 Range (R) Charts
22

Mean (x-Bar) Charts
• A mean control chart is often referred to as an x-bar chart. It is used to monitor
changes in the mean of a process.
• This chart serves mainly in validation.
• Changes in the process can be detected by these charts.
• Accuracy may also be monitored to some extent.
23

Construction of x-bar Chart
UCL = x + zx LCL = x - zx
x1 + x2 + ... xn
n
x ==
Where, x = average of sample means.
z =standard normal variable (2 for 95.44%
confidence, 3 for 99.74%confidence).
n = sample size.
x =  /√n,  =population (process) SD.
24

x-bar Chart Example:
Standard Deviation Known (cont.)
25

Standard Deviation Known (cont.)
26

Standard Deviation Unknown
OBSERVATIONS (RING DIAMETER, CM)
SAMPLE k 1 2 3 4 5 x R
1 5.02 5.01 4.94 4.99 4.96 4.98 0.08
2 5.01 5.03 5.07 4.95 4.96 5.00 0.12
3 4.99 5.00 4.93 4.92 4.99 4.97 0.08
4 5.03 4.91 5.01 4.98 4.89 4.96 0.14
5 4.95 4.92 5.03 5.05 5.01 4.99 0.13
6 4.97 5.06 5.06 4.96 5.03 5.01 0.10
7 5.05 5.01 5.10 4.96 4.99 5.02 0.14
8 5.09 5.10 5.00 4.99 5.08 5.05 0.11
9 5.14 5.10 4.99 5.08 5.09 5.08 0.15
10 5.01 4.98 5.08 5.07 4.99 5.03 0.10
50.09 1.15
27

Standard Deviation Unknown (cont.)
R = = = 0.115∑ R
k
1.15
10
UCL = x + A2R = 5.01 + (0.58)(0.115) = 5.08
LCL = x - A2R = 5.01 - (0.58)(0.115) = 4.94
=
=
x = = = 5.01 cm= x
k
50.09
10
28

UCL = 5.08
LCL = 4.94
Mean
Sample number
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
10
5.10 –
5.08 –
5.06 –
5.04 –
5.02 –
5.00 –
4.98 –
4.96 –
4.94 –
4.92 –
x = 5.01=
x- bar
Chart
Example
(cont.)
29

Range (R) chart
• Range (R) chart a control chart that monitors changes in the dispersion or
variability of process. Whereas x-bar charts measure shift in the central tendency
of the process.
• The method for developing and using R-charts is the same as that for x-bar
charts.
30

R- Chart
UCL = D4R LCL = D3R
R =
R
k
where
R = range of each sample
k = number of samples
31

R-Chart Example
OBSERVATIONS (RING DIAMETER, CM)
SAMPLE k 1 2 3 4 5 x R
1 5.02 5.01 4.94 4.99 4.96 4.98 0.08
2 5.01 5.03 5.07 4.95 4.96 5.00 0.12
3 4.99 5.00 4.93 4.92 4.99 4.97 0.08
4 5.03 4.91 5.01 4.98 4.89 4.96 0.14
5 4.95 4.92 5.03 5.05 5.01 4.99 0.13
6 4.97 5.06 5.06 4.96 5.03 5.01 0.10
7 5.05 5.01 5.10 4.96 4.99 5.02 0.14
8 5.09 5.10 5.00 4.99 5.08 5.05 0.11
9 5.14 5.10 4.99 5.08 5.09 5.08 0.15
10 5.01 4.98 5.08 5.07 4.99 5.03 0.10
50.09 1.15
32

R-Chart Example (cont.)
UCL = D4R = 2.11(0.115) = 0.243
LCL = D3R = 0(0.115) = 0
Retrieve Factor Values D3 and D4
33

R-Chart Example (cont.)
UCL = 0.243
LCL = 0
Range
Sample number
R = 0.115
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
10
0.28 –
0.24 –
0.20 –
0.16 –
0.12 –
0.08 –
0.04 –
0 –
34

Attribute control charts
• A control chart for attributes, is used to monitor characteristics that have discrete
values and can be counted. Often they can be evaluated with a simple yes or no
decision.
• Examples include color, taste, or smell.
• The monitoring of attributes usually takes less time than that of variables because a
variable needs to be measured.
• An attribute requires only a single decision, such as yes or no, good or bad,
acceptable or unacceptable
• e.g., the apple is good or rotten, the meat is good or stale, or counting the number of
defects e.g., the number of broken cookies in the box, the number of dents in the
car, the number of barnacles on the bottom of a boat.
35

Control Charts for Attributes
 p-chart
uses portion defective in a sample
 c-chart
uses number of defective items in a sample
36

P-charts
• P-charts are used to measure the proportion of items in a sample that are defective.
Examples are the proportion of broken vials in a batch .
• P-charts are appropriate when both the number of defectives measured and the size of
the total sample can be counted.
• The center line is computed as the average proportion defective in the population, . This
is obtained by taking a number of samples of observations at random and computing
the average value of p across all samples.
37

C-charts
• C-charts are used to monitor the number of defects per unit.
• Examples are the number of recalled products in an industry in a month, and the
number of bacteria in a milliliter of water.
• Note that the types of units of measurement we are considering are a period of time, or
a volume of liquid.
38

Advantages
• A process that is in control is stable over time, but stability alone does not guarantee
good quality. The natural variation in the process may be so large that many of the
products are unsatisfactory. Nonetheless, establishing control brings a number of
advantages.
• In order to assess whether the process quality is satisfactory, we must observe the
process operating in control, free of breakdowns and other disturbances.
• A process in control is predictable. We can predict both the quantity and the quality of
items produced.
39

• When a process is in control we can easily see the effects of attempts to
improve the process, which are not hidden by the unpredictable variation
which characterises a lack of statistical control.
• A process in control is doing as well as it can in its present state.
40

Application
• Drug potency
• Tablet or capsule in process characteristics
• Powder characteristics like mean particle size
• Microbial count
• Drug content application (nasal spray )
• Fill weight and fill volume
• Liquid characteristics like viscosity and refractive index
• Consumer complaints and industrial safety measurements
41

Conclusion
• Statistical process control improve the quality of the processes. Several
drawbacks in the conventional approach of drug formulation development can
be solved by using control charts as tools in statistical process control to
determine whether a manufacturing of dosage form in pharmaceutical industry
is in a state of statistical control or not.
42

Reference
1. John S. Oakland, Statistical Process Control, Fifth Edition, Butterworth-Heinemann, A
imprint of Elsevier Science, Linacre House, Jordan Hill, Oxford OX2 8DP, 200 Wheele
Road, Burlington MA 01803,pp 3-193
2. AIDT - Statistical Process Control - October 5, 2006.
3. S. Shah, P. Shridhar, D. Gohil, ̎ Control chart: A statistical process control tool i
pharmacy̎ , AsianJ. of Pʻceutics , 2010, pp 184-192.
43

Statstical process control

More Related Content

What's hot (20)

Similar to Statstical process control (20)

Recently uploaded (20)

Statstical process control