A REPORT
ON
BY
PRANAW KUMAR
08DDCS456
CSE
AT
AMBER ENTERPRISES (India) Pvt. Ltd
.
Selaqui, Dehradun
An IP Station of
FACULTY OF SCIENCE AND TECHNOLOGY, ICFAI University
DEHRADUN
July, 2010
A REPORT
ON
QUALITY CONTROL
BY
PRANAW KUMAR
08DDCS456
CSE
Prepared in partial fulfillment of the
Internship Program-II Course
AT
AMBER ENTERPRISES (India) Pvt. Ltd.
SELAQUI, DEHRADUN
An Internship Program-II station of
Faculty of Science & Technology, ICFAI University
July, 2010
Acknowledgement
I would like to express my sincere gratitude to Prof. R C Ramola, Dean FST,
Dehradun for allowing me to take up this course on Internship Programme II.
I would also like to thank Dr. S K Joshi, Faculty In Charge, Amber Enterprises,
Selaqui, Dehradun, Internship Programme II, for giving useful knowledge and
clarifying on Quality Control Process and above all for giving me this opportunity
to present my work with this report.
I would like to thank my friends n classmates who have helped me in gathering
the data for this report.
iii.
Table of contents
ACKNOWLEDGEMENT
III
ABSTRACT
VI
1. Introduction
1
2. History
2
3. Process of Quality Control
4
3.1. Incoming Quality Control
5
3.2. In-Process Quality Control
6
3.3. Outgoing Quality Assurance
7
4. Planning of Quality Control
8
4.1. Sampling
4.1.1. Parts of Sampling
4.2. Control Chart
4.2.1. Common types of Charts
4.3. Approaches
9
10
4.4. Real Time Quality Control
4.5. Quality System Elements and Data life Cycle
10
4.5.1. Planning
4.5.2. Implementation
4.5.3. Assessment
4.6. Standards for Digital Elevation Models
4.6.1. Quality Control tests
11
4.6.2. Uses of data and charts
5. Quality Control software
12
5.1. Software control methods
5.2. Verification and Validation of methods
5.3. Testing
5.4. Software used in pharmacy for QC
6. Quality Improvement
6.1. Statistical Process Control (SPC)
13
17
18
6.1.1. Pareto Analysis
6.1.2. Scatter Diagram and Regression Analysis
19
6.1.3. Control Charts
20
6.2. Engineering Process Control (EPC)
23
6.3. Six- Sigma Approach
24
7. Quality Control in Pharmaceutical Company
26
7.1. Brief Introduction about Pharmaceutical Company
7.2. Quality Control Management
27
7.3. Quality Control in Torrent Pharmacy
29
7.4. Quality Control in Aglowmed Pharmacy
33
7.5. Technologies used in pharmacy for quality control
34
8. Conclusion
48
9. List of References
VII
10. Bibliography
VIII
Faculty of Science & Technology, ICFAI University
Station: AMBER ENTERPRISES (India) Pvt. Ltd.
Centre: Selaqui, Dehradun
Duration: 26 May- 17 July
Date of Start: 26th May 2010
Date of Submission: 15th July 2010
Title of the Project: Quality Control & Quality Control in pharmacy
ID No.:08DDCS456
Name: PRANAW KUMAR
Discipline: CSE
Name & Designation of the Expert: Mr. SANDEEP KUMAR, Manager
Name of the IP Faculty: Dr. S K JOSHI
Key Words: Process, Management, Technology, Planning, Software, Pharmacy.
Project Area: CSE
Abstract:
This report gives an overview on quality control process and its use in the various
pharmaceutical companies.
Quality Control is an integral part of the production process. The main purpose of
quality assurance and quality control (QA/QC) is to identify and implement
sampling and analytical methods and to decrease the errors into
analytical data. Manufactured products and services are tested to determine if they
meet customer standards or not. Quality control is a service to maintain
consistently high standards not for creating the standards.
For the quality control processes companies use control charts, quality control
software according to the ISO: 9000 standard, visual verification, accuracy
verification and real time quality control.
Quality Control process is very useful and the vital and the lifeblood for all the
manufacturing firms.
Signature of Student
Signature of IP Faculty
Date:
Date:
1. INTRODUCTION
What is Quality Control?
Quality Control is a process of making product or a service to maintain
consistently high standards. It is a special which is followed in manufacturing of
products or service for maintenance of standards of quality of manufactured goods.
In quality control incoming materials are tested to make sure they meet the
appropriate industry specifications. Quality Control is an integral part of the
production process.
The main purpose of quality assurance and quality control (QA/QC) is to identify
and implement sampling and analytical methods and to decrease the errors into
analytical data. Manufactured products and services are tested to determine if they
meet customer’s standards or not.
Quality Control is the process of maintaining standards of incoming or outgoing
products ,not for creating the standards ,it is the vital for all the manufacturing
companies whether pharmaceutical company, logistics assembling company, airconditions/microwave oven assembling company, textile company, electrical
,hardware or software company.
For maintaining the standards they follow statistical process control (use of control
charts), sampling, using of ISO: 9000 standard software, manual verification and
many other modern process. Quality Control not only affects the quality of the
product but also the production cost.
1.
2. HISTORY
In the early 1900s, the beginning of Factory Productions, the final products were
inspected for the purpose of accepting or rejecting the same. During these times, in
his list of basic areas of manufacturing management, F. W. Taylor, emphasized on
quality by including Product Inspection into it. Radford’s was of the view of
involving quality consideration early in the product design stage and also to
connect-together Quality, Productivity and Costs.
In 1924, Walter Shewhart introduced ‘Statistical Process Control (SPC)’ by means
of ‘Control Charts’ in order to keep a control over production. After five years or
so, Dodge & Romig introduced Acceptance Sampling Inspection Tables popularly
known as Dodge-Romig Tables. The concept of SPC found a little acceptance in
the Manufacturing Industry till 1940s.
Historically, Second World War remarkably increased the importance of Quality
Control. W. Edward Deming introduced SQC in Japanese Industry. This resulted
in creation of a quality manufacturing facilities in Japan. The devastated country in
this Second World War posed a tough competition to other leading nations in the
area of manufacturing, especially the American Manufacturing Firms.
After this war, in the mid-twentieth century, professionals and engineers in the
industry hugely benefited by the American Universities in terms of training in
quality control. This has seen the emergence of ‘Quality Assurance’ evolved out of
this development taken place around ‘Quality Control’ concept. At about the same
time, Joseph Juran began his `Cost of Quality’ approach, emphasizing accurate and
complete identification and measurement of Costs of Quality, In the mid 1950s,
Armand Fiegen Baum proposed Total Quality Control which enlarged the focus
of Quality Control from manufacturing to include Product Design.
During the 1960s, the concept of “Zero-defects” gained favor. Philip Crosby, who
was the champion of “Zero defects” concept focused on employee motivation and
awareness. In this decade from 1950 to 1960; quality control and management
became synonymous with the growth of Industrial Revolution in Japan.
In the 1970s, Quality Assurance methods were used in services such as
government operations, health care, banking etc. During this period the world
started importing heavily from Japan including America and European countries.
In the late 1970s, there was a dramatic shift from quality assurance to a Strategic
Approach to quality. The reactive approach of finding and correcting defectives in
products manufactured was changed to a pro-active’ approach of focusing on
preventing defects from recurring altogether. During the same period ‘British
Standards’ (BS 5750) emerged along with ISO 9000 Standards of Quality.
In late 1980s, Total Quality Management (TQM) gained a lot of popularity even
outside Japan and became the main theme revolving around the concept of Quality
Control. In the twenty first century the concept of quality has been gathering a total
or gross approach in terms of ‘Business Excellence’.
3.
3. PROCESS OF QUALITY CONTROL
Quality Control mainly consists of three processes:
According to the
Quality is not only a mindset, but also a formalized system. Through strict
documentations and procedures, our engineers and operators maintain control of
quality throughout every step of production.
Our division of the quality control process into three separate processes ensures
that specialized expertise is applied to each stage of our operation. This system
also provides the redundancy necessary to prevent any quality problem from
evading detection.
4.
3.1. Incoming Quality Control
It is the job of the IQC process to conduct inspections and handle quality problem
before the assembly process starts.
Specific tasks of IQC include:
Perform approved vendor list check;
Evaluate supplier quality records;
Perform sampling of incoming materials based on the MIL-STD-105E
standard;
Assess dimension, visual and functional inspection of material samples;
Monitor quality control chart of inspected properties and alert engineering
staff of significant deviations;
Continuously enhance the IQC process.
5.
3.2. In-Process Quality Control
IPQC process governs the quality systems during the assembly process, to detect
and handle problems that may arise as a result of assembly.
Specific tasks of IPQC include:
Perform inspections on assembled and in-process materials according to
IPC-A-610D standards;
Conduct in-line automated and manual inspections
Apply first-article inspection after process setup;
Utilize statistical control techniques and watch for significant deviations;
Perform in-process audits to ensure processes are up to standard, and to
identify factors needing improvement.
6.
3.3. Outgoing Quality Assurance
OQA is the last process before products ship to customers, and hence is every
important to ensure our shipment is defect-free. Numerous redundancies with IQC
and IPQC is performed here to ensure the validity of previous processes.
Specific tasks of OQA include:
Perform visual and functional inspection;
Verify first-article inspection;
Repeat approved vendor list check;
Apply sampling based on the MIL-STD-105E standard;
Conduct reliability testing;
Submit failure analysis reports and alert engineering staff.
7.
4. PLANNING & IMPLEMENTATION OF
QUALITY CONTROL
4.1. Sampling:
It is also called as statistics. This sampling plays key role in Quality Control
process. Sampling is called as the process of selecting a suitable
sample
for
study
from
the
whole
lot.
4.1.1. Different sampling parts are there. Below are few examples:
1.Single-Sampling-Plan
2.Double-Sampling-Plan
3.Sequential-Sampling-Plan
4.2. Control Charts:
Control Charts are used to tell the difference between normal and abnormal
variations of a process. It explains whether the process is
running smoothly or not. Control Charts are used as a Tool in Data Quality
Improvement process. Control Charts gives indicate at a glance of production
process. Control charts are often referred to as statistical process control (SPC).
4.2.1Common Types of Charts
The types of charts are often classified according to the type of quality
characteristic that they are supposed to monitor: there are quality control charts for
variables and control charts for attributes. Specifically, the following charts are
commonly constructed for controlling variables:

X-bar chart. In this chart the sample means are plotted in order to control
the mean value of a variable (e.g., size of piston rings, strength of materials,
etc.).
8.



R chart. In this chart, the sample ranges are plotted in order to control the
variability of a variable.
S chart. In this chart, the sample standard deviations are plotted in order to
control the variability of a variable.
S**2 chart. In this chart, the sample variances are plotted in order to control
the variability of a variable.
For controlling quality characteristics that represent attributes of the product, the
following charts are commonly constructed:

C chart. In this chart (see example below), we plot the number of defectives
(per batch, per day, per machine, per 100 feet of pipe, etc.). This chart
assumes that defects of the quality attribute are rare, and the control limits in
this chart are computed based on the Poisson distribution (distribution of
rare events).

U chart. In this chart we plot the rate of defectives, that is, the number of
defectives divided by the number of units inspected (the n; e.g., feet of pipe,
number of batches). Unlike the C chart, this chart does not require a constant
number of units, and it can be used, for example, when the batches (samples)
are of different sizes.
Np chart. In this chart, we plot the number of defectives (per batch, per day,
per machine) as in the C chart. However, the control limits in this chart are
not based on the distribution of rare events, but rather on the binomial
distribution. Therefore, this chart should be used if the occurrence of
defectives is not rare (e.g., they occur in more than 5% of the units
inspected). For example, we may use this chart to control the number of
units produced with minor flaws.

9.

P chart. In this chart, we plot the percent of defectives (per batch, per day,
per machine, etc.) as in the U chart. However, the control limits in this chart
are not based on the distribution of rare events but rather on the binomial
distribution (of proportions). Therefore, this chart is most applicable to
situations where the occurrence of defectives is not rare (e.g., we expect the
percent of defectives to be more than 5% of the total number of units
produced).
4.3.Approaches:
*Quality-reviews
* Auto Software assessment and software measurement.
4.4. Real-time quality control:
It is also called as Batch monitoring. It is displayed as displayed as batch control
charts.
4.5. Quality System Elements and Data life Cycle:
4.5.1.Planning:
• Data Quality Objectives (DQOs)
• Quality Assurance Project Plans (QAPPs)
• Standard Operating Procedures (SOPs)
4.5.2.Implementation:
• QAPPs
• SOPs
• Data collection
• Assessments and audits
4.5.3.Assessment:
• Data validation and verification
• Data Quality Assessment (DQA)
10.
4.6.Standards for Digital Elevation Models in Quality Control:
4.6.1.Quality Control tests:
• Accuracy Verification
• Statistical Testing
• Water Bodies
• Hydrographs
• Slopes
• Logical and Physical Format Verification
• Visual Verification
• Editing
4.6.2.Uses of quality control data and control charts:
• Control charts can Measure uncertainty
• Used in method validation
• Used in method comparison
• It can estimate Limit of Detection (LOD)
• Quality Control data can do person comparison or qualification
• These charts can use in evaluation of proficiency tests
• Also used in environmental parameters and similar checks
11.
5. QUALITY CONTROL SOFTWARE
Software Quality Control is the set of procedures used by organizations (1) to
ensure that a software product will meet its quality goals at the best value to the
customer, and (2) to continually improve the organization’s ability to produce
software products in the future.
Software quality control refers to specified functional requirements as well as nonfunctional requirements such as supportability, performance and usability. It also
refers to the ability for software to perform well in unforeseeable scenarios and to
keep a relatively low defect rate.
These specified procedures and outlined requirements leads to the idea of
Verification and Validation and software testing.
5.1. Software Control Methods






Rome laboratory Software framework
Goal Question Metric Paradigm
Risk Management Model
The Plan-Do-Check-Action Model of Quality Control
Total Software Quality Control
Spiral Model Of Software Development
5.2. Verification and Validation of Methods



Independent Verification and Validation
Requirements Verification Matrix
Software Quality Assurance
5.3. Testing



Unit testing
Integration testing
System testing
12.
5.4. Software used in pharmacy for QC:
MultiQC
Medical Laboratory
Quality Control Software
MultiQC is a Windows software application for quality control in clinical
chemistry laboratories. It was created by a chemical pathologist disappointed by
the heterogeneity, the inappropriate design and the poor efficiency of the
ancillary QC programmes supplied with analyzers or LIS. The daily investigation of
issues arising at the workbench combined with the great improvements in
industrial QC for the last three decades led to MultiQC, a programme which
provides technicians with a comprehensive control panel to monitor and improve
the quality of analytical processes.
QC driven by capability index
The aim of QC in clinical chemistry is to keep analytical uncertainty within
medical tolerance at the lowest cost. The difficulty of the job is reliant on the
relative extents of tolerance and uncertainty intervals. The capability index is the
ratio of the former to the latter. It decides on the best way to perform QC :
- Low capability methods: Quality control is essential to keep
them
in-control.
- High capability methods: They may perform out-of-control
and produce however acceptable results. An acceptance chart
(pdf file 130 KB) should be preferred because it saves time and
cuts
costs
in
comparison
to
control
charts.
- Incapable methods: They must be improved or discarded.
12.
Up-to-date statistical tools
In addition to Shewhart (Levy and Jennings) charts, MultiQC
implements more recent statistical tools:
- Exponentially weighted moving average
(EWMA)
to
monitor
the
bias
- Exponentially weighted moving variance
(EWMV)
to
monitor
the
imprecision
- Multivariate process control (Hotel ling’s T2) to
monitor
multi-level
quality
control.
- Calibration charts, a new approach to the analysis
of QC data.
MultiQC disregards the old "Westgard rules" formerly brought in as a standard for
clinical laboratories but which turned out to be totally inappropriate to today's
analyzers. Read: Misconceptions in medical laboratory quality control (pdf file 610
KB).
Versatile QC methods and parameter modes
QC methods
Parameter modes
Non
statistical
1) Control intervals specified by the reagent maker
2) Statistics estimated from a reference pool
3) Specified statistics
Univariate
Multivariate
Acceptance
4) Learning mode when a new analyte is started
5) Semi-learnig mode when a new lot of control materials
is started
Plotting QC data and EQA returns on the same chart
MultiQC plots external quality assessment (EQA) returns
superimposed on the same charts as QC data. The position of the lab
in comparison to the peer group is thus made permanently visible by
the involved staff for an easy and complete follow-up of the
analytical process (precision and trueness).
A built-in management for changes of QC material batches
When it is necessary to switch to a new lot of QC material, control charts
can be temporarily duplicated to keep the analytical method under control of
the older batch while a reference pool is collected for the newer batch.
Monitoring the time of QC
MultiQC can monitor whether QC assays have
been performed in compliance with a timetable
specific to each analyte. When QC assays have
been missed the program opens a warning
window which displays a list of the missing
tests and sends out a customizable music.
Continuous method validation
MultiQC provides tools to continuously evaluate the performance characteristics of
each analytical method that it controls. A unique feature is that performance is
always related to medically allowed error. The resulting data and plots are stored
within QC charts as "analytical events", easy to consult to troubleshoot out-ofcontrol situations or bad EQA returns.
6. QUALITY IMPROVEMENT
In today's competitive market place, there is a need for business organizations to
ensure continual improvement. Manufacturing companies experience growing
pressure to improve quality, increase productivity, and reduce cost with limited
resources. Service organizations need to reduce response time, eliminate errors,
and improve customer satisfaction.
Though system certifications such as ISO9001:2008 could bring in some degree of
discipline and quality improvement in organizations, it is not sufficient to address
the real challenges.
We need to take a closer look at the manufacturing and service processes and
deploy suitable techniques to enhance process capability. While 'percentage
defects' is a thing of the past, achieving PPM (parts per million) defect levels is the
challenge before today's managers.
Now comes the question of HOW?
It is true that there are many approaches suggested by various quality experts.
Corporations all over the world have been experimenting with one approach after
the other, with little or no success. In this context, choosing a feasible path has
become very important.
Many top management personnel are not aware that relatively simple techniques
like SPC and EPC can be put to use to achieve quantum jumps in quality
improvement and cost reduction.
17.
6.1. Statistical Process Control (SPC):
SPC is a time-tested and effective control scheme used for process capability
analysis and process monitoring. SPC techniques consist mainly of Pareto
Analysis, Scatter Diagram and Regression Analysis, and Statistical Control Charts.
6.1.1. Pareto Analysis
It is perhaps the most useful tool in the early stages of quality improvement
initiatives. It can be deployed to identify the vital few and screen out trivial many.
Let us look at the following data on defect counts, taken from the inspection log of
a garment manufacturing unit:
Date
Day
Production
(Pcs)
Improper
Stitch
Missing
Button
Length
Mismatch
Reverse
Buckle
Total
05/07
Mon
200
12
5
2
1
20
06/07
Tue
250
8
10
5
0
23
07/07
Wed
200
7
9
3
2
21
08/07
Thu
150
4
6
2
1
13
09/07
Fri
350
12
15
5
2
34
10/07
Sat
250
10
12
4
0
26
What is the general conclusion?
As the production increases, proportionately more defects are reported on those
days. Is this justification sufficient if you are looking for defect reduction? Let us
summarize the same data in a different way, as shown below (Pareto Table):
Weekly Total
% Total
Cum. %
Missing Button
57
41.61
41.61
Improper Stitch
53
38.69
80.30
Defect
Length Mismatch
21
15.33
95.63
Reverse Buckle
6
4.37
100.00
137
100.00
-
TOTAL
Now, what is the conclusion?
Missing buttons and improper stitches contribute 80% of total defects. If the
corrective and preventive actions can be focused on elimination of the root causes
of these two dominant defects from the process, we can easily achieve a significant
reduction in overall defect tally.
Pareto Analysis can be effectively utilized for...




Machine down time analysis
Dominant fault analysis
Floor rejection analysis
Customer complaint analysis etc.
6.1.2. Scatter Diagram and Regression Analysis
They are very useful in the study of inter-relationship between a key process output
variable (KPOV) and a key process input variable (KPIV). If there is a significant
relation between the two, the process output can be controlled effectively by
controlling the process input.
There are many practical situations where measurement of product quality is not
easy. For example, in case of mechanical properties of heat-treated steel, by the
time the product is cooled, sample is taken and tested, a lot of production could
have already happened. In case the test piece fails, you have already generated
huge pile of scrap.
In such situations, it is worthwhile to explore whether the product quality (Y) can
be controlled by controlling one/more process parameters (X's).
Consider the following data:
Sl. No.
Str. Rate
(X)
Imp. % (Y)
.
Sl. No.
Str. Rate
(X)
Imp. % (Y)
1
16
7.1
.
11
36
16.4
2
18
8.0
.
12
38
15.5
3
20
8.4
.
13
40
18.9
4
22
9.5
.
14
42
18.5
5
24
11.8
.
15
44
20.6
6
26
10.4
.
16
46
19.8
7
28
13.3
.
17
48
21.7
8
30
14.8
.
18
50
22.8
9
32
13.2
.
19
52
23.6
10
34
14.7
.
20
54
25.4
The scatter diagram and regression line fo
this set of data shall be as below:
We can predict the value of Product Characteristic (Y) for various values of
Process Characteristic (X) using the following equation:
Y = -0.5011 + 0.4635 X
6.1.3. Control Charts
Statistical Process Control charts (or simply, SPC charts) are used for monitoring
the process performance and process variations. These charts may be constructed
for monitoring of process parameter or product characteristic.
A control chart differs from an ordinary chart in the following aspects:



Control chart has a centre line depicting the average process performance.
It has two control lines, namely, Lower Control Limit (LCL) and Upper
Control Limit (UCL). The control limits are calculated on the basis of
natural (short- term) variations in the process.
When a plotted point falls within the control limits, no action needs to be
taken. But, any point falling outside the control limits requires further
investigation / process adjustment. Control charts can be constructed for
both the variable (say, diameter) and attribute (say, surface defects) data.
The most commonly used variable control charts are the X-Moving Range chart,
Xbar-Range chart, and Xbar-Sigma chart.
Widely used attribute control charts are the p-chart, np-chart, c-chart and u-chart.
Let us consider the following data on weight of tablet, taken from a pharmaceutical
company:
Upper Specification Limit (USL) = 1.1 gram
Lower Specification Limit (LSL) = 0.9 gram; Target Value (T) = 1.0 gram
Sl. No.
Time
Weight
(Grams)
.
Sl. No.
Time
Weight
(Grams)
1
06:00
1.05
.
11
11:00
1.06
2
06:30
1.02
.
12
11:30
1.09
3
07:00
1.06
.
13
12:00
1.01
4
07:30
1.09
.
14
12:30
1.00
5
08:00
1.05
.
15
13:00
0.99
6
08:30
1.01
.
16
13:30
0.96
7
09:00
1.08
.
17
14:00
1.00
8
09:30
1.10
.
18
14:30
0.99
9
10:00
1.06
.
19
15:00
1.02
10
10:30
1.02
.
20
15:30
1.04
For this data, XMoving
Range
chart is most
appropriate. Let
us see the chart
drawn by the SPC
software.
In this case, all the data points are within control limits. Therefore, no process
stoppage / adjustment are required to eliminate any assignable cause of variation.
Now let us see the capability statistics.
Process Potential Index (Cp) : 1.0991
Process Capability Ratio (Cr) : 0.9099
Process Performance Index (Cpk) : 0.7144
Taguchi's Index (Cpm) : 0.7198
As Cp > 1, the process has the potential to just meet the product specifications.
However Cpk < 1 indicates that the process is off-centered, i.e., the overall process
average is not at the target. Under the assumption of Normal Distribution of data,
the expected defective tablets (in this case, over weight tablets) are 16056 per
million produced (or 1.6 %).
6.2. Engineering Process Control (EPC):
Engineering Process Control (EPC) is fast gaining popularity these days. While
SPC charts provide a good check against assignable causes of variation, EPC
charts can be used for prediction and run-on-run adjustment of process average.
Consider the data on weight of tablets discussed earlier. You may ask two
interesting questions.
What would be the process average at 16:00 Hrs?
What amount of adjustment is required NOW to bring the process average to target
value?
Process mean at 16:00 Hrs (predicted)= 1.01823
Process adjustment required now (at 15:30 Hrs) = -0.016
6.3. Six Sigma:
Six Sigma is a business initiative first introduced by Motorola in early 1990s.
Recent Six Sigma success stories come from companies like General Electric,
Allied Signal, and Sony etc. According to GE's 1997 annual report, Six Sigma
initiatives contributed more than 300 million US Dollars!
In general, Six Sigma implementation involves the following SEVEN phases:
1. DEFINE the processes that contribute to the problem.
2. MEASURE the capability of critical processes.
3. ANALYSE the data.
4. IMPROVE the key product / service characteristics.
5. CONTROL the key process variables.
6. STANDARDISE the methods for best-in-class process performance, and
7. INTEGRATE the standard methods and processes with the product / service
design stage.
The Six Sigma strategy involves extensive use of statistical techniques such as
control charts, design of experiments, response surface methodology etc. in order
to minimize process variations and product / service defects. These techniques
need to be applied in a structured manner.
While reporting the process improvement, Six Sigma teams use certain numeric
values, known as Six Sigma Metrics. The most common metrics are 'Defects per
Million Opportunities (DPMO)', 'Sigma Quality Level', and 'Yield'.
'Defects per Million Opportunities (DPMO)' is the number of critical defects that
the process is estimated to generate per million opportunities (operations or steps).
In shop-floor process control, this is also called defective 'Parts per Million (PPM)'
pieces produced by a single process / operation.
24.
'Sigma Quality Level' is an indicator of process centering and, process variation
viz-a-viz technical tolerance. A process at six sigma quality level is expected to
generate only 3.4 defective Parts per Million.
'Yield' is the estimated percentage of defect-free items (probability of zero defects)
churned out by a process.
Based on the quality characteristic under study (variable / attribute data type), one
or more metrics may be used for process monitoring and reporting.
It may be noted that the six sigma metrics are just the indicators of process quality.
Sustaining and improving the process performance require process monitoring and
control schemes such as Statistical Process Control (SPC), Engineering Process
Control (EPC) etc.
Six Sigma initiatives aim at reduction of process variations and defects. SPC and
EPC are two important techniques for achieving these goals. Relatively
inexpensive and easy to understand (requiring minimal support from external
experts), it is a feasible proposition to implement these techniques in any
organization.
25.
7. QUALITY CONTROL IN
PHARMACEUTICAL COMPANY
7.1. Brief introduction about pharmaceutical company:
The pharmaceutical industry develops, produces, and markets drugs licensed for
use as medications. Pharmaceutical companies can deal in generic and/or brand
medications. They are subject to a variety of laws and regulations regarding the
patenting, testing and marketing of drugs.
The earliest drugstores date back to the middle Ages. The first known drugstore
was opened by Arabian pharmacists in Baghdad in 754 and many more soon began
operating throughout the medieval Islamic world and eventually medieval Europe.
By the 19th century, many of the drug stores in Europe and North America had
eventually developed into larger pharmaceutical companies.
Most of today's major pharmaceutical companies were founded in the late 19th and
early 20th centuries. Key discoveries of the 1920s and 1930s, such as insulin and
penicillin, became mass-manufactured and distributed. Switzerland, Germany and
Italy had particularly strong industries, with the UK, US, Belgium and the
Netherlands following suit.
 Multinational leaders in pharmaceutical companies:
Novartis Switzerland, Pfizer USA, Bayer Germany, GlaxoSmithKline UK,
Johnson and Johnson USA, Sanofi-Aventis France, Hoffmann-La Roche
Switzerland, AstraZeneca UK/Sweden, Merck & Co. USA, Abbott Laboratories
USA, Wyeth USA , Bristol-Myers Squibb USA, Eli Lilly and Company USA,
Amgen USA, Boehringer-Ingelheim Germany, Schering-Plough USA, Baxter
International USA, Takeda Pharmaceutical Co. Japan, Genentech USA ,Procter &
Gamble USA .
26.
7.2. Quality Control management:
Pharmaceutical companies of all sizes outsource at least some quality control (QC)
testing to contract analytical testing laboratories. Virtual and smaller companies
may not have the staff to conduct such testing; whereas mid- to large-size
companies may outsource testing that they do not wish to perform in-house. In the
relationship between a pharmaceutical company and its outsourcing partner, each
partner has clearly delineated responsibilities, both business and compliance
related. Focus on those of the contractor (contract acceptor), limiting the attention
to responsibilities in the contractee–contractor relationship. Neither discussion
addresses purely business-related concerns such as revenue growth, development
and retention of staff, and shareholder reward.
7.2.1. Quality control management in pharmaceutical companies:
The pharmaceutical sector is a highly regulated sector. This is totally justified as
medicines can either save someone’s life or make matters worse if proper
information about the medical ingredients and their effects are not known. Hence,
quality
plays
a
huge
role
in
this
industry.
Quality control essentially deals with designing and producing products as well as
services in a way that they either meet or exceed the requirements of the customer.
Failure testing and assurance in design as well as production are two important
activities of quality control programs.
Pharmaceutical industries have to adhere to a number of quality standards and
practices for being able to sell medicines in the market. Quality systems like good
laboratory practice (GLP), good manufacture practice (GMP) and good clinical
practice (GCP) are specific to pharmaceutical industry regulations. The principles
of these systems primarily cover the procedure quality aspects on a formal basis
and not much evaluation is done of the technical aspects. Technical performance
gets covered only when a company gets these accreditations. The research and
development requirements for satisfying these regulations are different from one
country to another, with the strictest of them being in Europe.
27.
7.2.2.Quality-Assurance:
This covers providing evidence for supporting the claim that quality has been
established in work, product or service. For this purpose, suitable standard
operating procedures (SOP’s) are to be introduced for defining a standardized
procedure of doing operations in an effective manner. This ensures adherence to
maximum efficiency and safety requirements of the clinical research activities that
have been performed. Such defined procedural information assures auditors and
regulatory inspectors of requirements adherence. These SOPs should be
sufficiently proliferated amongst all the individuals involved in the procedure and
proper training should be provided to them. Such planned implementation of
procedures is in complete concordance with the basic PDCA cycle of quality
control that asks quality implementers to plan, perform, measure and take
necessary actions as per the measured data. For this purpose, SOPs are tailored for
clinical, pre-clinical, pharmacokinetics, bio-analysis, regulatory affairs, data
management, drug safety, project management, vendor management, supply chain
management,
change
control
and
crisis
management.
7.2.3.Failure-Testing:
Pharmaceutical research involves trying various combinations of ingredients and
individually gauging the effectiveness of each combination. As can be seen, such
combinations can run into millions and hence failure testing becomes an important
part of quality control in pharmaceutical companies. For instance, even if two
ingredients are involved in a medicine, their relative composition can be varied to
the extent limited by available measuring technology, so that the ideal combination
can be found out. Advanced quality techniques like Design of Experiments are
extensively employed in this regard. They greatly reduce the effort involved in the
testing process and the results provided by them are definitely more reliable than
human
analyzed
results.
Pharmaceutical industries must comply with the highest quality regulations for
winning accreditation and acceptance in the global market. Quality control is an
extremely effective tool in this regard.
28.
7.3. Quality control at
pharmaceutical limited:
They have a modern and well-equipped Quality Control (QC) laboratory, which
ensures that our products are pure, safe and effective and are released only after
thorough analysis as per stringent specifications, methods and procedures
developed according to international guidelines viz. EU CGMP, MHRA, WHO,
TGA,
etc.
Their QC Lab is a one of its kind laboratory and amongst the first few pharmacy
companies to get the NABL accreditation. It is also the first in India
Pharmaceutical industry to successfully pass the re-accreditation by NABL.
Our QC department has all necessary instruments for analysis of API, finished
products,
packaging
and
related
materials
used.
29.
The QC department performs following activities:




RM/PM analysis
Finished Products analysis
In-process Checks
Stability Studies
The QC activities are managed through four sections:




Instrumental Analysis and Finished Products
Wet Analysis Laboratory
Microbiological Testing Laboratory
Packaging Material -Testing Laboratory
30.
Quality Control for API / PM, Finished Products & In-Process Control is as
follows:
Flow Chart - RM / PM Inspection:
Flow Chart - Finished Products Inspections ::
31.
Flow Chart - In process Checks:
32.
7.4. Quality Control at Aglowmed Pharmacy:
Quality Policy / Processes
Aglowmed's has multi location manufacturing facilities at Ankleshwar (Gujarat),
Daman & Roorkee (Uttarakhand) for strategic convenience. The state-of-the-art
manufacturing facilities are WHO GMP certified where all products are
manufactured under strict quality control and GMP (good manufacturing practices)
conditions. The plant is maintained in clean and hygienic state with every area
having stipulated access controls and contamination safeguards. Quality control
department is fully equipped with sophisticated instruments such as H.P.L.C. / IR
& UV Spectrophotometers, etc
Their full-fledged quality control department and expert, technically qualified
scientists ensure that strict quality control is maintained and medicines are
produced meticulously, which guarantees excellent quality and effectiveness. A
separate quality assurance department has been set up to constantly supervise
each and every production stage and to ensure CGMP compliance as well as inhouse standard operating procedures. The quality control department has been
equipped with all latest instruments like HPLC, Gas Chromatography,
Spectrophotometer, Humidity chambers etc., for complete in-house quality
control. All Reference Standards are provided from the respective Pharmacopeia
Conventions like USP, BP, and IP etc., to ensure strict compliance during
analysis. Calibration and Validation of all instruments are carried out at regular
intervals to ensure reproductively of the results obtained.
.
33.
7.5. Technologies used in pharmacy for controlling the quality:
7.5.1. Recording Powder Flow meters (RPF) have received relatively little
attention in the literature. This paper reports on a modified RPF which utilizes a
Metter PR-1200 electronic balance rather than the traditional specifically
fabricated flow meter. The versatility of this modification is discussed as a cost
effective modification. This modification provides for the use of RPF's in many
areas of pharmaceutical technology. The reproducibility of RPF data with its
sensitivity to processing and formulation variables has been investigated.
Suggestions for the application of RPF data in quality control, reformulations and
other research areas of pharmaceutical manufacturing.
7.5.2. As a leading pharmaceuticals company, Taisho is constantly striving for strict
quality assurance and working to deliver world standard products for the safety of
consumers.
In 1980, Taisho formulated its Good Manufacturing Practice (GMP), a set of group
wide standards for manufacturing and quality assurance, and immediately set about
establishing
a
standards-compliant
manufacturing
structure.
Taisho established its Production Technology Laboratories in April 1999, with the
aim of conducting specialized research in comprehensive technologies relating to
prescription pharmaceuticals. In December 2001, the Company stepped up efforts
to bolster quality maintenance and at the same time enhanced efficiencies by
consolidating the quality assurance and manufacturing technology functions. As
OTC medicines are products comprised of a various number of ingredients,
consumer safety is paramount. To meet this need, Taisho has installed the most
comprehensive testing equipment used in Japan's pharmaceuticals industry. Other
measures adopted by the Company include testing for the presence or absence of
minute faults in product containers through the use of X-ray irradiation.
Taisho also introduced Supply Chain Management (SCM) at all manufacturing
bases with the aim of quickly ensuring high-quality products and stable supply. We
have positioned speed and efficiency in the manufacturing process as a key
priority, linking manufacturing, sales and logistics to the information network.
In an environment characterized by legislative reform and the anticipated benefits
from Taisho Toyama Pharmaceutical as it becomes fully operational, it is
imperative that we continue to increase our lineup of prescription drugs.
The Company has acquired ISO 14001, the international standard for
environmental management systems. 34.
7.5.3. Different lab equipments & their functions in different companies:
Alpha Biotech:
The Art Robbins Phoenix protein crystallography dispenser features accuracy,
speed and precision.
The Cobra 148 is a range of non-contact nano dispensers with dispense volumes of
50nl-5ml.
Brandel plate sealers offer a quick and easy way to seal plates of almost any size.
Emerald Bio-Systems’ micro capillary protein crystallization system, the MPCS
Plug Maker™, uses technology that generates up to 800 experiments in one Crystal
Card.
35.
Climet-Instruments-Company:
Climet’s microbial samplers are specially designed to minimize particle emissions.
CI-3100 series remote particle sensors detect microscopic particles in controlled
environments and critical processes.
Climet is the world's leading manufacturer of application-driven optical particle
counting instruments.
Dr.Schleuniger-Pharmatron:
8M: universal tablet tester for manually fed measuring of weight, thickness,
diameter and hardness.
36.
8M: versatile with quick-change system for all tablet types and shapes.
8M hardness tester extended with modular HS8 handling system for semiautomatic measuring.
AUTOTEST 4: fully automatic measuring of weight, thickness, diameter and
hardness.
AUTOTEST 4: reliable processing and testing of all tablet types including round,
capsules and oblong shapes.
37.
Ellab :
The TrackSense® Pro Sky system is used for real-time process monitoring.
E-Val Flex is Ellab’s cable-based system used for thermal validation.
ETI is a temperature-indicating device and can replace MIG thermometers.
Ellab’s TrackSense® Pro high-temperature sensors are ideal for monitoring
processes with extreme heat such as depyrogenation.
38.
Testo :
The Testo Saver is data logging system offers an extensive range of radio and
Ethernet probes, making it a truly flexible temperature and humidity monitoring
solution for a wide range of pharmaceutical applications.
The 810 is ideal for air and surface temperature infrared measurement with 1-point
laser spot marking and 6:1 optics.
The Testo 720 is a high accuracy thermometer for use as a reference standard
against working instruments in a wide range of laboratory and pharmaceutical
applications.
With a memory of up to 16,000 readings, the Testo temperature data logger range
is the ideal solution for continuous monitoring of air temperatures.
The Testo 206 pH meters with built-in temperature probes are versatile, easy-touse instruments for fast measurement checks on liquids.
Micromeritics
Micromeritics offers an extensive line of particle characterization instruments for
use in fundamental research, product development, quality assurance, quality
control, production, and process control applications.
Micromeritics manufacturers a wide variety of particle size analyzers, surface area
analyzers, porosimeters and pycnometers.
Micromeritics' confirm 21 CFR Part 11 software assists with compliance to FDA
regulations. Combined with Micromeritics' IQ and OQ services, the user can be
assured that the system is validated for accuracy, reliability, consistent intended
performance, and provides safeguards to protect the integrity of analysis records.
Micromeritics Pharmaceutical Services - particle size analysis, particle shape
analysis, surface area analysis, pore size, pore volume, micropore volume, density,
water adsorption, surface energy, methods development, methods validation, and
much more.
Micromeritics provides superior sales, service, and applications support to the
global pharmaceutical marketplace through seven direct offices and a
representative network covering 65 countries.
Metrohm :
The Titrando - the intelligent titrator without reliability gaps.
The 797 VA Comp trace is a modern measuring stand that
allows voltammetric and polarographic determinations to be performed.
41.
850 Professional IC with 858 Professional Sample Processor: an intelligent ion
chromatography system for parallel determination of anions and cations in
pharmaceutical products.
Labtech Machinery
SP-25 single-punch tablet press, output: 3,600tablets/hr.
MRP-10B mini rotary press, output: 18,000tablets/hr.
The CM-30 tablet coating machine ensures that an even coating is created.
42.
M-Tech Diagnostics:
The Pro Line challenge device is inserted into the mid-point of tubing run to
demonstrate sterilization conditions have been achieved during the cycle.
Nanoceram® Alumina filters are available in flat membrane, syringe, capsule and
pleated membrane cartridge format.
Whirl-Pak® sample bags provide a quick, reliable and convenient way to collect
liquid, semi-solid and solid samples.
43.
Mesa Laboratories
The Data-Trace MPIII system offers a wide variety of logger’s ideal for real-time
process monitoring and validation.
The Data-Trace Radio Frequency (RF) system allows for wireless and accurate
data collection, monitoring and validation during pharmaceutical processes.
The DTRF software package allows for fast and easy set-up, allowing for up to 250
loggers of any model or type to be synched together for accurate and efficient
reporting.
Mesa Laboratories' thermal barriers can be utilized with the Data Trace MPIII
loggers for monitoring processes involving extreme heat.
44.
JASCO
The P-2000 is designed to be a customizable polarimeter which can be equipped
with various options to handle a researcher’s initial requirements and budget.
Unparalleled optical performance and optionally available measurement modes are
combined in a manner to make the J-815 true "chiro-optical spectroscopy
workbench".
The NRS-3000 Series of bench top, singly dispersive micro-Raman spectrometers
are based on JASCO's proven technology.
The model DT-810 Dissolution Tester is fully automated and designed for
flexibility to provide dissolution testing of up to 8 samples.
45.
7.5.4. Process Analytical Technologies (PAT):
The term "Process Analytical Technologies (PAT)" has been used to describe "a
system for designing and controlling manufacturing through timely measurements
(i.e. during processing) of critical quality and performance attributes for raw and
in-process materials and also processes with the goal of ensuring final product
quality". The PAT initiative focuses on building quality into the product and
manufacturing processes, as well as continuous process improvement.
Process analytical technology (PAT) is one of the objectives contained in the
Initiative for Pharmaceutical CGMPs for the 21st Century published by the Food
and Drug Administration (FDA). In a few words and according to the FDA’s
guideline, PAT can be defined as a system for designing, analyzing, and
controlling pharmaceutical Manufacturing through the measurement of critical
quality and performance parameters.
PAT encourages technological innovation, specifically the adoption of new
analytical techniques by the pharmaceutical industry designed to improve the
understanding and control of manufacturing processes. Both the FDA and industry
experts expect benefits over conventional manufacturing practices: higher final
product quality, increased production efficiency, decreased operating Costs, better
process capacity, and fewer rejects. Correspondingly, fundamental Changes are
also expected within the regulatory framework.
The future of pharmaceutical production will require innovative technological
approaches and more science - based processes. PAT will boost collaboration
between research and development(R & D) and manufacturing departments inside
companies and increase overall efficiency. Approvals and inspections will
increasingly focus on scientific and engineering principles. As a result, regulators
will set higher expectations for new products from the outset. One purpose of PAT
is to provide a motivating framework to bring quality into a product from the
outset. It is thus essential for it to be involved in the R & D phase.
A typical illustration of a PAT approach to quality improvement is the use of Near
Infrared Spectroscopy (NIRS) to qualify percipients and active pharmaceutical
ingredients just before they enter the production process, E.g. for example, in
dispensing, near - infrared (NIR) spectra are informative about product structure
and overall quality. Because with substances such as percipients the quality range
was investigated at some time in the past and fixed into a calibration, NIR
Measurement can provide simultaneous non-destructive confirmation of the
predominant physical and chemical parameters. This is an effective method of
reducing uncertainties about possible causes of failure or poor quality during
production.
The goal of the PAT-oriented approach is to continue to ensure patient health by
the availability of safe, effective, and affordable medicines. A key driver of PAT
comes from the regulatory side, where the FDA recognized that its traditional
approvals procedures were actually hindering manufacturing innovation.
PAT may have an impact on qualification profiles in respect to scientific data
analysis, statistics, process control, etc. Similar to implementing Six Sigma,
implementing a PAT program may require dedicated training.
The implementation of process analytical technology (PAT) is occurring in what is
perhaps the most exciting period of change in pharmaceutical manufacturing of the
past three decades. A host of technological, regulatory, and market forces have
converged during the last five years, yielding new opportunities for innovation in
the development and operation of pharmaceutical production processes. A major
driving force for change is the Food and Drug Administration (FDA) initiative to
implement a modern, risk based framework for regulation and oversight of
pharmaceutical manufacturing.
Cost control, resulting partly through more efficient production processes, and
partly through the minimization of the necessity of final discard (or reprocessing)
at the QA final test point, is an important justification for exploring PAT.
47.
8. CONCLUSION
Quality Control is an important part of any manufacturing operation. It is the
lifeblood and vital for all manufacturing process. Quality control process affects
both production cost n the product quality.
The quality control statement of the company is to produce and distribute defect
free products, reduce waste, reduce variation in the manufacturing process, and
establish policies and procedures that will provide for continuous improvement of
its products and services.
In many companies there are quality control departments which controls the
production cost, quality of product, employee’s efficiency and daily work. Many
companies are hiring employees as a quality control manager, supervisor etc. So
the scope of jobs in this department is very good.
Nowadays there is much software for quality controlling is available in the market
which reduces the manual work of control process and companies are using them
frequently.
Pharmaceutical companies are using many advanced technologies for improving
the quality of product and they are recruiting many staves for controlling and
assuring the quality of the product.
48.
9. LIST OF REFERENCES

















This article incorporates public domain material from the General Services
Administration document "Federal Standard 1037C" (in support of MILSTD-188).
Godfrey, A. B., Juran's Quality Handbook, 1999. ISBN 007034003.
Pyzdek, T., Quality Engineering Handbook, 2003. ISBN 0824746147.
Clapp, Judith A, Software Quality Control, Error Analysis, and Testing,
1995 William Andrew In.
http://www.sqa.net/softwarequalitycontrol.html
Wesselius, Jacco, "Some Elementary Questions on Software Quality
Control"
http://satc.gsfc.nasa.gov/assure/agbsec5.txt
Deming, W E (1975) "On probability as a basis for action." The American
Statistician. 29(4), pp146–152
Deming, W E (1982) Out of the Crisis: Quality, Productivity and
Competitive Position ISBN 0-521-30553-5.
Mandel, B J (1969). "The Regression Control Chart" Journal of Quality
Technology. 1 (1), pp 1–9.
Oakland, J (2002) Statistical Process Control ISBN 0-7506-5766-9.
Shewhart, W A (1931) Economic Control of Quality of Manufactured
Product ISBN 0-87389-076-0.
Shewhart, W A (1939) Statistical Method from the Viewpoint of Quality
Control ISBN 0-486-65232-7.
Wheeler, D J (2000) Normality and the Process-Behavior Chart ISBN 0945320-56-6.
Wheeler, D J & Chambers, D S (1992) Understanding Statistical Process
Control ISBN 0-945320-13-2.
Guidance for Industry. PAT — A Framework for Innovative Pharmaceutical
Development,
Manufacturing,
and
Quality
Assurance.
http://www.fda.gov/cder/guidance/6419fnl.htm
Weinberg Sandy “Process Analytical Technology-An Emergent Biomedical
Regulatory Methodology”.
vii.
10. BIBLIOGRAPHY
1. www.google.com
2. www.aglowmed.com
3. www.novartispharma.com
4. www.torrentpharma.com
5. www.statsoft.com/textbook/qualitycontrolchart.htm
6. www.rajeshtimane.com
7. www.multiqc.com
8. http://www.globalqualityvillage.com/spc.php
9. http://en.wikipedia.org/wiki/Talk:Software_quality_control
10. www.aristopharamaltd.com
11. www.lotsofessays.com
12. http://www.allbusiness.com/34709451.html?query=quality+control&x=0&y=0
13. http://www.indiamart.com/company/1943270/aboutus.html
14.
http://www.sgs.com/search.htm?query=quality+control&lob=&x=10&y=4&q=qua
lity%20control
15. www.pharmainfo.net/pat
viii.