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 9

4.3. Approaches 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 11

4.6.1. Quality Control tests

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 13

6. Quality Improvement 17

6.1. Statistical Process Control (SPC) 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. INTRODUCTIONWhat 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, air-conditions/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 non-functional 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

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5.4. Software used in pharmacy for QC:

MultiQC Medical LaboratoryQuality 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).

   

 

   

QC methods Parameter modes

Non statistical

1) Control intervals specified by the reagent maker

Univariate 2) Statistics estimated from a reference pool

Versatile QC methods and parameter modes

MultivariateAcceptance

3) Specified statistics

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.

 

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-of-control situations or bad EQA returns.

Continuous method validation

 

 

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  DayProduction

(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):

 Defect  Weekly Total % Total  Cum. %

Missing Button 57 41.61 41.61

Improper Stitch 53 38.69 80.30

Length Mismatch 21 15.33 95.63

Reverse Buckle 6 4.37 100.00

TOTAL 137 100.00 -

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

For this data, X-Moving Range chart is most appropriate. Let us see the chart drawn by the SPC

 Sl. No.  TimeWeight (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

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 .

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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.

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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.

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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

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Quality Control for API / PM, Finished Products & In-Process Control is as follows:

Flow Chart - RM / PM Inspection:

Flow Chart - Finished Products Inspections ::

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Flow Chart - In process Checks:

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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 in-house 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.

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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.

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8M: versatile with quick-change system for all tablet types and shapes.

8M hardness tester extended with modular HS8 handling system for semi-automatic 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.

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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.

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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-to-use 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.

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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.

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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.

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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.

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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.

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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.

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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.

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9. LIST OF REFERENCES

This article incorporates public domain material from the General Services Administration document "Federal Standard 1037C" (in support of MIL-STD-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 0-

945320-56-6. 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”.

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1. www.google.com

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15. www.pharmainfo.net/pat

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