e_journal (1).pdf

JOURNAL OF ENGINEERING, ICT & MANAGEMENT 2015

ISSN 2230-9756 GNIOTCOLLEGE OF MANAGEMENT Page 1

ISSN 2230-9756

GNIOT COLLEGE OF MANAGEMENT

ISSUE-8 January-March, 2015

JOURNAL OF

ENGINEERING,

ICT & MANAGEMENT

GNIOT GROUP OF INSTITUTIONS

_____________________________________________________

*GREATER NOIDA INSTITUTE OF TECHNOLOGY

*GNIOT-MANAGEMENT SCHOOL

* GNIOT COLLEGE OF MANAGEMENT



ISSUE- 1

January, 2015

PATRONS

SHRI KISHAN LAL GUPTA - CHAIRMAN

SHRI BISHAN LAL GUPTA -VICE CHAIRMAN

SHRI GAURAV GUPTA - MEMBER OF MANAGEMENT

SHRI DEEPAK GUPTA - MEMBER OF MANAGEMENT

EDITOR IN - CHIEF

PROF. (DR) ANSHUL SHARMA

PROF. (DR) SAVITA MOHAN

MANAGING EDITOR

MR VIVEK SRIVASTAVA

EDITORIAL MEMBERS

DR HIMANSHU MITTAL

MR PRASHANT DEV YADAV

MS NEHA SHARMA



Being the Editor In chief of the GNIOT College of Management, E journal it gives me

great pleasure to bring to you this issue. It was quite inspiring to watch and witness

the potential of our students unfolding at various stages and situations each day. It is

designed to present to its readers the various events, methods and applications related

with new developments and perspectives in the field of management and business.

With a sense of pride and satisfaction I would like to say that with the active support

of the management, faculty and students, this journal has come alive. With all the

efforts and contributions put in by the students, I hope the journal will bring creative

talents of the students of the institute.

Congratulations to the editorial team for their determined efforts in bringing out this

journal.

Dr.Anshul Sharma

(Editor In Chief)



Dear Readers,

I feel privileged in presenting the third issue of our institute e-journal. I would like to

place on record my gratitude and heartfelt thanks to all those who have contributed to

make this effort a success. My special thanks are to Dr Sunjay Yadav ,Director and

Dr.Anshul Sharma, Head of Department- for their guidance which enabling me to

bring out this volume. It is my moral duty to thank him for giving support and

encouragement and a free hand in this endeavor. The journal also showcases the

talents of our faculty members and students. With a sense of pride and satisfaction I

would like to say that with the active support of the management, faculty and

students, e magazine has come alive .With all the efforts and contributions put in by

the students, I truly hope that the pages that follow will make some interesting

reading.

Last but not the least I am thankful to all the authors who have send their articles and

readers who made this journal so popular.

VIVEK SRIVASTAVA

MANAGING EDITOR



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TQM Tools for Gain Competitive Edge

1. Dr. K.K.Garg, Lingayas Lalita Devi Institute of management & Sciences, Mandi, New Delhi-110047

Email:- [email protected]

2. Prof Jivan Choudhary, Lingayas University, Faridabad Email: - [email protected]

3. Mr Pranav Mishra, 1., Lingayas Lalita Devi Institute of management & Sciences, Mandi, New Delhi-110047

Emai.:- [email protected]

ABSTRACT

Purpose- The increasing competition is motivating the Indian automobile industry to implement

Total Quality Management (TQM) by using a wide variety of Tools of TQM. Purpose of this

paper is to review Tools Focus on Total Quality Management (TQM) Framework in Indian

Automobile Industry.

Design/Methodology/Approach- In this paper, an effort is made for the analysis covering a sample

of 35 organizations from Indian automobile industry in a case study mode.

Finding- The Survey findings indicate that quality scenario in the Indian automobile industry is

improving and automobile manufacturer (OEMs) & Suppliers (Tier-I) are more focusing on

Tools of TQM practices and sub-contractors (Tier-II) is not responding as the changing needs of

market.

Keyword(s): Total Quality Management, Tools, Competitive Edge

INTRODUCTION

The automobile industry is one of the core industries in Indian economy, whose prospect is reflective of

the economic resilience of the country. With 4 percent contribution to the GDP and nearly 5 percent of

the total industrial output, the automotive industry has become a significant contribution to the exchequer.

Continuous economic liberalization over the years by government of India has resulted in making India as

one of the prime business destination for many global automotive players. The Indian automobile industry

comprises of the automobile and auto component industry. India is the largest three-wheeler market and

second largest two wheeler market in the world and is the fourth largest and fastest growing passenger

car market in Asia. India is also the second largest producer of motorcycles in the world.

The TQM concept refers to company-wide quality assurance from supplier to customer using system

approach of documented sets of procedures and control of process variability in a team sprit with top

management commitment. No system can work with people who are not interested in their work; Primary

task of organizations is to motivate people to work. An organization that wants to build capability for

superior performance must recognize that people are its core strength. It is the peoples motivation,

commitment, creativity and teamwork that provide strength and spirit for performance. People should be

able to work in an atmosphere that fosters self motivation, self-confidence and self-assessment for

learning and improvement. These are keys in getting the people of the organization involved in the

mailto:[email protected]:[email protected]:[email protected]



process of Total Quality Management. Tools for TQM play an important role in the analysis whether it is

statistical or not. The use of tools is the more visible evidence of TQM.

RESEARCH METHODOLOGY

To obtain an insight on the awareness of Cost reduction programmed through TQM practices in the

Indian Automobile Industry, a sample of 35 respondents (05 automobile manufacturers, 20 suppliers, and

10 sub-contractors) were obtained while 200 organizations were requested to participate in the study. The

study indicated a comparative level of awareness and practice of Tools for Total Quality Management in

the Automobile Industry. The questionnaire was checked for reliability and validity by expert and

practiceners. The questionnaire was validated by sample data from original equipment manufacturers

(OEMs), suppliers (Tier-I), sub-contractors (Tier-II). In the global scenario, the automobile components

manufacturing companies are classified as Tier-I and Tier-II suppliers. The Tier-I Suppliers are those

which supply components to the original equipment manufacturers (OEMs). Tier-II suppliers are those

which supply components to Tier-I suppliers. The survey reported here was conducted from March, 2013

to May, 2013 and was restricted to companies located in NCR region of India.

Survey questionnaire, so designed, was validated with a pilot survey. The modifications were made in

order to get the required and necessary information from the different organizations under the purview of

the survey. The survey questionnaire was also designed to survey the applicability of quality tools in the

Indian automobile sector. The respondents are specifically requested to ignore their personal feelings and

use their best judgment on a five-point scale. The identified groups of respondents from the selected units

were contacted either personally or through telephone taking care that the sample represented different

departments and managerial levels. The process of administering the questionnaires was clearly

communicated personally and in writing or over telephone as applicable. It was emphasized that absolute

privacy of an individuals responses would be maintained. The respondents were also requested to only

respond to the statements keeping in view how things actually were in their organizations and not to

respond by giving their individual preferences or by comparing their organization of some ideal state.

Accordingly, a survey has been conducted in the automobile sector of the Indian organizations. The

automobile sector has been divided into three categories i.e. automobile manufacturers, suppliers and sub-

contractors. The 1st tier vendors have been termed as suppliers. In accordance with the nomenclature

prevailing in ISO 9000:1994 and QS 9000:1998, we have considered sub-contractor as a term to identify

suppliers supplier.

Tools for TQM

Ishikawa proposed seven elemental (Q-7) tools based on statistical techniques. These are:

(i) Histograms

A Histogram is a graphical representation of the variation in a set of data. These known as Bar-charts.

These displays category-wise measures of the numbers as vertical or horizontal bars. It shows the

frequency or number of observation of a particular value or within a specified group. The histogram can

show whether or not the distribution is normal by plotting the estimates of such distribution.



(ii) Scatter Diagrams

The scatter diagram was developed so that intuitive and qualitative conclusions could be drawn about the

paired data or variables. A scatter diagram is composed of a horizontal axis containing the measured

values of one variable (independent, i.e. cause) and a vertical axis, representing the measurements of the

other variable (dependent, i.e. effect). These diagrams provide a way to study the relationship of one

variable with another which is useful for specific requirements such as product design, such as the

percentage of an ingredient in an alloy, or the number of employees errors and overtime worked. The

resulting scatter diagram could, for example, provide useful information for material selection for design

purpose.

(iii) Stratification

Interpreting data can be a creative process. There are always instances of data which seem to define a

rational explanation. Office equipments which work perfectly well one day may not work at all, the next

day. Often, a suitable stratification will be obvious to the people who have experience with a product,

process and materials and situations. So, stratifications are a method of analysis of data by grouping it in

different ways.

(iv) Pareto Analysis

Wilfred Pareto was an Italian economist who discovered a universal relationship between value and

quantity. Pareto analysis helps in identification of Vital few from the trivial many at a glance and

provides help in selecting directions for improvements. Using the Pareto principle to priorities can help

managers achieve a focus strategy, namely to focus on key revenue earning products.

Pareto analysis is a prioritization technique that identifies the most significant items among many. This

technique implies that about 80% of the problems or effects are produced by about 20% of the causes.

Pareto analysis (sometimes referred to as the 80/20 rule and as ABC analysis) is a method of classifying

items. Events or activities are according to their relative importance. It is frequently used in inventory

management where it is used to classify stock items into groups based on the total annual expenditure for

or total stockholding cost of each item. Organizations can concentrate more detailed attention on the high

value/important items. Pareto analysis is used to arrive at this prioritization.

(v) Check-Sheets

A Check sheet is an aid used in assembling and compiling data concerning a problem. It is used to collect

data on a process in order to determine whether any unusual or unwanted elements are present. The

functions of a Check sheet are Production process distribution check, Defective item check, Defect

location check, Defect cause check.

(vi) Cause and Effect (C & E) Diagrams/Ishikawa Diagrams/Fishbone Diagram

A Cause-and-Effect diagram is simply a graphical representation of an outline that presents a chain of

causes and effects. The effect is the quality characteristics, which need improvements. Causes are

sometimes broken down into the major causes include Material, Machines, Measurement, Methods and

Manpower (5Ms).



Cause and Effect diagrams are a problem solving technique, developed in 1943 by Ishikawa in Japan as a

result of workers being confused by the number of factors which influence a process and, thus, finding it

difficult to solve process-related problems. The diagram is developed after brainstorming by identifying a

problem to be solved (i.e. effect) and the likely causes. The effect or incident being investigated is shown

at the end of a horizontal arrow. Potential causes are then shown as labeled arrows entering the main

cause arrow. Each arrow may have other arrows entering it as the principal factors or causes are reduced

to their sub causes and sub-sub-causes by brainstorming. A useful variant of this is negative

brainstorming and cause and effect analysis. Here, the group brainstorms all the things that would need to

be done to ensure a negative outcome, thereby, identifying potential road blocks and marking it easier to

dismantle these. Another variation of C & E diagrams was developed by Sumitomo Electric and is known

as Cause-and-Effect Diagram with Addition of Cards (CEDAC). The effect side of CEDAC is a

quantified description of the problem with a visually quantified target and continually updated results on

progress of achieving it. The cause-side uses two differently colored cards for writing facts and

improvement ideas, placed after duly initialled by people submitting the cards, on left and right side of

the spines respectively in the C & E diagram, and regularly updated. It could be used in analyzing high

scrap levels, quality problems, material waste, etc. The technique has got team spirit with creativity

involved.

The most useful tool for identifying the causes of problems is a Cause-and-Effect diagram, also known as

a Fishbone or Ishikawa diagram (after Kaoru Ishikawa, a Japanese quality expert, who first developed this

tool).

(vii) Control Charts

Material and Supplies Workers

Problem:

Dents

in a part

Machine Methods

Lack of training

Improper Handling Uneven surface on

Coming Material

Poorly Laid Out Work

Procedure

Improve Alignment of

Tools

Conveyers

Drop Parts Too

Far

Machine Part-Wear

and Tear

Fig.1: C&E Fishbone diagram



The backbone of Statistical Process Control (SPC) is control charts; these were first proposed by walter

shewhart in 1924. This gives a clear visual display that quickly tells when a process is out of control.

Control charts are important and display the result of statistical process control measures which depict

whether product samples conform to specified limits or tolerances. A control chart displays a central

horizontal line usually corresponds to the average value of the quality characteristics being measured.

Two other horizontal lines represent the upper and lower control limits.

Most frequently used control charts, e.g. those used in automobile and aerospace industry are simple run

charts where data is plotted against time or sample number. For variables, mean (X) and range (R) charts

are used and number defective (np) or defects (c) charts are used for attributes. Proportion defective (p)

charts are also quite commonly used for attributes. However, Cumulative Sum (CUSUM) charts offer

powerful management tool for detection of small changes in attributes and variables, especially in

chemical industry.

NEW Q-7 TOOLS

The new Q-7 tools for quality improvement are as follows:

(i)Affinity Diagram

Affinity diagram (sometimes, referred to as a KJ diagram, after the initials of the person who created this

technique, Jiro Kawakita) is a special kind of brainstorming tool. Affinity diagram is used to gather large

amounts of ideas, opinions or issues and group those items that are naturally related and, for each

grouping, a single concept ties the group together. So it is group-decision making technique designed to

sort a large number of ideas, process variables, concepts and opinions into naturally related groups.

(ii) Inter-Relationship Diagraph

Inter-relationship diagraph is a graphical representation of all the factors in a complicated problem,

system or situation. It is typically used in conjunction with one of the other quality tools, particularly, the

affinity diagram. Frequently, the header-cards from the affinity diagram are used as the starting point for

the interrelationship diagraph. Inter-relationship diagraph is a tool for finding causes to a problem. It not

only clarifies the relationship between cause and effect but also between the various causes.

(a) Identifying key or driver issues from a list of important issues.

(b) Identifying the most important problems for solving when the number of problems exceeds the

resources available to solve all of them.

(c) Identifying the root cause of existing problems.

(d) Identifying key factors needed to make a decision when there is insufficient information available

to make a data-driven decision.

(iii)Tree Diagram

Tree diagram is a tool used to generate the ideas for developing a list of alternative solutions to a problem.

A team, when faced with a problem, first uses a cause-and-effect diagram or inter-relationship diagraph to

determine the causes. After identifying the major causes for the problem, it collects data to confirm the

causes that contribute most towards the problem. This information helps the team generate ideas for



solving the problem. Tree diagram is a tool which helps the team to do so, effectively. As we know, for

inter-relationship diagraph, one starts with the problem and develops a list of alternatives causes for the

problem. For creating tree diagram, one has to start with the solution or the desired outcomes and develop

a list of means to achieve the set objectives.

(iv) Matrix Diagram

A Matrix Diagram is a tool that is used to systematically organize information that must be compared on a

variety of characteristics in order to make a comparison, selection or choice. It is a tool which depicts the

relations between two sets of factors in the form of a table or a matrix. Matrix diagram is, sometimes,

referred to as a quality table and is the starting point in building a house of quality.

(v)Matrix Data Analysis

Matrix data analysis is the only tool among the New Seven which uses numerical data and produces

numerical results. It is, somewhat, similar to a matrix diagram with a difference that numerical data is

used instead of symbols indicating the existence and strength of relationship. With numerical data

replacing the symbols, matrix data analysis is now really a table and some people prefer to call the tool as

matrix data analysis method. The technique is used in a graphical manner in Principal Component

Analysis where only two characteristics can be studied, at a time.

(vi)Process Decision Programmed Chart (PDPC)

Process decision programmed chart is to map out conceivable and likely events and contingencies that

can occur in any implementation plan along with appropriate and reasonable counter-measures. It is a

planning tool. It forces proactive thinking on what can go wrong with ones plan and what would one do

to overcome the effect of such adverse occurrences. The tool helps to anticipate undesirable occurrences

and enables one to prepare with plans to neutralize their effect. It encourages negative thinking with a

view to plan for achieving ones goals in spite of obstacles in ones path. Instead of thinking positively

like, Do not worry: everything will be fine and being surprised and shocked when something goes

wrong, the tool encourages thinking of the worst that can happen and prepares one for it.

(vii)Arrow Diagram

An arrow diagram is a simplified kind of critical path analysis, especially developed for scheduling

activities-particularly assembly operations. It is another term for a PERT or CPM chart. It is a graphic

description of the sequential steps that must be completed before a project can be completed. It is

essentially a planning tool that determines the critical path of a project or a process. Arrow diagram is a

flow chart of the process or the project with few differences. In an arrow diagram, event nodes are

stages which denote the completion of a step or a number of steps.

The line connection the event-nodes represent the steps in the process. The time at each individual step is

used to calculate the time by which an event can be accomplished, at the earliest, and also the time by

which it must be accomplished, at latest, to complete the process in time. It can also be used for assembly-

operations.

OTHER TQM TOOLS ARE FOLLOWING:-



1. Benchmarking Its a systematic and continual measurement process, comparing the

performance on ones organization process against competition/business leaders available anywhere in

the World to gain information, which in turn help the organization to improve its performance. The

purpose of Benchmarking is to improve products and processes. Delivery and services to meet the

customer needs. The connection between business process and customer needs is critical to efficient

Benchmarking. Benchmarking consists of measuring performance against best practices/performance

studying how they achieve performance levels and using the information, adopting them to their

environment to achieve breakthrough performance.

2. Quality Function Deployment (QFD) The concept of QFD was developed in the early 1970 in

Japan. QFD is a systematic and organized approach of taking customer needs and demands into

consideration while designing new products and services or while improving the existing products and

services. QFD is a powerful tool for use within a TQM program. The term QFD represents the overall

concept that provides a means of translating customer requirements into the appropriate technical

requirement for each stage of product development and production. Thus QFD is both a philosophy and a

set of planning and communication tools that focuses on customer requirements in coordinating the

design, manufacturing and marketing of goods.

3. Failure Mode and Effect Analysis (FMEA)

To remain competitive it is very important not only to identify present causes of failure but also to

identify potential causes of failure during design and process stage itself. FMEA helps in building the

prevention while planning through designing and process stage.

4. Fish Bone Diagram

Today, market needs are rapidly becoming more diversified and sophisticated, technical innovations are

arriving on the scene at a bewildering pace, and competition is becoming more and more ferocious. To

ride out these successive waves of change, every company now urgently requires people with a superior

capacity for solving problems. Fish Bone Diagram, which was developed in 1943 by Ishikawa in Japan as

a problem solving technique is being used worldwide.

5. Brainstorming

Brainstorming is an idea generating technique pioneered by Alex Osborn. Worldwide organizations are

using this tool to encourage team working and group discussion and progressively build on the collective

wisdom in a non threading, non criticizing atmosphere.

6. Run Chart

Run Chart displays the trend of changes of a characteristic over a period of time It is specialized graph,

which uses connected lines instead of bars to illustrate data. Decision based on facts and figures is very

important for effectiveness of any organizations.

7. Suggestion Scheme:

In order to improve the performance of the organization, the organization should consider employees as

their most valuable assets. Globally there is a trend to improve the involvement of the employees through

Suggestion Scheme. It has been an established fact that through involvement of people, organization can

get better results.

8. Six Sigma:



A critical part of six sigma work is to define and measure variation with the intent of discovering its

causes and to develop efficient operational means to control and reduce the variation. The expected

outcomes of six sigma efforts are faster and more robust product development, more efficient and capable

manufacturing processes, and more confident overall business performance (Sanders and Hid, 2000). In

order to reduce the variation to a very low level, the first step is to design for productivity. This means

that designers configure a product in such manner that its performance is shielded against variation. By

doing this, the organization can be sure that its specified levels; i.e., all of the product will be on target

with minimum difference between units of product.

Table 1: Sigma conversion table

Quality Level

(Yield)

Defects per million

Opportunities(DPMO)

Sigma Cost of poor

Quality(%of

Sales)

Types of

Companies

30.9 690,000 1.0 >40 Non-

Competitive

69.2 308,000 2.0 30-40 Industry

Average 93.3 66,800 3.0 20-30

99.4 6,210 4.0 15020

World Class 99.98 320 5.0 10-15

99.9997 3.4 6.0



Fig 2: PDCA Cycle

10. PERT/CPM: In the context of TQM, many global organizations are using PERT/CPM achieve

customer deadline and to complete a complex task with the best available approach

11. Statistical Process Control (SPC) SPC is used to separate out random variation (often called

common cause or non-assignable variation) from the real variation caused by changes to the process.

Control charts allow decisions to be made about processes on the basis of fact rather than gut feeling

12. Kaizen Activities: Kaizen is a Japanese term meaning Change for the Better; the concept

implies a continuous improvement in all company functions at all level. It is found that the number of

companies engaged in Kaizen activities through quality circles are about 97% in Japan and Korea.

13. Why Why Analysis: Why Why Analysis helps in the identification of root causes of the

problem for taking effective corrective and preventive action.

14. Design Of Experiment (DOE): Design Of Experiment (DOE) is a technique for the optimization

of products or processes. Taguchi involves a two stage experimental design that gives the benefits of

robustness and efficiency with the minimum number of experiments. Experimental design usually

involves attempt to optimize a process, which can involve several factors (e.g., temperature, time,

chemical composition) at several levels (e.g., five possible temperatures, four possible times, six possible

chemical composition).World class organizations are very effectively using this tool particularly when

these organizations are working for Six Sigma .

15. Total Productive Maintenance (TPM): Total Productive Maintenance (TPM) was introduced in

Japan by Seiichi Nakazima. TPM first took root in the automobile industry and rapidly became the part of

the corporate culture in companies such as Toyota, Nissam, and Mazda, and their suppliers and affiliates.

Today, organizations are using TPM to achieve startling results, particularly in reducing equipment

breakdowns, minimizing idling and minor stoppages, reducing quality defects and claims, boosting

productivity, trimming labor and cost, shrinking inventory, cutting accidents and promoting employee

involvement.

16. 5S Activities: 5S is foundation of the majority of improvement activities. Ever growing customer

demands for quality product is forcing organizations to rethink about the workplaces. This phenomenon

has been understood as product improvements cannot be done in filthy organizations and people cannot

work to their maximum potential under such dismal environment. Therefore, 5S has gained worldwide

importance. This is one of major reasons, despite 62%j of automobile sector claim to be following TQM

philosophy, why organizations have not been able to sustain improvements activities.

17. Business Process Re-engineering (BPR): The definition of BPR is the fundamental rethinking

and radical redesign of business process to achieve dramatic improvements in business performance

.Worldwide organizations are using BPR as an innovative tool for dramatic improvement. American

automotive industry, which was in depression in early 1980s got benefited through this technique. Ford

motor achieved a 75% reduction in head count in their accounts payable departments, Reengineering

should be deployed when a need exists for heavy blasting



RESULTS & DISCUSSION

On the basis of the feedback received from the respondents the following data were observed in the three

category viz. automobile manufacturing category, Supplier category and Sub Contractor category.

Quality Tools

Usage of the Quality

Tools

Overall

Usage

Automobile

manufacturer Category Supplier Category

Sub Contractor

Category

5S Category 74% 35% 5% 39

Pareto Analysis 78% 80% 33% 70

Benchmarking Tools 70% 28% None 33

TPM 62% 15% None 24

DOE 37% 14% None 17

Why-Why Analysis 70% 57% None 48

Control Charts 87% 72% None 61

Kaizen Activities 65% 35% 5% 36

Scatter Plots 45% 30% None 28

SPC 70% 69% 5% 56

PERT/CPM 28% 18% None 17

PDCA Circle 87% 45% 15% 49

BPR 23% 15% None 14

Six-Sigma 10% None None 3

Suggestion Scheme 95% 69% None 61

Run Chart 95% 55% 25% 59

Brainstorming 78% 58% 25% 56

Fish Bone Diagrams 78% 58% 25% 56

QFD 37% 26% None 23

FMEA 25% 20% None 17



Histogram 70% 54% 10% 49

Table 2: Overall usage of tools for TQM

These are clearly shown category wise in the following

Figure

74%

78%

70%

62%

37%

70%

87%

65%

45%

70%

28%

87%

23%

10%

95% 95%

78% 78%

37%

25%

70%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

5S C

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Why

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Scat

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PDCA

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Six-

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enta

ge

Fig.3: Quality Tools Used in Automobile Manufacturers

35%

80%

28%

15% 14%

57%

72%

35%

30%

69%

18%

45%

15%

69%

55%58% 58%

26%

20%

54%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

5S C

atego

ry

Paret

o Ana

lysis

Benc

hmark

ing To

ols TPM

DOE

Why

-Why

Ana

lysis

Contr

ol Ch

arts

Kaize

n Acti

vities

Scatt

er Plo

tsSP

C

PERT

/CPM

PDCA

Circ

leBP

R

Six-S

igma

Sugg

estio

n Sch

eme

Run C

hart

Brain

storm

ing

Fish B

one D

iagram

sQF

DFM

EA

Histo

gram

Quality Tools

Perc

enta

ge

Fig. 4: Quality Tools used in Suppliers Category



5%

33%

5% 5%

15%

25% 25% 25%

10%

0%

5%

10%

15%

20%

25%

30%

35%

5S C

ateg

ory

Pare

to A

nalys

is

Benc

hmar

king

Tools TP

MDO

E

Why

-Why

Ana

lysis

Cont

rol C

harts

Kaize

n Ac

tivitie

s

Scat

ter P

lots

SPC

PERT

/CPM

PDCA

Circ

leBP

R

Six-

Sigm

a

Sugg

estio

n Sc

hem

e

Run

Char

t

Brain

storm

ing

Fish

Bon

e Di

agra

ms

QFD

FMEA

Histo

gram

Quality Tools

Perc

enta

ge

Fig. 5: Quality Tools used in Sub-contractors Category

In the automobile sector Fish Bone Diagram, Brainstorming, Run Chart and Pareto Diagram are very

popular and are being used for solving quality related problems where as the TQM tools of lesser use are

Histogram, FMEA, QFD, Suggestion Scheme, Six Sigma, BPR, PDCA, PERT/CPM, SPC, Scatter Plot,

Kaizen activities, Control Chart, Why-Why Analysis, DOE, TPM, Benchmarking and 5S activities, which

are also very essential for continuous improvement and better housekeeping. The overall usage of quality

tools by the Indian automobile sector is shown in the Fig.

0

10

20

30

40

50

60

70

80

Pare

to A

nalys

is

Cont

rol C

harts

Sugg

estio

n Sc

hem

e

Run

Char

tSP

C

Brai

nsto

rmin

g

Fish

Bon

e Di

agra

ms

PDCA

Circ

le

Hist

ogra

m

Why

-Why

Ana

lysis

5S C

ateg

ory

Kaize

n Ac

tivitie

s

Benc

hmar

king

Tool

s

Scat

ter P

lots

TPM

QFD

DOE

PERT

/CPM

FMEA BP

R

Six-

Sigm

a

Quality Tools

Per

cen

tag

e

29

34

2928

27 27 27

24 2423

19

1716

13

1211

8 8 87

2

Fig. 6: Overall applicability by the Indian automobile industry



The overall applicability of various quality tools by the respondent organizations of the Indian automobile

sector have been summarized in Table

Table3: Summary of overall applicability of Quality Tools in Indian Automobile Industry.

Respondent

Organization

Applicability of Quality Tools No. of quality tools

used by respondent

organizations

60-70% Pareto Diagram, Control charts and Suggestion Schemes 3

50-60% Fish Bone Diagram, Brainstorming, Run Chart, Suggestion

Scheme, SPC, Control Chart and Pareto Diagram and 5S

activities.

7

40-50% Histogram, Fish Bone Diagram, Brainstorming, Run Chart,

Suggestion Scheme, PDCA, SPC, Control Chart, Why-Why

Analysis, and Pareto Diagram.

10

30-40% Histogram, Fish Bone Diagram, Brainstorming, Run Chart,

Suggestion Scheme, PDCA, SPC, Control Chart, Kaizen

Activities, Why-Why Analysis, Benchmarking, and Pareto

Diagram and 5S activities.

13

20-30% Histogram, QFD, Fish Bone Diagram, TPM, Brainstorming,

Run Chart, Suggestion Scheme, PDCA, SPC, Scatter Plot,

Kaizen Activities, Control Chart, Why-Why Analysis,

Benchmarking, and Pareto Diagram and 5S activities.

16

10-20% Histogram, FMEA, QFD, Fish Bone Diagram, Brainstorming,

Run Chart, Suggestion Scheme, BPR, PDCA, SPC, Scatter

Plot, Kaizen Activities, Control Chart, Why-Why Analysis,

DOE, PERT/CPM, TPM, Benchmarking, and Pareto Diagram

and 5S activities.

20



respectively. However, the least understood tool is Six Sigma, which is being used in less than 10% of the

organizations.

Conclusion

To be gain competitive edge it is very important that Indian automobile Industry must focus for the

implementation tools for TQM. For effective process control it is very important to use Histogram but

only 48% of the responding Indian automobile organizations claim to be using Histogram as a quality

tool. Overall there is a need for improving process control for getting better results using this vital quality

tool. Only 18% of the Indian automobile Industry is using FMEA as a tool for assessing potential failure

for investigation, which is very low. The usage of QFD in the Indian automobile sector is very low as

only 24% of the organizations are focusing on QFD.. About 59% of the Indian automobile organizations

are using Fish Bone Diagram as vital tool for solving quality related problems, which is still quite low.

Indian automobile Industry has understood the vitality of this tool and 59% of the organizations are using

this tool. In the surveyed organizations about 63% of the organizations are using this chart to understand

the trends and effects of various countermeasures being implemented in the plant. Though 56% of

organizations claim to have suggestion scheme in their organizations, still the organizations have not

been able to make full use of the potential of all employees. This is evident with the results of

organization having rejections more than 4% as discussed before. For the Indian automobile Industry, 6-

sigma implementation is not well adopted as a tool for making continuous improvement. Only 5% of

respondent organizations claim to follow 6-sigma tool. That is the reason why Indian automobile sector

in general have not been able to achieve consistent level of quality based on + - Sigma, which means that

the parts falling outside the normal process range will be around 2700 parts-per- million (2700ppm)

where as rejections in automobile sector is more than 4%(40,000ppm).Motorola Corporation that received

Malcolm Baldrige National Quality Award (MBNQA) in 1988 based its major efforts on 6-Sigma.

Though 55% of the Indian automobile sectors claim to be following PDCA circles the earlier result of

higher rejection level and few organizations seriously focusing on cost reduction speaks to the contrary.

The result of the survey indicates that the Indian automobiles sector has not effectively used the potential

of this tool, which is reflected in the fact that only 19% of the organizations are using this tool. That is

one of the reasons why most Indian projects undergo in over runs. Though 53% of the Indian automobile

organizations claim to be using SPC, however the higher average rejection rate of 4% (40,000 ppm) does

not corroborate the same. In our survey, only 28% of the organizations claim to be using Scatter Diagram.

Though 40% of the Indian automobile organization claim to follow Kaizen activities, yet effective

participation in quality circles is almost negligible considering the vast population of the country.

Therefore, to be globally competitive it is essential for the Indian automobile sector to create conducive

atmosphere where everyone is involved in Kaizen activities individually or through quality circles. Only

56% of the surveyed Indian automobile sector claim to follow control charts. The low percentage is

mainly due to extremely poor use of this tool by sub-contractor of the automobile sector. The high

rejection level of the organizations does not corroborate the effective use of this vital tool. The survey

indicates that 46% of the Indian automobile sector claim to follow why-Why Analysis. Majority of the

organizations are working only on symptoms that are the reason why problems keep on repeating. Overall

there is a need for using this effective tool for getting better results. However, by and large the Indian

automobile sector has not understood the utility of DOE and only 20% of the organizations have

responded that they are using this tool. Still Indian automobile sector has not understood the utility of



TPM and only 29% of the organizations claim to be using TPM .To achieve the world class results, the

Indian automobile sector has to focus on TPM much more vigorously. However, the Indian automobile

sector has not effectively used this tool. Only 35% of the organization are using benchmarking tool to

improve the performance of the organizations. The survey indicates that 66% of the Indian automobile

organizations are using this tool. To be competitive globally, Indian automobile organizations have to

prevent the problems from recurring. Organizations are required to do root cause analysis to prevent the

problems from recurring. This is also confirmed by the low percentage of Indian automobile

organizations focusing on 5S activities. As can be seen form Figure 4.7, only 43% of the organizations are

using 5S activities. However, in the Indian automobile sector only 16% of the organizations is using this

tool to achieve higher performance results). If Indian automobile sector wants to be globally competitive

it must focus on this vital tool.

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NPA: MANAGEMENT OF NATIONLISED BANKS

*Mr Deepak, Assistant Professor, Gniot Group of Institution, Greater Noida

**Mr Vivek Srivastava, Associate Professor, Gniot Group of Institution, Greater Noida

**Mr Mridul Dharwal, Faculty, Sharda University, Greater Noida

**Dr M L Maurya, Professor and Head, Bundelkhand University, Jhansi

Abstract

As far as the present scenario is concerned, the banking industry is in a transition phase. The

Public Sector Banks, which are the foundation of the Indian banking system account for more

than 78 per cent of total banking industry assets. Unfortunately they are burdened with excessive

Non Performing Assets (NPA), massive manpower and lack of modern technology. It is also

dangerous for banks profitability credibility and economics of scale. Narasimham Committee

report recommended various guidelines to RBI in 1993 to identify and reduce of NPAs be treated

as national priority.NPA indicated the bankers credit risks and efficiency of allocation of

sources. The Financial reforms have helped largely to reduce NPA in Indian Bank Industry. This

paper attempts to analyze the performance of different banks. To compare the performance of

public sector, private sector and foreign banks selective indicators were taken into

considerations. These Indicators were Gross NPAs and Gross Advances.

Keywords

Issue and Challenges for Indian Banking Industry, Impact and Causes of NPAs, Co-relation

between Gross NPAs & Gross Advances.

Introduction

The Indian banking industry regulated by the Banking Regulation Act of India, 1949.It can be

divided by two categories .One is scheduled banks and other is non scheduled banks.

Scheduled Banks consisted commercial banks and the co-operative banks. Commercial Banks

comprise nationalized banks, The SBI & its associated banks, foreign banks, regional rural banks

and the old/new private sector banks on the basis of ownership.

Indian Banking has a long period history which has evolved over many years passing through

different various phases .It has undergone note worthy transformation following financial sector

reforms but at present ,it is passing through a decision phase .Indian Bank adopt international

best practices in regulation and supervision of money market. So, It create a strong competitive

and vibrant banking system into due prudential norms .It means to allow entry of new private

sector banks and foreign banks, access the capital market permission , flexibility in operational



work and financial autonomy to public banks ,improve to corporate governance practices and

maintain standards practices. Banks have diversified our role into non-traditional activities and

result comes in the form of conglomerate .Therefore, deregulation has opened up new height for

banks to augment income.

Issue and Challenges for Indian Banking Industry

The Indian Banking System faces a series of reform over the last years in respect of deregulation

of interest rates, the role of government increases in public sector banks and the increased

participation of private sector banks. New foreign banks are very crazy to cover all Indian

customer financial market because of Indian Public and Private bank have not capable to tap the

domestic market.

There are several challenges for Indian banks in a competitive environment as follows:-

Non Performing Assets (NPAs)

Risk Management & Basel II

Consolidation

Overseas Expansion

Technology

Government Reforms

Skilled Manpower

Consumer Protection

Meaning of NPA

An asset called NPA when the borrower fails to repay the interest and/or principal amount on

agreed terms. It means stop to generate income for the bank. A NPA treated as past due to

amount in respect of credit facility in terms of interest and /or installment or principal amount for

two quarters or more. The past due means the amount has not been paid within 30 days from the

due date .This concepts comes with effect from 31 march 2001.

Impact of NPAs on Banking Operation



The efficiency of bank is reflected the size of its balance sheet but the level of return on its assets

is also important factor .The NPAs adversely affected to generate interest income but as parallel

banks are required to give some provision for NPAs from their current profits.

The NPAs have blasting impact on the return on assets in the following ways:-

1. The capital adequate ratio is disturbed and cost of capital will go up.

2. The Economic Value Addition (EVA) by banks gets upset.

3. The current profits of banks are eroded.

4. The interest income of banks reduced due to non-receipt basis.

5. Banks profitability is affected adversely because of the provision of doubtful debts and

consequent write off as bad debts.

6. Return on Investment (ROI) is reduced.

7. The assets and liability mismatch will widen.

8. It limits recycling of the funds.

Causes for mounting NPAs

The NPAs in PSBs are increasing by external and internal factors. PSBs are facing more problem

than the private and foreign banks. Directed loans system is one of the main causes of NPAs in

which commercial banks are required to give 40 percentage of their credit to priority fields.

Directed loans system issue the loan to the micro sector like sick or weak units. The another

important sources of NPAs is poverty elevation programs like IRDP, RREP,SUME,JRY,PMRY

etc. failed due to some reasons in fulfilling their objectives. The maximum amount of loan

granted under these schemes .These amount was totally unrecoverable by political manipulation,

misuse of funds and wrong target audience of these sections. Some of the important reasons for

NPA, mentioned in various literatures are summarized below:

1. Inadequate support from RBI/NABARD in meeting the fund-needs of these banks.

2. Corruption, nepotism, favoritism and Undue intervention by political bigwigs.

3. Improper, ineffective proposal appraisal system and inefficient credit risk management.



4. Direct lending under subsidy schemes and Lack of proper pre-appraisal and follow up.

5. Un-sound financial condition of the borrower and improper selection of borrowers/activities.

6. Inadequate working capital leading to operational issues under financing/untimely financing.

7. Delay in completing the project and Diversion of funds for expansion\modernization\setting

up new projects\ helping or promoting sister concerns.

8. In-ability of the corporate to raise capital through the issue of equity or other debt instrument

from capital markets.

9. Business failures to identify problems in advance.

10. Deficiencies on the part of the banks viz. in credit appraisal, monitoring and follow-ups,

delay in settlement of payments\ subsidiaries by government bodies etc.

Guidelines for minimizing NPAs:

Banks are required to follow certain guidelines to minimize the non-performing assets. These are

listed below:

1. Use of proper internal systems: Banks should establish appropriate internal systems to

eliminate delays or postponement in the identification of NPAs, especially for high value

accounts.

2. Classification of accounts with short-term deficiencies: The classification of an asset

as a NPA should be based on the record of recovery. Banks should not classify an

advance account as an NPA simply due to the existence of some deficiencies, which are

temporary in nature, such as non-availability of adequate drawing power based on latest

stock.

3. Asset classification should be borrower-wise and not facility wise: It is difficult to

predict a situation where only one asset of a borrower becomes a problem with respect to

recovery. Therefore, all the assets loaned by a bank to a borrower have to be treated as

NPAs, and not a particular asset.

4. Advances under consortium arrangements: Asset classification of advances taken

from consortiums should be based on the record of recovery of the individual member

banks, as well as other aspects that have a bearing on the recoverability of these

advances.

5. Agricultural advances: In respect of advances granted for agricultural purposes, if the

interest or installment of principal remains unpaid for two harvest seasons after it

becomes due, this advance is to be treated as a NPA. When natural calamities impair the

repaying capacity of agricultural borrowers, banks may convert a short-term production



loan into a term loan, or re-schedule the repayment period as a relief measure. In this

case, the term loan as well as fresh short-term loan may be treated as current dues and

need not be classified as a NPA.

6. Restructuring/rescheduling of loans: A standard asset, where the terms of the loan

arrangement regarding interest and principal have been renegotiated or rescheduled after

the commencement of production, should be treated as a sub-standard asset and should

remain in that category for at least one year of satisfactory performance under the

renegotiated or restructured terms. In case of sub-standard and doubtful assets also,

rescheduling does not entitle a bank to upgrade the quality of advances automatically,

unless there is satisfactory performance under the rescheduled/renegotiated terms.

Procedures for NPA identification and resolution in India

Since a high number of NPAs dampens the performance of banks, it is important to identify

potential problem accounts and monitor them closely. Though most banks have Early Warning

Systems (EWS) for identification of potential NPAs, the actual processes followed differ from

bank to bank. The EWS enables a bank to identify the borrower ac-counts that show signs of

credit deterioration and initiate remedial action. Many banks have evolved and adopted an

elaborate EWS, which allows them to identify potential distress signals and plan their options

beforehand, accordingly.

The early warning signals indicate potential problems in the ac-counts, which include persistent

irregularity in accounts and delays in servicing of interest payments. In addition, some of these

banks review their exposure to borrower accounts every quarter based on published data, which

serves as an important additional warning system. These early warning signals used by banks

are generally independent of the risk rating systems and asset classification norms prescribed by

RBI.

The major components or processes of the EWS followed by banks in India, as brought out by

a study conducted by Reserve Bank of India at the insistence of the Board of Financial

Supervision, are as follows:

1. Appointing a relationship manager/credit officer for monitoring accounts

2. Preparation of know your client profiles

3. Following a credit rating system

4. Identification of watch list /special mention category accounts

5. Monitoring of early warning signals

Literature Review



A large number of researchers have been studied to the issue of NON PERFOMING ASSET in

banking industry .A review of the relevant literature has been described as under:-

Krishnamurthi, C.V.(2000) observed that the rising NON PERFOMING ASSETS is serious

diseases for the public sector banks .It shows that the gross NON PERFOMING ASSET of

PUBLIC SSECTOR BANKS are mounting very heavily .The NON PERFOMING ASSET

curses lie between a gross of Rs.39.253 crores in 1992 -93 to Rs.45,463 crores in1997-98.

Munniappan (2002) studied the diseases of NON PERFOMING ASSET into two factors

.One is internal factor in respect of portfolio of funds for expansion, modernization and

diversification, accept new projects etc. Second is external factor in respect of recession in

economy, other countries suffered from non performing assets assessment, input/power shortage,

price up and downs uncertain natural calamities etc.

Das & Ghosh (2003) studied non-performing loans of Indian PUBLIC SECTOR BANKS on the

basis of various indicators like as assts size, operating efficiency, and macroeconomics condition

and credit growth.

Gupta, S and Kumar ,S (2004) defined that redeeming features of banking sector reforms is the

continuing downfall in gross and net NON PERFOMING ASSET as a proportion of total assets

for all bank groups .NON PERFOMING ASSETS needs resolution otherwise it can break the

backbone of entire economic system with financial system .

Banerjee,B. and Dan,A.K (2006) analyzed that NON PERFOMING ASSETs are one of the most

crucial problem which is faced by bank to require attention for improvement in the management

of PSBs are increasing very speedily at present scenario due to following reason . one is

government has got to bail out banks with monetary fund provisions sporadically and ultimately

taxpayers bear the value .Second is cash borrowed for investment ,for not utilized properly

,affects the creation of assets and therefore the growth of economy is vulnerable .The author has

urged many strategic measures to manage Non playing assets of Public sector banks.

Jatna, Ranu (2009) states main cause of mounting NON PERFOMING ASSETs in public sector

banks is malfunctioning of the banks. Narasimham Committee identified the NON

PERFOMING ASSETs as one of the possible effects of malfunctioning of PUBLIC SECTOR

BANKS.

Dong he (2002) in his study focuses on the nature of NON PERFOMING ASSET in Indian

banking system and define the important role of assets reconstruction companies in resolving



NON PERFOMING ASSETS.

Prof G.V.Bhavani Prasad and Veera D (2011) examined that the reason behind the falling

revenues from traditional sources is 78% of the total NON PERFOMING ASSETs accounted in

PUBLIC SECTOR BANKS.

Dr.P.Hosmani & Hudagi Jugdish(2011) found that a slight improvement in the asset quality

reflected by downsize in the NON PERFOMING ASSET percentage.NON PERFOMING

ASSET is an improvement scale for assessing financial performance of Indian banks. The

mounting value of NON PERFOMING ASSETS will adversely affected to financial position in

term of liquidity, profitability and economic of scale in operation. Bank has to take timely

necessary steps against degradation of good performing assets.

Manish B Raval (2012) studies to understand the major composition of NON PERFOMING

ASSETS in Indian Banks and compared the three compositions i.e. Priority sector, Non Priority

sector and others sector of NON PERFOMING ASSETS between Nationalized and SBI and its

associates. The researcher stated that there is no significant difference between three

compositions of NON PERFOMING ASSETS to total NON PERFOMING ASSETS in

nationalized banks and SBI and its associates.

Dr.A Dharmendran (2012) examine the position & growth of standard assets ,substandard assets,

loss assets ,gross nonperforming assets provision for non performing assets & net non

performing assets with the help of percentage analysis method & compound growth rate for all

the state Co-operative banks in India.

Nature and scope of the study

The present study is empirical and descriptive in nature. The study is confined to examine

the state of Nonperforming assets in Commercial banks operating in India under

consideration of six years.

Objective of the study

This research has been undertaken with the following objectives:

1. To find out the NPA position of selected public sector banks. (All Nationalised banks in

India)

2. To find the trend in NPAS of the Nationalised banks.

3. To analysis the comparative position of NPAS in Nationalised banks.



Hypothesis of the study

1. There is significant difference between Gross NPAs and Gross Advances ratios of

banks in respect of Non Performance Assets (H0).

2. There is significant difference between Net NPAs rations public sector banks in India

(H1).

Methodology

The study is descriptive in nature .It evaluate the NPA level in public sector banks for a period

between 2008-2013.By going through the path of objective set for the study ,the relevant

secondary data has been collected through various sources like, RBI website ,Trend and

progress in banking various issues. Collected data has been tabulated and analyzed by using

various ratio techniques. The study also examines the trend of NPA in various sample banks.

The findings of the study are inconformity with the statistical tools applied as such Mean,

Standard Deviation, compound annual growth rate and one-way ANOVA correlation.

Analysis and Interpretation

Table-1: Gross NPAs of Different Years and ranks of individual banks.

200

8

200

9

201

0

201

1

201

2

201

3

Mea

n SD CGAR

Ran

k

Allahabad Bank 2 1.81 1.71

1.80 1.91

3.92 2.19

0.8

5

11.87

% 9

Andhra Bank

1.1

0.83 0.86

1.38 2.12

3.71 1.67

1.1

1

22.46

% 4

Bank of Baroda

1.8

1.27 1.64

1.62 1.89

2.4 1.77

0.3

7 4.91% 6

Bank of India

1.7

1.71 3.31

2.64 2.91

2.99 2.54

0.6

8 9.87% 15

Bank of Maharashtra

2.6

2.29 2.96

2.47 2.28

1.49 2.35

0.4

9 -8.86% 12

Canara Bank

1.3

1.56 1.53

1.47 1.75

2.57 1.70

0.4

5

12.03

% 5

Central Bank of India

3.2

2.67 2.32

1.82 4.83

4.8 3.27

1.2

8 6.99% 19

Corporation Bank

1.5

1.14 1.02

0.91 1.26

1.72 1.25

0.3

0 2.31% 1

Dena Bank

2.4

2.13 1.8

1.86 1.67

2.19 2.01

0.2

8 -1.51% 7

IDBI Bank Limited 1.9 1.38 1.54 1.79 2.57 3.22 2.07 0.7 9.19% 8



0

Indian Bank

1.2

0.89 0.76

0.99 1.94

3.33 1.52

0.9

8

18.54

% 3

Indian Overseas Bank

1.6

2.54 4.71

2.71 2.79

4.02 3.06

1.1

2

16.60

% 17

Oriental Bank of

Commerce

2.3

1.53 1.74

1.98 3.17

3.21 2.32

0.7

2 5.71% 11

Punjab and Sind Bank

0.7

0.65 0.63

0.99 1.65

2.96 1.26

0.9

2

27.16

% 2

Punjab National Bank

2.7

1.77 1.71

1.79 3.15

4.27 2.56

1.0

2 7.94% 16

Syndicate Bank

2.7

1.93 2.43

2.65 2.75

1.99 2.41

0.3

6 -4.96% 13

UCO Bank

3

2.21 2.15

3.31 3.73

5.42 3.30

1.2

1

10.36

% 20

Union Bank of India

2.2

1.96 2.25

2.37 3.16

2.98 2.49

0.4

7 5.19% 14

United Bank of India

2.7

2.85 3.21

2.51 3.41

4.25 3.15

0.6

3 7.85% 18

Vijaya Bank

1.6

1.95 2.37

2.56 2.93

2.17 2.26

0.4

7 5.21% 10

Table 1 shows the gross NPA ratio of nationalised banks for last six years with necessary

statistics like mean, growth rate of NPAS via CAGR. From the table-1, it is seen that gross

NPA of nationalised banks is in the upward trend generally in all the banks with varying growth

except same banks like Bank of Baroda, Dena Bank, and Syndicate Bank. The compound

annual growth rate of banks under study is in the range of 8.86 to 18.54 and banks are having

value of compound annual growth rate of gross NPAS during this range. As per the mean

which is representative of a group of data, banks are ranked in ascending order. The reason for

ranking them in ascending order is from the interpretation of NPA that better the performance,

lower the ratio and vice versa. From the above table it is found that Central Bank of India is

ranked first as it was able to manage lowest means GNPA ratio of 1.25 percent, followed by

Punjab and Sind Bank at second position with mean GNPA ratio of 1.26 percent and third rank

achieve by Indian bank. Central Bank of India and Uco Bank got lowest rank of 19 and 20 with

a mean ratio of 3.27 and 3.30 percent respectively.



FIG. 1

Figure-1 portrays the GNPA ratio of all the banks for a six year period and break of individual

bar shows the annual gross NPA ratios for a six year period. It is seen from the diagram that

some banks are having high gross NPA ratio from year to year while others have kept it under

controlled conditions. From the figure-1, it is depicted that Punjab and Sind bank, Indian bank,

corporation bank and Andhra bank have kept strict control on their NPAS and their total NPA

for the six year period is lowest relative to others. The bars of central bank, Indian overseas bank,

UCO bank, united bank are having highest level of bars which shows their higher level of

NPAs relative to other banks.

Table-2: Net NPAs of Different Years and ranks of individual banks.

2008 2009 2010 2011 2012 2013 Mean SD CAGR rank

Allahabad Bank 0.8 0.72 0.66 0.79 0.98 3.19 1.19 0.99 25.93% 12

Andhra Bank 0.15 0.18 0.17 0.38 0.91 2.45 0.71 0.90 59.29% 3

Bank of Baroda 0.47 0.31 0.34 0.35 0.54 1.28 0.55 0.37 18.17% 1

Bank of India 0.52 0.44 1.31 0.91 1.47 2.06 1.12 0.62 25.79% 10

Bank of Maharashtra 0.87 0.79 1.64 1.32 0.84 0.52 1.00 0.41 -8.22% 8

Canara Bank 0.84 1.09 1.06 1.1 1.46 2.18 1.29 0.48 17.23% 15

Central Bank of India 1.45 1.24 0.69 0.65 3.09 2.9 1.67 1.07 12.25% 18

Corporation Bank 0.32 0.29 0.31 0.46 0.87 1.19 0.57 0.37 24.47% 2

Dena Bank 0.94 1.09 1.21 1.22 1.01 1.39 1.14 0.16 6.74% 11

IDBI Bank Limited 1.3 0.92 1.02 1.06 1.61 1.58 1.25 0.30 3.30% 14



Indian Bank 0.24 0.18 0.23 0.53 1.33 2.26 0.80 0.84 45.32% 4

Indian Overseas Bank 0.6 1.33 2.52 1.19 1.35 2.5 1.58 0.77 26.85% 17

Oriental Bank of Commerce 0.99 0.65 0.87 0.98 2.21 2.27 1.33 0.72 14.83% 16

Punjab and Sind Bank 0.37 0.32 0.36 0.56 1.19 2.16 0.83 0.73 34.19% 5

Punjab National Bank 0.64 0.17 0.53 0.85 1.52 2.35 1.01 0.79 24.21% 9

Syndicate Bank 0.97 0.77 1.07 0.97 0.96 0.76 0.92 0.12 -3.98% 6

UCO Bank 1.98 1.18 1.17 1.84 1.96 3.17 1.88 0.73 8.16% 20

Union Bank of India 0.17 0.34 0.81 1.19 1.7 1.61 0.97 0.64 45.46% 7

United Bank of India 1.1 1.48 1.84 1.42 1.72 2.87 1.74 0.61 17.33% 19

Vijaya Bank 0.57 0.82 1.4 1.52 1.72 1.3 1.22 0.44 14.73% 13

Source: Department of Banking Supervision, RBI

Table 2 displays the Net Non Performing Assets ratio of nationalised banks. This is the actual

burden on the shoulders of bank and calculated by deducting necessary provisions from the

gross nonperforming assets of bank. From the analysis of table-2, it is inferred that Net NPA of

nationalised banks is close vigilance and control in most of the banks by maintaining sufficient

level and provisions to counter balance for the quality of assets. The CAGR is varying in much

range compared to GNPA of nationalised banks. The Bank of Maharashtra is having lowest

CAGR of -8.22 and union bank having highest CAGR of 59.29 percent. The ranking of banks is

done on the basis of mean for last six years and ranking is done in ascending order i,e lower the

average better the rank, Bank of Baroda , corporation, Andhra bank got first second and third

rank respectively with their lowest mean for six years and united bank of India and UCO bank

19th and 20th rank respectively.



Graph-2 shows the annual ratios of net NPAs for each nationalised bank and height of bars

determine level of NNPAs ain respect of six years .Division of bars determine annual level of

NPPA ratio. Andhra Bank, Bank of Baroda Corporation Bank Indian Bank and Punjab and Sind

bank are positive in terms of Net NPA ratio .There level is minimum and rest having higher ratio

with varying level of bars .UCO banks displayed bar length is maximum.

Table-3

BANK/RANK

RANKS AS

PER GNPAS

RANKS AS

PER NNPAS

AVERAGE OVERALL

RANK

Allahabad Bank 9 12 10.5 6

Andhra Bank 4 3 3.5 2

Bank of Baroda 6 1 3.5 2

Bank of India 15 10 12.5 9

Bank of

Maharashtra 12 8 10 5

Canara Bank 5 15 10 5

Central Bank of

India 19 18 18.5 12

Corporation Bank 1 2 1.5 1

Dena Bank 7 11 9 3

IDBI Bank

Limited 8 14 11 7

Indian Bank 3 4 3.5 2

Indian Overseas

Bank 17 17 17 11

Oriental Bank of

Commerce 11 16 13.5 10

Punjab and Sind

Bank 2 5 3.5 2

Punjab National

Bank 16 9 12.5 9

Syndicate Bank 13 6 9.5 14

UCO Bank 20 20 20 13



Union Bank of

India 14 7 10.5 7

United Bank of

India 18 19 18.5 12

Vijaya Bank 10 13 11.5 8

Table 3 shows the composite rank of each bank, this is arrived by averaging the ranks of banks

as per GNPA and NNPA. Average performance will determine the real performance in the

management of nonperforming assets. So final ranks assigned to banks is based on the average of

earlier two ranks. It can be seen from the table that Corporation bank has got first rank followed

by Punjab and Sind, Andhra Bank, Bank of Baroda, Indian Bank at second rank and Dena bank

at third rank. In the management of nonperforming assets some banks have got the same rank

which is clearly shown in the table, four banks have got second rank, two banks have got 5th

rank, two banks have got 6th rank, two banks have got 9th

rank and another two banks have got

12th rank. These banks are at the same performance level in the management of nonperforming

assets.

Table -4

ANOVA

GNPA

Source of Variation SS df MS F Table value

at 5 level of

significance

F(5,114)

Between Groups 28.14337 5 5.628674 8.247972 2.29

Within Groups 77.79716 114 0.682431

Total 105.9405 119

The table 4 shows that calculated F value of 8.247972 is which is very much higher than table

value or critical value of 2.29 at 5 % level of significance with degrees of freedom (v1=5 and

v2=114) and hence our analysis supports our hypothesis that there is significant difference of

gross NPA ratios of nationalised banks. This shows that nationalised banks are having different

level of Gross NPAs and which shows their efficiency in management of gross NPAs, and

quality of their assets.

Table -5

ANOVA

NNPA

Source of Variation SS df MS F Table value at



5 level of

significance

F(5,114)

Between Groups 24.0491 5 4.80982 15.70756 2.29

Within Groups 34.908 114 0.30621

Total 58.95709 119

The table no 5 shows the ANOVA test of Net NPA to Net Advances of Nationalised banks. It is

seen from the table that calculated F statistics value of 15.70756 is higher than table value of

2.29 at 5 % level of significance. Results of our ANOVA analysis support our hypothesis that

there is significant difference between NNPA of nationalised banks, which shows their varied

performance of asset management.

Limitation of the study

1. Only nationalized banks are chosen for the purpose of the study.

2. Study is based on the availability of data.

Conclusion

The management of nonperforming assets is a daunting task for every bank in the banking

industry. The very important reason and necessity for management of NPA is due to their multi

dimensional affect on the operations, performance and position of bank. Results of study through

light on the level of nonperforming assets of different nationalised banks and

relation between different banks in the level of nonperforming assets . It is found that level of

nonperforming asset both gross and net is on an average in upward trend all the nationalised

banks but the growth rate is different. Banks got different ranks on the basis of mean and final

ranking was done on the basis of average gross NPA rank and net NPA rank. Corporation bank

got first rank among all the twenty and banks, From the ANOVA test it is was deduced that there

is significant difference between gross and net NPA of nationalised banks, this portrays their

efficiency in the management of NPAs The non performing asset is a major problem and hurdle

faced by banking industry. Wilful defaults, improper processing of loan proposals, poor

monitoring and so on are the causes for accounts for becoming NPAs. NPAs affect the position

as well as performance in several ways such as interest income, profits, and provisions against

NPAs and so on. Henc

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