advances in management september 2012

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GOD IS

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Govt.of India Registration No. MPENG/2008/24042 Print: ISSN No. 0974-2611; e-ISSN: 2278 - 4551

ADVANCES IN MANAGEMENTAn International Peer Reviewed Monthly Journal dealing with all aspects of Management, Commerce and Economics

Volume No. 5 (9), Pages 1-70, September (2012)

Editor - in -Chief (Hon.)

DDrr..SShhaannkkaarrGGaarrgghh

M.SC.,Ph.D.,M.B.A., LL.B., FICCE, FISBT, FISM, FICDM

Phone: +91-731-4004000 Mobile: 094250-56228

www.managein.net; www.shankargargh.net

Correspondence Address:

Advances In ManagementSector AG-80, Scheme No. 54, Vijay Nagar,

A.B. Road, Indore 452010 (M.P.), INDIA

Phone: 2552837, 4004000 FAX: +91-731-2552837

E-Mail: [email protected]

Editorial:

From Cookies to Consumers to the Bottom Line; Understanding and Applying Marketing ConceptsCarpenter Shari

3-5

Research Papers:

1 Human Resource Development: A New Roadmap on Success of Industrialization Panigrahi AnitaKumari

6-11

2 Micro Credit as a means of Socio Economic Empowerment A Survey in Andhra Pradesh Savitha B.and Jyothi P.

12-19

3 Application of Artificial Neural Networks for Sales Forecasting in an Indian Automobile

Manufacturing Company Chauhan Manish Kumar and Mittal M. L.20-24

4 The Voyage of the Renault-Nissan Alliance: A Successful Venture Weng Marc Lim 25-29

Case Study:

5 Personality Type A as a Moderator for Relationship between Occupational Stress and Organizational

Effectiveness Mahal Prabhjot Kaur30-35

6 Economic Problems in Micro, Small and Medium Enterprises (MSMES) in India Venkateswarlu P. and

Ravindra P.S.36-40

7 Creation of Demand for Millet Foods A Case Study of Successful Change Management of an Innovation

Project Manikandan P.41-49

8 Occupational Stress and its impact on QWL with specific reference to Hotel Industry Mohla Charu 50-54

9 Employees Perception on Welfare Practices- A Study of A.P. Eastern Power Distribution of AP

Limited Ramesh M.55-60

General Article:

10 Environmental Accounting and Reporting In India: An Appraisal Sharma V.K. 61-65

Book Review:.Steve Jobs written by Isaacson Walter Reviewed Rungta Shravan 66-67

INSTRUCTIONS TO AUTHORS: 68-69 EDITORIAL BOARD: P 2 MEMBERSHIP FORM: P 70

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CONTENTS


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Advances In Management Vol. 5(9) Sep. (2012)

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Editorial BoardEditor-in-Chief (Hon.):Shankar Gargh, Indore, INDIA

Ajax Persaud, Ottawa,Canada

Alfredo M. Bobillo,Valladolid,SpainArnulf D. Schircks, Zrich,Switzerland

Corrado loStorto,Naples, Italy

David Naranjo-Gil, Sevilla,Spai

Dobrai Katalin, Pecs,Hungary

Evangelos Manolas, Orestiada,Greece

Farouk Yalaoui, Troyes, France

Ferenc Farkas,Pecs,Hungary

Fotis Vouzas,Thessalonilci,Greece

Grace TR Lin, Hsinchu,Taiwan

Hui Tak-Kee, Singapore, Singapore

Imed Kacem, Troyes,FranceJamal Roudaki, Christchurch,New Zealand

Jitendra M. Mishra, Grand Rapids,USA

Joao Ferreira, Covilha,Portugal

Joel Wisner, Las Vegas,USA

John Currie, Galway,Ireland

John A. Consiglio,Msida, Malta

John R. Buffington, Colorado,USA

Jorn Stovring,Roskilde,Denmark

Laura Stefanescu, Craiova, Romania

Lucia Marchegiani, Rome, Italy

Malaya Kumar Nayak, London,UKManjula S. Salimath,Texas,USA

Marina Dabic, Zagreb, Croatia

Michael Harvey, Mississippi, USA

Miltiadis Boboulos, Halkida,Greece

Mosad Zineldin, Vxj,Sweden

Mu-Shang Yin, Taipei,Taiwan

Nawaz A. Hakro,Nizwa, Oman

Nicholas Odhiambo, Pretoria,S. Africa

Panagiotis Polychroniou, Patras,Greece

Paul C. Nutt, Ohio,USA

Paul D. Berger, Waltham,USAPedro Soto-Acosta, Murcia,Spain

Randy Drenth, Beme, Switzerland

Richard E. Kopelman,New York, USA

Richard J. Cebula,Jacksonville, USA

Rodger Morrison, Montgomery,USA

Ruth Alas, Tallinn,EstoniaShankar Chelliah, Penang, Malaysia

Shigeyuki Goto, Tokyo,Japan

Sultan M.Al-Salem,Safat, Kuwait

Wong Wai Peng, Penang,Malaysia

Yu Ming-Miin, Keelung,Taiwan

A. Suryanarayana, Hyderabad, India

Abhijit Bhattacharya,Lucknow,India

Ajeya Jha, Gangtok,India

Aryaa Jain, Indore,India

B.B. Pal, Kalyani,India

Gunjan Malhotra, Ghaziabad, IndiaJagannath Patil, Bangalore,India

K.B. Nidheesh, Pondicherry, India

K.L. Padmini, Holalkere,India

L. Suganthi, Chennai, India

M.S. Pabla, Jalandhar,India

Megha Jain, Indore,India

Mousumi Roy, Durgapur,India

Neera Jain, Gurgaon, India

P. Janaki Ramudu, Bangalore,India

P. Manikandan, Hyderabad,India

P. Natarajan, Pondicherry,IndiaPaul D. Madhale, Miraj, India

Pawan Chugan, Ahmedabad, India

Prabhat Kumar Pani,Jamshedpur, India

Priyanka Bhalerao, Mumbai,India

Puja Padhi, Mumbai, India

R.D. Biradar,Nanded,India

R.K. Jena,Nagpur,India

Rajashekhar Patil, Mumbai,India

Rajesh Bhatt, Bhavnagar,India

Ranjan Chaudhuri, Mumbai,India

S. Krishnakumar, Chennai, IndiaS. Muralidhar, Bangalore,India

Sanjay K.Govil, Hyderabad,India

V.V. Gopal, Hyderabad,India

Vimi Jham, Ghaziabad, India

If one is interested in becoming member of our Editorial Board, he/she should send

Bio-data / CV by Email to: [email protected]!


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Human Resource Development:

A New Roadmap on Success of IndustrializationPanigrahi Anita Kumari

Department of Humanities, Gandhi Academy of Technology and Engineering, Berhampur, Odisha, [email protected]

Abstract

HRD has emerged as an important concept in India as

in other parts of the developing and developed world

and is seen as an important strategy of facilitating

sustainable development of industries. It promotes

human resources through education, training,

employment creation, population, health . . . all of

which form the basis for enhancing and utilizing their

skills, knowledge and productive capacity as well as to

set up the goal of an organization. Everything production, management, marketing, sales, research

and development etc. may be more productive, if people

are sufficiently motivated, trained, informed, managed,

utilized and empowered. By applying a well-ordered

and professional HRD approach to work in the

protected areas field, the skills, knowledge and attitudes

of park personnel will be enriched and this overall

quality of work performed will improve. Thus, HRD

provides the essential tools needed to manage and

operate an industry in an effective way. Nowadays it is

one of the accepted key towards a developed and

industrialized society.

Keywords: HRD, Industrialization, Industrialized society.

Introduction

To cope with the fast changing environment, organizations

need to review their HRD approaches continuously. HRD is

neither a concept nor a tool, but an approach using different

personnel systems, depending upon the needs and priorities of

the industries. Industries need to be dynamic and growth-

oriented to sustain in the competitive environment. This is

possible only through the competence of the human

resources. At the industry level, human resource development

is not only essential but critical to a companys survival.

There is an intrinsic impermanence in modern industry,where market and technology are so volatile that failure to

proactive to change and be innovative will result in a

company driven out of business. A company that is short on

capital can borrow money but a company that is short of the

required human resources has little chances of survival either

in the short or long term perspectives. Realistic plans for the

development and utilization of human resources are made

after consideration of the external and internal factors

affecting the personnel objectives of each industry and

organizational unit. It is becoming apparent that without a

measure of industrialization, the economic growth of any

country will be stunted.

The industrialization strategy that a country pursues should

take into account the macroeconomic framework within

which industries have to operate. The orientation of

macroeconomic policies determines the direction and even

the type of industries that spring up and thrive. In addition, a

country has to operate within the global environment, whichwill dictate the kind of trade, investment and industrialization

pattern that enables it to survive. The underlying philosophy

was to use industrialization as a means of promoting

economic growth, creating employment and eradicating

poverty. But the fulfillments of industrialization visions share

a common motivation and reflect a fundamental commitment

to promoting the wellbeing and dignity of individuals in

society.

Industrialized countries like Japan, Switzerland and South

Korea have emerged from limited natural endowments. They

have no minerals and the climate, land structure and soil are

not conductive to agriculture. Yet, these countries have

achieved spectacular economic growth on the strength andingenuity of their human resources. On the other hand, there

are countries well endowed with natural resources such as

minerals or oil that have failed to capitalize on their given

wealth. They remain observes, not partaking in the

exploitation of their resources. They choose by their own

default to remain on the fringes of progress. They are content

to oscillate between developing and under developed status,

dependent on foreign technology and expertise to exploit

their dwindling resources. These countries have failed to

develop effectively their human resources, to capitalize on

their natural wealth and to steer their own destiny. Thus,

prospects and growth, productivity and profitability-of-an

organization depends maximum on effective utilization of

human resources employed in the effort of achieving

company objectives. The achievement of an organization can

be seen as a result of cooperation and hard work at all the

levels of functioning of an organization.

Evolution of human resource development:The origin of

HRD was suggested to have started in the USA during the


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advent of the Industrial Revolution in 1800s. But some

writers argued that the roots of HRD emerged in 1913 when

Ford Motor started training its workers to produce mass

production in the assembly line. However, a significant

historical event was suggested during the outbreak of World

War Two in the 1940s as it was during this period thatworkers were trained to produce warships, machinery and

other military equipments and armaments.5Blake

2argued that

HRD could have started a century later, in the early 1930s

and its roots emerged from the concept of organization

development (OD). On the other hand, Stead and Lee15

,

contested that the historical starting point of HRD was during

the 1950s and 1960s when theories on employees

developmental process was popularized and published by

organizational psychologists such as Argyris1, McGregor

13,

Likert10and Herzberg8.

Hence, Stead and Lee15

believed that the development of

human resources in an organization far encompasses merely

training but also motivation and development as suggestedby organizational psychologists.

2This was supported by other

writers. Desimone et al5 said that during 1960s and 1970s,

professional trainers realized that their role extended far

beyond classroom training and they were also required to

coach and counsel employees. Realizing, this extended role,

Nadler16

introduced the term HRD in 1970s and was placed

under the big structure of human resources with the function

of selection and development of employees under the term

HRD.2

Subsequently, in early 1980s, the term HRD was approved by

the American Society for Training and Development (ASTD)

because they believed that training and development

competencies expanded to include interpersonal skills such ascoaching, group process facilitation and problem solving.

And by then, organizations realized that human resources are

important assets and emphasis was placed in investing in

training and education for performance improvement to

increase productivity and business success.5

In the UK, Harrison7argued that the historical development

of HRD is more fragmented compared to the US. The history

of HRD in UK was suggested to have started during World

War Two in which training was the symbiotic term.

Similarly to the USA, during this period, training was the

term because workers were trained in the production and

manufacturing sector as well as becoming soldiers. The

emergence of HRD began in early 1980s when the

manufacturing industry was hit by a recession and a strategy

was required to overcome the crises especially in

multinational companies. Companies began to realize that

human resource is an important asset and started developing

their employees particularly to improve their performance

and develop or enhance their skills to increase productivity.

Since then, HRD is considered as an important business

strategy and processes7but viewpoints of HRD as a strategy

for business success were argued by writers such as Garavan,

Costine and Heraty.6

In Malaysia, HRD could have started as early as 1980s. The

historical development and emergence of HRD in Malaysia

lacked empirical evidence, the development of HRD during

this period was not very clear and focused. HRD may have

started when the Commonwealth Countries Secretariat began

developing the Human Resources Development Group

(HRDG) in 1983 with the intention to assist the ASEAN

countries in developing its human resources.3, 4

In 1984, the

ASEAN countries, including Malaysia being part of the

ASEAN Pacific Rim commenced their proposals in providing

assistance in developing human resources particularly, in

education, training and skills development for new

technology.3 It may be argued that the emergence of HRD

could have started during the mid 1970s when the

Government began developing the Bumiputras in businessesto improve economic disparities

11or it may have started like

the UK, during the economic recession in 1985 as it was

during this period that the Government began its aggressive

drive towards manufacturing and industrialization.12

However, clear evidence was seen when the Government of

Malaysia began to include HRD strategies in the countrys

development plans and policies in 1991 in the Second Outline

Perspective Plan (OPP2) and the Sixth Malaysia Plan (6MP).

One of the main thrusts of these plans is to become a fully

industrialized nation with skilled and knowledge-based

workforce by year 2020.12

Nevertheless, it could be argued

that HRD could have started even before Malaysias

independence, when workers migrated from India to work inthe tin-ore mining fields and oil palm plantations.

The human resource development in India is of recent origin

and the terms gained currency only in the early seventies. In

the opinion of Nadler14

, the term HRD was first applied in

1968 in George Washington University. It was used in Miami

at the conference of American Society for Training and

Development in 1969. According to Nadler14

, the term was

gaining more acceptances during the mid- 1970s, but many

used it as a more alternative term than Training &

Development. In the opinion of some management

professionals, Japan is the first country to begin with HRD

practices. Better People, not merely better technology, is

the surest way to a Better Society, is the most popular

belief in Japan. The term was first used in India in 1972 by

the State Bank of India. By the late seventies and early

eighties this professional outlook on HRD caught on to a few

PSUs, namely BHEL, MUL, SAIL, IA, AI and IOC. L & T

and TISCO were the first two organizations in the private

sector to begin with HRD.


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When we trace the concept tract of HRD, the theme of HRD

is interestingly observed about the occurrence of during

different periods and different places. Economics Adam

Smith, Karl Mark and a host of classical and modern of

Human resources focused on labor and skill development.

Even though there is a lot of controversies in the parameterregarding the quality of human resources in a country

preferred by the world organization, such as World Bank etc.

These parameters have been accepted based on the validity.

Linking Industrialization with Human Resource

Development

The important relationship between the quality of human

resource and the well-being of a nation has been repeatedly

proved through examples from the past and present. Based on

what transpired during the past 30 years, it is apparent that

the next 30 years will witness more rapid and volatile

changes in the industrial sector. To minimize ant dislocation

from the changes that are impacting externally, it is essentialthat strategic plans for HRD, with systematic links among the

players and the constituents involved, be developed. Periodic

review and fine tuning of the goals and the operational plans

of HRD are essential to ensure relevance and to avoid

wastage of resources which may disrupt the industrialization

goals. The concept of HRD is therefore a substantive and

complex field. It embraces three levels of strategic planning

and analysis of which are, the aggregate, sectoral and the

industry level.

The primary objective of HRD is the effective utilization of

scarce or abundant talent in the interest of both broad and

specific national objectives as well as the objectives of

industry, business and individual employee. In its broadestsense, it is the development of plans of action to meet the

manpower requirements in anticipation of the changing

conditions of the social, economic, industrial and business

environment. Thus, Human resource development is itself

critical to the sustainability of the industrialization process.

Human Resources has grown as an industry to include experts

in the field of Organizational Development, Change

Management, Continuous Process Improvement, as well as

those who gain impressive training and enjoy significant

tenure in Benefits Administration, Recruiting, Policy

Analysis and Training.

In planning for HRD, particularly for skill and expertise that

require long gestation periods, changes in technology fortargeted industries need to be monitored closely. This role

known as boundary spanning is vital as changes in

technology and market structure have far reaching

implications on human resource requirements and job

behavior. The followings are some of the pertinent changes in

the global trend that must be taken into consideration in

planning for HRD in industries, either in short term or long

term perspectives:

Compared to the 70s and 80s, the next three decades willwitness a formidable technological explosion facilitated

by the advance of information technology, modern

medicine and biotechnology. The technological

explosion may render most scientists technically

obsolete. It is highly probable that they would lack the

latest skills and knowledge in their own fields, if they are

not constantly kept abreast of the elements taking place.

The next thirty years will also witness a tremendouscommunication explosion with faster and shortened

distances for travel. There will be a substantial increase

in productivity for those nations that have the capacity of

changes in the relevant fields to acquire relevant

knowledge and new technology.

New knowledge and technology will affect powerrelationships and facilitate decentralization in

government or in industry.

Economic activities will be more global than they aretoday. This would mean that ownership of firms would

be very much internationalized.

There will be a tremendous expansion of the servicesector, in particular the information related industry.

Information replaces energy as the main transformingresource. It adds value to products and services by

increasing the efficiency of labour, materials and capital

used

An effective and realistic HRD plan must be able to absorb

the shocks and ride with the rapid changes taking place.

The Challenges of Industrialization to Human

Resource Development

Human resource development (HRD) has been an important

area of research practice. The purpose of HRD policy is the

development of Human Resources. Todays increasing

complex and volatile business environment characterized by

globalization, liberalization and the transnational invasion

ensures that managing would not be the same again. As we

are in 21st century, competitiveness in global market place

presents the ultimate challenge to policy makers, business

leaders and entrepreneurs in any industry. From this body of

work a number of major challenges have emerged. Thechallenge of industrialization is to build synergy and

strengthen linkages between industrialization and human

resources so that the two are mutually reinforcing. Companies

need to be proactive to foresee the challenges that its

functions face as a result in order to harness the opportunities

that future has to offer. The challenges of industrialization to


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human resource development include:

Competing in a Global Economy:As companies compete

increasingly, in a global economy many are introducing new

technologies that require better educated and trained

workers. In fact, in the United States today, over one-half of

all jobs require education beyond high school. Thus,

successful organizations must hire employees with the

knowledge to compete in an increasingly sophisticated

market. Competing in the global economy requires more than

educating and training workers to meet new challenges. In

addition to retraining the workforce, successful companies

will institute quality improvement processes and introduce

change efforts. The workforce must learn cultural sensitivity

to better communicate and conduct business among different

cultures and in other countries. Developing manages into

global leaders has been identified as a major challenge for

organizations.

The need for Lifelong Learning:Given the rapid changesthat all organizations face, it is clear that employees must

continue the learning process throughout their careers in

order to meet these challenges. This need for lifelong learning

will require organizations to make an ongoing investment in

HRD. Thus, it is a challenge to HRD professionals to provide

full range of learning opportunities for all kind of employees.

Lifelong learning can mean different things to different

employees. For example, for semi-skilled workers, it may

involve more rudimentary skills training to help them build

their competencies. To professional employees, this learning

may mean taking advantage of continuing education

opportunities. This is particularly important for certified

professionals who are required to complete a certain number

of continuing education courses to maintain theircertification. To managers, lifelong learning may include

attending management seminars that address new

management approach.

Technological Changes: Recent spurt in computerization

and technological upgradation is, on the one hand,

streamlining process and paper work and increasing quality,

service and speed and on the other hand making several jobs

obsolescent. Many companies which realize that they are not

adding value in all functional areas are increasingly

outsourcing all but the most critical functions. With the

advancement in telecommunications, employees can now

work in their homes. Tele work, as it is called, has freed them

from the trouble and inconvenience of travelling over long

distances. Companies can also save on office space and

overhead expenses. These changes may make workers

redundant at some places. The redundant workers everywhere

need to be rehabilitated through training. The change has to

be brought about with a human face. At this point, the HRD

manger has a critical role to play.

Upgrading Employee Skills in the Service Sector : With

changes in technology, the skill and knowledge components

of the human resource have to be constantly upgraded,

otherwise the assimilation or absorption process is weakened.

The Technology Atlas (United Nations Economic Social

Commission for Asia and PacificUNESCAP) definesvarious stages of sophistication of increasing human abilities

starting from operating, setting up, repairing and reproducing

to adapting, improving and innovating abilities.16

As a

developing country Pakistan needs to industrialize further, the

size and contribution to output and employment of the service

sector will need to increase. ILO9 paper cites that

liberalization will also increasingly affect the service sector.

The development and the productivity of the service sector

will become more important. Service industries which are not

exposed to international competitiveness, tend to have lower

productivity with reference to both quantity and quality

which also appear to be a big reflection of the need of world

level HRD practices. Developing countries will need to pay

greater attention to the development of the service sector and

the raising of its productivity, through healthy HRD practices.

The major ingredient in service quality is attitude, knowledge

and skills of workers. The upgrading of service skills is an

issue for many developing countries which lack a solid

educational ground. Training programmes should be tailored

to enhance the capacity of the workforce in terms of above

quoted qualities.

Increasing Workforce Diversity: The workforce has

become increasingly more diverse and this trend towards

diversity will continue.This includes increasing diversity

along racial, ethnic and gender lines, as well as an increasing

percentage of the workforce that is over age fifty-five.Effectively managing diversity has been identified as one of

five distinguishing features of organization that make it onto

Fortune magazines list of 100 best companies.Diversity

issues have several implications for HRD professionals. First,

organizations need to address racial, ethnic and other

prejudices that may persist, as well as cultural insensitivity

and language differences. Second, with the increasing

numbers of women in the workforce, organizations should

continue to provide developmental opportunities that will

prepare women for advancement into the senior ranks and

provide safeguards against sexual harassment. Third, the

aging of the workforce highlights the importance of creating

HRD programs that recognize and address the learning related needs of both younger and older workers. Diversity

can be a catalyst for improved organizational performance

though this is far from a sure thing.

Work force empowerment: For the corporate democracy to

become a reality, many companies are now vesting their

employees with greater authority, expanding their job titles


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and increasing their accountability. In a country where the

benevolent autocrat has been overwhelmingly preferred

style, real empowerment of the work force is going to pose a

big challenge for the HRD manager.

More research in HRD: Continuous research is needed to

discover new HRD methods and interventions. This is

possible only when there are HRD-oriented organizations to

pool and share their experiences in diverse areas.

Nowadays companies are facing daunting challenges in

hiring, training and retaining people. Globalization has

increased the demand for talent everywhere. So, it is our

conviction that a human resource development system has to

capture the workers imagination, so as to optimize their

strengths and energy systems (Physical, mental and

emotional) to make them capable of creative responses to

complex industrial situations. The challenges of HRD would

be to create an environment of resilience which can

accommodate and assimilate successfully changes intechnologies, policies, methods etc. so as to reorient culture

by getting individuals, teams and collectivities to feel a sense

of belonging, commitment and mobilization to welcome

change in the interest of organizational transformation.

Opportunities of Industrialization to Human resource

Development: Rapid industrialization also offers various

opportunities to employees of the organization because

Human Resource development is a key factor for new

technology advancement and industrialization. In order to

achieve the goal of optimization of resources, the

organization should give in positive work environment as

well as working opportunities. Organizations have many

opportunities for human resources or employee development,both within and outside of the workplace. Some of the

opportunities are narrated below:

Increase in employment opportunities: Massiveindustrialization provides employment opportunities on

massive basis. Massive industrialization means establish-

ment of more infrastructure facilities, which will in turn

lead to more employment. It will also lead to research

facilities and innovation of new products. Industrialists

will employ more capital equipment and labour for the

manufacture of new products.

Increase in living standard: Higher employment level,higher income level and greater variety of products meanhigher living standard. More industrialization will also

provide greater stimulus to outputs and stability to the

economy.

Performance Appraisal: It is used as a mechanism tounderstand the difficulties/weaknesses of the

subordinates and help/encourage them remove all these

and realize these. Other objective is to identify strength

and weaknesses of the subordinates too and to provide a

positive environment and help them to understand their

positive attitudes.

Career Planning: In HRD, corporate strategies andbusiness expansion plans should not be kept secret. Long

term plans of the organization should be made

transparent to employees. Most individuals want to know

their career growth and other possibilities. Hence the

managers should transform the organization plans to the

employees, thus making way for the employees to plan

their growth possibilities accordingly.

Training: The training is directly linked with the careergrowth and appraisal of the employees as such.

Employees are given the job training as well as off the

job training.

Potential Appraisal and Development: The capabilitiesshould be developed within the employees to grow

/perform new roles and responsibilities by themselves

continuously. A dynamic and growing organization

needs to continually review its structure and systems,

creating new roles and assigning new responsibilities.

Rewards: Rewarding employees is a significant part ofHRD. By this the organization helps in motivating and

recognizing the employee talents as such. It also helps in

communicating the values of the organization also.

Employee Welfare: HRD systems focus on employeewelfare and quality of work life by continually

examining employee needs and meeting them to the

extent possible.

Conclusion

To meet industrialization program, HRD is still a very

fragmented and imprecise activity. Its 'fuzziness' becomes

aggravated when one is focusing on a moving and dynamic

environment. A clear forecasting format on manpower

requirements, utilized at the macro and micro levels is vitally

needed. Precise demand patterns at the micro level and fine

tuned according to specific industries and jobs must be

undertaken. Each industry's growth rates in short and medium

terms need to be worked out not just for the purpose of

capital formation but also for HRD. A well thought out and

well-run HRD plan will achieve measurable positiveoutcomes for the company.

Current patterns of HRD appear to be aggregative, static and

often 'off tangent' to economic and industry trends. The

policy impetus through incentives and venture capitals given

to small and medium scale industries has not been adequately

matched by HRD. While the Government provides the


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necessary infrastructure and climate for the growth of small

and medium scale industries, the absence of a sufficient

number of entrepreneurs hampers the smooth take-off of the

industrialization plan. Thus, industrialization and economic

growth depend principally on the development of people of

their potentials, skills capabilities, resourcefulness andcommitment. Planning and investment in HRD is vital in

sustaining growth and maintaining social and economic

change.

References

1. Argyris C., Understanding Organizational Behavior, DorseyPress, Homewood, IL (1960)

2. Blake R. R., Memories of HRD, Training and Development,49 (3), 22-28 (1995)

3. Hashim F. Y., Commonwealth Report (1989) in PembangunanSumber Manusia di Malaysia: Cabaran abad ke-21. Kuala Lumpur,

Universiti Teknologi Malaysia (2000)

4. Commonwealth Secretariat Foundation for the Future: HumanResource Development, Report of the Commonwealth Working

Group on Human Resource Development Strategies, London (1993)

5. Desimone R.L., Werner J.M. and Harris D.M., HumanResource Development, 3rd ed., Orlando, Harcourt College

Publishers (2002)

6. Garavan T.N., Costine P. and Heraty N., The emergence ofstrategic human resource development, Journal of European

Industrial Training, 19 (10), 4-10 (1995)

7. Harrison R., Employee Development, 2nded., London, Instituteof Personnel and Development (2000)

8. Herzberg F., The Motivation-Hygiene Concept and Problems,of Manpower, Personnel Administration, 27, 2-7 (1959)

9. ILO, Human Resource Development in Asia and the Pacific inthe 21st Century - Issues and Challenges for Employers and their

Organizations, ILO Workshop on Employers Organizations in Asia

Pacific in the Twenty First Century, Turin, Italy (1997)

10. Likert R., New Patterns of Management, McGraw-Hill (1961)11. Malaysia, Government First Outline Perspective Plan, 1971-1990, National Printing Department (1971)

12. Kuala Lumpur Malaysia, Government Second OutlinePerspective Plan, 1991-2000, National Printing Department, Kuala

Lumpur (1991)

13. McGregor D., The Human Side of the Enterprise,New York,McGraw-Hill Inc., (1960)

14. Nadler L., The Hand Book of Human Resource Development,New York, John Wiley and Sons (1984)

15. Stead V. and Lee M., Inter-cultural perspectives on HRD, InStewart J. and McGoldrick J., eds., Human Resource Development:

Perspectives, Strategies and Practice, London, Prentice Hall (1996)

16. Virman B.R., Managing people in organizations: Thechallenges of change, India, New Delhi, Response Books, Sage

Publications (1999).

(Received 14thMarch 2012, accepted 10thJuly 2012)


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Micro Credit as a means of Socio Economic Empowerment

A Survey in Andhra PradeshSavitha B.* and Jyothi P.

School of Management Studies, Hyderabad Central University, Gachibowli, Hyderabad 500 046 (A.P), INDIA

*[email protected]

Abstract

The provision of credit to the poor and underprivileged

sections of the society is an important aspect of the

larger mandate of social banking today. In India, the

institutional credit delivery system of banks has been

aptly complemented by the growth of self-help savings

and credit groups. This has resulted in bringing

together the banking system and the poor for mutual

benefit. Irrespective of the model of credit delivery, the

availability of loans often brings about a change in the

household welfare of the borrowers and makes a

significant impact on their empowerment and socio

economic status. The present article which is based on

an empirical study of responses from a survey

conducted in a district of Andhra Pradesh provides an

insight into how the availability of credit and

membership in Self help groups brought about a change

in the socio economic status of the borrowers thereby

contributing to their empowerment.

Keywords:Micro credit, Self help groups, Socio economic

empowerment.

Introduction

Micro-Credit to the poor often works on the assumption that

availability of finance will enable them to come out of the

vicious circle of poverty. However when micro credit results

in bringing about the habit of savings for future needs, it will

augur a sense of social and economic security in the

borrowers. Micro- credit as a tool for development can be

successful when it is able to combine the advantages of

access to credit and financial sustainability.

Micro-credit has emerged as an important tool for

development and poverty alleviation as it promises delivery

of higher incomes and growth in assets for the poor by

promoting micro enterprises. Micro-credit providers canbring about a change in the lives of borrowers when they

focus on poverty alleviation by including livelihood

promotion, women empowerment, development of peoples

organizations and conducive institutional environment as

their core values. Micro-enterprises that are promoted by

individuals often face difficulties due to their inability to

spread the risk of borrowings.

*Author for Correspondence

As collective ownership of micro-enterprises mitigates the

borrowing risk, it leads to the emergence and popularity of

Self help groups in our country. The self help group is usually

formed with the help of an external agency usually a Non

governmental organization. It consists of upto 20 women

members who undertake mutual savings and credit. When

they are successful, they approach an external agency which

may be a bank or a micro finance institution to provide them

additional capital to facilitate on lending to the group

members. In India, the institutional credit delivery system of

banks has been aptly complemented by the growth of self-

help savings and credit groups. This has resulted in bringingtogether the banking system and the poor for mutual benefit.

Self Help Groups (SHG) play a vital role in the delivery of

micro-financial services. Their members take decisions

together on their savings, interest rates, provision of loans and

their recovery. This helps them to develop the right set of

attitudes and skills required for their sustenance. Members of

these groups also learn to mobilize resources, build linkages

which form the basis for their socio economic empowerment.

Irrespective of the model of credit delivery, the availability of

loans often brings about a change in the household welfare of

the borrowers and makes a significant impact on their

empowerment and socio economic status. The results of the

study provide an insight into how the availability of creditand membership in Self help groups brought about a change

in the socio economic status of the borrowers thereby

contributing to their empowerment.

Empowerment refers to control over resources and decisions.

Empowering the poor requires removal of all barriers which

stops them from taking actions that will result in improving

their welfare and limiting their choices. Any form of

institutional reform that will result in empowerment of the

poor will have to include access to information, informed

participation, political and administrative accountability and

improvement in ability of people to work together. The Self

help group movement was started in Andhra Pradesh to give

women greater access and control over resources, increasetheir income, develop linkages between SHGs and lending

institutions, providing greater access to health care and

education thereby leading to their social empowerment.

Membership of women in Self help groups engenders in them

a sense of self confidence and raises their level of awareness.

The solidarity and strength that they obtain from being

together with other women facing similar circumstances

serves as a powerful factor which empowers them.


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Review of Literature

Review of literature on micro-credit provides an

understanding on how it has enabled delivery of financial

services at a scale through appropriate mechanisms thereby

reaching out to the poor. Fisher and Sriram5in their research

suggested that micro-credit can be successful when itcombines outreach and sustainability. The micro credit

movement has brought about womens empowerment and

social change in India. Some studies in Bangladesh of the

clients of BRAC (Bangladesh Rural Advancement

Committee) showed that clients who were part of the micro

credit programme for more than four years had witnessed

significant rise in their household incomes and assets.

Morduch13

argues that micro credit is based on principles of

peer selection, peer review and dynamic incentives. Group

lending provides a distinct advantage over individual lending

as it improves repayment rates, allowing lower interest rates

and raising social welfare.

Namboodiri and Shiyani4observed strengths of SHG lending.

Siebel and Dave18

found that the effectiveness of group

lending in Self Help Groups resulted in lesser number of non

performing loans. Puhazhendhi and Satya Sai16

in their

research study found that SHGs have been instrumental in

economic and social empowerment of the rural poor. This

provided the incentive to take successive loans. The role of

Micro credit as a tool to foster economic and social

empowerment among women has been a subject of research

over the years. Successful efforts to empower poor people

will mean increasing their freedom of choice and action in

different contexts which include access to information,

inclusion and participation.3

There is no single definition of womens empowerment in the

literature. Empowerment is variously conceptualized as a

process, an end-state, and a capacity.1, 8-11

The majority of

efforts to measure womens economic empowerment

programmes focuses primarily on quantitative outcomes -

such as increased access to credit or increased business

revenue.

Studies by Rahman17

, Pitt and Khander15

and Hashemi,

Schuler and Riley6 have found positive results for women

empowerment in a micro credit program in Bangladesh.

Approaches to measuring womens empowerment generally

involve defining what is meant by empowerment and

identifying the different elements which make up thisdefinition. The elements of empowerment are: resources,

agency and achievements8; control over resources and

agency9; agency and opportunity structure

1; agency;

structures and relations; assets, knowledge, will and

capacity.2In most cases, these elements are then broken down

into sub-dimensions with associated indicators and sources of

measurement.

A review of theories and strategies to foster women's

empowerment in the development context by Malhotra10

defined empowerment as the ability of people to make

strategic choices in areas that affect their lives. Pitt et al15

found that the view that womens participation in micro credit

programmes helps to increase womens empowerment by

establishing a baseline of women's assets, knowledge, willand capacity. Mayoux et al

12 emphasized the importance of

studying the differential impacts of various types of financial

products and service delivery, and their influence on women

empowerment. Jupp et al7developed a participatory approach

to measuring empowerment at the project level in

Bangladesh.

The growth of Self help groups in India and provision of

micro credit to them by banks and microfinance institutions

has resulted in improving their participation in society and in

governance. The review of literature throws light on the fact

that programmes that aim at empowering women need to

measure the improvement in the ability of women to make

decisions. Their access to credit, finances, property, nutrition,education health care and improvement in social status and

participation in governance also need to be measured.

Hypotheses

Hypotheses were developed to judge the relationship between

loan use and its impact on income and financial independence

of the borrowers.

1. There is no significant relationship between receipt ofloan and change in overall household income.

2. There is no significant relationship between loan use forincome generating activity and financial independence of

the borrowers.

3. There is no significant relationship between training andits impact on way of life of the borrowers.

Methodology

Sample: The sample consisted of 200 respondents. Primary

data have been collected using convenient sampling method.

The sample consisted of 200 SHG members in Rangareddy

district of Andhra Pradesh.

Demographic profile of sample: The respondents were

women in the age group of 20 years to 60 years. Sample

consists of both married and unmarried women. A

questionnaire was specially designed for the purpose ofcollecting information. The questionnaire was translated into

the local language to elicit information and data from the

respondents.

Scoring: The responses were coded and subjected to SPSS

analysis.

Findings: Hypotheses developed were tested using chi square

test. The results are as follows:


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Table I

Hypotheses Results

NULL HYPOTHESESSIGNIFICANCE P

VALUERESULTS

H1: There is no significant relationship between receipt of loan and change inoverall household income.

0.000 Rejected

H2: There is no significant relationship between loan use for income generatingactivity and financial independence of borrowers

0.000 Rejected

H3: There is no significant relationship between training and its impact on way of

life of borrowers0.000 Rejected

*chi square statistic significant at 0.05 level

The Chi-square statistic was found to be significant at the

0.05 level and hence the null hypotheses were rejected

thereby establishing that there is significant relationship

between receipt of loan and change in overall household

income. The test results also show that the use of loan for

income generating activity has a positive impact on financialindependence. Borrowers felt that when loans are used for

income generating activities rather than for consumption, it

makes them feel financially independent.

The borrowers used the loan for income generating activities

such as agriculture (43%), setting up small business such as

grocery stores (12%), tea stalls (10%), animal husbandry

(9%), tailoring shop (7%), flower (8%) and fruit shops (11%).

The study indicates that when loans are used for Income

generating activities, it helps in earning profits which

becomes the main source of income for the borrowers.

Borrowers took decisions relating to the use of loans for

income generating activities, spending on food, clothing and

payment of school fees for childrens education. Ability tocontribute to decisions relating to household with control

over financial resources made them feel financially

independent. Those respondents who attended the training

programs felt a positive impact on their life which gave them

the confidence to set up their own small business units.

Training programmes attended included those on tailoring,

hand and machine embroidery.

Factors influencing Social Empowerment

SHG members often make use of loan facilities from their

groups, banks and MFIs. These loans affect various aspects

of social empowerment of these members. It has been thought

that it is apt to explore these factors that affect their social

empowerment. A structured questionnaire was distributed to

the SHG members. The sample consists of 200 SHG

members of Rangareddy district. The questionnaire contained

7 variables influencing the social empowerment. The

variables were measured using a five-point Likert Scale. The

opinion of respondents is taken on a scale of 1-5 with 1

being the least important and 5 being most important. The

variables affecting the social empowerment mentioned in the

questionnaire are as follows:

Table IIVariables Affecting The Social Empowerment

S. N. Variables

1 Comparison with others

2 Change in food3 Effect on person

4 Effect on welfare on household

5 Dependency on husband

6 Contribution to family budget decisions

7 Ability to prepare for emergencies

Source: Primary data

Factor analysis is a data reduction statistical technique that

allows simplifying the correlational relationships between a

number of continuous variables. The study is intended to

explore the important factors affecting social empowerment

of SHG members. Principal Component Analysis is used,

since it is an exploratory factor analysis. To test the

acceptability of data, the following steps were taken:

The correlation matrices were computed. It revealed thatthere is enough correlation to go ahead for factor

analysis.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy forindividual variance is studied. The test result is as

follows:

Table IIIKMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of

Sampling Adequacy .817

Bartlett's Test ofSphericity

Approx. Chi-Square 1373.016

Df 21

Sig. .000

It found sufficient correlation for all the variables/attributes.

A measure which is more than 0.8, is considered


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meritorious and is acceptable. The measure of sampling

adequacy is found to be 0.817. It indicates that the sample is

good enough for sampling. The overall significance of

correlation matrices is tested with Bartlett's Test of Sphericity

providing support for the validity of the factor analysis of the

data set as shown in table III. Principal Component Analysis

is employed for extracting the data which allowed

determining the factor underlying the relationships between a

number of variables.

Table IV

Total Variance Explained

Component Initial Eigen values Extraction Sums of Squared LoadingsRotation Sums of Squared

Loadings

Total% of

Variance

Cumulati

ve %Total

% of

Variance

Cumulative

%Total

% of

Varianc

e

Cumulati

ve %

1 4.961 70.871 70.871 4.961 70.871 70.871 3.159 45.122 45.122

2 1.014 14.479 85.350 1.014 14.479 85.350 2.816 40.228 85.350

3 .339 4.848 90.198

4 .277 3.951 94.149

5 .192 2.750 96.899

6 .152 2.165 99.064

7 .066 .936 100.000

Extraction Method: Principal Component Analysis

Total variance explained suggests that it extracts one factor

which accounts for 85.35 percent of the variance of the

relationship between variables/attributes. The criteria for

extracting initial factors are Eigen value of over 1. A total of

two factors are extracted with a total variance explained being

85.356%.

Rotation is necessary when extraction technique suggests that

there are two or more factors. The rotation of factors is

designed to give an idea of how the factors initially extracted

differ from each other and to provide a clear picture of which

item loads on which factor. There are only 2 factors, each

having Eigen value exceeding 1 (details as shown in table

IV). Varimax rotation is applied for the selected 7 variables.

The numbers in each column are the factor loadings for each

factor, roughly the equivalent of the correlation between a

particular item and the factor.

Varimax rotated factor analytic results for factors affectingthe social empowerment of SHG members. The factor

loadings of the 7 variables are observed and clubbed into 2

factors. The major factors influencing are depicted in figure

1.

Fig 1: Factors influencing the social empowerment of SHG members

From the above chart, it is clear that Household Welfare and

Social Status together influence Social Empowerment. The

detailed explanation on the factors affecting the Social

Empowerment as identified by the factor analysis is given

below:

Factor 1: Household Welfare

It is the most vital factor which explains 70.87 percent of

variation. The household welfare is the most important factor

and it has three loads to this factor: Ability to prepare for

Sudden Expenses/Emergencies (0.849), Ability to Contribute

to Family Budget Decisions (0.830) and Effect on Welfare of

household after the loan (0.825).

Factor 2: Social status

This factor has three variables which have 14.479 percent of

variation. The factors - Change in food since receipt of loan

(0.909), Effect on person (0.886) and comparison with

Factors Influencing

Social Empowerment

Household

Welfare

Social

Status


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others in your area (0.867) are the important concerns for the

SHG members which increase their social empowerment.

Thus the factor analysis proves that the most important

factors that contribute to social empowerment are variables

that directly impact household welfare of borrowers and their

social status.

On the same lines a factor analysis was conducted to establish

the factors that impact the socio economic status of members

after they have joined Self help groups. The Cronbachs

alpha, (Table VI) which determines the internal consistency

of the scale is .686. Kaiser-Meyer-Olkin Measure of

Sampling Adequacy (Table VII) for individual variance is

0.732 .The value is considered to be a middling value and

indicates that the sample is considered good.

The total variance explained (Table VIII) suggests that it

extracts one factor which accounts for 67.589 percent of the

variance of the relationship between the variables. The

criteria for extraction of initial factors are Eigen value of overone. A total of six factors were extracted with the total

variance explained being 67.589 percent.

Varimax rotation has been applied as there are more than two

factors for the selected nineteen variables (Table IX). The

numbers in each column are the factor loadings for each

factor, roughly the equivalent of correlation between a

particular item and the factor. The factor loadings of the

nineteen variables are observed and grouped into six factors.

Figure 2 explains that access to credit and health services,

social recognition, development awareness, individual

growth, income and decision making and mobility are the

factors which influence the socio economic status of the SHG

members. The various factors have been explained as

follows:

Table V

Rotated Component Matrix (A)

Component

1 2

Dependency on Husband -.866

Ability to prepare for SuddenExpenses/Emergencies

.849 .349

Ability to contribute to Family Budget

Decisions.830 .398

Effect on Welfare of household after theloan

.825 .364

Change in food since receipt of loan .909

Effect on person .351 .886

Comparison with others in your area .329 .867

Extraction Method: Principal Component Analysis; Rotation

Method: Varimax with Kaiser Normalization; Rotation converged in

3 iterations.

Fig. 2: Factors influencing the socio economic status

Factorsinfluencing

SocioEconomicStatus

1.AccesstoCreditandHealthServices

2.

SocialRecognition

3.DevelopmentAwareness

4.

IndividualGrowth

5.IndividualIncome

6.Mobility


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Table VI

Reliability Statistics

Cronbach's Alpha N of Items

.686 19

Table VII

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy.732

Bartlett's Test of

Sphericity

Approx. Chi-

Square1711.936

Df 171

Sig. .000

Table VIIITotal Variance Explained

Component Initial EigenvaluesExtraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total

% of

Varianc

e

Cumulativ

e %Total

% of

VarianceCumulative % Total

% of

Varianc

e

Cumulati

ve %

1 4.702 24.745 24.745 4.702 24.745 24.745 3.905 20.551 20.551

2 2.984 15.705 40.450 2.984 15.705 40.450 2.590 13.629 34.181

3 1.606 8.455 48.905 1.606 8.455 48.905 1.989 10.470 44.650

4 1.416 7.453 56.358 1.416 7.453 56.358 1.965 10.344 54.995

5 1.100 5.791 62.149 1.100 5.791 62.149 1.216 6.402 61.396

6 1.034 5.440 67.589 1.034 5.440 67.589 1.177 6.192 67.589

7 .982 5.168 72.757

8 .860 4.524 77.281

9 .793 4.171 81.452

10 .635 3.341 84.793

11 .556 2.925 87.718

12 .500 2.630 90.348

13 .448 2.358 92.706

14 .369 1.940 94.646

15 .323 1.700 96.34716 .269 1.416 97.763

17 .208 1.093 98.855

18 .146 .769 99.624

19 .071 .376 100.000

Extraction Method: Principal Component Analysis.


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Table IXRotated Component Matrix (A)

Component

1 2 3 4 5 6

Access to sanitation .938Access to immunization .912

Access to health services .843

Access to credit sources .778

Skills .563 -.373

Recognition in community .908

Recognition in family .840

Interaction with outsiders .740 .327

Literacy .558 .447

Nutrition awareness .405 .698

Girl child awareness .637 .340

Voicing concern .627

Family planning awareness .601 .463

Asset building .791

Family income .650

Health awareness .469 .437 -.485

Individual income .860

Decision making related to child .436 .392

Mobility -.812

Extraction Method: Principal Component Analysis: Rotation Method: Varimax with Kaiser Normalization; Rotation converged in 7 iterations.

1. Access to credit and health services: It is the mostimportant factor which explains 24.745 percent of the

variation. Access to Credit and Health Services is the

most important factor and has 5 loadings, namely, access

to sanitation (0.938), access to immunization (0.912),

access to health services (0843), access to credit sources

(0.778), skills (0.563) signifying that they are key

influencers of the socio economic status.

2. Social recognition:This factor has four variables whichaccount for 15.705 percent of the variation. Recognition

in community (0.908), recognition in family (0.840),

ability to interact with outsiders (0.740) and literacy(0.558) are considered to be the variables which impact

the social status of the members.

3. Development awareness: This factor has four variablesand accounts for 8.455 percent of the variation. Nutrition

awareness (0.698), girl child development awareness

(0.637), voicing concern (0.627) and family planning

awareness (0.601) are the key variables which contribute

to betterment of health and well being of individual and

families.

4. Individual growth: It has five variables which accountfor 7.453 percent of the variation. Asset building (0.791),

family income (0.650), literacy (0.447) are some of the

key variables which contribute to individual growth.

Health awareness (-0.485) seems to have an inverse

relationship with the other factors.

5. Individual Income: This factor accounts for 5.791percent of the variation. Growth in individual income

(0.860) will definitely improve the socio economic

status.

6. Mobility: This factor accounts for 5.44 percent of thevariation although it has an inverse (-0.812) relationship

with other factors. Increase in mobility is not considered

as a major contributor to the betterment of the socio

economic status.

Findings and Conclusion

1. There is significant relationship between receipt of loan


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and change in overall household income.

2. There is significant relationship between use of loan forincome generating activity and financial independence of

borrowers.

3.

There is significant relationship between training and itsimpact of life of respondents

4. The factor analysis established:a) Two factors namely household welfare and social

status as key influencers of social empowerment.

b) Six factors namely Access to credit and health

services, Social recognition, Development awareness,

Individual growth, Individual Income, Mobility as

influencers of socioeconomic status.

Micro credit when invested in an income- generating activity

or productive assets leads to improvement in income levels of

the SHG members. Profits from the enterprise provides

increased income and a general strengthening of incomesources. The study proves that SHGs have facilitated better

interaction of its members with the external environment and

have promoted a larger consciousness and awareness of the

world. SHG members keep their own accounts and are able to

engender in themselves a sense of financial discipline as peer

pressure ensures timely repayment and end use of micro

credit.

References

1. Alsop R., Power, Rights and Poverty: Concepts andConnections, a working meeting sponsored by DFID and the World

Bank (2004)

2. Charlier S. and Caubergs L., The Women EmpowermentApproach: a methodological Guide, Commission on Women andDevelopment, Brussels (2007)

3. Narayan Deepa, ed., Empowerment and Poverty Reduction: ASource Book, The World Bank, W.D.C. and Ibid (2002)

4. Djankov S., Miranda P., Seira E. and Sharma S., Who are theUnbanked?, World Bank Policy Research Working Paper 4647,

World Bank, Washington, D.C. (2008)

5. Thomas Fisher and Sriram M.S., Beyond Micro-Credit Puttingdevelopment Back into Micro-Finance, Oxfam, Oxford, U.K in

association with New Economics Foundation, London, Vistaar

Publications (Sage), New Delhi (2002)

6. Hashemi S., Schuler M.S.R. and Riley A.P., Rural CreditProgrammes and Women Empowerment in Bangladesh, World

Development,24(4), 635-653 (1996)

7. Jupp D, Ibn Ali S and Barahona C., Measuring Empowerment?Ask Them Quantifying qualitative outcomes from peoples own

analysis, Sida, Sweden (2010)

8. Kabeer Naila, Conflict over Credit: Reevaluating theEmpowerment Potential of Loans to Women in Rural Bangladesh,

World Development, 29 (1), 63-84 (2001)

9. Malhotra A., Measuring Womens empowerment, unpublishedbackground paper for workshop on poverty and gender, New

perspectives (2002)

10.Malhotra Anju, Measuring Womens Empowerment as aVariable in International Development, World Bank, Gender and

Development Group, Washington DC (2002)

11.Martinez E., The Courage to Change: Confronting the limits andunleashing the potential of CAREs programming for women,

Synthesis Report: Phase 2, CARE International Strategic ImpactInquiry on Womens Empowerment, CARE International (2006)

12.Mayoux.L. and Hart L., Gender and Rural Microfinance :Reaching and Empowering Women, A guide for Practioners, IFAD

Rome (2009)

13.Morduch Jonanthan, The microfinance promise, Journal ofEconomic Literature, 37, December,1569-1614 (1999)

14.Namboodiri N.V. and Shiyani R.L., Potential role of Self HelpGroups in Rural financial deepening,Indian Journal of Agricultural

Economics,56 (3), July-Sept, 401-409 (2001)

15.Pitt M. and Khander S., Household and Intra HouseholdImpacts of the Grameen Bank and similar targeted credit

programmes in Bangladesh (1995)16.Puhazhendhi V. and Satya Sai K.J.S., Economic and SocialEmpowerment of Rural Poor through Self Help Groups, Indian

Journal of Agricultural Economics, 56 (3) July-Sept, 450-451

(2001)

17.Rahman R.I., Impact of Grameen Bank on the situation of PoorRural Women, Grameen Evaluation Project, Bangladesh Institute of

Development Studies BIDS, Working Paper No. 1, Dhaka (1986)

18.Siebel Hans and Dave Harish Kumar, Commercial aspects ofSHG Banking in India, NABARD (2002).

(Received 14thMay 2012, accepted 12

thAugust 2012).


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Application of Artificial Neural Networks for Sales Forecasting in

an Indian Automobile Manufacturing CompanyChauhan Manish Kumar1and Mittal M. L.2*

1. Mechanical Engineering Department, Krishna Institute of Management and Technology, Moradabad (UP), INDIA2. Mechanical Engineering Department, Malaviya National Institute of Technology, Jaipur (Rajasthan), INDIA

*[email protected]

Abstract

Sales forecasts are essential part of any business as

planning such as facility planning, human resource

planning, manufacturing planning and scheduling, and

planning of sales promotions are driven by it.

Traditionally, time series analysis was used as a tool for

sales forecasting which suffers from limitations such as

efforts needed for analysis and the accuracy of the

resulting forecasts. Recently, Artificial Neural Networks(ANNs) have been developed for a variety of

applications in engineering and management. Sales

forecasting is one such application where ANNs have

been successfully applied. This study reports the

application of ANN in an Indian auto manufacturer for

sales forecasting. The results are compared with the

time series models.

Keywords: Sales Forecasts, Artificial Neural Networks,

Automobile Industry.

Introduction

Forecasting in general is prediction of some future event.Businesses use a variety of forecasts such as forecast of

technology, economy and sales of product or service.

Forecast of sales drive many important planning decisions in

manufacturing organizations. Typical decisions include

number and capacities of the facilities, hiring of personnel,

required inventory levels, quantity and timing of

purchasing/production, distribution network and transporta-

tion. The accuracy of sales forecasts thus becomes primary

condition for effective management of operations in the

manufacturing organization. Although having accurate

forecasts has never been easy, it has become more difficult in

recent years due to increased uncertainty, complexity of

business and reduced product life cycle.

Owing to its importance, the researchers had put lot of efforts

towards development of better methods of forecasting. These

methods are broadly classified as Qualitative and Quantitative

methods. Qualitative methods are used when either no past

data is available or the planning horizon is too long.

Quantitative methods, on the other hand, are used when

sufficient past data is available and the planning horizon is

relatively short.

Traditionally, statistical methods have been used for

quantitative forecasting which are usually divided into time

series analysis and causal models. In time series analysis we

try to separate the underlying patterns (constant, trend,

seasonality and cycle) in the past sales data and use different

techniques to build a model to forecast the future sales.

Averaging, time series decomposition, regression analysis,

exponential smoothing or a combination of these are used in

time series analysis. In causal models it is assumed that the

sale of certain product is function of some other variable/s

and the job of the forecaster is to identify that relationshipusing statistical technique such as regression. The details of

the above techniques can be found in any elementary text

such as Henke and Wichern2. Irrespective of the technique the

statistical methods are model based which are more difficult

to develop, mostly based on assumptions of linearity and are

unable to learn from experience.

During last three decades, a large variety of Artificial Neural

Networks (ANNs) have been developed for a number of

applications such as pattern recognition, nonlinear

optimization and forecasting. ANN have also been used for a

large number of business problems also. Some of the early

business applications are reviewed by Wilson and Sarda5and

forecasting application by Zhang et al.6One of the important

applications of ANN is in the area of sales forecasting. This

study reports application of ANN for sales forecasting in a

leading Indian two wheeler manufacturing Company.

An Overview of Artificial Neural Networks

Artificial Neural Networks are inspired by biological neural

networks of human brain having important characteristics

such as parallelism, learning and generalization ability and

associative storage of information. The first mathematical

model of a neuron was proposed by McCulloch and Pitts3in

1943. In this model of neuron (perceptron) a weighted sum of

m input signalsxj,j=1,...,mwas computed and the output was

forced to 1 if the sum was pre-decided threshold value (U);0 otherwise. This model of neuron though paved the way of

ANN, had many limitations for its use in practice. Since 1943

researchers had put lot of efforts towards development of

varieties of ANNs with their real life applications in areas

such as optimization, pattern recognition and forecasting etc.

Detailed description of the ANN techniques can be seen in

Anderson1.

An ANN essentially is a weighted directed graph in which


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nodes represent artificial neurons and directed edges (with

weights) are connections between the neurons. Depending

upon the presence or absence of loops in the network ANNs

are broadly classified into feed-forward and recurrent

networks respectively. Single layer perceptron, Multilayer

perceptron and Radial bias function nets are examples of feedforward networks and Competitive networks, Kohonons self

organizing maps, Hopfields networks and ART networks are

some the recurrent networks.

Learning or training is an important trait of the ANN.

Learning essentially is adjustment of connection weights and

network so that it can perform its intended task. There are

mainly two types of learning: supervised and unsupervised. In

supervised learning, there is an output associated with every

input pattern. Weights are adjusted to get output from the

network as close as possible to the known output. In un-

supervised learning no correct output is provided for the

given input pattern. In this situation the learning takes place

by identifying the underlying structure or pattern in the data.Hybrid learning combines supervised and un-supervised

learning. Multilayer Feed-forward Networks (MFN) along

with supervised learning are mostly used for forecasting

applications.

Multilayer Feed-forward Networks

Basic Multilayer Feed-forward Network:Multilayer Feed-forward networks are probably the most used models of ANN

along with the back propagation learning. A multilayer feed-

forward network consists of an input layer, an output layer

and a number of hidden layers. Figure 1 shows a feed-

forward network with one hidden layer.

Each connection (arrow) has an associated weight and eachneuron has an associated transfer function. The sum of the

weighted inputs at each neuron forms an input to the transfer

functionf( ) of the neuron. The behavior of an ANN depends

on the weights and the transfer function used. Three transfer

functions: linear, threshold and sigmoid are commonly used

for ANNs.

Design of Feed-forward ANN: The design of ANN is animportant part of building an effective ANN. One part of the

network design is deciding the number of layers in the

network and number of neurons in each of the layers. The

numbers of neurons in the input and output layers are based

on the number of inputs and outputs respectively. The

number of hidden layers can be one or more. One hidden

layer, however, is sufficient in most of the situations. Number

of neurons in the hidden layer may vary from one to any

number which is to be decided on the basis of the efficacy of

the model.

If the number of neurons in a layer is too small, the outputs

will not be able to fit all the data points causing under fitting.

On the other hand, if number of neurons is too large,

oscillations may occur between data points causing over

fitting. The number of neurons in the hidden layer needs to be

decided by trial and error method. The inputs needs to be

coded which could be binary number or real number

depending on the problem. Another important element of

design of an ANN is the type/s of transfer function/s to be

used for the neurons in the model. Similar to deciding thenumber of neurons in the hidden layer the transfer function

needs to be decided by trial and error method.

Training of Feed-forward ANN:Training of ANN consists

of adjusting the connection weights of the network so that it

can perform the intended function. Back-propagation

algorithm4 is the most used algorithm for training MFFN

which is based on the calculation of the change in the error

(difference between the desired output and the actual output)

as each weight is increased or decreased slightly. This

essentially involves two phases. In the first phase the input

signal is propagated in forward direction in the network and

error is determined. In the second phase parameters (weights)

of the network are adjusted to minimize the error.

The Case

Data Collection: The first and foremost step of the model

building is collection of appropriate data. The quality,

availability, reliability and relevance of the data used to

develop and run the Artificial Neural Network system are

critical to its success. For implementation of Artificial Neural

Networks for sales forecasting in the case company, the

monthly sales data for last ten years starting January 2001 to

December 2010 were collected. The sales considered in this

study were for the total number of motorcycles across all

types of the models (aggregate sales). This ten years monthly

sales data are shown in table 1.Design and training of ANN:As discussed above, selectionof an appropriate ANN architecture is the key to successful

forecasting and it involves deciding number of layers and

number of neurons in each of them. For the current

application one hidden layer is considered in addition to input

and output layers. The input layer consists of six neurons (one

for each of the factors considered important for forecasting)

and the out layer consist of one neuron representing the

forecast. The factors included in the model along with their

coding are as below:

a. Festivals:Festivals are an integrated part of the Indianculture and considered to affect the sales of two

wheelers. During Deepawali and Navratra, the sales of

vehicles in general is very high and thus the sales of the

motorcycles. The month of Navratra is coded as 2,

Deepawali as 1 and other months are coded as 0.

b. Season:The weather seasons in a year have been foundto affect the sales of motorcycles in India. For example

the sale during rainy seasons is generally very low. Four

seasons are considered in this study. Three main seasons

are rain, winter, summer while spring and autumn put


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together in forth season. The rainy months are coded as

0 winter as 1, summer as 2 and spring and autumn

as 3.

c. General economic condition:The third factor affectingthe sales of the motorcycles is the market condition. The

recession period is coded as 0, good economic

condition as 2 and normal condition as 1. This is the

general factor which affects the sales of all costly

products.

d. New model launch:It has been found that launching ofthe new models of motorcycle by the company affects

the sales and thus considered in the model. The month in

which a new model has been launched is coded as 1

and otherwise 0.

e. Marriage season: In India many people purchasemotorcycles at the time of marriages which take place

during certain months of the year. During these months,

sales of the motorcycles are generally higher than non-

marriage period. Non marriage months are coded as 0

while marriage months are coded as 1.

f. Month: The last factor considered in the model is themonth of the year. The months are coded from 1-12 with

January 1 and December as 12.

There is no easy way to determine the number of neurons in

the hidden layer except trial and error. There are two methods

prevalent for this: forward and backward. In forward

(backward) selection we start with smaller (large) and

increase (decrease) in steps recording performance at each

step. The optimum number of neurons can be then selected.

In the current study 8 neurons were found to be optimum.

The next step in the architecture of this Artificial Neural

Network is the selection of the transfer functions. Based on

trial and error method Tansigfunction (tansig(n) = 2/(1+exp(-

2*n))-1) was found to be suitable for hidden neurons and

Logsigfunction (logsig(n) = 1/(1 + exp(-n)) for output layer.

An important aspect of ANN modeling is identification of the

data points for training validation and test sets. In the current

study the first 70% (January 2001 to December 2007) of the

data points are considered for training. Training of the

network is performed over 1000 epochs. The performance of

the training with respect to number of epochs is shown in

figure 2.

Results and Discussion

After training of the network it was used to forecast sales for

the whole period (Jan 2001 to Dec 2012). The forecasts are

compared with the actual sales and MAPE (Mean Absolute

Percent Error) and MSE (Mean Squared Error) are

determined. MAPE and MSE are found to be 6.31 and 3.53

E+08 respectively.

In order to evaluate the performance of the proposed ANN,

forecasts were also generated using the traditional time series

analysis. The time series model was developed using the

feature expert modeler available in the SPSS 16.0. This

feature provides the most appropriate model for the given

time series data. The time series model was fitted using 100%of the data (Jan 2001 to Dec 2010).

The expert modeler found Winters additive model with the

parameters as: Alpha (level) =0.196; Beta (trend) =0.001; and

Gamma (season) = 0.001 as the most appropriate model for

the data. With this fitted model MAPE and MSE were found

to be 15.32 and 1.06 E+09 respectively. This show that

proposed ANN provides better forecasts than the traditional

time series analysis. The sales forecasts are also plotted

against actual sales over with entire period given in figure 3.

Conclusion

Artificial Neural Networks are model free techniques that are

capable of learning by examples. These have beensuccessfully applied to a wide variety of engineering and

managerial applications. Sales forecasting is one such

application. Here a case study is reported in which multilayer

feed forward neural networks have been applied for

forecasting of sales in a leading two wheeler manufacturer in

India. A number of factors that are specific to Indian

conditions are considered in developing the ANN.

Performance of the proposed ANN is evaluated by comparing

the forecasts with the actual sales which give MAPE and

MSE as 6.31 and 3.53 E+08 respectively. The proposed

forecasting system is also found to be superior than a time

series model (Winters additive model which is found to be

most appropriate for the data).

References

1. Anderson J. A., Introduction to Neural Networks, MIT Press,Cambridge, MA (1995)

2. Henke J. and Wichern D.W., Business Forecasting, PearsonPrentice Hall, New Delhi(2007)

3. McCulloch W. S. and Pitts W., A Logical Calculus of IdeasImmanent in Nervous Activity, Bull. Mathematical Bio-physics, 5,

115-133(1943)

4. Werbos P. J., Beyond regression: New tools for prediction andanalysis in the behavioral sciences, Ph.D. Thesis, Harvard

University, Cambridge, MA(1974)

5. Wilson R. and Sharda R., Neural networks, OR/MS Today,Operational Research Society,Computers and Operations Research,

August, 3642 (1992)

6. Zhang G., Patuwo B. E. and Hu M. Y., Forecasting with artificialneural networks: The state of the art, International Journal of

Forecasting, 14 (1), 35-62 (1998).


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Table 1

Ten years aggregate monthly sale of motorcycles

MONTH 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

JANUARY 67,432 58,454 83,821 101276 143023 132456 237163 224734 243098 276376

FEBRUARY 72,461 81,327 98,056 122481 156476 139076 189367 245431 265431 287245

MARCH 98,267 101032 87,578 187592 185612 198521 289291 320594 325628 372156

APRIL 105345 118671 95,634 132621 173714 202591 292392 316561 323543 387521

MAY 115178 107491 110163 150042 192417 192472 285109 312317 305245 382201

JUNE 94,234 84,012 67,592 101438 145698 176431 212621 307,261 299256 356356

JULY 78,465 72,982 52,841 162511 134623 178921 246342 306254 287653 355635

AUGUST 67,231 78,267 57,830 110721 131056 212025 240875 305576 285983 312387

SEPTEMBER 102345 99,319 102921 224291 312512 298297 314567 385262 378543 382067

OCTOBER 156545 123498 169045 296494 356265 361167 365022 398458 425541 456742

NOVEMBER 122546 111067 123151 258623 276034 291201 288027 401345 412371 434367

DECEMBER 112324 187521 156481 241371 245629 252462 240532 385521 398045 312276

Fig. 1: Typical multilayer feed-forward network with one hidden layer

1

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2

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..

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Inputlayer OutputlayerHiddenlayer

X1

X2

X3

Xm

y1

yn

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