An Empirical Study on the application of Ergonomics Approach at Public Universities of Ethiopia with Special Reference to Adigrat University.

Tewelde Gebresslase

Abstract: It is obvious that either public or private institutions might be profit or service oriented in their nature and to achieve this; employee wellbeing should be primarily concerned. One move toward is to integrate the concepts of quality ergonomics which is the main human factors, and safety into such higher academic institutions experiences for all community that make the competitive in today’s working environments of the institutions. Literally speaking Ergonomics means the study or measurement of work therefore this paper focuses on the relationship between physical and logical environment setting and institutional performance with especial reference to Adigrat University. Hence, this paper is literature and personal observation based research article on the role of ergonomics approach of workplace in case of the stated University which is one of the third generation higher academic institutions in Ethiopia., the researcher tried to put a possible suggestions based on a practical observation on what is going practically. At the end with a proper plan of ergonomics approach the tangible and intangible costs due to unhealthy working condition could be reduced since the outcome of this paper could attract the attention of the management bodies in particular and community of the institution i.e Adigrat University in general.

 Key words: Professional safety, ergonomics, employees’ motivation, productivity, Adigrat University.

  1. Introduction:

Ergonomics is the study and means to enhance the compatibility between human beings and surrounding systems. Ergonomics satisfies some of the key needs of the operators including reduction of stress and fatigue, improvement in safety, comfort level and quality of the work life. It promotes the well-being of the operator by maintaining a safe, healthy and efficiency driven environment (Viraj Bakshi, 2016). Ergonomics is defined as the design of workplace, equipment, machine, tool, product, environment and system, taking into consideration the human’s physical, physiological, psychological capabilities and optimizing the effectiveness and productivity of work system while assuring the safety, health and wellbeing of the workers.rgonomics focuses on the work environment and items such as the design and function of workstations, controls, displays, safety devices and tools to fit the employee’s physical requirements, capabilities and limitations to ensure his/her health and well being.

Ergonomics is the study and means to enhance the compatibility between human beings and surrounding systems. Ergonomics satisfies some of the key needs of the operators including reduction of stress and fatigue, improvement in safety, comfort level and quality of the work life. It promotes the well-being of the operator by maintaining a safe, healthy and efficiency driven environment (Viraj Bakshi, 2016). Ergonomics is defined as the design of workplace, equipment, machine, tool, product, environment and system, taking into consideration the human’s physical, physiological, psychological capabilities and optimizing the effectiveness and productivity of work system while assuring the safety, health and wellbeing of the workers.

According to the collection literature for ergonomics concept the following are some of the definitions. Ergonomics is the scientific study of people and their working conditions, especially done in order to improve effectiveness (Cambridge dictionary). Ergonomics is the science of refining the design of products to optimize them for human use. (…) it is sometimes known as human factors engineering (whatis.com). Ergonomics is a science that deals with designing and arranging things so that people can use them easily and safely (Merriam-Webster Dictionary). Ergonomics is an applied science concerned with designing and arranging things people use so that the people and things interact most efficiently and safely —called also biotechnology, human engineering, human factors (Merriam-Webster Dictionary). Ergonomics is a study of capacities and limitations of mental and physical work in different settings. Ergonomics applies anatomical, physiological, and psychological knowledge (call human factors) to work and work environments in order to reduce or eliminate factors that cause pain or discomfort (business dictionary).

Although the term Ergonomics has many but mutually inclusive definitions, the following definition is taken from Peter Vink (2006) as operational meaning for this paper. Hence,   Ergonomics (or human factors) is the scientific discipline concerned with the understanding of interactions among humans and other elements of a system, and the profession that applies theory, principles, data and methods to design in order to optimize human well-being and overall system performance. Having this operational definition for Ergonomics, this paper is an empirical study on human and none human factors for unhealthy working condition and tried to put possible observations on how Ergonomics Approach for workplace could help as a solution for related problems at Public Universities of Ethiopia with Special Reference in Adigrat University.

  1. Research Rationality
S

ince human resources are the ultimate user of the workplace environment, therefore labor should consider designing and equipping the workplace setting to suit their comfort. In this case the physical and logical design of working environments has a direct impact on the healthy workplace vis-a-vise wellbeing of the workers. As Joan Burton cited in WHO Regional Office for the Western Pacific; defines a healthy workplace as follows:

 “A healthy workplace is a place where everyone works together to achieve an agreed vision for the health and well-being of workers and the surrounding community. It provides all members of the workforce with physical, psychological, social and organizational conditions that protect and promote health and safety. It enables managers and workers to increase control over their own health and to improve it, and to become more energetic, positive and contented.”

Either knowingly or unknowingly the management of one organization could follow any leadership philosophy; whatever the response of the followers. Besides to this, the management body could ignore the humanitarian aspect to maximize the organizational performance. As a result the working environment could affect negatively since the relationship between the top and lower management level could badly affect. In this regard, “It is unethical and short-sighted business practice to compromise the health of workers for the wealth of enterprises.” Evelyn Kortum, WHO (2014).

A healthy workplace can be affected through two factors which are human and non human. In this case, human factors identify what employees are being asked to do, who is doing it, and where they’re working and Non human factors identify the tangible and intangible features of the environments. According Kerm Henrikse (2010) Human factors research applies knowledge about human strengths and limitations to the design of interactive systems of people, equipment, and their environment to ensure their effectiveness, safety, and ease of use.

As Peter V. (2006) cited in Vink, (2005), participatory ergonomics is the discipline that studies how different parties should be involved in a design process. Participatory ergonomics is the adaptation of the environment to the human (that is ergonomics) together with the proper persons in question (participants). Besides, different authors also argued that “good ergonomics is good economics”. However, the concepts of ergonomics are not implemented properly. It is known that there are a number of hidden reasons why the employees who are working in Adigrat University (where the author is working) are not well satisfied in their day to day working style. Thus, it is believed to have a careful observation what is going practically and assessing to what extent the Ergonomics approach (human factors) for workplace is implementation otherwise to forward possible alternative solution for healthy, conducive and productive working environment to Adigrat University.

  1. Research Questions
  • What are the human factors for institutional performance in the university?
  • How the physical or logical working environs could influence the institutional performance of Adigrat University?
  • What are the bottlenecks against practicing ergonomic approach of workplace?
  1. Research Objective
    • General Objective

The general objective of this article is to assess the factors affecting the healthy working environs and forwarding ways of practicing ergonomics approach for workplace in Adigrat University.

  • Specific Objective
  • To determine the human factors those affect the institutional performance of the university.
  • To examine the relationship between factors of the physical/logical environment towards institutional performance.
  • To point out the major bottlenecks for practicing ergonomic approach.
  1. Institutional System Analysis

Historically, the age of modern Education in Ethiopia is almost 108 years since Emperor Menelik II opened the first modern school at Addis Ababa in 1908. Next to this, according to Alemayehu Bishaw; another important event in the expansion of modern education was the advent of the late Emperor Haile Selassie I, as Regent and Heir to the throne in 1916. He was a graduate of the first school established in Menelik II‟s palace. This foundation of higher institution also started during Emperor Haile Selassie I, with his name Haile Selassie I University (now Addis Ababa University) in 1950.

Currently, Ethiopia becomes the owner of 33 (excluding the 11 new universities to be built in second GTP period of the nation) higher academic institutions and 59 accredited Non-Government Higher Education Institutions under its Ministry of Education. Adigrat University (3rd generation) is one of the public higher academic institutions which is established in 2011.

This academic year the University has 6 colleges and one institute, 41 departments with a regular student population of more than14000 and nearly 5000 continuing education students. The total number of its academic staff has reached nearly 1000 (more than 300 of them on their further study at home and abroad). The support staff is expected to reach 1500 this academic year (www.adu.edu.et retrieved at 15/8/16).

According to Higher Education Proclamation No. 650/2009 no. 17/3, every public institution shall exercise its autonomy in ways that, at the same time, ensure lawfulness, efficiency and effectiveness, transparency, fairness, and accountability. Through this the MoE gives autonomous power to the university. That’s why different universities of the country could not have consistent institutional structure. Most of them are indifferent on their institutional structure, way of students evaluation, payment policy in which the MoE should follow up and adjust. The following is the current institutional hierarchy of Adigrat University.

As one can understand from the next hierarchy, the two vice presidents are over loaded. The majority divisions under Academic, Research and community Service vice president are colored yellow and it shows it should divided in to at least two units for research and academic purpose.

 

Figure 1 Current institutional hierarchy of Adigrat University

It is due to over responsibility and centralized management in these vice presidents that the majority employees complain more on lack of good governance in different semi annual meetings.  These same is true in the purchasing unit of the university that requested teaching materials could not deliver on time. Even if the university has more than 5000 students in continuing education, there is no responsible unit to overcome related issues. Hence, it is better to have such productive divisions instead of having the current bureaucracy such as quality assurance at college level. It is a symptom for its weakness campus assistant administrator under basic service unit; significant numbers of personal and institutional properties were stolen by thefts.

It is also due to lack of having a close linkage with the external community that domestic and foreign staff are suffering badly by home thefts in the town. When we see about the management system, individuals are treated as they are member of local political party rather than their merit. It is an example for that; not only for Adigrat University but also for almost higher institutions, the presidents and vice presidents are assigned from the local society rather than from any ethnic group. Not only this, directors, deans and head of center institutes of the university are assigned as they are member of local political organizations rather than through merit. This is against to article 9.2/a, of the legislation on the requirements to hold a position in the University which states as follows.

The candidate must have excellent communication and interpersonal skill and proven ability to participate successfully in a complex, highly professional organization, with demonstrated competence in leadership, motivation, collaboration and working with teams, teaching, research and community service activities relevant to the position;

Although fast physical expansion is one of the positive sides of the University, the internal environment is not well equipped rather lack of staff cafeteria and discount students hotel and entertainment service, shortage of pure water, too late of staff’s condominium.

  1. Research Methodology

It is obvious any research paper has its own methodology; this paper is also casual and descriptive by nature and it is literature and observation based. The researcher develops conceptual framework which assumed relevant to ergonomic approach. Then, after the theoretical or literal concepts are analyzed, the authors tried to see to what extent they are practicing in Adigrat University. Since the author is a permanent academic staff of the university, it is good opportunity to identify every aspects of the human factor and lastly the paper will have its own significant in enhancing institutional performance through overcoming the de-motivational factors of employees.

  1. The Theory Versus the Practice

As far as their appropriateness Hierarchy of Needs theory (Abraham Maslow) and Alderfer’s ERG theory of motivation are taken as a conceptual framework.   In this case the researcher tried to assess either these theories are practicing in Adigrat University or not; because, it is believed that these theories involves human factors relationship (ergonomics) and otherwise, these factors can related to the physical design (internal and external environmental features) and logical design (policies, working system and management philosophy…) of the institution. As to these theories the employee demands the following needs from their home and from their working institutions.

According to Maslow, we seek first to satisfy the lowest level of needs. Once this is done, we seek to satisfy each higher level of need until we have satisfied all five needs. Thus, related factors are arranged as a concept and their necessity in this case institution.

Need Home Job In Adigrat University
Physiological food water shelter and cloth Heat, air, base salary Cafeteria service or center of entertainments (for staff and students), discount business, attractive dormitory and office, on time payments and fringe benefits, pure water
Safety freedom from war, poison, violence work safety, job security, health insurance Internal (Teaching material, transport service, pleasant physical infrastructure, campus community safety), external (free fear of war, peace and stability, home) free of theft or creating risk free compound.
Belongingness family, friends, clubs teams, departments, colleague, clients, supervisors, subordinates Participative decision, decentralized management philosophy, two way communication, meritocracy of positions, feeling of ownership
Esteem approval of family, friends, community recognition, high status, responsibilities Encouragements, recognitions and moral, letting competent for higher management, confidentiality, achievement, reduce employees turnover
self-actualization education, religion, hobbies, personal growth education, religion, hobbies, personal growth Short bureaucracy of promotion, workers educational opportunity, encouraging for innovation and creativity, investigation and freedom

Table 1: Hierarchy of Needs Theory (yellow column) and author’s view (green column)

As to the human expectation, either in group or individually, it is assumed that every employee of Adigrat University needs to acquire and to satisfy these needs. According to the connotations of the hierarchy of needs theory, individual employees must have their lower level needs met by, for instance, safe working conditions, adequate pay to take care of one’s self and one’s family, and job security before they will be motivated by increased job responsibilities, status, and challenging work assignments. Despite the simplicity of application of this theory to Adigrat University, the human factors as to the ergonomics approach is not practicing.

ERG theory, developed by Clayton Alderfer, is a modification of Maslow’s hierarchy of needs. Alderfer’s theory also categorized work force needs into three categories and the related factors to these categories are summarized as follows. As one can observe from the table 1 and table 2, these theories are powerful to maximize the performance of the institution if well practiced. As to the factors for employee’s motivation, the factors could affect the institutional performance positively; because, institutional performance is the sum of departmental or individual performance.

Needs Implication To Motivating the employees

 

To enhance institutional performance
Existence needs Include all material and physiological desires Ø  Pay one time (load and overtime)

Ø  Avoiding bad noise and sounds

Ø  Minimize meetings

Ø  Prioritize institutional goals

Ø  Keeping clean area

Ø  Keeping quality and clean buildings and classrooms

Ø  Prioritize institutional before political goals

Relatedness needs Encompass social and external esteem; relationships with significant others v  Trust and Delegate both power and authority

v  Giving recognition and respect

v  Two way communication

v  Activity review day and celebrate success

v  Avoiding destructive informal groups

Ø  Avoid political agendas

v  Create transparency

v  Creating external relation (within outside the country)

v  Creating and encouraging social friendship among employees

v  Care about safety

·         Growth needs

 

Internal esteem and self actualization; these impel a person to make creative or productive effects on himself and the environment ü  Give motivational challenges

ü  Encouraging human needs

ü  Keep employees, students and stockholders well informed

ü  Know what motivates the employees

ü  Letting trained and educated/career development

ü  Avoid unproductive follow up for academic staff

ü  Encourage creativity and innovation

ü  Avoiding unnecessary bureaucracy of promotion

ü  Apply decentralized management philosophy

ü   Promote meritocracy

ü  Promote computation

Table 2: Alderfer’s theory of needs and author’s view (green column)

Literally speaking motivation is one of the forces that lead to performance. Motivation is defined as the desire to achieve a goal or a certain performance level, leading to goal-directed behavior. As the human factor affect the institutional performance, environmental factors such as having the resources, information, and support one needs to perform well are critical to determine the performance the University.

According to human resource approach for motivation people want to contribute to organizational effectiveness and are able to make genuine contributions. The organization’s responsibility is to create a work environment that makes full use of available human resources. ERG theory’s implications for managers are similar to those for the needs hierarchy; top level management of the university should focus on meeting employees’ existence, relatedness, and growth needs, though without necessarily applying the condition that, say, job-safety concerns necessarily take precedence over challenging and fulfilling job requirements. Is so, the ergonomics or human factor of the institution become realized. And it directly implies the  performance could enhance since the workplace (internal and external) become healthy and safe.

  1. Summery Suggestions

Like any changes (BPR, TQM, BSC and Kaizen) which have being implementing through time in the University, Ergonomics could also practiced. Relatively ergonomics approach for workplace highly focuses on human factor of employees. It is rational implication that if human factor of the institution got primary attention, the employees’ motivation, individual performance and then institutional performance could be maximized in Adigrt University. For this, the two theories of motivation with their respective factors are a good example which needs especial emphasize at any institutional level. For easily applicable it is summarized as follows.

Hierarchy of Needs Theory ERG theory Human Factors

(Direct impact)

Institutional Factors

(Indirect impact)

Ladder for

practicing

Ergonomics

Physiological Existence needs Ignoring humanitarian aspects Bad physical and logical design Audit Human and Institutional needs (Team work): move from individual to the overall institutional system
Safety Healthy workplace Weak security

Inside & out side

Verify logical and physical human and institutional needs’ gap (Team work)
Belongingness Relatedness needs Push factors: Bad relations Deficiency of Pool factors Re-structuring and  system Validation  (Team work)
Esteem Internal Weakness of formal groups Centralized Decision making Externalize and communication (Bottom-up) (Team work)
External Growth needs Less external competition Internal &External Competitiveness Action Realization through human development (Team work)
Self-actualization Narrow minded: focusing on minor things… Have Practical  and long lasting Vision Empowerment of the long lasting Human and institutional Achievement

Table 3: comparative of the theory and the practice in Adigrat University

The goals of ergonomics are to provide a positive working environment in which the design of equipment, work layouts and work environment matches the capabilities of people so they can lead healthy and productive lives. Thus, this indicates the application of Ergonomics starts from individual, departments then in to the institution.

According to the literal analysis and practical observation, the researcher believes to develop an alternative institutions hierarchy that could be pleasant to practice ergonomic concept in workplace of the institution. Hence, through its autonomous power from MoE, these which are ranked as too broad working units should divide or restructure in to sub-systems. In general the author needs to forward the following suggestions accordingly.

  • Presidents and vice presidents of the university should assigned merit based from all over the nation and the world since it is a national institution. Because, due to lack of diversity in ethnicity in the higher positions, meritocracy is not practicing.
  • It is recommended that the management philosophy of the university should participatory and decentralized. Tasks should fairly distribute among the institutional divisions.
  • Campus community especially students needs orientation to keep classrooms clean.
  • Supportive office materials like photo copy, papers, desks and chairs should nearly available.
  • Discounted business firms like separate cafeterias for staffs, commodity shops, and pure water and clean dormitory are mandatory for students. To do this intake capacity of the university should as to its resources.
  • Since the institution is across the border, the federal government should care and as much as possible unnecessary sounds from training of the fighters should out of the campus community.
  • The human factors should consider as institutional factors because the institution living which could grow, die like human as the employees feel discomfort.
  • The internal and external threat of theft could avoid by practicing article 7.2.9/a/ viii, of the legislation which stated “Establish contacts with external bodies (city administration, city police, nearby administration, security, and other relevant offices) that help maintenance of peaceful teaching in the campus.”
  • It is better for the employees and the institution if Ergonomics Approach of workplace could executed in collaboration with other changes or independently.

Finally, after the above suggestions are taking in to consideration it is easy to practice Ergonomics approach then after the University become benefited in reducing its tangible and intangible costs, it could easily improves its performance, quality, employees participation and creates better safety culture and healthy workplace.

Reference

Adigrat University Senate Legislation (2004 E.C) Adigrat, Ethiopia.

Alemayehu Bishaw Education in Ethiopia: Past, Present and Future Prospects: African Nebula, Issue 5, 2012 available at http://nobleworld.biz/images/5-Lasser_s_paper.pdf

Alderfer, C., & Guzzo, R. (1979, September). Life experiences and adults’ enduring strength of desires in organizations. Administrative Science Quarterly, 24(3), 347- 361. Retrieved from http://www2.johnson.cornell.edu/publications/asq/

Alderfer, Clayton P. (1972) Existence, Relatedness, and Growth: Human Needs in Organizational Settings. New York: Free Press; Available at: http://www.referenceforbusiness.com/management/Mar-No/Motivation-and-Motivation-Theory.html#ixzz4HTqkn5VC

Dickson, V., Fox C., Marshall K., Welch N., & Willis, J.(2014).”What really improves employee health and wellbeing”, International Journal of Workplace Health Management, Vol. 7.

Kerm Henrikse (…) Patient Safety and Quality: An Evidence-Based Handbook for Nurses: available at http://www.ncbi.nlm.nih.gov/books/NBK2666/

Habtemariam Markos (1970)., Amharic as the medium of instruction in primary schools in Ethiopia.‟‟ In T.P. Gorman, (ed.), Language in Education in Eastern Africa. Nairobi: Oxford University Press.

Maslow, Abraham H. (1954) Motivation and Personality. New York: Harper & Row;
Available at: http://www.referenceforbusiness.com/management/Mar-No/Motivation-and-Motivation-Theory.html#ixzz4HTqwCsrQ

Nour Eldin M. (2014) Role of Ergonomics on Sudanese higher education Institutions ICT class Rooms e-material available at http://www.ijaiem.org/Volume3Issue9/IJAIEM-2014-09-13-20.pdf

Viraj Bakshi (2016) Study to Implement Lean and Ergonomics Concepts in a Production Environment

Joan Burton (2010) WHO Healthy Workplace Framework and Model: Background Document and Supporting Literature and Practices. E-book available at http://www.who.int/occupational_health/healthy_workplace_framework.pdf

P.Vink, (2006) Positive outcomes of participatory ergonomics in terms of higher comfort and productivity

Additional visited websites

www.adu.edu.et official website of Adigrat University

www.businessdisctionary.com visited at 10/08/16

www.whatis.com visited at 12/08/16

www.Merriam-WebsterDictionary.com visited at 01/08/16

 

Tombstones Of The War Dead: A Spectacle of Epitaphs and Emblems

 

 Dr.H.Rasi,

            The Madras War Cemetery (1939-1945) is a celebration of war dead laid to rest in St. Thomas Mount in the border of Madras known for its history and heritage. The cemetery, one among the 34 of its kind in India, is meant to keep alive the memories of soldiers, sailors, and airmen–from Australia, Burma, Canada, India, New Zealand, Poland, the United Kingdom, and West Africa–who served in garrisons and died in India on their way to battle fields in far off places to fight in the Second World War on behalf of the (British) Commonwealth of Nations. They died “thousands of miles away from their hearth and home, leaving a void in their families and a trail of grief” but their mortal remains found a haven in the Madras War Cemetery.

            The cemetery in St. Thomas Mount contains 856 Commonwealth burials. Each burial is commemorated with a tombstone–813 mm tall, 375 mm broad and 75 mm wide. C. Venkatesan, in a paper presented to the Tamil Nadu History Congress and published in its Proceedings, goes into raptures when he says: “Each headstone is a moving memorial, a mound of stone, a little mount, a miniature pyramid designed to last forever”.1 The Commonwealth War Graves Commission has ensured that on each stone is engraved “the national emblem or the service or regimental badge, followed by the rank, name, unit, date of death, age, and usually a religious emblem; and at the foot, in many cases, an inscription chosen by relatives”2–in short, a resume of the profile of the warrior.

            Walking across the lawns of the cemetery, I felt I was in the presence of angels. Brave men and women sleeping in silence and solitude, the headstones executed with  immaculate elegance, the regimental emblems sculpted with amazing precision, the epitaphs chosen mostly from sacred and secular literature of a bygone era, the lovely lawn resembling a green carpet of grass, the bronze sword representing the military character of the cemetery, the rain trees, the Rangoon creepers, the Indian laburnums, the west Indian jasmine, the roses, the shrubs, and the whole cemetery bound by a ledge of Madras thorn, white clouds floating in the blue sky of St. Thomas Mount make one  feel that he is wandering across an earthly paradise, an Elysium so to speak.

            The epitaphs and the emblems are the highlights of the tombstones; I was enthralled by the former, and excited by the latter.

            The epitaphs are expressions of love, of admiration, of gratitude, and, of course, of grief and sorrow; they are the family’s attempts to communicate with the dead. The dead have been so much a part of the living , have shared so much of their thoughts, have dreamt so many of their dreams that their sudden loss devastates them. The living open their hearts for the dead in exquisite prose and poetry – and we call it epitaphs. The epitaphs are usually not more than a couple of lines but carry the marvel of moving people to tears. Never in history has so much been said in so few words.

            The emblems are drawings of the banner under which the combatants fought their battles. They are like the royal insignia of the Cheras, Cholas, and Pandyas of the Sangam age and the Pallava, Maratha and Vijayanagar kings of a later time. The emblems symbolise the traditions and values of the respective regiments, their weapons of war, their valour, the myths of their people, and the fauna and flora native to their land. The persons who sculpted the pictures in stone had imagination, were steeped in the knowledge of legend and literature, believed in the efficacy of the emblems to bless their countrymen with victory–the result is an exhibition of emblems of everlasting value.

            C.Venkatesan, a specialist in the study of cemeteries, especially war cemeteries, describes in his characteristic way the designs of the varied emblems in the Madras War Cemetery:

            Profiles of regimental symbols sculpted on the stones are lovely little pieces of art. Reliefs showing the Egyptian sphinx, fierce lions, antlers of reindeer, short swords of the Gurkhas, fast-footed couriers, gun carriages, prancing horses, flying eagles, and medieval castles have been carved with great care, understanding, and even feeling. I was particularly struck by the sculpture of the enigmatic sphinx having a lion’s body with a twisted tail and a woman’s head; only a sculptor steeped in the knowledge of Egyptian history and civilization could have created such splendid works of art.

            One tombstone carries the figure of a dragon; the representation is so frightening and it is doubtful whether the dragon known to mythology would have been this dreadful. Another shows a ram carrying a flag in its fore legs; I could see arrogance writ large in the ram’s face – arrogance arising out of the privilege given to it to carry the country’s flag. Yet another stone shows a courier running fast with what appears to be a coded message; I could see strength and stamina oozing out every inch of his muscle. Many a stone contain falcons in flight in search of prey with a beak sharper than a razor. Each of these of sculptures is a treasure, and worth a king’s ransom.

Rest in Peace:

            Many of the well wishers, as in civilian cemeteries of simple folks, are content with a recording of “Rest in Peace”3 on the epitaphs. This is prayer, this is seeking God’s intervention to grant them peace and quietitude in His kingdom. Dying is a journey into the unknown, and people are anxious that the dead should not meet with any harm in their new abode. On the surface “Rest in Peace” may appear to be simple in substance, but one can see hidden eloquence even in this unpretentious invocation: the dead should rest in silence and solitude, should rest in His arms free from the hue and cry of this turbulent and tumultuous world.

            The words Rest in Peace may have been allowed to stand alone: I find a tendency to prefix or suffix these words with some other wish. It looks as though that Rest in Peace is not given the focus that is its due.

In Memory of:

            Love is the bond between husbands and wives, sons and daughters and their fathers and mothers, brothers and sisters, and among friends. When a warrior dies in war, his loved ones are drenched in a million tears, feel a void in their life, and after a period of mourning, inscribe on the headstones the trait, the quality, the feature that impressed them most. Love is the dominant motif behind the memories projected in these monuments. Different people have different perceptions of the memories of the dead. They are either “in memory of”4, or “in lasting memory of”5, or “in loving memory of”6, or “in ever loving memory of”7; a few speak of “beautiful memories”8 and “sweet memories”9; there is atleast one which refers to “grateful memory”10; references to “glorious memories”11, “proud memories”12, “precious memories”13, and “treasured memories”14 are seen here and there; there are a couple of solemn allusions to “sacred memories” 15and “divine memories”.16

            I would not like to see much of a difference between memories and lasting memories and loving memories and ever loving memories because love is there everywhere linking people like a human chain. It seems to be a manner of writing, and there is no need to distinguish between different shades of love.

            But I admit, though grudgingly, that there may be something in speaking of “grateful memories”, “glorious memories”, “proud memories”, “precious memories”, and “treasured memories”. Some act of kindness, some deed of courage, some showing of chivalry may have touched a chord in the living, and therefore they are going a little out of the beaten track. But specific references to special acts would have been helpful to appreciate the appropriateness of adjectives, but of course there are constrains of space.

            Allusions to “sacred memory” and “divine memory” appear to be somewhat awe-inspiring, but even here I don’t see any need to consider such references as “God-connected”, because there is a belief that all the dead, especially the war dead, go to the kingdom of God, “live” in his presence, and bask in the sunshine of his grace. In this context I don’t want to be misunderstood as denigrating the memory of those who fell dead in the Second World War. I am only looking at the whole scene with an open mind, a neutral platform as it were.

I shall Remember:

            Memories are the stuff of which history and heritage are made; they are the unwritten archives, the invisible artefacts of humanity’s long trek towards freedom and honour; if they are not renewed and remembered, if they are not refreshed and “revisited”, the memorials on which they are cut would cease to convey any meaning, and would not serve the purpose they were meant to serve. Those who have commissioned the memorials have assured in the short space of the stone slabs that the dead would be “ever remembered”17, or “always remembered”18, or “proudly remembered”19. A few have undertaken to cherish their memories “not just today, but everyday”20; some have recorded that though the dead are gone, they are not forgotten.21

            The most eloquent expression of remembrance is: “At the going down of the sun, and in the morning, we will remember them”22. This is more or less a pledge, a promise to keep alive the memory of the dead. Man is at peace with himself, he is in union with god “at the going down of the sun” and “in the morning”, and these are the best moments for remembrance. But there is no need to insist that references to timings are references                     to sunset and sunrise; that kind of interpretation would be rather pedantic. The promoters of the epitaphs may have meant in all probability: everyday, preferably twice a day. A reference to 6 a.m to 6 p.m. may not have dictated their prescription.    Therefore neither time nor place need stand in the way of the living remembering the dead. What matters is the will to remember.

            Fortunately, there is no such direction regarding the place of remembrance. One need not visit a cemetery, a church, a darga to remember a dead person; one’s own home where the living and the dead lived and laughed, played together, worked and worshipped and shared whatever there was to share would do. What is required is a place where there is peace; the time recommended in the memorial is a time associated with peace.

            The discussion on remembrance can be rounded off with a reference to a remark that “God’s greatest gift is remembrance”23. By this the writer probably means that God has endowed the living with the power to remember, and among the several powers he has given him, that is the greatest gift. The faculty to remember is a faculty which can be exercised without physical strain, mental stress, paraphernalia of ritual, financial expenditure, guidance of a guru, and a host of similar constraints. This is simplicity personified and presented as a quotable quote. Though it is conceded that remembrances are reminders of the gratitude of the living to the dead, a query that calls for a response is how long remembrances shall continue. May be for a generation or two; society is in a state of flux, and the old order changeth yielding place to new; after some years the present becomes past, and fades from the thoughts of the future. The impermanence of human lives determines the impermanence of remembrances as well. The fact that remembrances cannot be carried on endlessly is brought home in the headstone of Sgt M.T. Jones of Royal Air Force: “as long as we live, we treasure his name”24. It is surprising that the question has been anticipated and answered.

God, King, and Country:

            It was in response to a call from “God, King, and Country”25 that men fought to defend freedom and honour, and it was a duty they performed with pride, and with no thoughts whether they would survive or perish in battle. The headstones inform us repeatedly that it was “duty nobly done”26, “duty fearlessly…  done”27, and “perilous path of duty”28; at least one headstone informs us that the warrior fell “a martyr to duty”29.

            The epitaphs contain evidences of supreme sacrifices: Warrant Officer J.B. Duggan of Royal Air Force and his brother Bertram,30 Gunner A. Knight of Royal Artillery and his brother William John,31 Gunner M.W. Upson of Royal Artillery and his brother Ronald Mervyn,32 Warrant Officer K. Webster of Royal Air Force and his brother Vincent,33 and Sgt A. Powrie of Royal Electrical and Mechanical Engineers and his son Ernest Peter34 all died on service. The death of two persons in the same family would make them grief stricken beyond words and consolation, and these are rare instances of supreme sacrifice in the chronicles of war anywhere in the world. The loss of Major A.C. Greene of Indian Medical Service35 whose feats of courage were mentioned in Despatches thrice should be treated as too big a tragedy for his family; there may be many more who have earned such honours and distinction. In all these cases, it is the call of duty that made them to lay their lives, and the thought of God, King and Country propelled them to new height of sacrifice.

Grief and Sorrow:

            The hurt and the pain of the loss of one whom they loved dearly, the sorrow that followed, was too much to bear for many. They were haunted by memories of “a happy face”36, “of a heart of gold”37, “of a loving smile”38. Fathers and mothers, brothers and sisters, sons and daughters were troubled by the memories of the departed, and lamented: “to the world he was one, to us he was the world”39; he was a sun in the sky, the focus of their love and affection, the hope of their future, and his death left them rudderless.

            The call to battle was abrupt and sudden; there was no time for the warrior to say goodbye to his family and friends. “He bade no last farewell, the heaven’s gates were open, a loving voice said ‘Come’”40, and he went. “No loved ones stood around him to bid a last farewell”41. What is distressing is the cry: “without farewell he left as all”42, and “without farewell he fell asleep”43. These are all moving passages, and make our heart bleed for the departed.

            The death of the warriors is not to be viewed as ordinary death; they did not die of old age, of disease, of execution, at the hand of an assassin. They died fighting for their country, for their land and people, for their right to live in peace and liberty. Many of the epitaphs would want us to remember that “(they) died so that we might live”44, that “(they) gave their life that we may live forever”45, that “for our tomorrow (they) gave (their) today”46, that “they gave their life that you may live in peace”47. Dying so that others might live is the noblest death one can imagine.

Far away from home:

            Many grieved that the graves were “in India”48, they were “far away” from England49, that they “never shall see” 50 the graves, that the families and the graves were divided by “land and sea”51. A mother cries “A foreign grave is a painful thing to a mother’s aching heart”52.  One is angry that “no flowers can I place in the grave where you lie”53. The anguish of another that her son languishes alone in India reaches us across time and distance, and makes us feel sad.

            I wish to assure parents, husbands and wives, and siblings of the dead that their beloved are not alone in India, and the 1,239,450,000 million people of India keep vigil over their graves. We would preserve the grave as among our national treasures like the sculptures of Mamallapuram, the Taj Mahal, and the St.Mary’s church in Fort.St.George.

             Please tell us the occasion, we will lay a garland on the grave, we will lit a candle on the tomb, we will say a prayer in folded hands so that they will be reborn in your midst, We will treat the War Cemetery as a place of pilgrimage.

End notes

  1. Proceedings of Tamil Nadu History Congress
  2. Annual Report of the Commonwealth War Graves Commission 2000-2001.
  3. Grave Reference: 1.A.13, 1.A.10, 1.B.10, 1.B.4, 1.C.16, 1.C.8, 1.D.12, 1.D.2, 1.F.12, 1.F.11, 1.J.11, 1.J.3, 1.J.2, 4.A.18, 4.A.17, 4.A.15, 4.B.15, 4.D.15, 4.D.13, 4.D.2, 4.E.15, 4.F.7, 5.C.14, 5.D.17, 5.D.6, 5.F.18, 6.A.10, 6.B.2, 6.D.6, 7.A.6, 8.A.5, 8.B.6, 8.B.3, 8.E.6, 8.E.4, 8.E.2, 9.C.4, 9.D.15, 9.D.11, 9.E.10, 9.E.5.
  4. Grave Reference: 1.C.13, 1.H.16, 1.L.13, 4.D.16, 4.E.16, 8.E.10, 8.F.1, 9.E.7.
  5. Grave Reference: 7.A.9, 9.A.15.
  6. Grave Reference: 1.B.6, 1.D.15, 1.D.13, 1.D.11, 1.E.4, 1.F.5, 1.G.6, 1.H.18, 1.J.14, 1.J.6, 1.K.4, 4.E.13, 4.E.7, 4.E.1, 4.F.8, 5.C.6, 5.D.5, 5.D.2, 6.A.10, 6.B.16, 6.B.12, 6.B.4, 6.D.4, 6.D.9, 7.A.4, 7.B.9, 7.B.7, 7.A.13, 8.B.16, 8.C.18, 8.F.16, 9.B.9, 9.F.7, 9.F.4.
  7. Grave Reference: 1.E.14, 1.H.7, 1.J.8, 1.K.9, 4.D.14, 8.E.2, 9.E.10, 9.E.3.
  8. Grave Reference: 1.B.16, 1.H.17, 1.H.2, 1.K.5, 7.A.2, 8.B.5, 8.D.11, 9.F.16.
  9. Grave Reference: 1.A.16, 1.E.8, 4.E.2, 8.C.14, 9.C.1, 9.D.7.
  10. Grave Reference: 1.C.14.
  11. Grave Reference: 5.C.11.
  12. Grave Reference: 1.K.6, 4.B.17, 4.F.1, 9.C.5, 9.C.1.
  13. Grave Reference: 4.C.13, 4.E.1.
  14. Grave Reference: 4.B.9, 6.C.4, 7.F.10, 9.A.2, 9.E.6.
  15. Grave Reference: 8.C.3, 9.C.8.
  16. Grave Reference: 9.B.6.
  17. Grave Reference: 1.D.17, 1.G.1, 4.E.3.
  18. Grave Reference: 1.H.6, 4.B.13, 4.D.11, 6.D.8, 7.F.8, 8.D.14.
  19. Grave Reference: 9.D.17.
  20. Grave Reference: 1.D.10, 1.K.2, 6.B.1.
  21. Grave Reference: 1.A.7, 1.H.18, 1.J.6, 4.A.18, 6.B.15, 6.B.4, 7.B.1, 9.D.18.
  22. Grave Reference: 1.A.11, 1.B.18, 1.B.12, 1.H.14, 1.K.7, 4.A.10, 4.E.6, 5.C.8, 5.E.2, 5.F.3, 6.A.5, 6.B.11, 6.D.10, 7.F.16, 7.F.3, 7.F.2, 8.C.8, 8.D.7.
  23. Grave Reference: 8.E.12.
  24. Grave Reference: 4.D.16.
  25. Grave Reference: 1.D.17.
  26. Grave Reference: 4.A.2, 4.B.10, 4.C.7, 9.B.5.
  27. Grave Reference: 4.B.18
  28. Grave Reference: 1.L.1
  29. Grave Reference: 8.B.12.
  30. Grave Reference: 6.D.12.
  31. Grave Reference: 4.C.6.
  32. Grave Reference: 7.D.9.
  33. Grave Reference: 1.L.6
  34. Grave Reference: 9.D.7; Madras War Cemetery Register, p.50.
  35. Grave Reference: 7.F.5
  36. Grave Reference: 6.A.6.
  37. Grave Reference: 1.A.13.
  38. Grave Reference: 6.D.11.
  39. Grave Reference: 4.C.9, 4.E.9, 8.D.16, 9.D.6.
  40. Grave Reference: 9.E.8.
  41. Grave Reference: 7.A.3.
  42. Grave Reference: 6.C.14.
  43. Grave Reference: 6.B.9, 8.B.17, 9.C.15.
  44. Grave Reference: 1.L.4, 4.A.18, 4.F.12, 9.B.15, 9.C.1.
  45. Grave Reference: 8.C.17
  46. Grave Reference: 1.L.6, 7.E.4.
  47. Grave Reference: 4.F.17.
  48. Grave Reference: 6.D.4.
  49. Grave Reference: 4.B.9, 4.B.4.
  50. Grave Reference: 5.B.12.
  51. Grave Reference: 8.E.18.
  52. Grave Reference: 4.F.14.
  53. Grave Reference: 1.D.9.

A Study On Success Factors Towards Rural Marketing On Non Durable Products In Thanjavur District

Dr. N.Sumathi,

Mrs.M.Elampirai,

Introduction

 The success of any company depends on its customers. There is a wide range of opportunity to sell in rural areas by these companies due to the untapped markets in those areas. Factors like pricing; advertisement, product quality etc. are involved in the success of rural marketing. The companies can become successful if they concentrate on these factors and take marketing decisions based on these factors This study explains about the success factors towards rural marketing on non durable products in Thanjavur District.

Key Words: Rural Marketing, Success Factors, Non Durable Products

Research Methodology

Review of Literature

Ms.Deepti Srivastava, Faculty of IILM Institute of Higher Education, Gurgaon, Haryana has explained about the changing paradigm in rural India in her research paper “Marketing to Rural Indi: A changing Paradigm” , APJRBM Volume 1, Issue 3 , December 2010.

Mr.B.Amarnath ,Associate Professor, Department of MBA, Sri Venkateswara University,Tirupathi, Andhra Pradesh and G.Vijayudu, Research Scholar, Sri Venkateswara University, Tirupathi, Andhra Pradesh have explained about consumer perceptions and attitudes towards branded packaged products in their research paper “ Rural Consumers’ Attitude towards Branded Packaged Food Products” in the  Asia Pacific Journal of Social Sciences, Vol III(1),Jan-June 2011.

Mr.V.V.Devi Prasad Kotni, Assistant Professor, Department of Management Studies, GVP College for Degree and PG Courses, Rushikonda, Endada, Visakhapatnam, Andhra Pradesh has done SWOT Analysis and found out the various opportunities and problems of rural markets in India in his research paper “Prospects and Problems of Indian Rural Markets” in the Zenith International Journal of Business Economics & Management Research Vol.2, Issue 3,March 2012.

Objectives of The Study

  1. To study the purchase behaviour of the rural consumers in Thanjavur District.
  2. To identify the success factors towards rural marketing on non durable products in Thanjavur District.
  3. To provide suggestions to the marketer for achieving success in the rural markets of Thanjavur District.

Sampling Methods

Sample Size: The sample size consists of 40 respondents.

Sampling Method: Simple Random Sampling is followed in this research.

Method of Data Collection:

Primary Data and Secondary data collection methods have been followed. Structured close ended Questionnaire with 22 questions has been used for this study. Secondary data has been collected from the government websites.

            Ten villages of Orathanadu Taluk, Thanjavur District namely Ambalapattu, Kannnanthangudi, Okkanadu, Paruthikottai, Pudur, Thekkur, Thelungankudikadu, Thennamanadu,Thenmandalakkottai and  Thirumangalakkottai have been selected randomly for data collection.

Research Tool: Simple Percentage Analysis has been used for this research study.

Limitations of the Study

  1. The study is conducted in the villages in and around Orathanadu Taluk, Thanjavur District.
  2. The sample size is only 40.
  3. The time taken to conduct the study is one month only.
  4. There may be bias in understanding the questionnaire by the respondents

Findings

Attributes Not preferred by rural consumers of Thanjavur District

Number of Respondents Percentage
Advertisement 2 5
Credit Facility 14 35
Discount Offer 0 0
Friends and Relatives 2 5
Brand Image 2 5
Convenience 6 15
Influence of Dealers and Agents 14 35
Total 40 100

From the above table it is found that 35% of the respondents do not prefer credit facilities provided by the shops and 35% of the respondents do not prefer the influence of dealers and agents while purchasing non durable products.

Mode of Purchase of non durable products

  Purchase at Town Purchase at Nearby Shop Purchase through agents Purchase at abroad Purchase through online shopping Total
Food Items 30 10 40
Fruits& Vegetables 28 10 2 40
Toiletaries 20 18 2 40
Edible Oil 24 16 40
Beverages 14 22 4 40

From the above table it is found that majority of the respondents purchase food items, fruits and vegetables, toiletaries and edible oil at town. Majority 22% of the respondents purchase beverages at nearby shop.

Brand is not a concern

  Number of Respondents Percentage
Food Items 12 30
Toiletaries 04 10
Edible Oil 02 05
Footwear 08 20
Brand is important 14 35
Total 40 100

From the above table it is found that majority 35% of the respondents give importance to brand for all the non durable products they purchase. 30% of the respondents do not give importance to food items they purchase.

Reasons for switching the brand

  Number of Respondents Percentage
Price 06 15
Change in the Market Trend 06 15
Habit 04 10
Promotional Strategies by companies 06 15
Non Availability of the product 14 35
Others 04 10
Total 40 100

35% of the respondents feel that they switch their brand due to non availability of the product.

Affordability per month

  <500 500-1000 1001-2000 >2000 Total
Food Items 30 10 40
Fruits& Vegetables 28 10 2 40
Toiletaries 20 18 2 40
Edible Oil 24 16 40
Beverages 14 22 4 40

From the above table it is found that the respondents spend Rs. 500 per month for product food items, fruits and vegetables, toiletaries and  edible oil . Majority 22% of the respondents spend between Rs.500 and Rs.1000 for beverages per month.

Bargaining by Consumers

  Number of Respondents Percentage
Bargain 34 85
Do not Bargain 06 15
Total 40 100

              From the above table it is found that 85% of the respondents bargain while purchasing non durable goods.

Best Advertising Technique

  Number of Respondents Percentage
Shop Display 10 25
TV Ad 22 55
Ad in Cinema Theatres
Pamplet
Wall Painting
Newspaper 08 20
Total 34 100

              From the above table it is found that majority 55% of the respondents feel that advertising in Television is the best advertising technique and 25% of the respondents feel that shop display is the best advertising technique.

Recommendation of non durable goods to friends

  Number of Respondents Percentage
Definitely not 04 10
Probably not
Not sure 02 05
Probably 18 45
Definitely 16 40
Total 40 100

              From the above table it is found that majority 45% of the respondents probably recommend and 40% of the respondents definitely recommend the non durable products they use to their friends.

 

Suggestions

 

 

  • From the research it is found that the respondents do not prefer credit facilities and influence of dealers and agents while purchasing non durable products. Hence the marketer can do direct selling instead of selling their products through dealers and agents.
  • It is found that majority of the respondents purchase the non durable goods in town. They do not prefer nearby shops for these purchases. The main reason for their preference in town is due availability of quality products and reduction of cost due to their bulk purchase in town. Hence if the marketer introduce new markets in the rural villages and provide the same facilities like town he can become successful.
  • Majority 35% of the respondents say that brand is very important. Hence brand is an important factor to be successful in the rural markets of Thanjavur District.
  • Majority 35% of the respondents say that they switch their brands due to non availability of the products. Hence we can conclude that these rural customers are more loyal to the brand they use. Hence creating loyalty among rural customers and making sure that the non durable products are available regularly to them.
  • Majority of the respondents afford Rs. 500 and less than Rs. 500 per month for food items, fruits and vegetables, toiletaries and edible oil. Hence packaging is an important factor for the success of the company. The company can be successful in selling the products through small packets and sachets.
  • Majority 85% of the respondents bargain while purchasing their products. Majority 35% of them always bargain and 35% of them bargain depending upon the shop they purchase. Therefore the marketer has to take steps to overcome this problem to be successful in the rural market.
  • Majority 55% of the respondents feel that Advertisement in Television is the best way of advertising. Advertisment in cinema theatres, providing pamphlets and wall painting advertisements are not preferred by the respondents. Hence the companies can reduce the expenditure towards advertising in cinema theatres, providing pamphlets and wall painting and give more importance to advertise in television.
  • Majority 45% of the respondents recommend the non durable products to their friends. Hence if the companies concentrate on satisfying the rural consumers and take steps to retain them. These satisfied consumers may recommend the non durable products to their friends.

Conclusion

         The rural consumers are influenced by various factors like quality of the products, selling and distribution techniques, packaging, branding and advertisements etc. If these factors are identified and take necessary steps the companies can become successful in selling and achieving profits.

References                      

 

  • S.G. Krishnamacharulu, Lalitha Ramkrishnan, Rural marketing- Text and Cases , PE Singapore , 2003.
  • Golden, S. A. R., & Regi, S. B. (2015). Satisfaction of Customers towards User Friendly Technological Services offered by Public and Private Sector banks at Palayamkottai, Tirunelveli District.International Journal of Research2(3), 775-787.
  • Neelamegham S, Marketing in India (Cases and Readings), Third edition, Vikas Publishing House Pvt. Ltd., 2000
  • Regi, S. B. S, ARG (2014).“.A DESCRIPTIVE STUDY ON THE ROLE OF CONSUMER PSYCHOLOGY AND BEHAVIOUR IN PRODUCT PURCHASING”. Indian Streams Research Journal3.
  • Regi, S. B. S, ARG (2014).“.A DESCRIPTIVE STUDY ON THE ROLE OF CONSUMER PSYCHOLOGY AND BEHAVIOUR IN PRODUCT PURCHASING”. Indian Streams Research Journal3.
  • Regi, S. B., & Golden, S. A. R. (2014). A Study On Attitude Of Employee Towards Working Environment With Special Reference To RR Pvt Ltd.Review Of Research, 2 (2), 1,5.
  • Regi, S. B., Golden, S. A. R., & Franco, C. E. (2014). A DESCRIPTIVE STUDY ON THE PROSPECTS OF E-COMMERCE IN INDIA.Golden Research Thoughts, 3 (9), 17.
  • Regi, S. B., Golden, S. A. R., & Franco, C. E. (2014). A DESCRIPTIVE STUDY ON THE PROSPECTS OF E-COMMERCE IN INDIA.Golden Research Thoughts, 3 (9), 17.
  • Kotler, Philip, Keller, Lane “Marketing Management”, Prentice Hall, (2005)
  • http:/www.populationcomission.nic.in
  • http://www.mbauniverse.com/ruralmarket.php
  • http://zenithresearch.org.in/
  • http://www.indiainfoline.com
  • http://shodhganga.inflibnet.ac.in/bitstream/10603/107364/9/09_chapter%201.pdf
  • ijarcsse.com

Awareness Among Consumers About Green Marketing In Tanjore District

 

 Dr M. Mary Anbunathy

  ABSTRACT

             According to the American Marketing Association, green marketing is the marketing of products that are presumed to be environmentally safe. Thus green marketing incorporates a broad range of activities, including product modification, changes to the production process, packaging changes, as well as modifying advertising.  The movement of green marketing has been expanding rapidly in the world, no exception to India particularly in Tamilnadu. Consumers’ awareness and motivational champion are the driving force in the market, they go for green marketing. Now a day the environment has been changed and the mindset of the consumers also changed go for green marketing. When compare to other countries in India, the level of awareness is lower about the green marketing like organic food and eco friendly products ect.  The Indian consumer has much less awareness of global warming issues. Initiatives from industry and the government are still ice blue. Green is slowly and steadily becoming the symbolic color of eco-consciousness in India. The growing consumer awareness about the origin of products and the concern over impending global environmental crisis there are increasing opportunities to marketers to convince consumers. With this background data have been collected to know the level of awareness’ of the consumers in Tanjore town. For the purpose of the study both primary data and secondary data have been collected and chi square test is used for testing the hypothesis. The study reveals that there is a relationship between the educational qualification and their income level of the consumers in Tanjore town.

IMPORTANCE OF THE STUDY Green marketing definitions can be a little confusing, since green marketing can refer to anything from greening product development to the actual advertising campaign itself. Going by alternative names such as sustainable marketing, environmental marketing, green advertising, eco marketing, organic marketing, all of which point to similar concepts though perhaps in a more specific fashion, green marketing is essentially a marketing message in order to capture more of the market and services that are better for the environment. There are many environmental issues impacted by the production of goods and rendering of services, and therefore there are also many ways a company can market their eco-friendly offerings. Green marketing can appeal to a wide variety of these issues such as the items can save water, reduce greenhouse gas emissions, cut toxic pollution, clean indoor air, and be easily recyclable. Now a day there is awareness among the consumers about the green products. With this back ground the study is considered as an important one.

Review of Literature

  • Merilänen, S., Moisander, J. & Personen, S. (2000). The Masculine Mindset of Environmental Management and Green Marketing. Business Strategy and the Environment, 9(3), pp. 151-162. Environmental management systems and green marketing programmes have gained increasing popularity in western market economies.  They are viewed as cost-efficient, effective and just means of tackling problems associated with the impact of economic activity on the environment.  It is argued in this article, however, that these optimistic views are based on a number of ideas, images and metaphors that retain many and centric and inadequate assumptions about self, society and nature that may be incompatible with long-term environmental protection goals.
  • Prothero, A. & Fitchett, J.A. (2000). Greening Capitalism: Opportunities for Green Community. Journal of Macromarketing, 20(1), pp. 46-56. In this paper, the authors argue that greater ecological enlightenment can be secured through capitalism by using the characteristics of commodity culture to further progress environmental goals.  The authors reject both naive ecological romanticism and revolutionary idealism on the grounds that they fail to offer any pragmatic basis by which greater environmental responsibility can be achieved.  Drawing on the now well-established theoretical tradition of post-Marxist cultural criticism, the authors offer a conceptual justification for the development and implementation of a green commodity discourse.  For this to be achieved and implemented, prevailing paradigms regarding the structure, nature, and characteristics of capitalism must be revised.  Marketing not only has the potential to contribute to the establishment of more sustainable forms of society but, as a principle agent in the operation and proliferation of commodity discourse, also has a considerable responsibility to do so.
  • Oyewole, P. (2001). Social Costs of Environmental Justice Associated with the Practice of Green Marketing. Journal of Business Ethics, 29(3), Feb, pp. 239-252. This paper presents a conceptual link among green marketing, environmental justice, and industrial ecology.  It argues for greater awareness of environmental justice in the practice for green marketing.  In contrast with the type of costs commonly discussed in the literature, the paper identified another type of costs, termed ‘costs with positive results,’ that may be associated with the presence of environmental justice in green marketing.  A research agenda is finally suggested to determine consumers’ awareness of environmental justice, and their willingness to bear the costs associated with it.

Objectives of the study

  1. To know the evaluation of green marketing
  2. To know the contribution of companies towards the green marketing
  3. To know the challenges for green marketing
  4. To know the level of awareness of consumers about the green marketing
  5. To know the attitude among the consumers towards green products.

Methodology of the study   For the purpose of the study, both secondary and primary data have been collected and analyzed. The secondary data have been collected from articles, reports and professional information concerning green marketing studies in general using the internet and academic databases.  The primary data was collected through questionnaire. The statistical methods used for the analysis are percentage analysis and chi square test

Hypotheses for the study

  • There is no significant relationship between the Income and Awareness about the green products
  • There is no significant relationship between the occupation and Awareness about the green products.
  • There is no significant relationship between the educational level and Awareness about the green products.

Evolution of Green Marketing Green marketing term was first discussed in a seminar on ―Ecological Marketing‖ organized by American Marketing Association (AMA) in 1975 and took its place in the literature. The term green marketing came into prominence in the late 1980s and early 1990s. The first wave of green marketing occurred in the 1980s. The tangible milestone for the first wave of green marketing came in the form of published books, both of which were called Green Marketing. They were by Ken Pattie (1992) in the United Kingdom and by Jacquelyn Ottman (1993) in the United States of America. According to Peattie (2001), the evolution of green marketing has three phases.

  • First phase was termed as “Ecological” green marketing, and during this period all marketing activities were concerned to help environmental problems and provide remedies for environmental problems.
  • Second phase was “Environmental” green marketing and the focus shifted on clean technology that involved designing of innovative new products, which take care of pollution and waste issues.
  • Third phase was “Sustainable” green marketing. It came into prominence in the late 1990s and early 2000concerned with developing good quality products which can meet consumers need by focusing on the quality, performance, pricing and convenience in an environment friendly way.

Characteristics of Green Products

  1. Products those are originally grown.
  2. Products those are recyclable, reusable and biodegradable.
  3. Products with natural ingredients.
  4. Products containing recycled contents and non toxic chemical.
  5. Products contents under approved chemicals.
  6. Products that do not harm or pollute the environment.
  7. Products that will not be tested on animals.
  8. Products that have eco-friendly packaging i.e. reusable, refillable containers etc.

Initiatives Taken Up By Business Organizations’ towards Green Marketing

  • Going Green: Tata’s New Mantra Tata Motors is setting up an eco-friendly showroom using natural building material for its flooring and energy efficient lights. The Indian Hotels Company, which runs the Taj chain, is in the process of creating Eco rooms which will have energy efficient mini bars, organic bed linen and napkins made from recycled paper. And when it comes to illumination, the rooms will have CFLs or LEDs. and Paper Sector. The initiatives undertaken by this top green firm in India includes two Clean Development Mechanism projects and a wind farm project that helped generate 2,30,323 Carbon Emission Reductions earning Rs. 17.40 Crore.
  • Oil and Natural Gas Company (ONGC) India’s largest oil producer, ONGC, is all set to lead the list of top 10 green Indian companies with energy-efficient, green crematoriums that will soon replace the traditional wooden pyre across the country. ONGC’s Mokshada Green Cremation initiative will save 60 to 70% of wood and a fourth of the burning time per cremation.
  • Wipro Green It. Wipro can do for you in your quest for a sustainable tomorrow- reduce costs, reduce your carbon footprints and become more efficient – all while saving the environment.
  • Wipro’s Green Machines (In India Only) Wipro Infotech was India’s first company to launch environment friendly computer peripherals. For the Indian market, Wipro has launched a new range of desktops and laptops called Wipro Greenware. These products are RoHS (Restriction of Hazardous Substances) compliant thus reducing e-waste in the environment.
  • India’s 1st Green Stadium The Thyagaraja Stadium stands tall in the quiet residential colony behind the Capital’s famous INA Market. It was jointly dedicated by Union Sports Minister MS Gill and Chief Minister Sheila Dikshit on Friday Dikshit said that the stadium is going to be the first green stadium in India, which has taken a series of steps to ensure energy conservation and this stadium has been constructed as per the green building concept with eco-friendly materials.
  • Suzlon Energy The world’s fourth largest wind-turbine maker is among the greenest and best Indian companies in India. Tulsi Tanti, the visionary behind Suzlon, convinced the world that wind is the energy of the future and built his factory in Pondicherry to run entirely on wind power. Suzlon’s corporate building is the most energy-efficient building ever built in India.
  • Tata Metaliks Limited (TML) Every day is Environment Day at TML, one of the top green firms in India. A practical example that made everyone sit up and take notice is the company’s policy to discourage working on Saturdays at the corporate office. Lights are also switched off during the day with the entire office depending on sunlight.
  • Tamil Nadu Newsprint and Papers Limited (TNPL) Adjudged the best performer in the 2009-2010 Green Business Survey, TNPL was awarded the Green Business Leadership Award in the Pulp soon replace the traditional wooden pyre across the country. ONGC’s Mokshada Green Cremation initiative will save 60 to 70% of wood and a fourth of the burning time per cremation.
  • IndusInd Bank Green banking has been catching up as among the top Indian green initiatives ever since IndusInd opened the country’s first solar-powered ATM and pioneered an eco-savvy change in the Indian banking sector.

Present trends in Green Marketing in India  Governmental Bodies are forcing Firms to become more responsible. In most cases the government forces the firm to adopt policy which protects the interests of the consumers. Competitors’ Environmental Activities pressure the firms to change their Environmental Marketing Activities.

The Future of Green Marketing There are many lessons to be learned to avoid green marketing myopia, the short version of all this is that effective green marketing requires applying good marketing principles to make green products desirable for consumers. Evidence indicates that successful green products have avoided green marketing myopia by following three important principles

  1. Consumer Value Positioning
  • Design environmental products to perform as well as (or better than) alternatives.
  • Promote and deliver the consumer desired value of environmental products and target relevant consumer market segments.
  • Broaden mainstream appeal by bundling consumer desired value into environmental products.
  1. Calibration of Consumer Knowledge
  • Educate consumers with marketing messages that connect environmental attributes with desired consumer values.
  • Frame environmental product attributes as “solutions” for consumer needs.
  • Create engaging and educational internet sites about environmental products desired consumer value.
  1. Credibility of Product Claim
  • Employ environmental product and consumer benefit claims that are specific and meaningful.
  • Procure product endorsements or eco-certifications from trustworthy third parties

Challenges of Green Marketing Implementing green marketing is not going to be an easy job. The firm has to face many problems while trading products of green marketing. Challenges which have to be faced are listed under

  • Green marketing encourages green products / services, green technology, green power / energy.
  • The firm ensures that they convince the customer about their green product, by implementing
  • Eco labeling schemes. Eco labeling schemes offer its “approval” to “Environmentally harmless” products and they are very popular in Japan and Europe. Convincing the Indian customer’s is a great challenge.
  • The profits will be very low since renewable and recyclable products and green technologies are more expensive. Green marketing will be successful only in long run.
  • Many customers may not be willing to pay higher price for green products which may affect the sales of the company.

Analysis of Primary Data

       The following table gives the socio economic back ground of the respondent those who are purchasing the green products for their use in Tiruchirapalli district.

TABLE – 2  DEMOGRAPHICAL   PROFILE OF THE RESPONDENTS
Particulars No. of the Respondent % of the respondent
Age of the respondent Up to 25yrs 18 18
  25-35yrs 39 39
  35-45yrs 17 17
  45-55yrs 15 15
  Above 55 years 11 11
  Total 100 100
Gender of the respondent Male 53 53
  Female 47 47
  Total 100 100
Education  level of the respondent Up to 12th std 12 12
  Graduate 36 36
  PG 41 41
  Professional 7 7
  Others  4  4
  Total 100 100
Marital status of the respondent Married 72 72
  Unmarried 28 28
  Total 100 100
Occupation of the respondent Student 6 6
  Housewife 27 27
  Employed 38 38
  Entrepreneur 26 26
Retired persons 3 3
Total 100 100
Monthly income of the respondent No income 4 4
  Below Rs.10000 22 22
  10001-20000 34 34
  20001-30000 27 27
  Above30000 13 13
  Total 100 100

Sources primary data

        With the help of the above table it is observed that 39% of the respondents are from the age group of 25 – 35. 53 percent of the respondents are male. 41 percent of the respondent have been completed their post graduation.72 of them are married. 38 of them are working people, of which majority of them are in private sector institutions. Majority of them are getting a monthly salary of Rs more than 10000 and less than 20000 per month.

 

TABLE – 2

SOURCES OF INFORMATION ABOUT THE GREEN PRODUCTS

Sl.No Particulars No. of Respondent % of Respondent
1 Friends and Relatives 36 36
2 News paper and Magazines 22 22
3 Television and Radio 9 9
4 Internet 26 26
5 others sources 7 7
  Total 100 100

              Sources primary data

       With the help of the above table, it is observed that 36 of the respondent have got the information about the green products from their friends and relatives. The major media of spreading the awareness is ward of mouth.  The web site is another media among the youngsters for getting information.

 

TABLE -3

 AMOUNT SPEND FOR A MONTH FOR PURCHASING THE GREEN PRODUCTS

                                                                                                          Rs in Hundreds

Sl.No Particulars No. of Respondent % of Respondent
1 Below 500 18 18
2 500 -750 27 27
3 750 – 1000 32 32
4 1000-1250 14 14
5 above 1250 9 9
  Total 100 100

             Sources primary data

With the help of the above table, it is observed that 32 percent of the respondent spending up to 1000 for their monthly purchase of green products.

TABLE -4

NATURE OFGREEN PRODUCTS PURCHASED IN A MONTH

Sl.No Particulars No. of Respondent % of Respondent
1 Organic Food items like Vegetables, Rice, Fruits etc 34 34
2 Cosmetics(soap, Shampoo ect) 47 47
3 Toiletries 9 9
4 Electricals 6 6
5 others 4 4
  Total 100 100

               Sources primary data

               With help of the above table 4 shows the purchase of type of Eco friendly products. 34% of respondents purchase organic food items like rice, vegetables, and fruits only. 47% of the respondent purchased cosmetic items and minority of them are purchased toiletries, electrical and others.

Testing of Hypotheses

  • There is no significant relationship between the Income and Awareness about the green products
  • There is no significant relationship between the occupation and Awareness about the green products.
  • There is no significant relationship between the educational level and Awareness about the green products.

 

                Factors                  Method Calculated value Table value(5% level significance, 12 Degree of freedom) Result
Income Awareness about the green products   42.47 21.026 Rejected
Occupation Awareness about the green products 38.96 21.026 Rejected
Educational level Awareness about the green products 28.96 21.026 Rejected

 

FINDINGS The findings of the study were summarizes and presented.

  • 39% of the respondents are from the age group of 25 – 35
  • 53 percent of the respondents are male.
  • 41 percent of the respondent have been completed their post graduation.
  • 72 of them are married
  • 38 of them are working people, of which majority of them are in private sector institutions.
  • Majority of them are getting a monthly salary of Rs more than 10000 and less than 20000 per month.
  • 36 of the respondent have got the information about the green products from their friends and relatives. The major media of spreading the awareness is ward of mouth. The web site is another media among the youngsters for getting information.
  • 32 percent of the respondent spending up to 1000 for their monthly purchase of green products.
  • There is a significant relationship between the Income and Awareness about the green products
  • There is a significant relationship between the occupation and Awareness about the green products.
  • There is a significant relationship between the educational level and Awareness about the green products.

Suggestions

  • Manufactures’ should concentrate to produce recyclable products, reuse of packaging and they can use energy saving equipments for production and other purpose.
  • More green products should be offered to the retailer, and then they can sell green products to the consumers.
  • Government should offer subsidies for purchasing the equipments and machinery helping in keeping environment green. The manufacturers can be offer loans from the banks to install the equipments at lower rate of interest.
  • Word of mouth and internet (social networks face book, whats app) play a vital role in promoting the awareness about the green products and the advantages of green products. The advertisement should be modified and explain in detail about the green products and then it will reach the consumers.
  • Government should make necessary for creating the awareness about the benefit of green products.

Conclusion

                   The current low levels of consumer awareness about global warming, environmental pollution the Government of India, manufacturers, and retailers need to help raise consumer consciousness. Indian manufacturers have yet to find a market for green products, even as consumers have a low awareness of them because of the insufficient efforts made by the marketers.  Overall, it is clear that the Indian consumers especially Tanjore consumers are having less awareness about the usage of green products. Now a day consumers are spending lesser amount to purchase green products. But they ready to pay more prices for the products which are causing less environmental pollution. They also prefer promotional campaign which protects the environment, and distribution channels which are not causing environmental pollution. Government, companies, consumers and other stockholders have to join hands to come out of the situation. The opinion of the retailers is green products are liked by consumers but because of poor awareness and high prices have not been fully adopted by them. As far as consumers are concerned the awareness level is increasing and has started implementing them in their normal life.  The intermediaries should include consumer’s attitude measurement programme in their marketing plan and adopt all aspects of green marketing, then only they can achieve their goal and fulfill the social responsibility of their business concern. There is a need in this situation to save our earth is  joint hands actions from Government, NGOs, Manufactures’, retailers regulators, scientific community and environmental education groups should create an awareness programmes among the consumers at regular intervals for reviving, maintaining and safeguarding the earth’s eco system.

RERFERENCES

  • Ina landau (2008) – “Gaining Competitive Advantage through Customer Satisfaction, Trust and Confidence in Consideration of the Influence of Green Marketing “Master Thesis- University of Gavle
  • Kanupriya Gupta and Rohini Somanathan (2011), – “Consumers Responses to Incentives to reduce plastic bag use: Evidence from a field experiment in Urban India” – Thesis – Delhi school of Economies., Delhi – 110 007
  • Merilänen, S., Moisander, J. & Personen, S. (2000). The Masculine Mindset of Environmental Management and Green Marketing. Business Strategy and the Environment, 9(3), pp. 151-162.
  • Oyewole, P. (2001). Social Costs of Environmental Justice Associated with the Practice of Green Marketing. Journal of Business Ethics, 29(3), Feb, pp. 239-252.
  • Polonsky, Michael Jay. 1994. “An Introduction to Green marketing” – Electronic Green Journal, 1(2)-Article 3 (1994) – Pg2
  • Prothero, A. & Fitchett, J.A. (2000). Greening Capitalism: Opportunities for Green Community. Journal of Macromarketing, 20(1), pp. 46-56
  • Regi, S. B., Golden, S. A. R., & Franco, C. E. (2014). A DESCRIPTIVE STUDY ON THE PROSPECTS OF E-COMMERCE IN INDIA.Golden Research Thoughts, 3 (9), 17.
  • Renee Wever (2009) – “Thinking about the Box – A holistic approach to a sustainable design engineering of packing for Durable consumer goods “–Thesis– Delft University of Technology – Delft, Netherland.
  • Soren Bohne and Rikke Thomson (2011) – “Influencing consumer perception of and attitudes towards CO2 neutral and biodegradable carrier bags“ – Thesis – Department of Business administration – Aarhus University.

Challenges Faced By The Select Urban Public Sector Bank Customer’s While Using Atm/ Debit Card –

A Descriptive Analysis

* S.Bulomine Regi.,

ABSTRACT

“Banking is essential, banks are not”. It is noted that, traditional bank branches (bricks and mortar) are going to vanish through innovative banking services i.e. electronic banking and plastic cards which continue to attract new users. The main objective focused in this paper is to measure the challenges faced by the customers’ using ATM/Debit Card offered by selected public sector banks i.e. State Bank of India and Canara Bank. 360 respondents were selected using purposive stratified random sampling. This paper mainly focused on the challenges faced by the customers using ATM/Debit card.

KEYWORDS: ATM, Debit Card, ATM user, Challenges, Public Sector Banks

INTRODUCTION

ATMs are now a routine part of banking transactions but when they were introduced in 1960s, they were the high- tech technology. The Automated Teller Machine (ATM) is now such a normal part of daily life that it’s strange to think it was ever cutting-edge technology. But in 1960s, when the first cash-dispensing ATM was installed at a branch of Barclays Bank in London, it was innovative and revolutionary. What’s more, over the decades, ATMs have become much more than just cash dispensers. They also allow customers to carry out a range of banking activities, including deposits and mobile phone top-ups. Given that the ATM is such a prominent feature in people’s lives, it’s important to understand its background, technical development and its capabilities. Here’s a quick introduction to the ATM and its global significance.

While the first card-accepting ATM was introduced by Barclays in London in 1968, this was not in fact the very first incarnation of the automated teller. CitiBank, then known as First National City Bank, launched a version of the ATM called the Bankograph in American branches in 1960. This machine did not let customers withdraw money but instead allowed them to pay bills without the assistance of bank staff. Moreover, Barclays’ 1968 addition was not foolproof and cards were regularly swallowed by these early ATMs.

Following these early developments, growth in North America and Western Europe was rapid. In 1969, the first machine to use magnetically encoded plastic was installed at Chemical Bank in New York, although initial take-up was slow as the running costs for these machines, known as Docutellers, outstripped the cost of hiring a human teller. However, as the modified Total Teller was introduced in the early 1970s, ATMs began spreading in banks across the two continents.

Today, ATMs have been popularised across the globe. Experts estimate that developed countries like the USA, Canada, the UK and Japan have a high concentration of ATMs per capita, while steady economic growth in India and China has meant that the number of bank machines in these countries has been growing in the last decade. However, it’s not just the number of ATMs throughout the world that has increased but also its functions. As well as withdrawing and depositing cash, modern ATMs also allow you to put credit on a mobile phone just by entering your phone number of the keypad. What’s more, some machines will let pay money into a beneficiary’s account, while others will print mini bank statements of your last few transactions.

However, as software changes, it does concern over ATM security. Today’s biggest worry for ATM industry professionals is how to maintain the security of global systems beyond the traditional advice to consumers to keep their PIN secret. The development of chip cards and Chip and Pin technology has helped to combat ATM fraud but there are still advances to be made.

STATEMENT OF THE PROBLEM

            Nowadays majority of the customers are using ATMs to withdraw cash from their account. The debit cards are used in very occasion for payments made through online, payments for purchases in shopping mall and so on. The use of ATM is increasing day-by-day, it is important to study the challenges towards use of ATM services. The customers were facing different types of problems with which ATM is directly related. Machine complexity, machine breakdown, poor quality notes, network failure, unsuitable location, forgot ATM pin number, High frequency of use, safety and security are the major problems of ATM users. Customers do not like ATMs because of impersonality, vision problem, fear of technology and reluctance to change and adopt new mode of delivery of service.

 

 

OBJECTIVES OF THE STUDY

The following are the objectives of the study:

  1. To study the socio-economic conditions of the respondents using ATM/Debit Card from select public sector banks in Tirunelveli District.
  2. To identify the challenges faced by the customers while using ATM/Debit Card from select public sector banks in Tirunelveli District.
  3. To give suggestions for the improvement of using ATM/Debit Card.

METHODOLOGY

Research design

A  research  design  is  a  plan  of  the  research  project  to  investigate  and  obtain answers  to research questions. Three types of research designs identified from the literature are exploratory, descriptive and explanatory design.[1] In  the  beginning  of  the  study, an  exploratory research  was  undertaken by an  in-depth review of literature in order to identify the research  problem,  constructs  and  to formulate hypotheses. Descriptive  research design  was  used  in  the  next  stage  of  the research for the purpose of describing the profile of the respondents and to determine the frequencies,  percentages, mean and standard  deviation  of  the  measures  and constructs used  in  the  study. Descriptive research could not explain the relationship among the variables [2] and therefore, to establish relationship and association between variables used in the study, explanatory research was used.

Survey  method  using  a  pre-structured  interview schedule was  used  for  collecting  primary data from the respondents because it offers more accurate means of evaluating information about  the  sample  and  enables  the  researcher  to  draw  inferences about  generalising  the findings  from  a  sample  to  the  population.[3]  The study  also  made  use  of secondary  data  collected  from  published  sources  such  as  records  and  reports of RBI and IRDBT, books, bank official websites, bank magazines, reports, newspapers, journals and websites.

Two banks were selected for the study and 180 customers were selected from each bank purposively those who are using innovative banking services namely ATM/Debit Card, Credit Card, Internet Banking and Mobile Banking. Two banks were selected based on IBA Banking Technology Awards 2014-2015.[4] The select banks are State Bank of India and Canara Bank which are public sector.

Sample design

Details of customers using innovative banking services (IBS) could not be obtained from the banks due to banks’ privacy issues and topic sensitivity. Therefore, the researcher decided to contact the respondents from ATM outlets of the select banks and other urban ATM outlets in the district. Simple random sampling method was adopted to select the ATM outlets and purposive sampling method was adopted to select the respondents. Customers who are using innovative banking services (IBS) visiting ATM outlets on the days of survey were selected as sample respondents. The respondents were selected after having ensured that they have account with any of the two banks and they are using all the two selected IBS. It was also ensured that the respondents have been using IBS for a minimum period of two years.

 Determination of Sample Size

Where

         Z       =       Standardized value corresponding to a confidence level of 95% = 1.96

         S       =       Sample SD from Pilot study of 60 sample = 0.484

         E       =       Acceptable Error =5% = 0.05

         Hence, Sample size = n = (ZS/E) 2

                                                = (1.96*0.484/0.05)2

                                                = 359.96

Hence, Sample Size n= 360

360 respondents who were selected for the study out of those 180 respondents are from State Bank of India and 180 respondents are from Canara Bank. The collected data were analysed with the help of SPSS 21 and AMOS. In order to obtain the score of the attitude of customers Likert Five Point Scaling Technique was used.

Results of Reliability Test Using Cronbach’s Alpha

Variables No. of Items Cronbach’s Alpha
Measuring  level of attitude of  the customers’ towards IBS 28 0.892

LIMITATIONS OF THE STUDY

Each research work is subjected to certain limitations and this study is also not an exception. The present study has the following limitations:

  • The responses for the study have been solicited from the District of Tirunelveli in Tamilnadu alone. The expectation and attitude of the customers in Tirunelveli may vary from those of the rest of the Districts in Tamilnadu and other states in India.
  • The study may suffer from the element of biasness.
  • The customers of two banks were selected for the study to study the attitude towards IBS. As a result, the generalisation of the findings of the present research has to be done with utmost care.
  • Furthermore, the sample was restricted to commercial banks. The other major banks like private, co-operative banks and foreign banks are excluded from the study.
  • The analysis of innovative banking services offered to corporate banking customers are excluded from the study.
  • No published data were available on number of customers availing all the four select services and no banks provided much data.
  • As regard users of card, no categorisation has been done such as users of classic, platinum and alike.
  • The study was restricted to urban customers only.

ANALYSIS AND INTERPRETATION

MILIEU OF THE RESPONDENTS- A DESCRIPTIVE ANALYSIS

  • Majority (61.4%) of the customers using innovative banking services (IBS) are male.
  • Majority (31.7%) of the respondents using innovative banking services (IBS) belongs to the age group of 31-35.
  • Majority (75.3%) of the customers using innovative banking services (IBS) are married.
  • Majority (45.8) of the IBS users are graduates.
  • Majority (44.7) of the respondents are employed.
  • In public sector, customers using innovative banking services (IBS) are earning above
  • Overall 78.3 per cent of the respondents are having savings account.
  • Majority (47.2%) of the respondents are having account with the bank between 2-5 years.

 

Problems faced by the customers while using ATM/Debit Card

         Customers are using maximum ATM/Debit Card service at the maximum in their day to day transactions. It is evident that, majority of the customers are using Debit Card up to 5 times. While using ATM/Debit Card customers are facing problems in performing their task. The below table shows the major problems faced by the customers while accessing ATM/Debit Card.

Table No. 1

Mean of Problems Faced by the Respondents while using ATM/Debit Card

ATM/DEBIT CARD Public Sector of Bank
Poor network 2.05
Lack of infrastructure 1.99
Long waiting queue 3.40
Machine out of service 2.52
Out of cash 1.81
Limited ATM centres 2.52
Unable to print statement 2.41
Letters printed in the statement disappear after few days 3.62
Card blocked 2.58
Misuse of card and frauds 2.27
Lack of confidence 2.18
Swiping is difficult 1.86
ATM centre doors are always open 3.67
Without security guards 3.44
Non-availability of CCTV  (Inside and Outside ATM centre) 2.37
Damaged Currency 2.23
Reduction of balance without cash disposal 2.36
Over/Under value of withdrawal amount 2.29
Location of ATM centre  is safety 2.56
No proper Air Conditioner 1.74
No parking  facilities in front of ATM centres 3.60
2 or more people in a single machine 3.57
Not giving proper intimation about charges 3.54
Magnetic Strip easily damaged 1.89
If misplaced, blocking card is difficult 2.29
Prompt service to get new card and PIN 3.12
Time Consuming 2.29
ATM premises are full of Receipts on the floor. 3.17
Shoppers also charging for using card 2.76

    Source: Primary Data

            Based on the mean score, public sector customers using ATM/Debit Card services are facing problems like ATM outlet doors are always open (3.67), letters in printed statement disappear after few days (3.62), no parking facilities in front of ATM outlets (3.60), two or more people tend to use a machine at a time (3.57), banks are not giving proper intimation about charges (3.54), lack of security guards (3.44) in the ATM outlets and long waiting in queue (3.40).

Inference: It is inferred that, public sector customers are facing the similar problems i.e. lack of infrastructure facilities and not proper maintenance of ATM outlets. It is evidence that, urban ATM outlets in the study area are accurately having these types of problems cited by the sample respondents.

SUGGESTIONS

  • Nowadays, there is sufficient number of ATMs but no proper facilities to access the ATM outlets like parking, shed to stay in queue, paper free ATM center, Air Conditioner, Security guards and CCTV camera in and out of ATM outlets to avoid physical attack and theft occurred in the place of ATM outlet. So, proper care should be given to maintain ATM outlets.
  • The banks should instruct the outsourcing agents to put quality paper for printing receipt. Because, the letter in the printed receipt disappear after few days.
  • The customers should follow the security guidelines given by the banks while accessing ATM/Debit Card.
  • The customers should not disclose the PIN to anybody.
  • The customers should avail ATM/Debit Card with utmost care.

CONCLUSION

Banking sector plays a vital role in the growth of economic development in India. Banking is still under evolutionary stage as it is adopting new technologies to facilitate further the customer convenience in the secured environment. IBS is becoming popular amongst customers who are familiar with the technology up graduation but it is gradually spreading to mass especially at metropolitan and urban cities. Few banks have taken an early lead by introducing technology based banking services. The study on the customers’ attitude towards innovative banking services (IBS) in banking sector reveals that customers are satisfied in some aspects and they want to continue with their respective banks. The shift from cutomerised service to personalized services is highly essential to satisfy all groups of customers. The findings of the study stress upon the importance of the security and safety expected by the customers especially in the case of innovative banking services (IBS) like ATM/Debit Card. The future of internet banking lies in offering personalized internet based services that are not only valued by their customers but are also unique to them. This would help distinguish themselves in the crowd. This would also help them evolve continuously to meet customers’ needs, capitalizing on new technology to build stronger customer relationship.

REFERENCES

  1. Eugine, F. D. C. & Regi, S. B., “Advantages and Challenges of E-Commerce Customers and Businesses: In Indian Perspective” International Journal of Research–Granthaalayah,4(3), 7-13.
  2. Golden, S. A. R. (2015). Regional Imbalance affecting quality of e-banking services with special reference to Tuticorin District-An Analysis.International Journal of Research2(3), 788-798.
  3. Golden, S. A. R., & Regi, S. B. (2014). Attitude of Rural People Towards Technology Inclusion In Banking Services At Tirunelveli District, IGJAE – Indo Global Journal Of Applied Management Science, 2(2).
  4. Golden, S. A. R., & Regi, S. B. (2014). Customer Preference Towards E- Channels Provided By State Of Bank Of India, Kongunadu College Of Arts And Science, Special Edition 1(1).
  5. Golden, S. A. R., & Regi, S. B. (2015). Satisfaction of Customers towards User Friendly Technological Services offered by Public and Private Sector banks at Palayamkottai, Tirunelveli District.International Journal of Research2(3), 775-787.
  1. http://ezinearticles.com/?A-Brief-Introduction-to-the-Automated-Teller-Machine&id=5397483
  2. http://worldwidejournals.com/paripex/file.php?val=July_2013_1374047900_e453d_54.pdf
  1. Regi, S. B., & C. Eugine Franco, “MEASURING CUSTOMERS’ ATTITUDE TOWARDS INNOVATIVE BANKING SERVICES OF PUBLIC AND PRIVATE SECTOR IN TIRUNELVELI DISTRICT” International Journal of Research – Granthaalayah, Vol. 4, No. 5: SE (2016): 58-66.
  2. Regi, S. B., & Golden, S. A. R. (2014). Customer Preference Towards Innovative Banking Practices Available In State Bank Of India At Palayamkottai.Sankhya International Journal Of Management And Technology, 3 (11 (A)), 3133.
  3. Regi, S. B., & Golden, S. A. R. (2014). Customer Preference Towards E-Channels Provided By State Of Bank Of India.
  4. Regi, S. B., and Dr.C. Eugine Franco, “MEASURING CUSTOMERS’ ATTITUDE TOWARDS INNOVATIVE BANKING SERVICES OF PUBLIC AND PRIVATE SECTOR IN TIRUNELVELI DISTRICT” International Journal of Research – Granthaalayah, Vol. 4, No. 5: SE (2016): 58-66.
  5. Regi, S. B., Golden, S. A. R., & Franco, C. E. (2014). ROLE OF COMMERCIAL BANK IN THE GROWTH OF MICRO AND SMALL ENTERPRISES.Golden Research Thoughts, 3 (7), 15.

[1] Cooper, D.R. and Schindler, P.S. (2001).  Business Research Methods (7th edition). Singapore: McGraw-Hill- Irwin.

[2] Zikmund, W.G. (2000).  Business Research Methods (6th edition). Chicago: The Dryden Press.

[3] Creswell,J.W.  (1994). Research Design: Qualitative and Quantitative Approaches. Thousand Oaks: Sage Publication

[4] http://www.iba.org.in/Documents/FINAL_AWARDS.pdf dated 10/04/2015 time 23.59 p.m

Econometric Models  to Water Use Estimation in Power Plants: An Experiential Analysis

 PERINI PRAVEENA SRI

 

ABSTRACT

The purpose of this paper is to examine water use estimation in hydel and thermal electric power plants in selected regions i.e. Coastal, Rayalaseema and Telangana regions of Andhra Pradesh. The study primarily focuses on the realistic fundamental premise that thermal electric and hydro electric energy generation is responsible for the largest monthly volume of water withdrawals in four seasons (i.e. summer, rainy, winter and post monsoon season) of a year. These enormous water withdrawals by these hydel and thermal power plants can have significant influence on local surface water resources. However there are very few studies of determinants of water use in hydel and thermal electric generation. Analysis of hydel and thermal electric water use data in the existing power plants clearly indicates that there is wide variability in unitary hydel and thermal electric water use within the system. The multivariate regression procedures were used to identify the significant determinants of thermal and hydel water withdrawals in various power plants i.e. five hydel and four thermal power plants. The estimated regression coefficients indicate that the best explanatory variables for the total quantity of hydel water withdrawals are storage capacity, tail water level and actual generation and thermal water withdrawals are condenser cooling and ash disposal. The unit variability of unit water usage indicates that there is significant potential for water conservation in existing power plants.

Keywords:

Thermal water withdrawals, hydel water withdrawals, storage capacity, tail water level, actual generation, condenser cooling and ash disposal.

  • INTRODUCTION

Water has become a growing source of tension especially in power sector in many parts of the World. For India hydro and thermal power projects are vital to fill in the serious electric energy shortfalls that crimp its economy. About 40 percent of India’s population is off the power grid and due to this the welfare of the economy was badly affected. The main stumbling block for this kind of worse situation are a genuine water shortage problem in India and the country’s inability to properly manage large quantities of water during rainy season has made matters worse, exposing it to any small variation in rainfall or river flow. Though the country has invested heavily on nuclear power to generate 30,000 MW and $ 19 billion to produce factories of major thermal, hydro and nuclear power stations, the electric energy shortages were very much prevalent in most parts of the country. For this the first and foremost thing is to judiciously manage the vital resource “water”. The country also planned for setting up of 20,000 MW solar power by 2020. The Government of India has an ambitious mission of Power for All By 2012. This would require an installed generation capacity of atleast 20,000 MW by 2012 from the present level of 144,564.97 MU. However the power requirement will double by 2020 to 400,000 MW. How India is able to meet this target with the on-going water shortage plight in Electricity Generation Industry is a matter of great concern. However the Electricity Generation Industry strategy should primarily focus on this invisible culprit “Water” causing huge generation losses through better water efficiency techniques and lay emphasis on technology up gradation and massive utilization of renewable sources of energy.

The purpose of this paper was to examine water use estimation at hydel and thermal electric power plants in selected regions i.e. Coastal, Rayalaseema and Telangana regions of Andhra Pradesh. The study primarily focuses on the realistic fundamental premise that thermal electric and hydro electric energy generation is responsible for the largest monthly volume of water withdrawals in four seasons (i.e. summer, rainy, winter and post monsoon season) of a year. These enormous water withdrawals by these hydel and thermal power plants can have significant influence on local surface water resources. Water use at the power station level (by fuel type) can be estimated indirectly by using multiple regression analysis. In regression models, water use relationships are expressed in the form of mathematical equations, showing water use as a mathematical function of one or more independent (explanatory) variables. The mathematical form (eg. Linear, multiplicative and exponential) and the selection of the Right hand side (RHS) or independent variables depend on the category and on aggregation of water demand represented by Left Hand side (LHS) or dependent variable.

2.0  THEORETICAL AND CONCEPTUAL REVIEW OF LITERATURE: DIFFERENT APPROACHES OF WATER USE ESTIMATION

The various studies relating to water demand for thermal power plants and its significant determinants are reviewed for explicit understanding of thermal electric energy water use. Cootner, Paul and George O Golf (1965) have build upon a systematic model for estimating water demand in conventional steam electric utility industry. They have regarded   water as a common factor input along with fuel. Here

TWD= f (Qf, Cw, EHe, CWH )

Where in TWD = Thermal water withdrawal demand,    Qf = Quantity and cost of fuel,   Cw = Cost of water,  EHe = Economics of heat exchange and recycle and  CWH= other costs of thermal power plant associated with the disposal of waste heat.

In other words the quantity of the fresh water withdrawals depends upon the above mentioned factors. In another study Wollman and Bonem (1971) found that the quantity of fresh water withdrawals for steam electric power generation depends upon (1) Thermal efficiency (with higher thermal efficiency less heat will be dissipated. Due to this smaller amount of cooling water are needed) (2) The extent to which sea or brackish water can substitute for fresh water (3) The rate of recirculation. Recirculation is a function of price of water availability. Young and Thompson (1973) in their study identified three factors that affect water use   in thermal electric energy generation. They can be listed as water pricing, change in generation, technology, price of electricity, price of substitutes used in electricity i.e. oil and gas, population and level of general economic activity. The other factors include waste and heat discharge to water and the changes in cooling technologies.

Gleick (1993) in his study reviewed the water requirement of electric energy. Taking as base of earlier studies, he estimated the consumptive water use in Electricity Generation Industry using different technologies. The system efficiency for conventional coal combustion (Once through Cooling Towers), natural gas combustion (Once Through Cooling Towers) and nuclear generation (CTs) stood at 35 percent, 36 percent and 40 percent. The estimates specifies that with the help of Once Through Cooling Technologies, the average consumptive use ranges from  1.2 m3/MWH  in case of conventional coal, for oil and natural gas consumption the average consumption use is less by 1.1 m3/MWH  , where as with cooling towers it was 2.6 m3/MWH. For nuclear power generation the average consumptive use of water with the aid of CTs was more that stood at 3.2 m3/MWH. There is a need for use of high efficient technology in cooling towers for water conservation. Electric Power Research Institute 2002, estimated the evaporation water loss from recirculating towers i.e., roughly 480 gal/MWH for a coal fired power plant. Mortenson, 2006 in his study have provided a technological breakthrough i.e. small scale tests of one technology (that uses cross-currents of ambient air for condensation) as a counteracting measure for these evaporation losses. By this technology the evaporation losses can be reduced to about 60-140 gallons/MWH (that can be applied even to hotter climates). In value terms, EPRI 2004 notified that the savings from reduction of evaporation losses will be $870,000.

There are very few studies of determinants of water use in hydel and thermal electric generation. The literature available relating to water use estimations is very few. Water use experts have to opt for estimation methods for many of the water withdrawals classes i.e. domestic, agriculture and industry because of the true fact that many legal, financial and political constraints limit for getting the hard data. For instance water withdrawals in domestic and live stock water use are usually estimated by multiplying population figures by coefficient. In case of agricultural sector, the irrigation water withdrawals are often estimated by multiplying the acreage by assumed water requirements of the crop rather than by measuring actual water pumped and applied.

Snavely (1986), explicitly details the water use data collection programs and maintaining regional data base of the Great Lakes St. Lawrence River Basin States.  The results are very much appealing indicating as how broad the range of estimation coefficient for water use can be within a geographic area with similar water availability. Mostly the estimated coefficients used for agriculture and domestic use vary by a factor of 10. The econometric studies relating to water use estimation in public supply use and thermo electric power use have the potential to explain temporal and geographic variability across USA. The aggregated water use estimates were provided by the National water Use Information Programme. These estimates primarily focus on measuring total water withdrawals (that includes annual extraction of fresh  surface water and ground water) for the period 1980-1985 to 1990-1995 in each of 48 states of USA for public supply water withdrawals , domestic, commercial, irrigation and live stock. The saline water withdrawals were estimated for industrial, mining and thermal electric categories. The public supply water withdrawals are estimated within geographical area i during year t using a set of explanatory variables that includes air temperature, precipitation, price of water, median household income and others.

Cohn et.al (1989) and Christensen et.al (2000) have used examples of such kind by using statistical techniques. The shorter time period used has the advantage of highlighting the recent trend of declining water use since the 1980 compilation. The mean withdrawal for the period (1980-1995) clearly indicates that it was 183.7 gallons per capita per day. This average water withdrawals would decrease by 7.8 gpcd, if the state GDP per capita increased by $1000. The inclusion of this state GDP captures the effects of relative volume of non residential uses (along with their ability to pay for water). The model also indicates that US was able to withdraw 17.2 gpcd, because of its surface water rights in comparison with riparian law states. The inclusion of temperature and precipitation variables also clearly shows the effect of weather on water withdrawals and can be used in normalizing water use for weather. The model indicates that average per capita demand for water in the state decreases by 2.1gallons per day per one inch increase in precipitation and vice versa i.e. water demand increases during summer months. i.e. average temperature.

Billings and Jones, 1996 employed indirect estimation of water use in urban and municipal planning using coefficient based methods. It calculates water use for commercial, residential and industrial categories. They assume constant water use rates and ignores trends i.e. changes in water use due conservation, technological change or economic forces. Mullusky et.al (1995), Wood Well and Desjardin (1995) for Washington D.C. metropolitan area have employed this water use coefficients for three categories of water users i.e. simple family homes, multiple family homes and employment water use.   Another approach of estimating National Water Use in USA includes Stratified random sampling followed by Census of Agriculture. They employed various methods of collecting data such as telephone, mail survey instruments to develop detailed country level estimates of national agricultural activities. According to Hutson et.al 2004 the thermo electric power water use refers to water that is removed from the ground or diverted from surface water sources (that includes fresh water and saline water) for use in the process of generating electricity with steam driven turbine generators. In this paper the term water withdrawals is used more often precisely. The term designates the amount of water that is extracted from natural water sources. Again it is essential to demarcate between water withdrawals and discharge as consumptive use. Water consumption is the quantity of water with drawn that is evaporated, transpired, incorporated in to crops, consumed by human or live stock.

At the end it can be said that different authors have notified different methods for estimation of water use for various uses of the economy. This paper employs multivariate models of water use for estimation of significant determinants of thermal and hydel water withdrawals.

Objectives of the paper

The objective is to determine if multiple regression models of unit hydel and

thermo electric water use have the potential

To identify significant determinants of total hydel and thermo electric water withdrawals across selected region wise power plants in AP using aggregated category wise water use estimates.

To estimate the future water withdrawals for hydel and thermal electricity generation plants expressed as cubic meters per second. (Cumecs) and cubic meters using the growth rate phenomenon.

The types of data used for estimation are monthly water withdrawals data (For surface fresh water resources)

Region level models for hydro and thermo electric water withdrawals

The potential dependent and independent variables for water withdrawals are identified for estimation purpose. Regional level data for thermal and hydel water withdrawals are more accurate data. The underlying reason being water withdrawals are usually metered.

Dependent Variable: Total Hydel Water Withdrawals

     Total Thermal Water Withdrawals

Independent Variables of Hydel Power Plant:

(a) Reservoir levels, (b) Inflows, (c) Storage capacity, (d) Evaporation losses, (e) Tail water level and (f) Gross Head

Independent Variables of Thermal Power Plant:

(a) Demineralised water, (b) Boiler Feedback, (c) Condenser Cooling (d) Ash disposal, (e) Others include colony domestic, drinking, sanitation, fire fighting, back wash filter, (f) Installed generation capacity, (g) Actual electric energy production (h) Total no. of cooling towers, (i) Water temperatures in summer, rainy and winter.

Multiple Regression analyses were performed using the data related to category wise water use in power plant, generating facility and weather conditions from month wise 1995-96 to 2008-09 data in respective thermal and hydel power plants. The effect of variables such as quantities of water used exclusively for the production of electricity i.e. Boiler feed, Demineralised water, Condenser cooling, Ash Disposal, colony domestic (Drinking, Sanitation, Fire Fighting, Back wash filter ), installed capacity generation, number of cooling towers, cooling temperature and electric energy generation on total water withdrawals of thermal power plants are explicitly analyzed. In addition to this, the effect of variables such as reservoir elevation, storage capacity, tail water level, evaporation losses, inflows, gross head, actual generation on total hydel withdrawals have also been looked in to. This paper explores the structure of power plant level aggregated water use data based on corresponding and routinely collected economic and climatic data. The purpose of this enquiry is to determine if multiple regression models have the potential to explain the temporal and climatic variability across various thermal and hydel power plants in Andhra Pradesh using aggregated water use estimates and most importantly to identify significant determinants of total water withdrawals of thermal and hydel power plants. The statistical models examined here are derived using data estimates of total water withdrawals for hydel and thermo electric power use.

Specification of Mathematical Model

WHEim = a +∑ bj Xj

                    j

Where WHEim  = Fresh water withdrawals for Hydel Electric Energy within region wise i during particular months m in a year.

     Xj is a set of explanatory variables. (Mentioned above)

WTEim = a +∑ bj Xj

                    j

WTEim = Fresh water withdrawals for Thermal Electric Energy within region wise i during particular months m in a year.

      Xj is a set of explanatory variables. (Mentioned above Coefficients a and bj can be estimated using multiple regression model.

Specification of the Econometric Model:

In Linear forms, these equations can be estimated as follows

Yt = B1+B2X2+B3X3+B4X4+B5X5+B6X6+B7X7+ µ

Model: 1 WTEim = B1+B2 CT+B3DB+B4CD+B5AS+B6WT+B7AG+µ ……… (1)

Where, WTEim = Water withdrawals for thermal electric energy in region i for particular months m.

CT = Condenser cooling (with Cooling Towers), DB = Demineralized water and Boiler Feed

CD = Colony Domestic, AS = Ash Slurries, WT= Water Temperature, AG= Actual generation

µ= random error term

Condenser Cooling: Water required for cooling hot turbines and condensers

Demineralized Water:  Water that is, free of minerals and salts. Water runs through active resin beds to remove metallic ions and filtered through sub micron filter to remove suspended impurities.

Colony Domestic: Water that is used for the purpose of colony maintenance, drinking purpose and plantation.

Ash Slurries: As coal burns, it produces carbon –di-oxide, sulphur –di-oxide and nitrogen oxides. These gases together with lighter ash are called fly ash. The electro static precipitators remove all the fly ash and are mixed with water to make in to ash slurries.

Water temperature: Recording the temperature of water during summer, rainy and winter seasons.

Actual Generation: The generation of electricity that is actually generated apart from installed generation.

Model 2: WHEim = B1+B2 RE+B3SC+B4 TW+B5GH+B6WT+B7AG+µ ……. (2)

Where WHEim= Water withdrawals for hydel electric energy in region i for particular months m.

RE = Reservoir Elevation, SC= Storage Capacity ,TW= Tail water level, El= Evaporation losses, GH= Gross Head, WT= Water Temperature, AG= Actual Generation,µ= random error term

Reservoir Elevation: This is defined as the foot of the dam. i.e. the level from which the reservoir storage level and the height of the dam are measured.

Storage Capacity: This corresponds to the flood level usually designated as the upper limit of the normal operational range, above which the spill gates come in to operation

Tail water Level:  Water immediately below the power plant. Tail water elevation refers to the level that water which can rise as discharges increase. It is measured in the feet above sea level.  1 foot = 0.305 meters.

Inflows: The inflow may be monsoonal rains or lakes, rivers. The average volume of incoming water, in unit period of time.

Evaporation Losses: Conversion of liquid to vapor state by latent heat. Water gets saturated in the form of vapor due to rise in water temperature.

Discharge: Volume of water released from power dam at a given time measured as cubic feet per second.

Gross Head: A dam’s maximum allowed vertical distance between upper stream’s surface water fore bay elevation and the down stream’s surface water (tail water) elevation at the tail race for reaction wheel dams.

Actual Generation: The amount of electricity actually generated apart from installed generation.

The collection of data includes a monthly time series data analysis during the period (1995-96 to 2008-09). Analysis of hydel and thermal electric water use data in the existing power plants clearly indicates that there is wide variability in unitary thermal and hydel electric water use within the system. The multi- variate regression  procedures were used to identify the significant determinants  of thermal and hydel water withdrawals in various power plants i.e. five hydel and four thermal power plants. The unit variability of unit water usage indicates that there is significant potential for water conservation in existing hydel and thermal electric power plants.

3.0 Approach and Methodology

 The study includes three main components. (a) A series of site visits and interviews with power plant personnel. (b) Field surveys of selected hydel and thermal power plants of Andhra Pradesh (c) The multiple regression analysis of power generation data and other associated information.

Summary of site visits: Site visits for selected five hydel namely Nagarjuna Sagar Main Power House, Nagarjuna Sagar Left Canal Power House, Nagarjuna Sagar Right Canal Power House, Srisailam Left canal power house and Srisailam right Canal Power House and four thermal namely Rayalaseema Thermal Power Plant, Kothagudaem Thermal Power Station O & M, Kothagudaem Thermal Power Station Stage V and Narla Tata Rao Thermal Power Plant have been made to assess the overall performance scenario of power plants and also to examine the extent of water irregularities .Appraisal of Power Plant Survey:  The research estimates of hydel and thermal Electric Energy water withdrawals are based upon the authenticated sources of data provided by respective hydel and thermal power plants of Andhra Pradesh Generation Corporation of India Limited. In order to transparently clarify the way that power generation officials responded to this kind of field survey in practice and to solicit information from them on factors responsible for water use at power generation facilities, site visits have been taken up.  At various Power plants several personal interviews with power plant officers helped to identify the types of onsite water uses, the measurement of these uses and provision of information on various types of cooling systems and water use procedures employed by hydel and thermal electric energy generation facilities.

The purpose of conducting a series of personal interviews with power plant officials can be listed as follows:

(a)    Scrutinize and examine the power generation water use and water withdrawals from intake (surface water) to discharge in various types of facilities.

(b)   Observing the fact that all the water with drawals (hydel and thermal) are metered.

(c)    Detailed analysis about important onsite uses of water and its significant determinants

(d)      To obtain feedback on the cooling system level of water use in power stations.

Multiple Regression Models of Water Use

The principal sources of data used in the multi variate analyses of thermal and hydel power plants are most accurate and provides a fairly comprehensive review of plant characteristics, power generation and water withdrawal details. The data in electronic format and in official records was available for the years 1996-97 to 2008-09. The weather data i.e. especially related to water temperatures during summer, rainy and winter was collected in order to examine the influence of it on total thermal and hydel water withdrawals.

At the end it can be concluded that the site visits and field surveys helped to identify important concerns about water measurement and use at thermal and hydel electric power plants. Added to this, these factors have received attention in the development of models to describe hydro and thermal electric water use. All the above mentioned information proved very much useful in the design of data analysis that was used to develop water use bench marks.

4.0 RESULTS AND DISCUSSION: ESTIMATION AND INTERPRETATION OF MODEL SPECIFICATIONS

Hydel based Electric Energy Power Plants

Model Specification I Nagarjuna Sagar Main Power House

 (Appendix table: A1)

In model 1 the estimated regression equation for total hydel water withdrawals is in the linear form as follows:

*              * *                          *

WHE = -146.238-0.080RE-0.258SC+0.350TW+0.133GH+50.67AG

                                               (-3.96)         (3.144)                      (119.87)

N= 154, R2 =0.99, f= 5543.05

  • The estimated equation indicates that the total hydel water withdrawals are inelastic with respect to storage capacity. This kind of negative relationship indicates that the hydel water withdrawals are somewhat in responsive to changes in the storage capacity. The coefficients are statistically significant at 1 % level.
  • The total hydel water withdrawals are elastic with tail water level and actual generation that hold a positive relationship. The coefficients are statistically significant at 5 % and 1 % level.
  • The t-ratio of regression coefficients is highly significant for three independent variables namely SC, TW and AG. As the t ratio value is greater than 2.58 indicates that the relation between dependent variable and independent variables observed in the sample holds good.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as reservoir elevation and gross feet, as the t- value is very small.
  • The R2 (coefficient of determination) is 0.99. It means that the independent variables tail water level, actual generation and storage capacity can explain 99 percent of variation in the dependent variable (WD) and remaining 1 percent variation is unexplained by the model. As R2 is very high, the estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher and more significant at 1% level. This clearly indicates that the regressors are significantly associated with dependent variable.

Model SpecificationII Nagarjuna Sagar Left Canal Power House

         (Appendix Table: A2)

*                                 *            *                    *

WHE = 1660.770-3.516RE-21.705SC+9.653TW+491.286AG+0.130EL

            (3.314)                       (4.16)        (3.84)         (15.67)

 N= 166, R2= 0.78, f = 116.22

  • The estimated regression coefficients indicate that the best independent that have significant effect are storage capacity and actual generation with significant levels at 1 % for each of independent variables.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. As t ratio value is greater than 2.58, it indicates that the relation between Hydel Water withdrawal and independent (SC) and (AG) observed in the sample holds good.
  • The R2 is 0.78. It means that the independent variables SC and AG can explain 78 percent variation in the dependent variable and the remaining 22 % variation is unexplained by the model. The estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (116.22) and more significant at 1 % level. This indicates that the regressors SC and AG are significantly associated with dependent variable.

Model Specification III Nagarjuna Sagar Right Canal Power House 

         (Appendix Table: A3)

             *                                      *                                                     *

WHE = 6133.252+0.628 RL-58.029 SC+0.414EL+37.493TW+486.057 AG

          (7.314)                        (6.063)                                          (16.232)

N= 166, R2= 0.78, f value = 116.22

  • The estimated regression coefficients indicate that the best independent variables that have significant effect are storage capacity and actual generation with significant levels at 1 % for each of independent variables.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. The relation between water withdrawals and Storage capacity and actual generation in the sample holds good as the t-value is greater than 2.58.
  • The t-ratio of regression coefficients is not at all significant for other independent variables such as reservoir level, storage capacity and evaporation losses.
  • The R2 is 0.78. It means that the independent variables SC and AG can explain 78 % variation in the dependent variable and remaining 22 % variation is unexplained by the model. The estimated equation is considered as the equation of very good fit.
  • The overall model is statistically significant as f value is higher (116.22) and more significant at 1 % level. This indicates that the regressors are significantly associated with dependent variable (WD)

Model Specification IV Srisailam Left Bank Power House

                  (Appendix Table: A4)

                                                                *                          *

WHE = -2243.501-0.766RE+1.195SC+57.47AG+0.592EL+4.24TW+0.000IF

                              (-2.27)                         (18.81)                     (2.69)

N= 58   , R2= 0.96, f value = 221.872

  • The estimated regression coefficients indicate that the best independent variables that have significant effect are actual generation and tail water level with significant levels at 1 % and 10 % for independent variables.
  • The t-ratio of regression coefficients is highly significant with three independent variables namely reservoir elevation, actual generation and tail water level. The t-ratio value is greater than 1.96 value for reservoir level and greater than 2.58 value for actual generation and tail water level. This indicates that the relation between WD and independent variables AG and reservoir elevation observed in the sample holds good.
  • The t- ratio of regression coefficients is not at all significant for other independent variables such as evaporation losses and inflows.
  • The R2 is 0.96. It means that the independent variables reservoir level, actual generation and tail water level can explain 96 % of variation in the dependent variable and remaining 4% is unexplained by the model. Thus the estimated regression coefficient is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (221.872) and more significant at 1 % level. This indicates that the regressors AG and TW are significantly associated with dependent variable. (WD)

 

Model Specification V Srisailam Right Bank Power House

                   (Appendix Table: A5)

                 *                        *        *

Y = -7630.380-1.78RE+0SC+56AG+0.051EL+0.627TW+0.289GH

              (-4.199)             (-4.3)  (122.65)

  N= 138    , R2    = 0.99 and f value = 4.59

  • The estimated regression coefficients indicate that the best independent variables that have a significant effect are storage capacity and actual generation with significant levels at 1 % level each of independent variable.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. The t- ratio value is greater than 2.58 for SC and AG that indicates that the relation between WD and independent variables SC and AG holds good.
  • The t- ratios of regression coefficients is not at all significant for other independent variables such as evaporation losses, tail water level and gross head.
  • The R2 is 0.99. It means that the independent variables such as storage capacity and actual generation can explain 99 % variation in the dependent variable and remaining 1 % is unexplained by the model. Thus the estimated regression coefficient is considered as an equation of very good fit.
  • The overall relationship was statistically significant as f value is 4.59 and more significant at 1 % level. This indicates that the regressors SC and AG are significantly associated with WD.

Thermal based Electric Energy Power Plants

Model Specification VI Kothagudaem Thermal Power Plant O &M

      (Appendix Table: A6)

                                                     *                                                     *   

Y= -787978.047 + 1.021CC-2.130DB-12.190CD+146.699 OT +1.152 AD+4616.497 CT-817.112AG

                              (3.259)                                                        (3.841)

N= 84, R2 = 0.55, f value = 13.710

  • The estimated regression coefficients indicate that the best explanatory (independent) variables with significant effect on quantity of water with drawals per Kilowatt hour are condenser cooling with cooling towers (Natural Draft cooling system) and ash disposal with significant levels of 5 % and 1 % level.
  • The estimated equation indicates that the total thermal water withdrawals are elastic with respect to condenser cooling and ash disposal. This kind of positive relationship indicates that the thermal water withdrawals are responsive to changes in condenser cooling and ash disposal.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely condenser cooling with Natural Draft CTs and Ash Disposal. The t ratio value is greater than 2.58.
  • This indicates that the importance of technological alternatives (i.e. Condenser Cooling with natural draft CTs) is the significant determinant of water withdrawals. Next ash disposal takes second place as significant determinant of total thermal water withdrawals.
  • The t-ratio of regression coefficient is not at all significant for other independent variables such as DM and Boiler feedback, colony domestic, others (Drinking, Sanitation, Fire fighting, Back Wash Filter), cooling temperature and actual  electric energy generation.
  • The R2 is 0.55. It means that the independent variables such as condenser cooling and ash disposal can explain 55 % of variation in the dependent variable and remaining 45 % variation is unexplained by the model. The estimated equation is considered as good fit.
  • The overall model is statistically significant as f value is higher (13.710) and highly significant at 1 % level. This indicates that the regressor’s condenser cooling with Natural Draft CT’s and Ash Disposal are significantly associated with dependent variable WDs.

Model Specification VII Kothagudaem Thermal Power Station Stage V

          (Appendix Table: A7)

                                   *                *

Y= 98233.879+0.873 CC+1.186AD+0.111 DB-1688.373CT+32.019 AG

                               (20.91)       (15.247)

              N= 83, R2= 0.97, f value = 706.164

  • The estimated regression coefficients indicate that the best independent variables with significant effect on quantity of WD per million tonnes are Condenser cooling and ash disposal with significant levels at 1% level each.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely Condenser cooling with natural draft CT’s and Actual Generation. The t- ratio value is greater than 2.58. Here the significant determinant of WD’s are CC with natural draft CT’s. Next comes ash disposal as second good determinant.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as BF & DM, cooling temperature and Energy Generation.
  • The R2 is 0.97. It means that independent variables such as CC and AD can explain 97 % of variation in the dependent variable (Water withdrawal) and remaining 3 % variation are unexplained by the model. Thus the estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (706.164) and highly significant at 1 % level. This indicates that the regressors condenser cooling with NDCT’s and Ash Disposal are significantly associated with Water withdrawal’s (Dependent Variable)

Model Specification VIII Rayalaseema Thermal Power Plant

          (Appendix Table: A8)

                           *

Y = 10334.674+0.745 CC+8.725 BF+0.847 AS-4.143 AG-145.408 CT

     (2.677)                (3.007)

N= 35, R2 = 0.87 and f value = 33.145

  • The estimated regression coefficients indicate that the best independent variables with significant effect on quantity of Water Withdrawal Condenser cooling with significant levels at 5%.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for one independent variables namely Condenser cooling with natural draft CT’s .The t- ratio value is greater than 2.58. Here the significant determinant of WD’s are CC with natural draft CT’s.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as BF & DM, Ash Disposal cooling temperature and Energy Generation.
  • The R2 is 0.87. It means that independent variables such as CC can explain 87 % of variation in the dependent variable (WD) and remaining 13 % variation are unexplained by the model. Thus the estimated equation is considered as an equation of very good fit.
  • The over all model is statistically significant as f value is higher (33.145) and highly significant at 1 % level. This indicates that the regressors condenser cooling with NDCT’s are significantly associated with WD’s (Dependent Variable)

Model Specification IX Narla Tata Rao Thermal Power Plant

                     (Appendix Table: A9)

                          *                               *   

Y = 139993.709 + 1.002CC -0.863CD + 1.031 AS- 373.483 CT- 56.843 AG

                                    (1277.966)                 (19.88)

N=      R2 = 1.00, f value = 907849.564

  • The estimated regression coefficients indicate that the best explanatory (independent) variables with significant effect on quantity of water with drawals per Kilowatt hour are condenser cooling with cooling towers ( Induced l Draft cooling system) and ash disposal with significant levels of 1 % and 1 % level.
  • The estimated equation indicates that the total thermal water withdrawals are elastic with respect to condenser cooling and ash disposal. This kind of positive relationship indicates that the thermal water withdrawals are responsive to changes in condenser cooling and ash disposal.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely condenser cooling with Induced Draft CTs and Ash Disposal. The t ratio value is greater than 2.58.
  • This indicates that the importance of technological alternatives (i.e. Condenser Cooling with Induced draft CTs) is the significant determinant of water withdrawals. Next ash disposal takes second place as significant determinant of total thermal water withdrawals.
  • The t-ratio of regression coefficient is not at all significant for other independent variables such as, colony domestic, cooling temperature and actual electric energy generation.
  • The R2 is 1.00. It means that the independent variables such as condenser cooling and ash disposal can explain 100 % of variation in the dependent variable. This shows that we have accounted for almost all the variability with the variables specified in the model. The estimated equation is considered as very good fit.
  • The overall model is statistically significant as f value is higher (907849.564) and highly significant at 1 % level. This indicates that the regressor’s condenser cooling with Induced Draft CT’s and Ash Disposal are significantly associated with dependent variable WDs.

The pertinent conclusion of this study is there may be significant potential for water conservation after having identified the significant determinants of total thermal water withdrawals i.e. condenser cooling and ash disposal. The choice of explanatory variable for eg: Induced draft and natural draft technological innovation was able to address the significant changes of water withdrawals.

5.0  CONCLUSION AND RECOMMENDATION

The thermal and hydel power plants sustenance is very much under stake due to major reason of fresh water shortages in power generation. The most sophisticated technology followed in advanced countries namely Concentrated solar thermal power integrated with combined system of conventional steam plant, Fresnel Solar Collector and  Solar Flower Tower can be used as a replica even in developing countries like India though not cost effective in order to counteract the water shortage problem

REFERENCES

Benedy Kt Dziegielewski, Thomas Bik (August 2006), “ Water Use Bench Marks for Thermo Electric Power Generation” Project report, Southern Illinois University, United States

Geological Survey, 2004, USGS National Competitive Grants Program.

Gbadebo Oladosu, Stan Hadley, Vogt D.P. and Wilbanks J.J. (September, 2006), “Electricity

Generation and Water Related Constraints: An Empirical Analysis of Four South Eastern

States”, Oak Ridge National Laboratory, Oak Ridge Tennessee.

Sitanon Jesdapipat and Siriporon Kiratikarnkul, “ Surrogate pricing of water: The Case of micro Hydro –Electricity Co-operatives in Northern Thailand”.

 Xiaoying Yang & Benedy Kt Dziegielewski (February,2007), “ Water Use by Thermo Electric power plants in the United states” Journal of the American Water Resources Association, Vol 43, No.1.

“Estimating Water Use in United States: A new Paradigm for National Use Water Use Information Programme”(2002),

http://books.nap.edu/openbook.php?record_id=10484&page=95

 

Data Sources

Annual Report on the Working of SEBs and Electricity Departments, Planning Commission, Various Issues

Administrative Reports of Andhra Pradesh Generation Corporation of India Limited (APGENCO),Various Issues. Field Level data of selected thermal and hydel power stations authenticated  by APGENCO.

Econometric Models  to Water Use Estimation in Power Plants: An Experiential Analysis

 PERINI PRAVEENA SRI

Department of Social Science, Faculty of Economics

 Ethiopia, Aksum University

ABSTRACT

The purpose of this paper is to examine water use estimation in hydel and thermal electric power plants in selected regions i.e. Coastal, Rayalaseema and Telangana regions of Andhra Pradesh. The study primarily focuses on the realistic fundamental premise that thermal electric and hydro electric energy generation is responsible for the largest monthly volume of water withdrawals in four seasons (i.e. summer, rainy, winter and post monsoon season) of a year. These enormous water withdrawals by these hydel and thermal power plants can have significant influence on local surface water resources. However there are very few studies of determinants of water use in hydel and thermal electric generation. Analysis of hydel and thermal electric water use data in the existing power plants clearly indicates that there is wide variability in unitary hydel and thermal electric water use within the system. The multivariate regression procedures were used to identify the significant determinants of thermal and hydel water withdrawals in various power plants i.e. five hydel and four thermal power plants. The estimated regression coefficients indicate that the best explanatory variables for the total quantity of hydel water withdrawals are storage capacity, tail water level and actual generation and thermal water withdrawals are condenser cooling and ash disposal. The unit variability of unit water usage indicates that there is significant potential for water conservation in existing power plants.

Keywords:

Thermal water withdrawals, hydel water withdrawals, storage capacity, tail water level, actual generation, condenser cooling and ash disposal.

  • INTRODUCTION

Water has become a growing source of tension especially in power sector in many parts of the World. For India hydro and thermal power projects are vital to fill in the serious electric energy shortfalls that crimp its economy. About 40 percent of India’s population is off the power grid and due to this the welfare of the economy was badly affected. The main stumbling block for this kind of worse situation are a genuine water shortage problem in India and the country’s inability to properly manage large quantities of water during rainy season has made matters worse, exposing it to any small variation in rainfall or river flow. Though the country has invested heavily on nuclear power to generate 30,000 MW and $ 19 billion to produce factories of major thermal, hydro and nuclear power stations, the electric energy shortages were very much prevalent in most parts of the country. For this the first and foremost thing is to judiciously manage the vital resource “water”. The country also planned for setting up of 20,000 MW solar power by 2020. The Government of India has an ambitious mission of Power for All By 2012. This would require an installed generation capacity of atleast 20,000 MW by 2012 from the present level of 144,564.97 MU. However the power requirement will double by 2020 to 400,000 MW. How India is able to meet this target with the on-going water shortage plight in Electricity Generation Industry is a matter of great concern. However the Electricity Generation Industry strategy should primarily focus on this invisible culprit “Water” causing huge generation losses through better water efficiency techniques and lay emphasis on technology up gradation and massive utilization of renewable sources of energy.

The purpose of this paper was to examine water use estimation at hydel and thermal electric power plants in selected regions i.e. Coastal, Rayalaseema and Telangana regions of Andhra Pradesh. The study primarily focuses on the realistic fundamental premise that thermal electric and hydro electric energy generation is responsible for the largest monthly volume of water withdrawals in four seasons (i.e. summer, rainy, winter and post monsoon season) of a year. These enormous water withdrawals by these hydel and thermal power plants can have significant influence on local surface water resources. Water use at the power station level (by fuel type) can be estimated indirectly by using multiple regression analysis. In regression models, water use relationships are expressed in the form of mathematical equations, showing water use as a mathematical function of one or more independent (explanatory) variables. The mathematical form (eg. Linear, multiplicative and exponential) and the selection of the Right hand side (RHS) or independent variables depend on the category and on aggregation of water demand represented by Left Hand side (LHS) or dependent variable.

2.0  THEORETICAL AND CONCEPTUAL REVIEW OF LITERATURE: DIFFERENT APPROACHES OF WATER USE ESTIMATION

The various studies relating to water demand for thermal power plants and its significant determinants are reviewed for explicit understanding of thermal electric energy water use. Cootner, Paul and George O Golf (1965) have build upon a systematic model for estimating water demand in conventional steam electric utility industry. They have regarded   water as a common factor input along with fuel. Here

TWD= f (Qf, Cw, EHe, CWH )

Where in TWD = Thermal water withdrawal demand,    Qf = Quantity and cost of fuel,   Cw = Cost of water,  EHe = Economics of heat exchange and recycle and  CWH= other costs of thermal power plant associated with the disposal of waste heat.

In other words the quantity of the fresh water withdrawals depends upon the above mentioned factors. In another study Wollman and Bonem (1971) found that the quantity of fresh water withdrawals for steam electric power generation depends upon (1) Thermal efficiency (with higher thermal efficiency less heat will be dissipated. Due to this smaller amount of cooling water are needed) (2) The extent to which sea or brackish water can substitute for fresh water (3) The rate of recirculation. Recirculation is a function of price of water availability. Young and Thompson (1973) in their study identified three factors that affect water use   in thermal electric energy generation. They can be listed as water pricing, change in generation, technology, price of electricity, price of substitutes used in electricity i.e. oil and gas, population and level of general economic activity. The other factors include waste and heat discharge to water and the changes in cooling technologies.

Gleick (1993) in his study reviewed the water requirement of electric energy. Taking as base of earlier studies, he estimated the consumptive water use in Electricity Generation Industry using different technologies. The system efficiency for conventional coal combustion (Once through Cooling Towers), natural gas combustion (Once Through Cooling Towers) and nuclear generation (CTs) stood at 35 percent, 36 percent and 40 percent. The estimates specifies that with the help of Once Through Cooling Technologies, the average consumptive use ranges from  1.2 m3/MWH  in case of conventional coal, for oil and natural gas consumption the average consumption use is less by 1.1 m3/MWH  , where as with cooling towers it was 2.6 m3/MWH. For nuclear power generation the average consumptive use of water with the aid of CTs was more that stood at 3.2 m3/MWH. There is a need for use of high efficient technology in cooling towers for water conservation. Electric Power Research Institute 2002, estimated the evaporation water loss from recirculating towers i.e., roughly 480 gal/MWH for a coal fired power plant. Mortenson, 2006 in his study have provided a technological breakthrough i.e. small scale tests of one technology (that uses cross-currents of ambient air for condensation) as a counteracting measure for these evaporation losses. By this technology the evaporation losses can be reduced to about 60-140 gallons/MWH (that can be applied even to hotter climates). In value terms, EPRI 2004 notified that the savings from reduction of evaporation losses will be $870,000.

There are very few studies of determinants of water use in hydel and thermal electric generation. The literature available relating to water use estimations is very few. Water use experts have to opt for estimation methods for many of the water withdrawals classes i.e. domestic, agriculture and industry because of the true fact that many legal, financial and political constraints limit for getting the hard data. For instance water withdrawals in domestic and live stock water use are usually estimated by multiplying population figures by coefficient. In case of agricultural sector, the irrigation water withdrawals are often estimated by multiplying the acreage by assumed water requirements of the crop rather than by measuring actual water pumped and applied.

Snavely (1986), explicitly details the water use data collection programs and maintaining regional data base of the Great Lakes St. Lawrence River Basin States.  The results are very much appealing indicating as how broad the range of estimation coefficient for water use can be within a geographic area with similar water availability. Mostly the estimated coefficients used for agriculture and domestic use vary by a factor of 10. The econometric studies relating to water use estimation in public supply use and thermo electric power use have the potential to explain temporal and geographic variability across USA. The aggregated water use estimates were provided by the National water Use Information Programme. These estimates primarily focus on measuring total water withdrawals (that includes annual extraction of fresh  surface water and ground water) for the period 1980-1985 to 1990-1995 in each of 48 states of USA for public supply water withdrawals , domestic, commercial, irrigation and live stock. The saline water withdrawals were estimated for industrial, mining and thermal electric categories. The public supply water withdrawals are estimated within geographical area i during year t using a set of explanatory variables that includes air temperature, precipitation, price of water, median household income and others.

Cohn et.al (1989) and Christensen et.al (2000) have used examples of such kind by using statistical techniques. The shorter time period used has the advantage of highlighting the recent trend of declining water use since the 1980 compilation. The mean withdrawal for the period (1980-1995) clearly indicates that it was 183.7 gallons per capita per day. This average water withdrawals would decrease by 7.8 gpcd, if the state GDP per capita increased by $1000. The inclusion of this state GDP captures the effects of relative volume of non residential uses (along with their ability to pay for water). The model also indicates that US was able to withdraw 17.2 gpcd, because of its surface water rights in comparison with riparian law states. The inclusion of temperature and precipitation variables also clearly shows the effect of weather on water withdrawals and can be used in normalizing water use for weather. The model indicates that average per capita demand for water in the state decreases by 2.1gallons per day per one inch increase in precipitation and vice versa i.e. water demand increases during summer months. i.e. average temperature.

Billings and Jones, 1996 employed indirect estimation of water use in urban and municipal planning using coefficient based methods. It calculates water use for commercial, residential and industrial categories. They assume constant water use rates and ignores trends i.e. changes in water use due conservation, technological change or economic forces. Mullusky et.al (1995), Wood Well and Desjardin (1995) for Washington D.C. metropolitan area have employed this water use coefficients for three categories of water users i.e. simple family homes, multiple family homes and employment water use.   Another approach of estimating National Water Use in USA includes Stratified random sampling followed by Census of Agriculture. They employed various methods of collecting data such as telephone, mail survey instruments to develop detailed country level estimates of national agricultural activities. According to Hutson et.al 2004 the thermo electric power water use refers to water that is removed from the ground or diverted from surface water sources (that includes fresh water and saline water) for use in the process of generating electricity with steam driven turbine generators. In this paper the term water withdrawals is used more often precisely. The term designates the amount of water that is extracted from natural water sources. Again it is essential to demarcate between water withdrawals and discharge as consumptive use. Water consumption is the quantity of water with drawn that is evaporated, transpired, incorporated in to crops, consumed by human or live stock.

At the end it can be said that different authors have notified different methods for estimation of water use for various uses of the economy. This paper employs multivariate models of water use for estimation of significant determinants of thermal and hydel water withdrawals.

Objectives of the paper

The objective is to determine if multiple regression models of unit hydel and

thermo electric water use have the potential

To identify significant determinants of total hydel and thermo electric water withdrawals across selected region wise power plants in AP using aggregated category wise water use estimates.

To estimate the future water withdrawals for hydel and thermal electricity generation plants expressed as cubic meters per second. (Cumecs) and cubic meters using the growth rate phenomenon.

The types of data used for estimation are monthly water withdrawals data (For surface fresh water resources)

Region level models for hydro and thermo electric water withdrawals

The potential dependent and independent variables for water withdrawals are identified for estimation purpose. Regional level data for thermal and hydel water withdrawals are more accurate data. The underlying reason being water withdrawals are usually metered.

Dependent Variable: Total Hydel Water Withdrawals

     Total Thermal Water Withdrawals

Independent Variables of Hydel Power Plant:

(a) Reservoir levels, (b) Inflows, (c) Storage capacity, (d) Evaporation losses, (e) Tail water level and (f) Gross Head

Independent Variables of Thermal Power Plant:

(a) Demineralised water, (b) Boiler Feedback, (c) Condenser Cooling (d) Ash disposal, (e) Others include colony domestic, drinking, sanitation, fire fighting, back wash filter, (f) Installed generation capacity, (g) Actual electric energy production (h) Total no. of cooling towers, (i) Water temperatures in summer, rainy and winter.

Multiple Regression analyses were performed using the data related to category wise water use in power plant, generating facility and weather conditions from month wise 1995-96 to 2008-09 data in respective thermal and hydel power plants. The effect of variables such as quantities of water used exclusively for the production of electricity i.e. Boiler feed, Demineralised water, Condenser cooling, Ash Disposal, colony domestic (Drinking, Sanitation, Fire Fighting, Back wash filter ), installed capacity generation, number of cooling towers, cooling temperature and electric energy generation on total water withdrawals of thermal power plants are explicitly analyzed. In addition to this, the effect of variables such as reservoir elevation, storage capacity, tail water level, evaporation losses, inflows, gross head, actual generation on total hydel withdrawals have also been looked in to. This paper explores the structure of power plant level aggregated water use data based on corresponding and routinely collected economic and climatic data. The purpose of this enquiry is to determine if multiple regression models have the potential to explain the temporal and climatic variability across various thermal and hydel power plants in Andhra Pradesh using aggregated water use estimates and most importantly to identify significant determinants of total water withdrawals of thermal and hydel power plants. The statistical models examined here are derived using data estimates of total water withdrawals for hydel and thermo electric power use.

Specification of Mathematical Model

WHEim = a +∑ bj Xj

                    j

Where WHEim  = Fresh water withdrawals for Hydel Electric Energy within region wise i during particular months m in a year.

     Xj is a set of explanatory variables. (Mentioned above)

WTEim = a +∑ bj Xj

                    j

WTEim = Fresh water withdrawals for Thermal Electric Energy within region wise i during particular months m in a year.

      Xj is a set of explanatory variables. (Mentioned above Coefficients a and bj can be estimated using multiple regression model.

Specification of the Econometric Model:

In Linear forms, these equations can be estimated as follows

Yt = B1+B2X2+B3X3+B4X4+B5X5+B6X6+B7X7+ µ

Model: 1 WTEim = B1+B2 CT+B3DB+B4CD+B5AS+B6WT+B7AG+µ ……… (1)

Where, WTEim = Water withdrawals for thermal electric energy in region i for particular months m.

CT = Condenser cooling (with Cooling Towers), DB = Demineralized water and Boiler Feed

CD = Colony Domestic, AS = Ash Slurries, WT= Water Temperature, AG= Actual generation

µ= random error term

Condenser Cooling: Water required for cooling hot turbines and condensers

Demineralized Water:  Water that is, free of minerals and salts. Water runs through active resin beds to remove metallic ions and filtered through sub micron filter to remove suspended impurities.

Colony Domestic: Water that is used for the purpose of colony maintenance, drinking purpose and plantation.

Ash Slurries: As coal burns, it produces carbon –di-oxide, sulphur –di-oxide and nitrogen oxides. These gases together with lighter ash are called fly ash. The electro static precipitators remove all the fly ash and are mixed with water to make in to ash slurries.

Water temperature: Recording the temperature of water during summer, rainy and winter seasons.

Actual Generation: The generation of electricity that is actually generated apart from installed generation.

Model 2: WHEim = B1+B2 RE+B3SC+B4 TW+B5GH+B6WT+B7AG+µ ……. (2)

Where WHEim= Water withdrawals for hydel electric energy in region i for particular months m.

RE = Reservoir Elevation, SC= Storage Capacity ,TW= Tail water level, El= Evaporation losses, GH= Gross Head, WT= Water Temperature, AG= Actual Generation,µ= random error term

Reservoir Elevation: This is defined as the foot of the dam. i.e. the level from which the reservoir storage level and the height of the dam are measured.

Storage Capacity: This corresponds to the flood level usually designated as the upper limit of the normal operational range, above which the spill gates come in to operation

Tail water Level:  Water immediately below the power plant. Tail water elevation refers to the level that water which can rise as discharges increase. It is measured in the feet above sea level.  1 foot = 0.305 meters.

Inflows: The inflow may be monsoonal rains or lakes, rivers. The average volume of incoming water, in unit period of time.

Evaporation Losses: Conversion of liquid to vapor state by latent heat. Water gets saturated in the form of vapor due to rise in water temperature.

Discharge: Volume of water released from power dam at a given time measured as cubic feet per second.

Gross Head: A dam’s maximum allowed vertical distance between upper stream’s surface water fore bay elevation and the down stream’s surface water (tail water) elevation at the tail race for reaction wheel dams.

Actual Generation: The amount of electricity actually generated apart from installed generation.

Selected power plants in three regions of Andhra Pradesh

Power Plant by

Fuel Type

Rayalaseema Region Telangana Region Coastal Region
Thermal Rayalaseema Thermal Power Plant .Kothagudaem Thermal Power Station  O & M

 

.Kothagudaem Thermal Power Station Stage V

 Narla Tata Rao Thermal Power Plant
Hydel Nagarjuna Sagar Main Power House

 

Nagarjuna Sagar Left Canal Power House

 

Nagarjuna Sagar Right Canal Power House

Srisailam Left canal power house

 

Srisailam right Canal Power House

 

 

The collection of data includes a monthly time series data analysis during the period (1995-96 to 2008-09). Analysis of hydel and thermal electric water use data in the existing power plants clearly indicates that there is wide variability in unitary thermal and hydel electric water use within the system. The multi- variate regression  procedures were used to identify the significant determinants  of thermal and hydel water withdrawals in various power plants i.e. five hydel and four thermal power plants. The unit variability of unit water usage indicates that there is significant potential for water conservation in existing hydel and thermal electric power plants.

3.0 Approach and Methodology

 The study includes three main components. (a) A series of site visits and interviews with power plant personnel. (b) Field surveys of selected hydel and thermal power plants of Andhra Pradesh (c) The multiple regression analysis of power generation data and other associated information.

Summary of site visits: Site visits for selected five hydel namely Nagarjuna Sagar Main Power House, Nagarjuna Sagar Left Canal Power House, Nagarjuna Sagar Right Canal Power House, Srisailam Left canal power house and Srisailam right Canal Power House and four thermal namely Rayalaseema Thermal Power Plant, Kothagudaem Thermal Power Station O & M, Kothagudaem Thermal Power Station Stage V and Narla Tata Rao Thermal Power Plant have been made to assess the overall performance scenario of power plants and also to examine the extent of water irregularities .Appraisal of Power Plant Survey:  The research estimates of hydel and thermal Electric Energy water withdrawals are based upon the authenticated sources of data provided by respective hydel and thermal power plants of Andhra Pradesh Generation Corporation of India Limited. In order to transparently clarify the way that power generation officials responded to this kind of field survey in practice and to solicit information from them on factors responsible for water use at power generation facilities, site visits have been taken up.  At various Power plants several personal interviews with power plant officers helped to identify the types of onsite water uses, the measurement of these uses and provision of information on various types of cooling systems and water use procedures employed by hydel and thermal electric energy generation facilities.

The purpose of conducting a series of personal interviews with power plant officials can be listed as follows:

(a)    Scrutinize and examine the power generation water use and water withdrawals from intake (surface water) to discharge in various types of facilities.

(b)   Observing the fact that all the water with drawals (hydel and thermal) are metered.

(c)    Detailed analysis about important onsite uses of water and its significant determinants

(d)      To obtain feedback on the cooling system level of water use in power stations.

Multiple Regression Models of Water Use

The principal sources of data used in the multi variate analyses of thermal and hydel power plants are most accurate and provides a fairly comprehensive review of plant characteristics, power generation and water withdrawal details. The data in electronic format and in official records was available for the years 1996-97 to 2008-09. The weather data i.e. especially related to water temperatures during summer, rainy and winter was collected in order to examine the influence of it on total thermal and hydel water withdrawals.

At the end it can be concluded that the site visits and field surveys helped to identify important concerns about water measurement and use at thermal and hydel electric power plants. Added to this, these factors have received attention in the development of models to describe hydro and thermal electric water use. All the above mentioned information proved very much useful in the design of data analysis that was used to develop water use bench marks.

4.0 RESULTS AND DISCUSSION: ESTIMATION AND INTERPRETATION OF MODEL SPECIFICATIONS

Hydel based Electric Energy Power Plants

Model Specification I Nagarjuna Sagar Main Power House

 (Appendix table: A1)

In model 1 the estimated regression equation for total hydel water withdrawals is in the linear form as follows:

*              * *                          *

WHE = -146.238-0.080RE-0.258SC+0.350TW+0.133GH+50.67AG

                                               (-3.96)         (3.144)                      (119.87)

N= 154, R2 =0.99, f= 5543.05

  • The estimated equation indicates that the total hydel water withdrawals are inelastic with respect to storage capacity. This kind of negative relationship indicates that the hydel water withdrawals are somewhat in responsive to changes in the storage capacity. The coefficients are statistically significant at 1 % level.
  • The total hydel water withdrawals are elastic with tail water level and actual generation that hold a positive relationship. The coefficients are statistically significant at 5 % and 1 % level.
  • The t-ratio of regression coefficients is highly significant for three independent variables namely SC, TW and AG. As the t ratio value is greater than 2.58 indicates that the relation between dependent variable and independent variables observed in the sample holds good.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as reservoir elevation and gross feet, as the t- value is very small.
  • The R2 (coefficient of determination) is 0.99. It means that the independent variables tail water level, actual generation and storage capacity can explain 99 percent of variation in the dependent variable (WD) and remaining 1 percent variation is unexplained by the model. As R2 is very high, the estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher and more significant at 1% level. This clearly indicates that the regressors are significantly associated with dependent variable.

Model SpecificationII Nagarjuna Sagar Left Canal Power House

         (Appendix Table: A2)

*                                 *            *                    *

WHE = 1660.770-3.516RE-21.705SC+9.653TW+491.286AG+0.130EL

            (3.314)                       (4.16)        (3.84)         (15.67)

 N= 166, R2= 0.78, f = 116.22

  • The estimated regression coefficients indicate that the best independent that have significant effect are storage capacity and actual generation with significant levels at 1 % for each of independent variables.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. As t ratio value is greater than 2.58, it indicates that the relation between Hydel Water withdrawal and independent (SC) and (AG) observed in the sample holds good.
  • The R2 is 0.78. It means that the independent variables SC and AG can explain 78 percent variation in the dependent variable and the remaining 22 % variation is unexplained by the model. The estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (116.22) and more significant at 1 % level. This indicates that the regressors SC and AG are significantly associated with dependent variable.

Model Specification III Nagarjuna Sagar Right Canal Power House 

         (Appendix Table: A3)

             *                                      *                                                     *

WHE = 6133.252+0.628 RL-58.029 SC+0.414EL+37.493TW+486.057 AG

          (7.314)                        (6.063)                                          (16.232)

N= 166, R2= 0.78, f value = 116.22

  • The estimated regression coefficients indicate that the best independent variables that have significant effect are storage capacity and actual generation with significant levels at 1 % for each of independent variables.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. The relation between water withdrawals and Storage capacity and actual generation in the sample holds good as the t-value is greater than 2.58.
  • The t-ratio of regression coefficients is not at all significant for other independent variables such as reservoir level, storage capacity and evaporation losses.
  • The R2 is 0.78. It means that the independent variables SC and AG can explain 78 % variation in the dependent variable and remaining 22 % variation is unexplained by the model. The estimated equation is considered as the equation of very good fit.
  • The overall model is statistically significant as f value is higher (116.22) and more significant at 1 % level. This indicates that the regressors are significantly associated with dependent variable (WD)

Model Specification IV Srisailam Left Bank Power House

                  (Appendix Table: A4)

                                                                *                          *

WHE = -2243.501-0.766RE+1.195SC+57.47AG+0.592EL+4.24TW+0.000IF

                              (-2.27)                         (18.81)                     (2.69)

N= 58   , R2= 0.96, f value = 221.872

  • The estimated regression coefficients indicate that the best independent variables that have significant effect are actual generation and tail water level with significant levels at 1 % and 10 % for independent variables.
  • The t-ratio of regression coefficients is highly significant with three independent variables namely reservoir elevation, actual generation and tail water level. The t-ratio value is greater than 1.96 value for reservoir level and greater than 2.58 value for actual generation and tail water level. This indicates that the relation between WD and independent variables AG and reservoir elevation observed in the sample holds good.
  • The t- ratio of regression coefficients is not at all significant for other independent variables such as evaporation losses and inflows.
  • The R2 is 0.96. It means that the independent variables reservoir level, actual generation and tail water level can explain 96 % of variation in the dependent variable and remaining 4% is unexplained by the model. Thus the estimated regression coefficient is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (221.872) and more significant at 1 % level. This indicates that the regressors AG and TW are significantly associated with dependent variable. (WD)

 

Model Specification V Srisailam Right Bank Power House

                   (Appendix Table: A5)

                 *                        *        *

Y = -7630.380-1.78RE+0SC+56AG+0.051EL+0.627TW+0.289GH

              (-4.199)             (-4.3)  (122.65)

  N= 138    , R2    = 0.99 and f value = 4.59

  • The estimated regression coefficients indicate that the best independent variables that have a significant effect are storage capacity and actual generation with significant levels at 1 % level each of independent variable.
  • The t-ratio of regression coefficients is highly significant with two independent variables namely storage capacity and actual generation. The t- ratio value is greater than 2.58 for SC and AG that indicates that the relation between WD and independent variables SC and AG holds good.
  • The t- ratios of regression coefficients is not at all significant for other independent variables such as evaporation losses, tail water level and gross head.
  • The R2 is 0.99. It means that the independent variables such as storage capacity and actual generation can explain 99 % variation in the dependent variable and remaining 1 % is unexplained by the model. Thus the estimated regression coefficient is considered as an equation of very good fit.
  • The overall relationship was statistically significant as f value is 4.59 and more significant at 1 % level. This indicates that the regressors SC and AG are significantly associated with WD.

Thermal based Electric Energy Power Plants

Model Specification VI Kothagudaem Thermal Power Plant O &M

      (Appendix Table: A6)

                                                     *                                                     *   

Y= -787978.047 + 1.021CC-2.130DB-12.190CD+146.699 OT +1.152 AD+4616.497 CT-817.112AG

                              (3.259)                                                        (3.841)

N= 84, R2 = 0.55, f value = 13.710

  • The estimated regression coefficients indicate that the best explanatory (independent) variables with significant effect on quantity of water with drawals per Kilowatt hour are condenser cooling with cooling towers (Natural Draft cooling system) and ash disposal with significant levels of 5 % and 1 % level.
  • The estimated equation indicates that the total thermal water withdrawals are elastic with respect to condenser cooling and ash disposal. This kind of positive relationship indicates that the thermal water withdrawals are responsive to changes in condenser cooling and ash disposal.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely condenser cooling with Natural Draft CTs and Ash Disposal. The t ratio value is greater than 2.58.
  • This indicates that the importance of technological alternatives (i.e. Condenser Cooling with natural draft CTs) is the significant determinant of water withdrawals. Next ash disposal takes second place as significant determinant of total thermal water withdrawals.
  • The t-ratio of regression coefficient is not at all significant for other independent variables such as DM and Boiler feedback, colony domestic, others (Drinking, Sanitation, Fire fighting, Back Wash Filter), cooling temperature and actual  electric energy generation.
  • The R2 is 0.55. It means that the independent variables such as condenser cooling and ash disposal can explain 55 % of variation in the dependent variable and remaining 45 % variation is unexplained by the model. The estimated equation is considered as good fit.
  • The overall model is statistically significant as f value is higher (13.710) and highly significant at 1 % level. This indicates that the regressor’s condenser cooling with Natural Draft CT’s and Ash Disposal are significantly associated with dependent variable WDs.

Model Specification VII Kothagudaem Thermal Power Station Stage V

          (Appendix Table: A7)

                                   *                *

Y= 98233.879+0.873 CC+1.186AD+0.111 DB-1688.373CT+32.019 AG

                               (20.91)       (15.247)

              N= 83, R2= 0.97, f value = 706.164

  • The estimated regression coefficients indicate that the best independent variables with significant effect on quantity of WD per million tonnes are Condenser cooling and ash disposal with significant levels at 1% level each.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely Condenser cooling with natural draft CT’s and Actual Generation. The t- ratio value is greater than 2.58. Here the significant determinant of WD’s are CC with natural draft CT’s. Next comes ash disposal as second good determinant.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as BF & DM, cooling temperature and Energy Generation.
  • The R2 is 0.97. It means that independent variables such as CC and AD can explain 97 % of variation in the dependent variable (Water withdrawal) and remaining 3 % variation are unexplained by the model. Thus the estimated equation is considered as an equation of very good fit.
  • The overall model is statistically significant as f value is higher (706.164) and highly significant at 1 % level. This indicates that the regressors condenser cooling with NDCT’s and Ash Disposal are significantly associated with Water withdrawal’s (Dependent Variable)

Model Specification VIII Rayalaseema Thermal Power Plant

          (Appendix Table: A8)

                           *

Y = 10334.674+0.745 CC+8.725 BF+0.847 AS-4.143 AG-145.408 CT

     (2.677)                (3.007)

N= 35, R2 = 0.87 and f value = 33.145

  • The estimated regression coefficients indicate that the best independent variables with significant effect on quantity of Water Withdrawal Condenser cooling with significant levels at 5%.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for one independent variables namely Condenser cooling with natural draft CT’s .The t- ratio value is greater than 2.58. Here the significant determinant of WD’s are CC with natural draft CT’s.
  • The t- ratio of regression coefficient is not at all significant for other independent variables such as BF & DM, Ash Disposal cooling temperature and Energy Generation.
  • The R2 is 0.87. It means that independent variables such as CC can explain 87 % of variation in the dependent variable (WD) and remaining 13 % variation are unexplained by the model. Thus the estimated equation is considered as an equation of very good fit.
  • The over all model is statistically significant as f value is higher (33.145) and highly significant at 1 % level. This indicates that the regressors condenser cooling with NDCT’s are significantly associated with WD’s (Dependent Variable)

Model Specification IX Narla Tata Rao Thermal Power Plant

                     (Appendix Table: A9)

                          *                               *   

Y = 139993.709 + 1.002CC -0.863CD + 1.031 AS- 373.483 CT- 56.843 AG

                                    (1277.966)                 (19.88)

N=      R2 = 1.00, f value = 907849.564

  • The estimated regression coefficients indicate that the best explanatory (independent) variables with significant effect on quantity of water with drawals per Kilowatt hour are condenser cooling with cooling towers ( Induced l Draft cooling system) and ash disposal with significant levels of 1 % and 1 % level.
  • The estimated equation indicates that the total thermal water withdrawals are elastic with respect to condenser cooling and ash disposal. This kind of positive relationship indicates that the thermal water withdrawals are responsive to changes in condenser cooling and ash disposal.
  • The t-ratio of regression coefficients have expected signs and is highly statistically significant for two independent variables namely condenser cooling with Induced Draft CTs and Ash Disposal. The t ratio value is greater than 2.58.
  • This indicates that the importance of technological alternatives (i.e. Condenser Cooling with Induced draft CTs) is the significant determinant of water withdrawals. Next ash disposal takes second place as significant determinant of total thermal water withdrawals.
  • The t-ratio of regression coefficient is not at all significant for other independent variables such as, colony domestic, cooling temperature and actual electric energy generation.
  • The R2 is 1.00. It means that the independent variables such as condenser cooling and ash disposal can explain 100 % of variation in the dependent variable. This shows that we have accounted for almost all the variability with the variables specified in the model. The estimated equation is considered as very good fit.
  • The overall model is statistically significant as f value is higher (907849.564) and highly significant at 1 % level. This indicates that the regressor’s condenser cooling with Induced Draft CT’s and Ash Disposal are significantly associated with dependent variable WDs.

The pertinent conclusion of this study is there may be significant potential for water conservation after having identified the significant determinants of total thermal water withdrawals i.e. condenser cooling and ash disposal. The choice of explanatory variable for eg: Induced draft and natural draft technological innovation was able to address the significant changes of water withdrawals.

5.0  CONCLUSION AND RECOMMENDATION

The thermal and hydel power plants sustenance is very much under stake due to major reason of fresh water shortages in power generation. The most sophisticated technology followed in advanced countries namely Concentrated solar thermal power integrated with combined system of conventional steam plant, Fresnel Solar Collector and  Solar Flower Tower can be used as a replica even in developing countries like India though not cost effective in order to counteract the water shortage problem

REFERENCES

Benedy Kt Dziegielewski, Thomas Bik (August 2006), “ Water Use Bench Marks for Thermo Electric Power Generation” Project report, Southern Illinois University, United States

Geological Survey, 2004, USGS National Competitive Grants Program.

Gbadebo Oladosu, Stan Hadley, Vogt D.P. and Wilbanks J.J. (September, 2006), “Electricity

Generation and Water Related Constraints: An Empirical Analysis of Four South Eastern

States”, Oak Ridge National Laboratory, Oak Ridge Tennessee.

Sitanon Jesdapipat and Siriporon Kiratikarnkul, “ Surrogate pricing of water: The Case of micro Hydro –Electricity Co-operatives in Northern Thailand”.

 Xiaoying Yang & Benedy Kt Dziegielewski (February,2007), “ Water Use by Thermo Electric power plants in the United states” Journal of the American Water Resources Association, Vol 43, No.1.

“Estimating Water Use in United States: A new Paradigm for National Use Water Use Information Programme”(2002),

http://books.nap.edu/openbook.php?record_id=10484&page=95

 

Data Sources

Annual Report on the Working of SEBs and Electricity Departments, Planning Commission, Various Issues

Administrative Reports of Andhra Pradesh Generation Corporation of India Limited (APGENCO),Various Issues. Field Level data of selected thermal and hydel power stations authenticated  by APGENCO.

APPENDIX TABLES

Table: A1: Nagarjuna Sagar Main Power House

Variables Entered/Removed  
Model Variables Entered Variables Removed Method  
1 acutal_generation, tail_water_level, Reser_elevation, Gross_feet, Storage_capacitya . Enter  
a. All requested variables entered.    
b. Dependent Variable: water_discharge_cums  
Model Summary  
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .997a .995 .995 512.92868  
a. Predictors: (Constant), acutal_generation, tail_water_level, Reser_elevation, Gross_feet, Storage capacity  
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 7291771208.745 5 1458354241.749 5543.053 .000a
Residual 38675087.446 147 263095.833    
Total 7330446296.191 152      
a. Predictors: (Constant), acutal_generation, tail_water_level, Reser_elevation, Gross_feet, Storage capacity
b. Dependent Variable: water_discharge_cums      
 

Coefficientsa

 
Model Unstandardized Coefficients Standardized Coefficients t Sig.  
B Std. Error Beta  
1 (Constant) -146.238 1555.816   -.094 .925  
Reser_elevation -.080 .093 -.012 -.865 .389  
Storage capacity -.258 .065 -.091 -3.966 .000  
tail_water_level .350 .111 .031 3.144 .002  
Gross_feet .133 .094 .026 1.419 .158  
acutal_generation 50.669 .423 1.041 119.869 .000  
a. Dependent Variable: water_discharge_cums        

Table: A 2 Nagarjuna Sagar Left Canal Power House

Variables Entered/Removedb  
Model Variables Entered Variables Removed Method  
1 evaporation, energe_bus, twl_ft, storage capacity, reservior_levela . Enter  
a. All requested variables entered.    
b. Dependent Variable: water_drawals  
Model Summary  
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .864a .747 .739 2350.84646  
a. Predictors: (Constant), evaporation, energe_bus, twl_ft, storage capacity, reservior_level  
 

ANOVAb

Model Sum of Squares df Mean Square F Sig.
1 Regression 2626964399.664 5 525392879.933 95.068 .000a
Residual 889763133.646 161 5526479.091    
Total 3516727533.310 166      
a. Predictors: (Constant), evaporation, energy bus, twl_ft, storage capacity, reservior_level  
b. Dependent Variable: water_drawals        
Coefficientsa  
Model Unstandardized Coefficients Standardized Coefficients t Sig.  
B Std. Error Beta  
1 (Constant) 1660.770 501.102   3.314 .001  
reservior_level -3.516 3.411 -.157 -1.031 .304  
storage capacity -21.705 5.219 -.538 -4.159 .000  
twl_ft 9.653 2.510 .394 3.846 .000  
energy bus 491.286 30.765 .987 15.969 .000  
evaporation .130 .508 .015 .255 .799  
a. Dependent Variable: water_drawals        

Table: A3 Nagarjuna Sagar Right Canal Power House

Model Variables Entered      
1 generation bus, reservior_level, evaporation, storage capacity, tailwaterlevela      
b. Dependent Variable: water_drawals

Model Summary

 
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .885a .784 .777 3767.05581  
a. Predictors: (Constant), generation bus, reservior_level, evaporation, storage capacity, tailwaterlevel  
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 8246365913.182 5 1649273182.636 116.222 .000a
Residual 2270513515.133 160 14190709.470    
Total 10516879428.315 165      
a. Predictors: (Constant), generation bus, reservior_level, evaporation, storage capacity, tailwaterlevel  
b. Dependent Variable: water_drawals        
Coefficientsa  
Model Unstandardized Coefficients Standardized Coefficients t Sig.  
B Std. Error Beta  
1 (Constant) 6133.252 838.604   7.314 .000  
reservior_level .628 7.571 .016 .083 .934  
storage capacity -58.029 9.570 -.832 -6.063 .000  
Evaporation .414 .810 .027 .511 .610  
Tailwaterlevel 37.493 21.598 .263 1.736 .084  
generation bus 486.057 29.945 1.045 16.232 .000  
a. Dependent Variable: water_drawals        

Table:  A4 Srisailam Left Canal Power House

Variables Entered/Removedb  
Model Variables Entered Variables Removed Method  
1 inflow, Reservoir, evaporat, Actual generation, Tail water, storage_capacitya . Enter  
a. All requested variables entered.    
b. Dependent Variable: water_withdra  
Model Summary  
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .981a .963 .959 1454.18057  
a. Predictors: (Constant), inflow, Reservoir, evaporat, Actual generation, Tail water, storage capacity  
                                                                                   ANOVAb  
Model Sum of Squares df Mean Square F Sig.  
1 Regression 2815082375.894 6 4.692E8 221.872 .000a  
Residual 107846697.597 51 2114641.129      
Total 2922929073.491 57        
a. Predictors: (Constant), inflow, Reservoir, evaporat, Actual generation, Tail water, storage capacity    
b. Dependent Variable: water_withdra          
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -2243.501 2527.275   -.888 .379
Reservoir -.766 .337 -.239 -2.272 .027
storage capacity 1.195E-6 .000 .000 .004 .997
Actual generation 57.476 3.055 .953 18.814 .000
evaporat .592 .939 .081 .631 .531
Tail water 4.237 1.572 .248 2.695 .010
inflow .000 .002 -.017 -.339 .736
a. Dependent Variable: water_withdra        

Table: A5 Srisailam Right Canal Power House

Model Variables Entered Variables Removed Method  
1 Gross head, Tailwaterlevel, actual generation, Evaporation, storage, Reservoir . Enter  
a. All requested variables entered.    
b. Dependent Variable: water withdrawals  
 

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate
1 .998a .995 .995 631.39218
a. Predictors: (Constant), Gross head, Tailwaterlevel, actual generation, Evaporation, storage, Reservoir

ANOVAb  
Model Sum of Squares df Mean Square F Sig.  
1 Regression 1.099E10 6 1.832E9 4.596E3 .000a  
Residual 5.222E7 131 398656.090      
Total 1.105E10 137        
a. Predictors: (Constant), Gross head, Tailwaterlevel, actual generation, Evaporation, storage, Reservoir  
b. Dependent Variable: water withdrawals        
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -7630.380 1817.341   -4.199 .000
Reservoir -.178 .322 -.027 -.553 .581
storage .000 .000 -.068 -4.288 .000
actual generation 56.314 .459 1.022 122.651 .000
Evaporation .051 .139 .005 .365 .716
Tailwaterlevel .627 .334 .059 1.874 .063
Gross head .289 .320 .036 .904 .368
a. Dependent Variable: water withdrawals      

Table: A6 Kothagudaem Thermal Power Plant O &M

Variables Entered/Removedb  
Model Variables Entered Variables Removed Method  
1 energy generation , cooling temp, DM Water & Boiler Feed back , Ash Disposal , Condenser Cooling , Colony domestic , (Drin, Sani, Firefigh, Backwarhfiler) a . Enter  
a. All requested variables entered.    
b. Dependent Variable: Total water consumption  
 

Model Summary

 
Model R R Square Adjusted R Square Std. Error of the Estimate  
1 .747a .558 .517 289298.132  
a. Predictors: (Constant), energy generation , cooling temp, DM Water & Boiler Feed back , Ash Disposal , Condenser Cooling , Colony domestic , (Drin, Sani, Firefigh, Backwarhfiler)  
ANOVAb  
Model Sum of Squares df Mean Square F Sig.  
1 Regression 8.032E12 7 1.147E12 13.710 .000a  
Residual 6.361E12 76 8.369E10      
Total 1.439E13 83        
a.     Predictors: (Constant), energy generation , cooling temp, DM Water & Boiler Feed back , Ash Disposal , Condenser Cooling , Colony domestic , (Drin, Sani, Firefigh, Backwarhfiler)  
       
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -787978.047 1.334E6   -.591 .557
Condenser Cooling 1.021 .313 .551 3.259 .002
DM Water & Boiler Feed back -2.130 5.717 -.038 -.373 .710
Colony domestic -12.190 15.642 -.250 -.779 .438
(Drin, Sani, Firefigh, Backwarhfiler) 146.699 201.477 .467 .728 .469
Ash Disposal 1.152 .300 .409 3.841 .000
cooling temp 4616.497 10000.955 .039 .462 .646
energy generation -817.112 1096.318 -.295 -.745 .458
a. Dependent Variable: Total water consumption      

Table:  A7 Kothagudaem Thermal Power Plant Stage V

  Variables Entered/Removedb  
  Model Variables Entered Variables Removed Method  
  1 Energy Generation, ASH DIS-POSAL (MT), Cooling Temperature , Boiled Feed and DM plant Regeneration, COOLING TOWER MAKEUP        (MT)a . Enter  
  a. All requested variables entered.    
  b. Dependent Variable: TOTAL CONS.  (MT)  
  Model Summary  
  Model R R Square Adjusted R Square Std. Error of the Estimate  
  1 .989a .979 .977 64726.513  
  a. Predictors: (Constant), Energy Generation, ASH DIS-POSAL (MT), Cooling Temperature , Boiled Feed and DM plant Regeneration, COOLING TOWER MAKEUP        (MT)  
 

ANOVAb

Model Sum of Squares df Mean Square F Sig.
1 Regression 14792454121098.932 5 2958490824219.786 706.164 .000a
Residual 322593153570.889 77 4189521474.947    
Total 15115047274669.820 82      
a. Predictors: (Constant), Energy Generation, ASH DIS-POSAL (MT), Cooling Temperature , Boiled Feed and DM plant Regeneration, COOLING TOWER MAKEUP        (MT)
b. Dependent Variable: TOTAL CONS.  (MT)      
  Coefficientsa  
  Model Unstandardized Coefficients Standardized Coefficients t Sig.  
  B Std. Error Beta  
  1 (Constant) 98233.879 76676.230   1.281 .204  
  COOLING TOWER MAKEUP        (MT) .873 .042 .577 20.912 .000  
  ASH DIS-POSAL (MT) 1.186 .078 .484 15.247 .000  
  Boiled Feed and DM plant Regeneration .111 .978 .003 .114 .910  
  Cooling Temperature -1688.373 2158.260 -.014 -.782 .436  
  Energy Generation 32.019 115.619 .005 .277 .783  
  a. Dependent Variable: TOTAL CONS.  (MT)          

Table: A 8 Rayalaseema Thermal Power Plant

  Variables Entered/Removedb  
  Model Variables Entered Variables Removed Method  
  1 Cooling Temp, Ash slurry, Actual Generation, Power Generation, Boiler feed, Condenser cooling, BCWa . Enter  
  a. All requested variables entered.    
  b. Dependent Variable: Water consumption  
  Model Summary  
  Model R R Square Adjusted R Square Std. Error of the Estimate  
  1 .934a .873 .846 1324.085  
  a. Predictors: (Constant), Cooling Temp, Ash slurry, Actual Generation, Power Generation, Boiler feed, Condenser cooling, BCW  
  ANOVAb  
  Model Sum of Squares df Mean Square F Sig.  
  1 Regression 3.487E8 6 5.811E7 33.145 .000a  
  Residual 5.084E7 29 1753200.788      
  Total 3.995E8 35        
  a. Predictors: (Constant), Cooling Temp, Ash slurry, Actual Generation, Power Generation, Boiler feed, Condenser cooling, BCW  
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 10334.674 3861.078   2.677 .012
Condenser cooling, BCW .745 .248 .432 3.007 .005
Boiler feed 8.725 4.628 .244 1.885 .069
Ash slurry .847 .501 .230 1.692 .101
Power Generation -.595 .388 -.138 -1.532 .136
Actual Generation -4.143 5.478 -.077 -.756 .456
Cooling Temp -145.408 94.141 -.109 -1.545 .133
a. Dependent Variable: Water consumption        

Table : A 9 Narla Tata Rao Thermal Power Plant

  Variables Entered/Removedb  
  Model Variables Entered Variables Removed Method  
  1 Energy Generation, Condenser cooling & BCW (KL), Cooling Temperature , Ash slurry water (KL), Colony Domestic (KL)a . Enter  
  a. All requested variables entered.    
  b. Dependent Variable: Totalwaterconsumption  
  Model Summary  
  Model R R Square Adjusted R Square Std. Error of the Estimate  
  1 1.000a 1.000 1.000 50290.302  
  a. Predictors: (Constant), Energy Generation, Condenser cooling & BCW (KL), Cooling Temperature , Ash slurry water (KL), Colony Domestic (KL)  
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 11480277367590772.000 5 2296055473518154.000 907849.564 .000a
Residual 42994946072.977 17 2529114474.881    
Total 11480320362536844.000 22      
a. Predictors: (Constant), Energy Generation, Condenser cooling & BCW (KL), Cooling Temperature , Ash slurry water (KL), Colony Domestic (KL)
b. Dependent Variable: Totalwaterconsumption      
  Coefficientsa  
  Model Unstandardized Coefficients Standardized Coefficients t Sig.  
  B Std. Error Beta  
  1 (Constant) 139993.709 137540.088   1.018 .323  
  Condenser cooling & BCW (KL) 1.002 .001 .987 1277.966 .000  
  Colony Domestic (KL) -.863 .584 -.001 -1.476 .158  
  Ash slurry water (KL) 1.031 .052 .018 19.879 .000  
  Cooling Temperature -373.483 3763.081 .000 -.099 .922  
  Energy Generation -56.843 138.469 .000 -.411 .687  
  a. Dependent Variable: Totalwaterconsumption        

Econometric Competencies and Entrepreneurship Development 

Adebayo G ADEBAYO

Department of Accountancy

Rufus Giwa (Formally Ondo State) Polytechnic.

Owo, Ondo State, Nigeria

Abstract

This study is designed specifically to demonstrate the application of econometrics or quantitative analysis especially in helping the numerous small and medium enterprises in Nigeria in critical decision making. An Agro-business labour saving fabricating firm in Nigeria was selected as a case and pilot study for all other small or medium enterprises fabricates labour saving machines such as the cassava frying machine (CFM) and palm-oil extracting machine (PEM). The agro-business firm also fabricate cassava grater (GRT) and appliances (APP). The entrepreneur is worried on some critical problems. First, whether these special offers would affect the sale of the major products CFM and PEM since the same customers buy the major products and the special offers. This implies that money spent on special offers will not be spent on CFM and/or PEM. An increase in the sales of special orders may correspond to a decrease in the sale of CFM and PEM. Second is what would be the total sales taking into consideration especially the seasonal fluctuations and third how to identify existing customers who are most likely to respond to proposed improved offer on CFM and PEM. This is important as the Firm fabricates these products on demand. Three models were designed to find solution to each of the problems. The first model was the two-stage least square regression. Findings revealed that the special offer sales GRATER and APPLIANCES) had no negative effect on the sales of the major products (CDM and PEM). All the products significantly contributed to the total sales. The second model was forecasting. The model satisfied forecasting requirements and was able to forecast total sales from April to December 2016 taking into consideration seasonal variations referred to as fluctuations. The third model is the recency, frequency and monetary (RFM) analysis. This marketing tool is used to classify customers according to how recent they patronize, how often and how much is involved in individual customer’s cumulative patronage. The RFM analysis carried out on the customers identified the firm’s customers that would likely respond to new offer. An RFM score of 300 and above qualified a customer to be selected. Part of the recommendations was that entrepreneurs should avail themselves of the decision making tools for better management of their enterprises.

 Keywords: 

 Introduction

The world is facing many economic challenges and issues. These do not isolate the developed economies. Both are debt ridden, with regional economic imbalances and geo-political challenges. There is general economic meltdown in the world market. The Nigeria economy has been acutely affected because of the fall in the price of crude oil in the world market as a result of these economic imbalances and trade policies that are not conducive to Nigerian oil market. Since revenue from crude oil takes about 90% of the Federal government total revenue, there is the critical need to raise non-oil revenue to ensure fiscal sustainability while maintaining infrastructure and social spending.

The Federal government has taken a bold step towards revamping agriculture and overhauling its solid mineral resources. From an entrepreneurial perspective, the present economic meltdown in Nigeria would eventually be a blessing in disguise. The Central Bank of Nigeria has been instructed by President Buhari to create a “synergy” and organize soft loans to agro-based industries and other export businesses.

The main objectives of the 2016 Buhari government’s budget are to make the “synergy” work among all the different players in the country’s economy. These include the banking system, financial institutions, government entities, regulators and other arms of the government.

  • Entrepreneurial Development.

The credibility of the Buhari government among the Nigerian populace as a result of his zero tolerance to corruption and his “Big Bang” [a rapid reform which is economically necessary as a result of severe macroeconomic imbalances (Gelb, Jefferson and Singh 1993) such as this period of economic crisis in Nigeria] approach to major economic reforms, has spurred many investors into agro-based industries. They believe that the government meant business.

One of these respondents is a medium scale firm in Nigeria that fabricates machines to boost agricultural produce. The Firm has recently reactivated two of its machines-the Cassava Frying Machine (CFM) and the Automated Palm oil Extracting Machine (PEM)

  • Objectives of the Firm

The major objectives of the Firm in fabricating these labour saving machines is the issue of health hazard on the one hand. Many local manual cassava frying and palm oil producers especially women, had been subjected to untimely death due to incessant heat from the local frying system and the palm oil production. These machines would reduce drastically the feminine life wasted on daily basis. On the other hand, these machines will boost agricultural produce of “GARI”(a local name for the fine grain output from the frying process of cassava) and palm oil to contribute to increase the recent low GDP rate in Nigeria. [Economic growth in the last quarter of 2015 was 2.1% while total growth in the year was 2.8%, the slowest since 1999 to date (NBS, 2016). This statistics seems to toe the line of the global GDP growth projection of 2.5% which is 0.3% point less than November 2015 outlook (GEO, 2016)]. Therefore any entrepreneurial effort to boost the Nigerian GDP at this trying period is a right decision in the right direction.

  • The Cassava Frying Machine

The CFM is powered by electricity and is capable of frying about 5-10-kg of already peeled, washed and had been cut into smaller sizes and loaded into the machine drum. The machine grinds the cassava, presses it cause fermentation and fry the cassava into very fine grains. Adjustments by the use of some special appliances sold by the firm to its customers may cause the machine to dry cassava from the normal “GARI” into smoother form or into powder. It is an automated machine with 100% local components. A single CFM costs N45, 000 ($225)

  • The Automatic Palm oil Extracting Machine..

The PEM is an automated machine that produces fine, clear, well heated palm oil. Palm fruits are removed from the bunch after some period for partial fermentation, washed and then loaded into the machine tank or drum to the brim. The machine twists and separate kernels from tissues, extract the paste and heat into fine glossy oil. The slag is released from an outlet. It is also powered by electricity. The machine is made with 90% local components, that is, 10% components have to be imported. A single machine costs N50, 000 ($250), about N5000 ($25) more than CFM probably as a result of the cost of the imported components.

  • Statement of the Problem.

The two machines fabricated by the firm are the Cassava Frying Machine (CFM) and Palm oil Extracting (PEM) and are sold to customers on demand. Apart from these machines, there are two special offers that are also sold. These are grater (GRT) and appliances (APP). The APP is a device to enhance either the CFM or PEM, especially as a power saving device, at the customers’ option and is sold at the rate of N12, 000 per unit. These appliances are capable of making adjustments possible to CFM and PEM and other agro-based machines.  The GRT powered by electricity, the pealed, and washed cassava are loaded into its receptor and it grinds cassava very well. This is sold at the rate of N15, 000 per unit. Every month the Firm makes these special offers to customers who need the APP on previously bought machines or on a proposed purchase of CFM and /or PEM. The GRT is mostly purchased by customers who could not afford CFM. The firm is now concerned about the following problems:

  1. Whether these special offers would affect the sale of the major products CFM and PEM since the same customers buy the major products and the special offers. This implies that money spent on special offers will not be spent on CFM and/or PEM. An increase in the sales of special orders may correspond to a decrease in the sale of CFM and PEM.
  2. How to make a good forecast of the total sales from major products and the special offers.
  3. How to identify existing customers who are most likely to respond to proposed improved offer on CFM and PEM. This is important as the Firm fabricates these products on demand.
  • Objectives of the Study.

The primary objective of this study is to underscore the importance of econometric or quantitative analysis in solving most of the problems of entrepreneurs and hence enhance entrepreneurship development in Nigeria. In realization of this objective, the study had focused on helping to collect all relevant data on the Firm’s customers. These include each customer’s date of transactions with the Firm, amount of purchases each time, total number of transaction and the most recent transaction. There will also be collection of data on total monthly sales on CFM, PEM, APP and GRT for the past 51 months. Other secondary objective, in addition are:

  1. To create compactible models to find solution to each of the Firm’s area of concern.
  2. To discuss the findings and give expert recommendations on the findings.

  1. Review of Related Literature

2.1 The Nigerian Cassava

Cassava is well known as manihot esculenta or manilot utilissima (Yakasi, 2010). In Nigeria, cassava is grown in all the ecological zones andit is planted all the year round on the availability of moisture (Odoemenem and Otanma, 2011). Production is vital to the economy of Nigeria as the country is the world’s largest producer of the commodity. The crop is produced in 24 of the country’s 36 states. In 1999, Nigeria produced 33 million tonnes, while a decade later, it produced approximately 45 million tonnes, which is almost 19% of production in the world. The average yield per hectare is 10.6 tonnes.(Wikipedia, n.d.)

In Nigeria, cassava production is well-developed as an organized agricultural crop. It has well- established multiplication and processing techniques for food products and cattle feed. Cassava is processed in many processing centres and fabricating enterprises set up in the country. Cassava is used in the preparation of several household foods and derivatives such as paste, biscuits, bread sagos and sauce. Its starch is for industrial use such as baby food, jelly, custard poeders and confectioneries (Echebiri and Edeba, 2008).  Roots or leaves are made into flours. Flours are of three types, yellow garri, white garri, or intermediate colour. These varieties are a matter of choice and traditional attachment. Therefore it may be erroneous to classify any type as the best product in Nigeria. Its other products are as dry extraction of starch, glue or adhesives, modified starch in pharmaceutical as dextrines, as processing inputs, as industrial starch for drilling, and processed foods.

 2.2 Palm Oil in Nigeria

 

Palm oil is as old as Nigeria itself and has been an important subsistence, but until recently a supportive factor in the diet of many Nigerians. Palm oil is the world’s largest source of edible oil, accounting for 38.5 million tonnes or 25% of the global edible oil and fat production (MPOC, 2007). Palm oil is a product extracted from the fleshy mesocarp of the palm fruit (Elaeis guineensis). The global demand for palm oil is growing thus, the crop cultivation serves as a means of livelihood for many rural families, and indeed it is in the farming culture of millions of people in the country. Akanbge et al (2011), referred to this product as capable of having multiple values, a feature that underscores its acclaimed economic importance. Eventually oil graduated from domestic use to industrial application which had appreciated its production geometrically (Omereji,2005). Ekine and Onu(2008) estimated palm oil consumption of about two litres per a family of five per week for cooking. Today, consumption must have tripled since Nigerian house hold now uses palm oil beyond normal consumption. Palm oil is also an essential multipurpose raw material for both food and non-food industries (Armstrong, 1998). Palm oil is used in the manufacturing of margarine, soap candle, base for lipstick, waxes and polish bases in a condense form, confectionary (Embrandiri et at., 2011; Aghalino, 2000), and other uses in pharmaceuticals.

2.3 Forecasting

Because economic and business conditions vary over time, managers must find ways to keep abreast of the effects that such changes will have on their organizations. One technique that can aid in plan­ning for future needs is forecasting. Although numerous forecasting methods have been devised, they all have one common goal—to make predictions of future events so that projections can then be incorporated into the planning and strategy process.

2.3.1 Time-series forecasting meth­ods involve the projection of future values of a variable based entirely on the past and present obser­vations of that variable. Examples of economic or business time series are the monthly publication of the Consumer Price Index, the quarterly statements of gross domestic prod­uct (GDP), and the annually recorded total sales revenues of a particular company.(Levine et al, 2005)

2.3.2 Least-Squares Trend-Fitting and Forecasting. The component factor of a time series most often studied is trend. Trend is studied as an aid in making intermediate and long-range forecasting projections. As depicted in Figure 1 to obtain a visual impression of the overall long­term movements in a time series, a chart is constructed in which the observed data (dependent variable) are plotted on the vertical axis, and the time periods (independent variable) are plotted on the horizontal axis.(See figure 1 below)

The Forecasting add-on module provides two procedures for accomplishing the tasks of creating models and producing forecasts.

The Time Series Modeler procedure estimates exponential smoothing, univariate Autoregressive Integrated Moving Average (ARIMA), and multivariate ARIMA (or transfer function models) models for time series, and produces forecasts. The procedure includes an Expert Modeler that automatically identifies and estimates the best-fitting ARIMA or exponential smoothing model for one or more dependent variable series, thus eliminating the need to identify an appropriate model through trial and error. Alternatively, one can specify a custom ARIMA or exponential smoothing model.

AR(1)        = a first-order autoregressive to correct for residual serial correlation. It is regressing the dependent variable(s) with linear combination of its past values or lagged values.

MA(1)       = a first-order moving  average model ,i.e., regressing the dependent error with linear combination of its past error or lagged error. It also corrects serial correlation.

2.4 Research Questions.

The following research questions are formulated by the researcher

  1. What are the total monthly sales of the Firm for 51 months?
  2. How many are the numbers of customers of the Firm are their variable such as name, address, city, state- province, post code, gsm numbers, country, gender, age category are available?
  3. How can customers that would respond to new offer be identified?
  4. What is the forecast total sales from April to December 2016?

2.5 Research Hypotheses.

The following research hypotheses are formulated by the researcher at 5% level of significance.

  1. There will be no significant relationship between total sales of special offers and CDM sales.
  2. There will be no significant relationship between total sales of special offers and POE sales.
  3. There will be no significant relationship between total sales of special offers and APP sales.
  4. There will be no significant relationship between total sales of special offers and GRTsales.

 

  1. Methodology

 3.1 Data Collection

The data collected for this study is a demonstrative data only on behalf of the firm. It is characteristic of most small and medium enterprises of nearly the same status. This case study Firm is having about 90 customers. Sales records on CFM, PEM, APP and GRT were collected for 51 months (January 2012 to March 2016). In the Table below, the author computed the total sales for CFM, PEM, APP and GRT and special offer total sales (SPSALES). Others computed are discount on CDM at 3.5% from records and discount on PEM at 4.8%. A log discount on CFM and PEM and their lagged variables are automatically computed in the SPSS 21 and E-View 7.1 work files. The data are presented in Table 1 below.

 Table 1    Company Data for 51 Months – from January 2012 to March 2016

tsales cfm pem spsales grt app discfm dispem
N N N N N N N N
109.50

123.50

130.00

147.00

154.10

174.00

166.50

178.00

166.50

153.20

132.50

132.70

148.80

167.80

180.50

192.50

192.00

202.50

193.50

183.20

196.70

198.80

179.10

169.80

193.10

194.10

213.00

241.40

231.80

226.40

226.50

245.70

231.20

207.50

219.90

214.00

241.30

265.00

267.50

246.90

237.00

234.20

251.20

266.00

270.80

264.80

258.00

243.80

244.00

246.50

263.30

57.50

60.00

62.50

67.50

75.00

78.00

75.00

73.00

58.00

55.00

56.00

57.00

58.00

65.00

84.00

78.00

77.50

74.50

62.50

68.00

83.00

87.50

78.50

73.00

71.00

73.00

83.00

95.00

92.50

82.00

87.00

95.00

100.00

97.00

100.00

95.00

103.00

112.00

113.00

110.00

105.00

97.00

94.00

95.00

94.00

93.00

100.00

103.00

102.00

102.50

103.00

40.00

50.00

52.50

65.00

64.00

60.00

75.00

86.50

87.50

77.50

60.00

52.00

65.00

73.00

67.00

85.00

85.00

97.50

100.50

85.00

83.00

80.00

68.00

60.00

82.50

80.00

90.00

110.00

108.00

105.00

100.00

110.00

90.00

80.00

78.00

72.00

90.00

105.00

107.50

90.00

85.00

90.00

108.00

120.00

125.00

120.00

105.00

85.00

86.00

85.00

100.00

12.00

13.50

15.00

14.50

15.10

16.00

16.50

18.50

21.00

20.70

22.50

13.70

25.50

29.80

29.50

29.50

29.50

30.20

30.20

30.70

30.70

31.30

32.60

36.80

39.60

41.10

40.00

39.40

39.30

39.40

39.50

40.70

41.20

40.50

41.90

47.00

48.30

48.00

47.00

46.90

47.00

47.20

49.20

51.00

51.80

51.80

53.00

55.50

57.00

59.00

60.30

4.00

5.00

6.00

6.00

7.00

8.00

8.50

10.00

12.00

11.50

13.00

14.00

16.00

20.00

19.50

19.00

18.50

18.50

18.50

17.80

18.00

18.80

19.60

20.00

20.80

21.40

22.00

22.40

22.80

23.00

22.50

22.00

21.20

21.50

23.50

26.00

26.30

26.00

26.00

26.10

26.50

27.00

27.20

28.00

27.00

26.80

27.00

29.00

29.00

29.20

29.30

8.00

8.50

9.00

8.50

8.10

8.00

8.00

8.50

9.00

9.20

9.50

9.70

9.80

9.80

10.00

10.50

11.00

12.00

12.00

12.40

12.70

12.50

13.00

16.80

18.80

19.70

18.00

17.00

16.50

16.40

17.00

18.70

20.00

19.00

18.40

21.00

22.00

22.00

21.00

20.80

20.50

20.20

22.00

23.00

24.80

25.00

26.00

28.80

28.00

29.80

31.00

2.01

2.10

2.19

2.36

2.63

2.73

2.63

2.56

2.03

1.93

1.96

1.99

2.03

2.28

2.94

2.73

2.71

2.61

2.19

2.38

2.91

3.06

2.77

2.56

2.49

2.56

2.91

3.33

3.24

2.87

3.04

3.33

3.50

3.40

3.50

3.33

3.61

3.92

3.96

3.85

3.68

3.40

3.29

3.33

3.29

3.26

3.50

3.61

3.57

3.59

3.07

1.92

2.40

2.52

3.12

3.07

2.88

3.60

4.15

4.20

3.72

2.88

2.50

3.12

3.50

3.22

4.08

4.08

4.68

4.82

4.08

3.98

3.84

3.26

2.88

3.41

3.50

4.32

5.28

5.84

5.04

4.80

5.28

4.32

3.84

3.14

3.46

4.32

5.04

5.16

4.32

4.08

4.32

5.18

5.76

6.00

5.76

5.04

4.08

3.57

4.08

4.80

       *The average Sales discount on CFM is 3.5% and on PEM is 4.8%. No discounts for APP and GRT.

            The LAG variable for CFM and LAG for PEM are automatically Created Series in the SPSS 21 and E-                           View 7.1 work files.

    Figure 1    Graph of the Relationship between Total Sales, and the sales from Cassava Frying Machine, Palm oil Extracting Machine, Appliances and Grater

3.2 Models Specification

Three models are specified to the three major problems faced the Firm as stated under the statement of the problem. These are (1) Two-Stage Least Squares Regression (2) Forecasting Models and (3) The RFM Customer Analysis

 

3.2.1 Model 1.Two Stage Least Square Regression

A careful observation of the relationship between CFM, PEM and the special offers shows that there is a feedback loop between the response and the two major products which are predictors. One of the basic assumptions of the ordinary least-squares (OLS) regression model is that the values of the error terms are independent of the values of the predictors. When this “recursivity assumption” is broken, the two-stage least-squares (2SLS) model can help solve these problematic predictors. The 2SLS model assumes that there exist instruments, or secondary predictors, which are correlated with the problematic predictors but not with the error term.

Given the existence of instrument variables, the 2SLS model:

  1. Computes OLS models using the instrument variables as predictors and the problematic predictors as responses.
  2. The model-estimated values from stage 1 are then used in place of the actual values of the problematic predictors to compute an OLS model for the response of interest.

Fifty two months of sales information is collected. The file also includes a variable, special offer, displaying each month’s special offer, which has also been recorded into two indicator variables, Appliances offer and Grater offer that can be used as predictors in the regression procedures. Lastly, the monthly discounts (and log-discounts) offered to customers are also listed. Since the monthly discounts are chosen independent of special offer sales but do not affect CDM and POE sales, they should make good instrument variables. Additionally, the lagged CDM and POE should also make good instruments. The independent, predictors and instrumental variables are in the model description as in Table 2 below.

Table 2: Model Description

   Variables Type of Variable
SpecialOfferSales Dependent
CDM Predictor
POE Predictor
Appliances predictor & instrumental
Grater predictor & instrumental
Logdiccfm Instrumental
Logdispoe Instrumental

Special Offer Sales = α0 + α1CFM + α2POE + α 3APP + α4GRT + ε                                                        Eq 1

where

α 0             =    the intercept or constant term

α      α 4   =    the coefficients of both the predictors and instrumental variables

 ε            =    the stochastic error term

All other variables are as described in the model description in Table 2 above.

3.2.2 Model 2. Forecasting Models

The Model of Forecasting from an Equation, can be dynamic or Static. The static model is chosen because the static forecasting model performs a series of one-step ahead forecasts of the dependent variable:

For each observation in the forecast sample:

 yg+k = c(l) + c(2)xs+k+c(3)zs+k+c(4)ys+k1                                                         Eq 2

Such equation is always using the actual value of the lagged endogenous variable. This is translated into sales forecast of the Firm:

Total Sales    = βo + β1Total Sales (-1) + β2Pm Ar(1) + µi                                                 Eq 3

Where:

Total Sales        =     the total sales from CFM, POE, APP and GRT for 51 months. This is the dependent variable.

Total Sales (-1) = a lagged variable of the dependent variable. This is a one step ahead static forecasts that makes the static forecast more accurate than the dynamic forecast since, each period, the actual value of Total Sales(-1) is used in forming the forecast of Total Sales.

Pmi                  =     the period expressed in months with a total of 51 months. This is the independent variable.

  β 0                            =     the constant term or the model intercept.

  β1                   =     the coefficient of the lagged variable.

  β2                    =     the coefficient of the independent variable.

 µi                    =     the stochastic or error term

The tolal sales above is transformed into:

tsalesFp+k = Ф0 + Ф1 tsalesFp+k-1 2PMp+k + AR(1) +  ε                                                    Eq 4

after being subjected to the static forecasting model

where

tsalesF = total sales forecast

 tsalesFp+k-1 =   the previous month’s (lagged) sales to be added to current sales forecast.

Ф I              = constant and coefficients.

p                 = the base period (month) of start of forecast.

k                 = any month from the forecasting period (April to December 2016)

AR(1)        = a first-order autoregressive to correct for residual serial correlation. It is regressing the dependent variable(s) with linear combination of its past values or lagged values.  .

ε i               = the error term.

For seasonal adjustment of the forecast (fitted) sales.

tsalesfSA  = ⨍i(tsalesFi)                                                                                                     Eq 5

where

⨍i = multiplicative scoring factor for a 12 month period.

tsalesFi = total sales forecast for April to December.

The tsalesfSA  = the seasonally adjusted tsalesF for months (p52-60) i.e. 52, 53, 54, 55, 56, 57, 58, 59 and 60 for April, May, June, July, August, September, October, November and December.

The actual trend base is P51. In Table 1 the total sales corresponding to P51 is N263300. A forecast for p52-60 is required; i.e. April – December 2016.

Figure 3.  Graphical Relationship between Total Sales (tsales) and Fitted Sales (tsalesF)

3.2.3 Model 3.  RFM Analysis         

RFM (Recency, Frequency and Monetary) analysis is a direct marketing option that provides a set of tools designed to improve the results of direct marketing campaigns by identifying demographic, purchasing, and other characteristics that define various groups of consumers and targeting specific groups to maximize positive response rates.

It is a technique used to identify existing customers who are most likely to respond to a new offer. This technique is commonly used in direct marketing. RFM analysis is based on the following simple theory:

The most important factor in identifying customers who are likely to respond to a new offer is RECENCY. Customers who purchased more recently are more likely to purchase again than are customers who purchased further in the past.’The second most important factor is FREQUENCY. Customers who have made more purchases in the past are more likely to respond than are those who have made fewer purchases.

The third most important factor is total amount spent, which is referred to as MONETARY. Customers who have spent more (in total for all purchases) in the past are more likely to respond than those who have spent less.

How RFM Analysis Works

Customers are assigned a recency score based on date of most recent purchase or time interval since most recent purchase. This score is based on a simple ranking of recency values into a small number of categories. For example, if you use five categories, the customers with the most recent purchase dates receive a recency ranking of 5, and those with purchase dates furthest in the past receive a recency ranking of 1. The recency ranking for this Firm is based on the past 20 months as below:

Month Interval Ranking
Jan to Apr 2016 5
Sept to Dec 2015 4
May to Aug 2015 3
Jan to Apr 2015 2
Sept to Dec 2014 1

In a similar fashion, customers are then assigned a frequency ranking, with higher values representing a higher frequency of purchases. For example, in a five category ranking scheme, customers who purchase most often receive a frequency ranking of 5.

 

The number of times a customer made purchases up to a maximum of 5,(or simply, the Transaction Counts) represent frequency ranking for the Firm.

 

Finally, customers are ranked by monetary value, with the highest monetary values receiving the highest ranking. Continuing the five- category example, customers who have spent the most would receive a monetary ranking of 5. The monetary ranking for the firm is:

Naira Value of Purchases Ranking
N120,000 and above 5
N100,000 to N120,000 4
N75,000 to N100,000 3
N40,000 to N75,000 2
Less than 40,000 1

The result is four scores for each customer: recency, frequency, monetary, and combined RFM score, which is simply the three individual scores concatenated into a single value. The “best” customers (those most likely to respond to an offer) are those with the highest combined RFM scores. For example, in a five-category ranking, there is a total of 125 possible combined RFM scores, and the highest combined RFM score is 555.

 Results and Discussion

The results of the models had produced numerous tables in their outputs. The option of  table by table explanation and discussion had been taken.. Where appropriate, results had been discussed. Only the summary points need be discussed further. .Summary of the findings revealed that the special offer sales did not affect the sales of CFM and PEM. The assumption that there exist instruments, or secondary predictors, which are correlated with the problematic predictors but not with the error term may not hold. When CFM, PEM GRT AND APP were regressed on the total sales, they were all significant at less than 5%.

A multiplicative scoring factor to produce the total sales forecast with seasonal adjustments from April to December 2016.

The RFM analysis produced a combined RFM score for each customer at the concatenation of the three individual scores, computed as (recencyx100) + (frequencyx10) _ monetary. The recency, frequency, monetary score is 5 5 5 respectively. A customer must score at least 3 points for recency to qualify for customers that are likely to respond to new offer on CFM and PEM.

 

Conclusion and Recommendations

This study had been able to highlight the importance of econometric applicaton in business decision making especially for small and medium scale enterprises. Decision making is a prerogative of the entrepreneur but using the tools would help the effective management of their enterprises. I is important to add a caveat that professionals should be used and that the outcome of decision making tools are to assist entrepreneur only and should not be forced on them.

Notwithstanding, entrepreneurs are advised to avail themselves of the decision making tools from professionals with econometric competences. The present day business environment needs the sixth sense which business decision making tools afford entrepreneurs.

 

APPENDIX: (Al, A2 and A3 for Models 1, 2 and 3 respectively.)

 MODEL 1: TWO-STAGE LEAST SQUARES REGRESSION*

 

Table Al-1 The Lagged Variable Infusion into the Model

Series Name Case Number of Non-Missing No of Valid Creating
Values Cases Function
First Last
1 cfm_1 2 51 50 LAGS(cfm,1)
2 pem_1 2 51 50 LAGS(pem,1)

 

 

Table A1-2 Model Description

Type of Variable
                          Spsales Dependent
                                Cfm Predictor
                                Pem Predictor
Equation 1              Grt predictor & instrumental
                               App predictor & instrumenta  l
                       Logdiscfm instrumental
                      Loadispem instrumental

The model description table gives a summary of the model being fit. Variables specified as predictor will be regressed on the instrumental variables, and the model- estimate4d values will then be used in place of the actual values of these problematic predictors when computing the model for the dependent

 

Table A1-3 Model Summary

                                          Multiple R .995
                                           R Square .990
Equation 1
                             Adjusted R Square .989
                    Std. Error of the Estimate 1.410

The model summary table reports the strength of the relationship between the model and the dependent variable.

Multiple R, the multiple correlation coefficient, is the linear correlation between the observed and model- predicted values of the dependent variable. Its relatively high value indicates a very strong relationship.

R Square, the coefficient of determination, is the squared value of the multiple correlation coefficients. It shows that about 99 percent of the variation in Special offer sales is explained by the model.

Adjusted R Square is an r-squared statistic that is “corrected” for the complexity of the model, and is useful for comparing competing models. The larger values of the statistic indicate better models. It showed that the predictors were able to explain 98.9% of the variations in special offer sales.

Std. Error of the Estimate is the standard error in estimates of Special offer sales based on the model. The value this standard error of estimate compare to the standard deviation of 13.649 of Special offer sales shows that the model has reduced the uncertainty in the “best guess” for next month’s sales.

TableA1-4 ANOVA

Sum of Squares Df Mean Square F Sig
Regression 9221.545 4 2305.386 1160.333 0.000
Equation 1 Residual 91.394 46 1.987
Total 9312.940 50

The ANOVA table tests the acceptability of the model from a statistical perspective

The Regression row displays information about the variation accounted for by the model

The Residual row displays information about the variation that is not accounted for by the model

The regression sum of squares is considerably higher than the residual sum of squares, which indicates that about most of the variation in Special offer sales is explained by the model

The significance value of the F statistic is less than 0.05, which means that the variation that is explained by the model is not simply due to chance.

While the ANOVA table is a useful test of the model’s ability to explain any variation in the dependent variable, it does not directly address the strength of that relationship

Table A1-5 Coefficients

Unstandardized Coefficients Beta T Sig.
B Std. Error
(Constant) -3.237 8.202 -.395 .695
cfm .032 .202 .039 .158 .875
Equation 1 pern .026 .030 .036 .857 .396
grt .916 .146 .484 6.276 .000
app .988 .254 .481 3.892 .000

Dependent Variable  Special Sales

This table shows the coefficients of the regression line. It states that the expected special offer sales is equal to : -3.237 + 0.032CFM + 0.026PEM = 0.916GRT + 0.988APP.

The significance value for GRT and APP are less than 0.05, indicating that the effect of GRT and APP distinguishable from sales of CFM and PEM. In other words, the sales of GRT and APP may not be affecting sales from CFM and PEM. When CFM, PEM GRT and APP are regressed against Total Sales, in Table A1-6, all the variables are significant at 1% level. This shows that they all contribute and no “special sales’’ is limiting the sales of CFM and PEM.

 

 Table A1-6 Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients T Sig.
B Std. Error Beta
1 (Constant) -.193 3.180 -.061 .952
cfm 1.054 .053 .404 19.745 .000
pem .991 .036 .437 27.209 .000
grt .812 .173 .133 4.702 .000
app 1.000 .176 .151 5.683 .000
a. Dependent Variable: tsales

Table A1-7 Coefficient Correlation

cfm Pem Grt App
Equation 1 Correlations cfm 1.000
pem -.866 1.000
grt -.885 .661 1.000
app -.966 .843 .757 1.000

*SPSS 21 OUTPUT

 

 

MODEL 2: FORECASTING MODELS**

TableA2-1 Sales Forecast and Seasonally Adjusted Trend

S/N p MONTHS Tsalesf (rounded to a whole number) N’000 Multiplicative scoring Factor(𝒇i) TsalesfSA (rounded to a vhole number) N’000
1 0.89
2 0.99
3 51 MARCH 1.02
4 52 APRIL 269 1.05 282
5 53 MAY 273 1.06 289
6 54 JUNE 275 1.02 281
7 55 JULY 278 1.02 284
8 56 AUGUST 280 1.01 283
9 57 SEPTEMBER 283 1.05 297
10 58 OCTOBER 286 1.02 292
11 59 NOVEMBER 288 0.96 276
12 60 DECEMBER 291 0.91 265

The model equation is: tsalesF = 93.79 + 0.341tsalesf (-1) + 1.64pm +[(AR(1)= 0.233]

  For example April Forecast: tsalesF 52 = 93.79 + 0.341tsalesF51 + 1.64(Mouth 52) + 0.233      (See Eq 4)

                                                     = 93.79 + 0.341(263.3 = March Total Sales) + 1.64(52) + 0.233

                                                     = 93.79 + 89.78 + 85.28 + 0.233

                                                     = 269.09

                                    tsalesF53  = 93.79 + 0.341(269.08) + 1.64(53) + 0.233

                                                    = 272.7  e.t.c

Table A2-2     Augumented Dickey-Fuller Unit Root Test
Test Statistics Coefficients
Variable Intercept Intercept &Trend None Intercept Intercept& Trend None
At Level Tsalesf(Total Sales Forecast) -1.53 -4.152 1.183 -0.065 -0.577 0.0095
1st Diff -6.59 -6.557 -6.385 -0.982 -0.989-1 -0.939
2ndDiff -7.34 -7.253 -7.426 -1.835 -1.835 -1.834
Critical

Value

1%    -3.59

 

5%    -2.93

 

10%    -2.6

1%     – 4.18

 

5%     -3.51

 

10%    -3.19

1%        -2.62

 

5%        -1.95

 

10%       -1.61

TsalesF is stationary at First Difference in Table A2-2 above. This satisfies its expected forecasting property.

   Table A2-3 Model is Stationary at the First and Second difference:

 

 

 

AR Root(s)

  0.233330  0.233330
 No root lies outside the unit circle.
 ARMA model is stationary.
MA Root(s) Modulus Cycle
 -0.236191  0.236191
 No root lies outside the unit circle.
 ARMA model is invertible.

 

 

      **E-View 7.1 OUTPUT

MODEL 3: THE RFM ANALISIS

 

The Firm is having a total of about 90 customers on records. A random sample of 14 customers were made using Random Numbers, but customer ID-01 was purposive. RFM Analysis was limited to customers who made purchases between September 2014 and March 2016.

Table A3-1 Transaction Data

ID DATE AMT N’000 ID DATA AMT N ‘000
01 08/24/2014 212 16 03/15/2015 15
16 09/09/2014 50 83 03/20/2015 105
75 09/30/2014 12 30 04/12/2015 15
75 10/15/2014 15 01 04/14/2015 50
39 10/20/2014 50 72 04/15/2015 30
72 11/15/2014 15 35 05/18/2015 15
28 11/20/2014 50 16 05/25/2015 12
24 11/22/2014 12 89 07/16/2015 45
28 11/25/2014 12 16 07/18/2015 45
24 12/03/2014 15 49 07/25/2015 195
28 12/14/2014 15 08 08/10/2015 12
77 12/18/2014 15 01 18/18/2015 15
16 12/20/2014 15 72 08/20/2015 30
75 12/22/2014 12 35 08/22/2015 30
77 01/15/2015 15 81 09/14/2015 27
02 01/19/2015 62 08 10/20/2015 12
02 03/04/2015 69 08 01/13/2016 12
30 03/10/2015 15 89 01/20/2016 12

When you compute RFM scores from transaction data, a new dataset is created that includes the new RFM scores.

By default, the dataset includes the following information for each customer:

  • Customer ID variable(s)
  • Date of most recent transaction
  • Total number of transactions
  • Summary transaction amount (the default is total)
  • Recency, Frequency, Monetary, and combined RFM scores

The new dataset contains only one row (record) for each customer. The original transaction data has been aggregated by values of the customer identifier variables. The identifier variables are always included in the new dataset; otherwise you would have no way of matching the RFM scores to the customers.

 

Table A3-2 Customers with Transaction Summaries

ID DATE AMT N’000 ID DATE AMT N’000
01 08/24/2014 12 30 10/202014 50
01 04/14/2015 50 30 03/10/2015 15
01 08/18/2015 15 30 04/12/2015 15
02 01/19/2015 62 35 05/18/2015 15
02 03/04/2015 69 35 08/22/2015 30
08 08/10/2015 12 49 07/25/2015 195
08 10/20/2015 12 72 11/15/2014 15
08 01/13/2016 12 72 04/15/2015 30
16 09/09/2014 50 72 08/20/2015 30
16 12/20/2014 15 75 09/30/2014 12
16 03/15/2015 15 75 10/15/2014 15
16 05/25/2015 12 75 22/12/2014 12
16 07/18/2015 45 77 12/18/2014 15
24 11/22/2014 12 77 01/15/2015 15
24 12/03/2014 15 81 09/14/2015 27
28 11/20/2014 50 83 03/20/2015 105
28 11/25/2014 12 89 07/16/2015 45
28 12/14/2014 15 89 01/20/2016 12

The dataset must contain variables that contain the following information:

  • A variable or combination of variables that identify each case (customer).
  • A variable with the date of each transaction.
  • A variable with the monetary value of each transaction.

Table A3-3 RFM Analysis from Transaction Data -The New Data Set.

ID Date Most Recent Transa

ction

Counts

Amount

N’000

Recency Frequency Monetary

Score

RFM

Score

Customer with High Response to an Offer
01 08/18/2015 3 77 3 3 3 333 ü
02 03/04/2015 2 131 2 2 5 225
08 01/13/2016 3 36 5 3 1 531 ü
16 07/18/2015 5 137 3 5 5 355 ü
24 12/03/2014 2 27 1 2 1 121
28 12/12/2014 3 77 1 3 3 133
30 04/12/2015 3 80 2 3 3 233
35 08/22/2015 2 45 3 2 2 322 ü
49 07/25/2015 1 105 3 1 5 315 ü
72 08/20/2015 3 75 3 3 3 333 ü
75 12/22/2014 3 39 1 3 1 131
77 01/14/2015 2 30 2 2 1 221
81 09/14/2015 1 27 4 1 1 411 ü
83 03/20/2015 1 105 2 1 4 214
89 01/20/2016 2 57 5 2 2 522 ü

The combined RFM score for each customer is simply the concatenation of the three individual scores, computed as: (recency x 100) + (frequency x 10) + monetary.

For example, the RFM score for ID-16 is (3×100 + 5×10 + 5) = 300 + 50 + 5 = 355

The marked customers are those that are likely to respond to new offer on CFM and PEM. To qualify, a customer must score at least 3 points for recency.

 

References

Aghalino, S.O. (2000). British Colonial Policies and the Oil Palm Industry in the Niger Delta

        Region of Nigeria, 1900- 1960. African Study Monographs 21(1): 19-33.

Akangbe, J. A., Adesiji, G. B., Fakayode, S. B. and Aderibigbe, Y. 0. (2011). Towards Palm Oil

      Self-sufficiency in Nigeria: Constraints and Training needs Nexus of Palm Oil Extractors. J. Hum. Ecol. 33(2): 139-145.

Echebiri R.N. and Edeba, M.E.I. (2008) Production and Utilisation of Cassava in Nigeria:

      prospects for Food   Security and Infant Nutrition; PAT:Production Agriculture and

     Technology; 4 (1); 38-52

Embrandiri, A., Singh, R.P., Ibrahim, H. M. and Ramli, A.A.(2011). Land application of

      biomass residue generated from palm oil processing: its potential benefits and threats.

     Springer Science. Environmentalist, DOI 10.1007/s 10669-011-9367-0.

Gelb, A.; Jefferson, G. and Singh, I. (1993) Can Communist Economies Transform                         Incrementally? China’s Experiwnce: World Bank Working Papers WP 1189, October.

Levine, D.M.; Stephan, D.; Krehbiel, T.C. AND Berenson, M.L.(2005) Statistics for Managers,

      Upper-Saddle River, NJ. Pearson Educational Inc.

NBS (National Bureau of Statistics)(2015) Growth of Gross Domestic Product in Nigeria .

       Annual Abstract of Statistics. Federal Republic of Nigeria.

Odoemenem, I.U.,and Otanma, L.B. (2011) Economic Analysis of Cassava Production in Benue

        State, Nigeria. Current Research Journal of Social Sciences; 3(5), 406-411

Omereji, G.O. (2005) The Oil Palm Industry in Nigeria: cultivation, Processing and Trade.

       Mindex  Publishers, Benin City. Pp. 131-156

Cassava Production in Nigeria (n.d.) in Wikipedia. Reviewed April 10, 2016 from

          http://en.wikipedia.org/wiki/cassava production

Yakasi, M.T. (2010) Economic Contribution of Cassava Production (A Case Study of Kuje Area

        Council, Federal Capital Territory , Abuja Nigeria; Bayero Journal of Pure and Applied

       Sciences; 3 (1) ; 215-219.

 

The Effect of Project Management Information System on  Project Managers and Project Success

 

Shadi Fallah

Department of  management,  Islamic Azad University, Qaemshahr, Iran

shfallaah@gmail.com

Yousef Gholipour-Kanani

Department of Industrial Engineering, Islamic Azad University, Qaemshahr, Iran

Abstract. Project management information system (PMIS) helps managers in decision making, planning, organizing and controlling the project. Based on the importance of decision making, the aim of this study is assessing the effects of PMIS on the project management success. This study designed a comprehensive model to evaluate the impact of PMIS on the project management functions. This model included five factors such as: PMIS quality, the quality of output information of PMIS, PMIS application, PMIS impact on project management and the impact of PMIS on the project success. This study conducted in Iran in three dependent firms in Tehran petroleum. Data of this research is collected by using questionnaires from project managers. Results demonstrated the crucial role of PMIS on the project management success due to better planning, proper scheduling, regulating and controlling the project. At the end limitation and suggestion for future study is discussed.

Keywords: Project management information system, decision making, planning, project control.

INTRODUCTION

Information is one of the most important capitals in the organizations, because all physical facilities and environmental decision making are affected by information. Information can change competitive aspects withing the organization and lead to competitive advantage. Morever, successful organizations in information technology can change competition characteristics in the industry and benefit by being pioneer. Due to the importance of project management information system (PMIS) to implement project faster, less expensive and with higher quality, the general expectation from project managers enhanced (Welsch, 2006). Organizations apply information systems in their organizations to improve effeciency and profitibility; through this way they can adopt themselves easier to environmental changes and attain updated information. Also managers can estimated the project costs,  profits and budget by using PMIS. In general, PMIS can be used in Implementing activities, goods and materials management, collecting and classifying financial and non-financial information, and storage of information.

Despite of the importance of PMIS, there are still limited studies in this filed. Previous studies mostly considered on implementing information technology instruments in the organizations. Morever, due to importance of oil and gas projects more research about PMIS is needed. Because, in this industry PMIS can play the key role in recognizing the suitable resources, planning and scheduling, this study has considered on the effect of project management information system on  project managers and project success.

Forasmuch as PMIS accompanies managers in all process of implementing project, this study aims to assess the effect of PMIS on  project managers. Also  this study targeted to evaluate the effect of  PMIS on project success. Furthermore, the effects of PMIS on its application, project management characteristics and quality of outputs.

LITERATURE RIVIEW

An information system defined as ‘’ a set of related components that assist to collect, recovery, processing, storage and distribution of information within the organization’’ (Olson, 2004). This information is very useful for decision making and controlling the operation in an organization. Managerial team can use this information for analysis and making decision about future product line. Also Management information system defined as ’’ series of tools which provide to managers the required information in their professional fields on time, accurate and in appropriate conditions’’. Study of Welsch (2006) indicated that MIS assists middle managers through the provision of information in appropriate formats according to what they need.

According to Olson (2004) projects due to their diverse and uniqueness are structurally different from the current organizational routines. Project management is always more difficult than regular administration and requires more attention to different aspects of organization. The project combines the demands of the custodians, contractors and other stakeholders; so to create unity between all factors involved in the project, appropriate techniques and utilities should be exploited (Baker et al., 2009). Belout (2003) in an empirical study considered on the effects of PMIS on multi-project environment. Results showed that using project management information system is beneficial for project managers. However, no adverse effects have been observed due to the project, and information overload.

Commonly projects implement in certain time, cost and quality; they mostly done only for one time. Therefore it is essential to apply an instrument which helps organization to do projects more effective (Dietrich and Lehtonen, 2005).  Also another study has investigated the Challenges in information systems projects (Finch, 2003). Adams and Barndt (2008) in their study showed that PMIS has a positive effect on the management and execution of construction projects and managers should give careful consideration to overcome the weaknesses of the project. According to study of (Cooper et al., 2001) during the projects some changes may happen in process. Despite the theoretical accuracy in the preparation of plans and administered programs, managers should carefully pay attention to using on time and accurate information. Also project control team requires different expertise to accomplish the responsibilities as good as possible (Dai and Wells, 2004). For the purpose of control, various units should send information to control project department under proper discipline and coordination. Particularly, the activities which locate on the critical path of the project should regularly monitor and inspect to ensure that occurred delays don’t lead to delay in entire project. Bozeman and Kingsley (2007) showed that data quality of PMIS is related positively to the correct decisions, managers’ satisfaction from PMIS and increasing usage of PMIS within the organization. Also this study discussed about implementing multiple projects simultaneously causes that the project managers expand the results of the quality information for a project to all existing projects. Based on abovementioned literatures the following hypotheses were proposed:

H1. PMIS quality is positively related to output information quality.

H2. PMIS quality is positively related to PMIS application and project management factors.

H3. Output information quality of PMIS is positively related to PMIS application and project management factors.

H4. PMIS application influences positively on project management factors.

H5. PMIS application influences positively on project success factors.

H6. Project management factors influences positively on project success.

METHODOLOGY

Data of this research attained by distributing questionnaire which included 20 questions based on 6 main categories. This study targeted project control department of three firms which dependent to Tehran Petroleum Company. Targeted employees were 80 and all of them participated in this research. According to table of Morgan number of participants is 63. This study applied SPSS to test normality of study population and hypothesized relationships. Testing normality of population evaluated by using Kolmogorov–Smirnov test; also binominal test applied to assess the hypothesized relationships.

To consider on validity of questionnaire, all questions reviewed by several scholars and their opinions applied in the study instrument. After discussion and making some changes, the validity of questionnaire approved by them.  To test reliability of questionnaire, this study applied Cronbach’ alpha and all values ranged between .76 and .85; Therefore, result demonstrated the reliability of questionnaire. Scale format of questionnaire was based on five-point Likert scale (1=very low to 5=very high).

Table 1. Respondents information

                              Number           Percentage

Gender:

Male                                                      51                      81%

Female                                      12                  19%

Total                                  63                100%

Organizational tenure:

<5 years                                                19                   30%

6-10 years                       30                       48%

>11 years                                              14                        22%

Total                                        63              100%

Educational level:

Bachelor degree                 42                  67%

Master degree                              21                  33%

Total                                       63                100%

 

RESULTS

Results of Kolmogorov–Smirnov test showed that data of this study is not normal; therefore assessing data is done by using Binominal test which is one of the nonparametric tests. As can be seen in Table 2, P-Value related to H1 is less than 0.05; this result indicated that there is significant difference between two groups. Furthermore, according to the reality that 95% of responses were ‘’high’’ ad ‘’very high’’; it conclude that PMIS has positive and significant effect on quality of output information. Thus, H1 is supported. Similarly, P-Value related to H2 is less than 0.05 and majority of responses (94%) were ‘’high’’ ad ‘’very high’’. This finding reveals that quality of PMIS influences positively and significantly on application of PMIS and project management factors. Therefore, H2 is supported. Also table 2 shows that the responses of participants about H3 were mostly (93%) above “fair’’ with P-Value<0.05. This result indicates that quality of PMIS output information is positively and significantly related to PMIS application and project management factors. Therefore, it reveals the empirical support for H3. To assess H4 which indicates to the positive relationship between PMIS application and project management factors; Table 2 shows that 90% of responses were ‘’high’’ ad ‘’very high’’ and P-Value<0.05. Thus, H4 is empirically supported. Also 86% responses related to H5 are above ‘’fair’’; also P-Value related to H5 is less than 0.05; this result indicated that there is significant difference between two groups. Abovementioned values indicated the positive relationship between PMIS application and project success factors. Therefore, H5 is supported. The last hypothesis is also empirically approved with majority of ‘’high’’ and ‘’very high’’ responses (94%) and P-Value<0.05. Therefore, there is positive significant relationship between project management factors on project success. Generally, all hypotheses were supported.

Table 2. Results

                        Maen          SD         %Responses ≤3       %Responses>3          α          P-Value

H1                     4.56           .33             5%                     95%                  .05            0.00

H2                     4.40           .39                 6%                           94%                .05            0.00

H3                     4.43           .48                 7% 93%                .05                           0.00

H4                     4.46           .50                10%                         90%                 .05            0.00

H5                     4.44           .40                14%   86%                 .05            0.00

H6                     4.55           .47                  6%                          94%                .05            0.00

Discussion

This study is done based on direct and indirect effects of PMIS on project success. Easy usage of system, flexibility, respond time, easy learning, integrity of system, all have critical roles on quality of provided information. When quality of PMIS is high, information is more Reachable, reliable, accurate, comprehensive and secure. Result demonstrated that quality of information directly and intensively is related to applying PMIS and its effects on project management. But quality of information is not the only factor that should be considered; because it will be useful when it applies in organization through qualified managers. Using affective PMIS makes performance more professional due to its application in planning, controlling, setting and reporting in different steps of projects.

Results indicated that PMIS quality influences positively on output information quality, PMIS application and project management factors, project management project factors and project success factors. Results of this study were consistent with the result of existing empirical researches (e.g., Belout, 2003; Bozeman and Kingsley, 2007; Olson,  2004).

Limitation and suggestion for future research

The first limitation of this study is related to generalizability because data of this study collected from three dependent firms to Tehran Petroleum Company. It is beneficial if future studies consider on larger number of participants.   Also this study conducted in Iran which is known as developing country; therefore to generalizability of results to developed countries some problems may occur.  Also data of this study collected on limited period of time and does not cover information and changes may happen over time.  Therefore, it is useful to collect data using longitudinal design. Finally, further consideration should be done about other internal and external factors may effect on the quality of PMIS.

References

– Baker, B., Murphy, D., Fisher, D. 2009. Factors affecting project success. In Cleland, D., King, W. (Eds),Project Management Handbook, Wiley, New York, NY, 902-19.

– Kostalova, J., Tetrevova, L., Syedik, J. 2015. Support of Project Management Methods by Project Management Information System. Procedia – Social and Behavioral Sciences, 210, 96-104.

– Bozeman, B., Kingsley, G. 2007. Risk culture in public and private organizations. Public Administration Review, 58 (2), 109-18.

– Iyer, K.C., Banerjee, P.S., 2016. Measuring and benchmarking managerial efficiency of project execution schedule performance. Measuring and benchmarking managerial efficiency of project execution schedule performance, 34 (2), 216-236.

– Dietrich, P., Lehtonen, P., 2005. Successful management of strategic intentions through multiple projects. International Journal of Project Management 23 (5), 386–391.

– Ghaffari, M., Sheikhahmadi, F., Safakish, G. 2014. Modeling and risk analysis of virtual project team through project life cycle with fuzzy approach. Computers & Industrial Engineering, 72 (June), 98-105.

– Jafarzadeh, M., Tareghian, H.R. Rahbarnia, F., Ghanbari, R. 2015. Optimal selection of project portfolios using reinvestment strategy within a flexible time horizon. European Journal of Operational Research, 243(2), 658-664.

– Liu, S., Wang, L. 2016. Influence of managerial control on performance in medical information system projects: The moderating role of organizational environment and team risks. International Journal of Project Management, 34(1), 102-116.

– Welsch, W. 2006. “Input: state and local IT spending surge begins”, available at: http://www.washingtontechnology.com/news/1_1/daily_news/28292-1.html (accessed July 29, 2006).