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A Study of The Sales Forecasting Practice of Manufacturing Firms In Enugu
By
Ubani Blessing Nnenna PG/MBA/02/36948
Submitted to the Department of Marketing Faculty of Businees Administration
University of Nigeria Enugu Campus.
In Partial fulfilment of the requirements for the award of the Degree of Masters in Busines
Administration (M.BA) in Marketing.
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July, 2006.
APPROVAL PAGE This is to certify that this project has been read and approved by the supervisor as an original work, submitted in partial fulfilment of the requiremens for the award of the Degree of Masters of Business Administration(M.BA) in Marketing. BY ......................................... Ubani Blessing Nnenna
Student Date............................ ........................................ ........................................ Dr. Mrs. J. O. Nnabuko Prof. J. O. Onah
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Head of Department Supervisor Date........................... Date...............................
DEDICATION This work is dedicated to God Almighty whose mercies, love and favour abounded with me from the begining of this projecttotheend. To God be all the glory. Amen.
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ACKNOWLEDGEMENT My profound gratitude goes to my supervisor, Prof. J. O. Onah, Professor of Marketing, for the great knowledge I acquired from him while he supervised mywork. I also admire his supervisory skills. May my father in heaven bless and guide you all your days.
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Most grateful to Dr. (Mrs.) Nnabuko, the Head, Department of Marketing,for her encouragement and contribution towards this work. I owe you every appreciation. Mr. Abel, my research assistant, I count it a previlege to have worked with you during the couse of this work. It’s not common these days to meet people who are willing and ready to render assistance to othersat no cost. Your contribution to the success of this project is quite appreciated. I am grateful, thank you so much and may my God reward you. Also, my regards to these wonderful people: Sir & Lady G. N. Ubani, my parents; Mr. & Mrs. Mbanugo; my brother, Ifeanyi Ubani; Mr. & Barr. (Mrs.) Eme Nwosu & family; Mrs. Adaku Okoroafor-Nwosu, Mrs. Agomuo of the school of Post-graduate Studies; Prof. (Mrs.) Nnolim and Dr. Nwaizugbo, my lecturers; Victor Nwosu (Jnr.); James Agbo; and Miss C. Chinyere, staff of marketing department. You are all special to me. I pray for God’s blessings upon you all and your families. I realise there were people who encouraed me in the course of this work. I wonder how much work I would have been able to do alone. I appreciate all your effort. Thank you and God bless you.
Ubani, Blessing N.
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July, 2006.
ABSTRACT
In this work, sales forecasting practices in manufacturing firms have been discussed extensively. The focus is on the effects of sales forecasting on the growth and success of selected manufacturing firms in Enugu. The study consists of five chapters. Chapter one reviews the historical background of sales forecasting and its nature globally. It also highlights the statement of problem, objective of study, research hypothesis, scope of study, limitations to study and significance of study. In chapter two, we have a theoretical framework and literature review of relevant literature in sales forecasting. Chapter three covers the research methodology while chapter four deals with analysis of data and hypothesis testing. We have the conclusion and recommendations for further study in chapter five. This research employed sample survey. The empirical aspect was carried out using information obtained from sales/marketing managers of selected marketing firms involved in formalsales forecasting procedures supported with in formation from related published and unpublished materials. The research formulated four hypotheses which were tested with the Chi-square decision criterion and all tests were conducted at 5% level
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of significance. In case one, the null hypothesis (HO) was rejected while concluding that manufacturing firms’ operating environment has much impact on the process and outcome of sales forecasts. Hypothesis two also rejected the null hypothesis (HO) and accepts that there is a relationship between a firm’s organisational structure and the outcome of the firm’s choice of sales forecasting practice. Hypothesis three rejects the null hypothesis (HO) proving that sales forecasting practice has a direct impact on a firm’s revenue and market powers. Hypothesis four, however, fails toreject the null hypothesis (HO) but concludes that the number of persons involved in sales forecasting has no direct relationship with the frequency of error occurence in the process. Evidently, sales forecasting is a favoured approach to sales management in the manufacturing firms studied. Their approach is still less statistical and more subjective firms should rather adopt more scientific and proven statistical models, introduce computer-based technique in projecting sales volume. TABLE OF CONTENT Page Chapter One 1.1 Introduction 1 1.2 Statement of Problem 6
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1.3 Objective of Study 1.4 Research Hypothesis 7 1.5 Scope of Study 7 1.6 Limitations of Study 8 1.7 Significance of Study 9 Chapter Two 2.1 Litereture Review 11 2.2 Conceptual Meaning of Sales Forecasting 13 2.3 Objectives of Sales Forecasting in Business 16 2.4 Importance of Forecaasting in Business 17 2.5 Dimensions of Forecasting 22 2.6 Sales Forecasting Processes and Methods 27 2.7 Summary of Literature 56 Chapter Three: Research Methodology 3.1 Introduction 58 3.2 Population Description 58 3.3 Sampling Procedure 3.4 Method of Data Collection 3.5 Method of Data Analysis Chapter Four: Data Presentation and Analysis
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4.1 Introduction 63 4.2 Data Presentation 63 4.3 Test of Research Hypotheses 73 Chapter Five: Summary, Findings, Conclusion and Recommendations 5.1 Summary of Research Findings 78 5.2 Conclusion 80 5.3 Recommendations 83
Bibliography 85 Appendix 90
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CHAPTER ONE
INTRODUCTION
1.1 BACKGROUND OF THE STUDY
Rising level of competition, growing pressure from stakeholders,
environmental complexities and increasing customers’ enlightenment, among
others have reawakened the profit maximization goal of business firms. This is
understandable because both the survival and growth of any business lies on the
level of profit the firm is able to make out of its operations. The role of
marketing in this regard has become far more desirable than it used to be some
few years ago. Specifically, the entire marketing management roles are today
built around how to increase sales revenue. At the corporate level, sales
forecasting has become a very important aspect of the marketing management
function targeted at increasing sales.
Though as at the late sixties organizations had started applying the
principles of forecasting in facilitating and managing their sales levels, the scope
of forecasting has kept increasing. According to Bovee and Thill (1992:91),
forecasting has become one of the most challenging, intriguing, and frustrating
aspects of marketing, especially given that, mistakes in either over- or under-
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forecasting can be very costly to organizations. Consequently, based on this
recognition, efforts to be very careful in the methods or approaches adopted
have given more dimensions to forecasting the anticipated volume of sales upon
which production can be based.
In the same vein, the definitional concept of forecasting has kept changing
to suit the growing organizational dynamics. Whereas the initial definitions took
the concept to mean an assessment of the future, and the preparation of a
statement concerning uncertain events (Firth, 1972; and Sullivan and
Claycombe, 1977), its later definitions consensually see it as an information
management process targeted at a minimal error prediction. From these
changing dimensions, it is easy to depict the fact that the scope of forecasting in
organizations has been broadened beyond what it used to be some few years
ago.
Research efforts have also been directed towards diluting the alleged
claim that forecasting is the same with strategic planning, since both are usually
targeted at predicting the future and taking decisions based on the outcome of
such prediction. According to Wotruba and Simpson (1992:151), for instance,
sales forecasting is rather an integral part of planning, and can be viewed as
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what is going to happen to the company. They also argue further that forecasting
serves a vital role in planning when management uses it as a simulational
strategy in planning.
Forecasting and planning go so much hand in hand that many people
confuse the two. Vogt (1977:27) made an earlier distinction between the terms
forecasting and planning. According to him, “forecasting is predicting,
projecting and estimating some future event, matters mostly outside of
management control. Planning on the other hand, is said to be concerned with
setting objectives and goals and developing alternative courses of action to reach
them, matters generally within management control”. Thus, while forecasting is
not planning, forecasting is an indispensable, and even an automatic part of
planning; a vital planning input, a management tool for deciding now what a
business must do to realize in the future its profits and other goals (Vogt, 1977:
27).
Irrespective of how complex the art of forecasting has become in
manufacturing organizations, its importance has made it an unavoidable
exercise. Moon and Mentzer (1998: 44) contend that good sales forecasts are
important in providing good customer service. “When demand can be predicted
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accurately, it can be met in a timely manner, keeping both channel partners and
final customers satisfied. Accurate forecasts help a company avoid lost sales or
stock-out situations, and prevent customers from going to competitors”.
Accurate forecasts can also improve a company’s profits by enabling the firm to
more accurately plan its purchases. Transportation costs may be reduced if a
firm can more precisely predict what products need to be shipped and when they
need to be shipped (Moon & Mentzer, 1998). Firth (1972:1) contended that the
importance of the strategy of sales forecasting extends across both developing
and developed countries. According to him, even in developed countries, the
importance of forecasting has become more widely acknowledged in the recent
past due to substantial changes in the economic environment.
This growing importance of sales forecasting has been traced to the early
1970s. According to Sullivan and Claycombe (1977), the shortages and the
increased inflation of the early 1970s, followed by a major recession, led to
firms to focus renewed attention on forecasting and the benefits it can provide.
In an unstable economic environment like Nigeria, forecasting can therefore be a
desirable and complex approach. Desirable in the sense that organizations need
to know the likely outcome of their market participation in order to plan
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effectively; and complex because the predictability of the business environment
and customers’ reaction are usually based on the stability of government
economic planning and policy measures.
At present, therefore, the issue is no longer whether forecasting is a
desirable business tool or not. Organizational efforts are now being geared
towards developing more efficient techniques, methods and processes for a
result-oriented forecasting system in organizations.
In Nigeria, for instance, economic instability and lack of confidence in the
local economy have resulted in the shortage of essential commodities. This leads
to unnecessary price hikes that help in fueling inflation in the economy, and
consequently, costly interruptions and sometimes the abandonment of important
projects.
These implications have created the need for accurate forecasting for the
successful management of business organization. This is because accurate
forecasts of future revenues is important in capital budgeting, setting production
schedules, determining employment needs, and inventory levels, yet very little
appears to be known today of sales forecasting practices in our firms.
1.2 STATEMENT OF THE PROBLEM
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The business environments in most developing countries have become very
complex, with an increased difficulty in predicting the activities of firms in the
market. Notwithstanding, firms need to have a good knowledge of the present
and future outlooks of their respective environments to be able to cope with the
growing challenges. To do this, the old method of relying on subjective and
intuitive managerial and board views is no longer desirable. Instead, there is
now stronger need to apply scientific and systematic approaches in major areas
of decision making.
The need to have a good knowledge of the future revenue of a firm is the
greatest challenge in this respect. This is so because every other aspect of the
firm depends almost entirely on the ability of the marketing management to
evolve sound strategies capable of increasing the revenue and facilitating
efficient utilization of the scarce resources of the firm. In a harsh business
environment laced with stiff competition and unstable policies, a lot of scientific
approaches are required to make realizable future performance projections.
In the case of Nigeria, empirical research in marketing is unpopular
amongst manufacturing firms, thus resulting in the loss of the pulse of our firms
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in terms of the use into which they put old and recently developed sales
forecasting techniques.
Hypothesis Three
HO: Sales forecasting practice has no direct impact on firms’ sales revenues
and market powers.
HI: Sales forecasting practice has a direct impact on firms’ sales revenues and
market powers.
Hypothesis Four
HO: The number of persons involved in sales forecasting has no direct
relationship with the frequency of error occurrence in the process.
HI: The number of persons involved in sales forecasting has direct
relationship with the frequency of error occurrence in the process.
1.5 SCOPE OF THE STUDY
This study is focused on examining the practice of sales forecasting in
manufacturing firms. Its areas of coverage to this effect include the conceptual
meaning of sales forecasting, objectives and importance, as well as the methods
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and processes of sales forecasting. It is necessary to narrow the scope of study
so that it would be compatible with the time and other resource constraints.
Thus, the study is accordingly limited in scope to manufacturing firms in Enugu.
The reason for restricting the study to only manufacturing firms is because it is
expected that they could serve the research purpose.
1.6 LIMITATIONS OF STUDY
Although every effort was made to ensure the accuracy of the information
collected, a number of problems were acknowledged as hazards in the path of
the enquiries, such as the representativeness of the sample size, the control over
persons who actually wrote the responses, interviewer perceptions and
expectations and so on. Nevertheless these were not considered strong enough as
to render the result obtained deficient. Moreover, the researcher relied more on
primarily collected data for the analysis, instead of primary or historic data
arising from the firms used as case study. The choice of primarily sourced data
was because of the difficulties encountered in obtaining sales figures and
estimates from the firms.
1.7 SIGNIFICANCE OF THE STUDY
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As indicated above, this study was set to examine the practice of sales
forecasting among manufacturing firms. Although the study is set in Enugu, the
belief is that the result of the study would practically be of help to
manufacturing firms in Nigeria. Individuals and groups that would benefit
immensely from the study are the manufacturing firms themselves, the
government tax agents, the consumers, and the suppliers of industrial inputs in
Enugu State and beyond. It is equally expected that the study would aid future
researchers in developing better frameworks for sales forecasting.
To the firms, the study would expose the various alternative approaches to
sales forecasting. This is expected to assist in reducing the errors involved in the
result of forecasting. It would equally help firms in managing their sales process
and sales revenue, and as such, create a more reliable procedure for coping with
the complexities in the present day marketing environment.
To government tax agents and revenue officers, a good knowledge of the
mechanics for sales forecasting would assist them immensely in determining the
tax powers and capacities of manufacturing firms. Such expositions would also
help them in managing government revenue flow; and in contributing in more
realistic terms to government’s budgetary process.
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The expectation is also that the result of this study is going to assist
consumers and users of manufactured goods in managing their spending habits
and plans. For suppliers of industrial inputs, an understanding of how sales
forecasting works in firms would form a good basis for determining and
managing expected demand for their own respective products.
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CHAPTER TWO
REVIEW OF LITERATURE
2.1 INTRODUCTION
Decisions affecting the structure, behaviour and operations of corporate
organizations can be arrived at in two major ways. It can either be by a
judgmental or systematic scientific approach (Wotruba and Simposn, 1992:153).
Before the emergence of globalization, business decisions were based on
subjective judgments. This was probably because of the fact that the business
and economic environments were less complex and so more predictable. Also,
economic policies and programmes were more stable and reliable. It was very
easy to make reliable predictions based on subjective views and opinions. As
was observed by kotler (1980:64):
Many of today’s major corporations got their start by coming out with
the right products at the right time in a rapidly growing market. Many
of their past decisions were made without the benefit of formal
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strategic planning. Wise or lucky management decisions carried these
companies to where they stand today. However, management is
recognizing that intuition alone is no longer enough to succeed in
today’s environment. More and more companies are turning to formal
planning systems to guide their course.
However, with the growth in complexities and socioeconomic
interdependencies among world economies as indicated above, it is no longer
possible to rely on such judgment methodologies. This became so because the
results of errors arising from such predictions are ever increasing, involving
firms in far higher losses that were never imagined. Managers, therefore, have
started making decisions based on their knowledge of both past and present
events affecting not only the immediate system, but also the entire system upon
which their respective organizations operate. This has given rise to the adoption
of scientific and mathematical approaches to tracking down such expected
events in forecasting key sectional performances in organisations. Consequently,
the new approaches have grown in popularity, following series of modifications
targeted at error reduction and increased accuracy.
This chapter would attempt to carry out substantive review of the
conceptual meanings of forecasting/sales forecasting; the main objectives of
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forecasting in a corporate setting; the benefits accruing from the use of
structured, systematic sales forecasting approaches in firms; the optimal
methods and processes for sales forecasting, as well as the difficulties
encountered by firms in the adoption of existing models of forecasting in their
respective operations.
2.2 CONCEPTUAL MEANING OF SALES FORECASTING
The conceptual meanings given to sales forecasting have varied over
years. Before the 1980s, for instance, forecasting was basically seen as a mere
aid to decision making, with no significant practical value (McLaughlin,
1979:18). The initial conceptual framework upon which it was based made little
effort to align the concept of forecasting to the different functional areas of
human endeavour. As indicated by Robinson (1971:1), forecasting was an all-
pervasive art that had been used by mankind in all ages and in all cultures. All
decision making processes were seen mainly as forecasting. The need for
forecasting therefore sprang directly from the individual’s economic problem.
The central argument for forecasting was therefore based on the fact that
the future was fraught with uncertainties. For if the future was known with
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complete certainty, then most of an organization’s problems would disappear,
the allocation of resources to projects would be optimized and little or no control
would be required in running them.
In recent times, there have been some changes both in the dynamics and
application of various models for forecasting. The concept is now more aligned
to industries, human activities and even specific industrial areas. It can now,
according to Bolt (1994:17), be classified as: economic forecasting; market or
industry forecasting; product/service/process forecasting; sales forecasting; etc.
There can also be forecasting in different divisions of an organization, such as:
human resources forecasting; production forecasting, and so on.
Operationally, sales forecasting has been viewed by the Canadian
Business Service Centre (2003) and Botes (2005) as the process of organizing
and analyzing information in a way that makes it possible to estimate what your
sales will be. In the same way, Bolton and Chase (1997) reported that the firms
they studied used as many as 31 terms, such as: quota, projection, and estimate
to describe what they defined as a forecast. A sales forecast is an estimate of
sales (in a given currency or units) that an individual firm expects to achieve
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during a specific forthcoming time period, in a stated market, and under a
proposed marketing plan (Stanton & Spiro, 1999: 392).
The key word in that definition, of course, is an estimate that much of the
rest of the organization uses to make decision. Manufacturing, accounting,
shipping and many other functional areas in the organization make decisions
based on the sales forecast (Gordon, 1997). Demonstrating the importance of the
concept of ‘estimate’ in the definition, Bovee and Thrill (1992:91) and, Peter
and Donnelly (2001:147) stressed that sales forecasting involves predicting the
amount people would purchase, given the product features and the conditions of
the sales.
From these definitions, it can be inferred that modern sales forecasting
takes more of a scientific approach than the initial subjective phenomenon. This
is especially so because as a mathematical concept, estimates are usually
attributable to computational issues that can be reduced into logical expressions
or equations. Again, these definitions point clearly to the fact that the key
essence of forecasting includes not only predicting the amount or volume of
sales, but also synchronizing the performances and operations of other areas of
the organization so as to ensure the achievement of an optimal result. Through
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sales forecasting, therefore, an organization strives to link its external
environment with its internal operating capacity.
2.3 OBJECTIVES OF SALES FORECASTING IN BUSINESS
There are both broad and narrow applications of forecasting (Bovee and Thrill,
1992:91). It can also be for short and long-term goals (Sudman and Blair
(1998:102). Given this diverse nature and uses of forecasting, the objective of
formulating forecasts expectedly should be for implementation and realization
of the overall corporate goals of the organization. In the case of sales
forecasting, Bolt (1994:67) argued that setting objectives generally would be
effective when answers to a series of questions commencing with words such as:
Why, What, How, Who, Where and When were fully answered. Such questions,
according to him, would incorporate:
i. Why does the company need a forecast?
ii. What is it trying to achieve, in terms of accuracy, scope and
effectiveness?
iii. How are the forecasts to be compiled, and how many techniques are to be
used?
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iv. Who is going to do the forecasting?
v. Where will the forecast be done?
vi. When will the forecast be done?
Examining these questions closely would reveal the different kinds of
goals firms pursue with their respective sales forecasting approaches. This
specifically integrates issues like the need for forecasting, the quality and
accuracy of forecasts, the important agents involved, as well as the methods and
timing of forecasting.
2.4 THE IMPORTANCE OF FORECASTING IN BUSINESS
Most of the early writers on sales forecasting had strongly tried to assert
the need for firms to adopt the concept of forecasting in their business planning.
According to Makridakis and Wheelwright (1977):
Forecasting plays an important role in every major functional area of
business management. In the area of marketing however, forecasting is
doubly important, because not only does it have a central role in
marketing itself, but also in marketing, developing forecasts plays a
key role in the planning of production, finance, and other areas of
corporate activity.
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In his own earlier assertion supporting the relevance of the concept,
Ekpott (1981), stated that sales forecast is needed to tackle problems relating to:
low production, declining sales volume, high labour turn over, scarcity of key
material inputs, and piling stocks of finished goods. According to him,
forecasting and planning have come to be regarded as the manager’s first job.
Thus, his ability to anticipate, prepare for and possibly help mould the changes
by systematic fore-thought and planning, must clearly give the business an
advantage over one that merely tries to adapt to changing circumstances after
they have occurred (Copper-Jones, 1974:122).
Expectedly, the belief of many marketing practitioners is that sales
forecasting is important. In Dalrymples (1975), a survey of marketing executives
in US companies reveals that 93 per cent said that sales forecasting was “one of
the most critical” or “a very important aspect of their companies’ success’’.
Furthermore, formal marketing plans are often supported by forecasts
(Dalrymple, 1987). Given its importance to the profitability of the firm, it is
surprising that basic marketing texts devote so little space to the topic.
Armstrong (1987), in a content analysis of 53 marketing textbooks, found that
forecasting was mentioned on less than 1 per cent of the pages.
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Generally, modern business mangers have come to acknowledge that
businesses are rarely successful by chance unlike in the past, when forecasting
was often purely subjective and based on managerial or proprietorial ‘hunches’
which often amounted to little more than guesses.
The starting point in the planning exercise is, of course, the prediction of
future trends of demand. Forecasting the demand for finished products and for
the raw materials and services involved in their manufacture is necessary for the
effective planning of production and for programmes of plant expansion. From
the sales forecast, the company develops a number of related forecasts, plans
and budgets, which determine production plans and inventory plans, and as such
the level of business activity; and most importantly which could help investors
make decisions about investments in new ventures. They are equally vital to the
efficient operation of the firm and can aid managers in such decisions as the size
of a plant to build, the amount of inventory to carry, the number of workers to
hire, the amount of advertising to place, the proper price to charge, and the
salaries to pay sales people.
Donaldson (1990), Peter and Donnelly (2001:148), Wotruba and
Simpson (1992:153), and Bolt (1994:45) tried to relate the importance of sales
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forecasting to strategic areas of a company. Specifically, in his own way, Bolt
(1994:45) supportably argued that:
Market and sales forecasting are important tools of company
management and decision making as they assist in the appraisal of
investment projects, analysis, measurement and improvement of
current marketing strategies, and in the identification and/or
development of new products and new markets. They also promote
and facilitate the proper functioning of the many aspects of company
activity – that is, production, marketing, finance, research and
development, purchasing, etc.
In their own assessment, Wotruba and Simpson (1992:153) came up with the
opinion that:
Sales forecast usage is all encompassing and assists production to control
finished goods inventory; sales and marketing to establish sales quotas for the
sales force ad determine the size and character of advertisement budgets;
finance to estimate cash requirements and in preparation of operating and
capital budgets; purchasing in maintaining adequate stock of raw materials
and supplies to ensure uninterrupted production; personnel in manpower
planning; and engineering in maintenance and repair schedules.
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Baker (1999:278) went further to argue that profitability itself depends on: (1)
having a relatively accurate forecast of sales and costs; (2) Assessing the
confidence one can place in the forecast; and (3) properly using the forecast in
the plan. According to Marien (1999), demand planning and sales forecasting
(DP & SF) is a critical consideration for manufacturers, distributors, retailers,
and other supply chain members. It is a central activity for many mid– to senior-
level executives who manage their companies’ supply chain activities as well as
those specialists responsible for developing and monitoring sales to forecast
schedules and budgets. Summarily, Boulton (2004) reinitiated the benefits of
forecasting, stressing that being able to forecast demand more accurately has
major commercial advantages, whether the forecast is used:
1. to plan purchasing, production and inventory;
2. as the basis of marketing or sales planning; or
3. for financial planning and reporting or budgeting.
Moon and Mentzer (1998) contend in the same way that good sales forecasts are
important in providing good customer service: “When demand can be predicted
accurately, it can be met in a timely manner, keeping both channel partners and
finally, customers satisfied. Accurate forecasts help a company avoid lost sales
31
or stock-out situations, and prevent customers from going to competitors”
(p.44). Accurate forecasts can also improve a company’s profits by enabling the
firm to more accurately plan its purchases. Transportation costs may be reduced
if a firm can more precisely predict what products need to be shipped and when
they need to be shipped (Moon & Mentzer, 1998).
Despite the importance of demand planning and sales forecasting, lack of
communication between the company’s functional areas and across trading
partners often lead to separate and disjointed forecasts. To compensate for these
uncoordinated forecasts and the related negative impacts on customer service,
companies often resort to building excess inventories and fixed assets which
eventually turn into expensive premium freight. What are the specific challenges
faced by practitioners seeking to implement an effective sales forecasting
process? This has remained a critical question in the modern practice of sales
forecasting.
2.5 DIMENSIONS OF FORECASTING
One of the outcomes of the Benchmark Studies has become a framework
for analyzing forecasting practices in individual companies (Mentzer, Bienstock
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and Kahn, 1999; Moon, Mentzer, and Smith, 2003; Mentzer and Moon, 2004).
According to this framework, forecasting management can be thought of along
four dimensions: Functional Integration, Dimension of Approach, Systems, and
Performance Measurement. The following sections will discuss what World
Class Forecasting consists of across these dimensions.
Functional Integration
There are three themes articulated in the functional integration dimension,
each of which is critical to effectively managing the forecasting process. The
first involves a concept termed, ‘Forecasting C3’ – Communication,
Coordination, and Collaboration. Communication encompasses all forms of
written, verbal, and electronic communication between the functional business
areas of the company � marketing, sales, production, finance, and logistics
(including purchasing) – as well as with entities outside the company, primarily
customers. Coordination is the extent to which there is a formal process in place,
usually manifested through formal meetings that provide a structure for the
sharing of information between two or more functional business areas.
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Collaboration is an orientation among functional areas towards the common
goals of forecasting excellence.
The second theme found in functional Integration involves the way a
company organizes itself around the forecasting function. Finally, the third
theme is the extent to which different individuals in different areas of a company
are accountable for their contribution to the forecasting process. World class
forecasting companies achieve functional integration that stresses forecasting. In
addition, these companies extend their commitment to functional integration to
include external collaboration with key customers and suppliers. Whether this is
done in a formal CPFR (Collaborative Planning, Forecasting and
Replenishment) context, or whether it is done more informally through regularly
scheduled meetings with customers and suppliers, world class companies enjoy
the enhanced forecasting effectiveness that comes from open sharing of
information across company boundaries.
Such companies also structure forecasting as a separate functional area,
coordinating the forecasting needs of all functional areas, thereby, reducing the
adversarial negotiation approach exhibited by many companies – i.e., a true
consensus approach. As a separate functional group that is not accountable to
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sales, marketing or operations executives, forecasting can be far more unbiased.
This group is often responsible for orchestrating a Sales and Operations
Planning Process, and coordinating the flow of information from people who
have it (i.e., sales and marketing) to people who need it (i.e., production
logistics, purchasing, and finance). This independent forecasting group is also
frequently responsible for maintenance of forecasting systems, which provide
full access to information that impacts the forecasting process and outcomes
(e.g., capacity constraints, promotions, advertising campaigns). Also, in world
class forecasting companies, performance rewards are based on division or
corporate profitability goals, customer service goals such as improved fill rates,
or supply chain goals such as reduced inventory levels.
Dimension of Approach
The dimension of approach encompasses what a forecast is and how it is
done. There are seven themes that cut across the various stages of sophistication
in the Approach dimension. First is the orientation of the forecast, ranging from
plan-driven, to bottom-up, to top-down, to both top-down and bottom-up, with
reconciliation. The second theme is the approach the company takes to
conceptualizing historical demands. This ranges from a simple notion of
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“demand = historical shipments” to a full effort to document all demands, even
if that involves orders not placed. The third theme consists of the extent to
which companies differentiate between more and less important products or
customers in their forecasting process. The fourth theme involves the use of a
forecasting hierarchy. The fifth theme considers the level of technique
sophistication exhibited by the forecasting company. The sixth theme is the
relationship between forecasting and planning. Finally, the seventh theme
involves the level of training and documentation of the forecasting process.
World class companies recognize that top-down and bottom -up
forecasting approaches often result in two different answers, and they “dig into
the numbers” to reconcile and understand those differences. An example here
helps to illustrate these insights. Let’s say that a consumer packaged goods
(CPG) company first forecasts in a bottom-up approach. Each of this company’s
major retail customers predicts that demand for the CPG Company’s products
will increase by 5% next year, because of increased marketing activity at the
retail level. However, if a top-down forecast reveals that demand for this
particular product will be relatively flat. The “best” forecast is probably
somewhere in-between. Not every retail customer will increase demand by 5%,
36
but increased retail marketing support may increase overall demand to some
degree.
2.6 SALES FORECASTING PROCESSES AND METHODS
Some of the more important findings about sales forecasting methods can be
summarized as follows:
a. Methods should be selected on the basis of empirically-tested theories, not
statistically based theories.
b. Domain knowledge should be used.
c. When possible, forecasting methods should use behavioural data, rather
than judgment or intentions to predict behaviour.
d. When using judgment, a heavy reliance should be placed on structured
procedures such as: Delphi, role playing, and conjoint analysis.
e. Overconfidence occurs with quantitative and judgmental methods. In
addition to ensuring good feedback, forecasters should explicitly list all
the things that might be wrong about their forecasts.
f. When making forecasts in highly uncertain situations, be conservative.
For instance, the trend should be dampened over the forecast horizon.
37
g. Complex models have not proven to be more accurate than relatively
simple models. Given their added cost and the reduced understanding
among users, highly complex procedures cannot be justified at the present
time.
2.6.1 THE PROCESS OF SALES FORECASTING
In nearly all manufacturing companies, the sales department is heavily
involved in the sales forecasting process. Salespeople are frequently asked to
provide input about future demand in their accounts, and their inputs are
factored into the sales forecasts. Unfortunately, most of the companies who ask
their sales departments to contribute to forecasts do not take maximum approach
to ensure systematic compliance. Frequently, historical demand will follow
patterns, and statistical approaches to forecasting are designed to identify those
patterns, and forecast by projecting them into the future. Time series techniques
are designed to identify historical patterns that repeat with time, while casual
techniques are designed to identify historical patterns that exist between demand
and some other variables. Sales forecasting can be for new business or for
38
existing business. For a new business, about four major steps, according to the
Canadian Business Service Centre (2003), are involved, which include:
1. Develop a customer profile and determine the trends in your industry.
2. Establish the approximate size and location of the planned trading area,
using available statistics to determine the general characteristics of this
area, as well as using local sources to determine unique characteristics
about your trading area.
3. List and profile competitors selling in the firm’s trading area.
4. Using the research to estimate sales on a monthly basis for the first year.
On the other hand, for an existing business, the approach may prove very
simple if well handled. What it may require is the use of sales revenues from the
same month in the previous year to make a good base for predicting sales for
that month in the succeeding year.
In the same vein, Wotrub and Simpson (1992:151) demonstrated the
processes involved in sales forecasting, using an organogram. This is shown in
figure 2.1 below:
39
Bolton (1994:47) gave an expanded explanation to these processes and
identified that forecasting should be based on certain preliminary considerations,
which according to him include:
a. what business the company is involved in;
40
b. what product the company would be producing in the future to meet
anticipated needs and how these products can be related to the current
product range;
c. what markets the company would be operating in, and how the markets
are defined in terms of size, region, age groups, location, socioeconomic
groups, etc;
d. what factors affect demand in the various market segments, and whether
there are gaps left to be filled;
e. what the competitors are doing and how effective they are in the market;
g. what the price-value relationship between the existing and planned
product range is like; and
h. what levels of profit are desirable in the short-, medium- and long-term.
It is the answers provided for the above issues to be raised that would
determine basically the purpose for which the forecasting is to be subjected to,
the kind of data to be used, the various determinants of demand for the proposed
forecastable product, as well as the method of forecast consistent with the set
goals.
41
2.6.2 TECHNIQUES FOR SALES FORECASTING
Conceptually, forecasting involves methods that derive primarily from
judgmental sources versus those from statistical sources. Judgmental and
statistical procedures are often used together, and since 1985, much research has
examined the integration of statistical and judgmental forecasts (Armstrong and
Collopy, 1998). Going down the figure, there is an increasing amount of
integration between judgmental and statistical procedures. A brief description of
the methods is provided here.
Makridakis, Wheelwright and Hyndman (1998) provide details on how to
apply many of these methods. One way of making forecasts is through intention
studies. Intention studies ask people to predict how they would behave in
various situations. This method is widely used and it is especially important
where one does not have sales data, such as for new product forecasts. The
generated intentions can then be explained by relating the “Predictions” to
various factors that describe the situation. By asking consumers to state their
intensions, for instance, it is possible to infer how the factors relate to intended
sales. This is often done by regressing their intentions against the factors, a
procedure known as conjoint analysis.
42
Another way to make forecasts is to ask experts to predict how others will
behave in given situations. The accuracy of expert forecasts can be improved
through the use of structured methods, such as the Delphi procedure. Delphi is
an interactive survey procedure in which experts provide forecasts for a
problem, receive anonymous feedback on the forecasts made by other experts,
and then make another forecast. One principle is that forecasts by experts should
generally be independent of one another. Focus groups always violate this
principle. As a result, they should not be used in forecasting.
There is also the extrapolation method, which uses only historical data on
a series of interests. The most popular and cost-effective of these methods are
based on exponential smoothing, which implements the useful principle that the
more recent data are weighted more heavily. Another principle for extrapolation
is to use long time-series when developing a forecasting model. Yet, focus
forecasting, one of the most widely used time-series methods in business firms,
does not do this. As a result, its forecasts are inaccurate (Gardner and Anderson,
1997).
Another principle for extrapolation is the use of reliable data. The
existence of retail scanner data means that reliable data can be obtained for
43
existing products. Scanner data are detailed, accurate, timely and inexpensive.
As a result, the accuracy of the forecasts should improve, especially because of
the reduction in the error of assessing the current status. Not knowing where you
are starting from has often been a major source of error in predicting where you
will wind up. Scanner data are also expected to provide early identification
trends.
Empirical studies have led to the conclusion that relatively simple
extrapolation methods perform as well as more complex methods. For example,
the box-Jerkins procedure, one of the more complex approaches, has produced
no measurable gains in forecast accuracy relative to simpler procedures
(Makridakis et al., 1984; Armstrong, 1985).
There are also econometric models, which use data to estimate the
parameters of a model given various constraints. When possible, which is nearly
always done in solving management problems, one can draw upon prior research
to determine the direction, functional form, and magnitude of relationships. In
addition, they can integrate expert opinion, such as that from a judgmental
bootstrapping model. Estimates of relationships can then be updated by using
time-series or cross-sectional data. Here again, reliable data are needed. Scanner
44
data can provide data from low-cost field experiments where key features such
as advertising or price are varied to assess how they affect sales. The outcomes
of such experiments can contribute to the estimation of relationships.
Econometric models can also use inputs from conjoint models. Econometric
models allow for extensive integration of judgmental planning and decision-
making. They can incorporate the effects of marketing mix variables as well as
variables representing key aspects of the market and the environment.
Econometric methods are appropriate when one needs to forecast what will
happen using different assumptions about the environment or different
strategies.
1. Direct Extrapolation of Sales
If one does not have substantial amount of sales data, it may be preferable to
make judgmental extrapolations. This assumes that the person has a good
knowledge of the product. For example, the characteristics of the product and
the market, and future plans are all well known. When one has ample sales data,
it is often sufficient to merely extrapolate the trend. Extrapolation of the
historical sales trend is common in firms (Mentzer and Kahn, 1995).
Extrapolation methods are used for short-term forecasts of demand for inventory
45
and production decisions. When the data are for time intervals shorter than a
year, it is generally advisable to use seasonal adjustments, given sufficient data.
Seasonal adjustments typically represent the most important way to improve the
accuracy of extrapolation. Dalrymples’ (1987) survey results were consistent
with the principle that the use of seasonal factors reduces the forecast error.
Seasonal adjustments which also led to substantial improvements in accuracy
were found in the large-scale study of time series by Makridakis et al. (1984).
If the historical series involve much uncertainty, the forecaster should use
relatively simple models. Uncertainty in this case can be assessed by examining
the variability about the long-term trend line. Schnaars (1984) presented
evidence that the naïve forecast was one of the most accurate procedures for
industry sales forecasts. Uncertainty also calls for conservative forecasts. Being
conservative implies staying near the historical average. Thus, it often helps to
dampen the trend as the horizon increases (see Gardner and McKenzie, 1985, for
a description of one such procedure and for evidence of its effectiveness).
2. Causal Approach To Sales Forecasting
Instead of extrapolating sales directly, one can make a forecast based on
the factors that cause sales to vary. This begins with environmental factors such
46
as population, gross national product (GNP) and the legal system. These affect
the behaviour of customers, competitors, suppliers, distributors and
complementors (those organizations with whom you cooperate). Their actions
lead to a market forecast. Their actions also provide inputs for the market share
forecast. The product of the market forecast and the market share forecast yields
the sales forecast. Sales forecasting methods are a function of an aggregation
non-controllable environmental variables and marketing effort factors, which
have to be taken into consideration. Forecasting methods have been variously
classified. Kotler (1980:228) classified them into:
1. What people say: This includes surveys of buyers’ intentions, composites
of sales force opinion and expert opinions.
2. What people do: This includes market testing.
3. What people have done: This includes analysis of historical data, for
example time-series analysis.
Doyle and Fenwick (1981:17) classified the methods under the following
headings:
47
1. Qualitative Methods: Roughly corresponding to Kotler’s “what people
say” � market research, group discussion, Delphi and sales force
predictions.
2. Time-series Method: Corresponding to what people have done � trend
extrapolations, exponential smoothing, Box-Jan-Kings, Kings, Life cycle
projections and Auto-regressive models.
3. Model-based Regression analysis: Econometric methods, market
experiments, input-output, lending indicators.
Similarly, Chamber, Mullick and Smith (1971) classified them as:
1. Qualitative,
2. Time-series analysis, and
3. Causal model: This expresses mathematically, the casual relationship
between relevant factors.
According to Boulton (2004), there are three basic Methods of forecasting
sales for new start businesses:
1. Value-based – in other words, what the business has to sell;
2. Market-based – i.e., what the business can sell; and
3. Resource-based – what the business can produce to sell.
48
Value-Based Sales Forecast is calculated by dividing the estimated Annual
Overheads by the Gross Profit Margin as a percentage which then reflects what
the ‘break even’ sales figure is for the business; with the gross profit margin
computed as:
Selling Price - Direct Cost/Selling Price x 100 = GPM%
Market-based sales forecast is hinged on the results of the market research
that has been carried out. On its own, the resource-based approach is based on
the resource limitations of the business to provide the service or product.
Demonstrating the resource-based approach, a firm involved in manufacturing
with production capacity limited by machinery and/or staff, could be constrained
by labour/machine hours.
In order to be financially viable, the Resource-Based Sales Forecast must,
again, be greater than the value-based sales forecast. In other words, the firm
must be able to produce more than it has to sell and its market research should
show that it can sell as much, or more, than it has to. If the resource-based
forecast is lower than the market-based forecast, it means that the firm will not
49
be able to supply the demand. If they are the other way round it means that the
firm will be able to easily keep up with demand.
The weeks/months/annual sales forecast then becomes a realistic balance
between all three and should be something which the management feels
comfortable with and feels is achievable with effort. It is this dependence on the
judgments of experts that makes qualitative approaches for forecasting less
attractive than quantitative methods when we have a choice between the two.
The experts, argued Markridakis and Wheelwright, not only vary considerable in
their judgments, thus making the forecast dependent on the specific expert
concerned, but their employment is generally quite expensive, particularly when
the reliability that can be attached to their judgments in considered.
The above classifications were well summed by Hann and Berkey (2000),
who classified the techniques for forecasting into two: qualitative and
quantitative techniques.
Qualitative Forecasting Techniques
According to them, until the 1960s, senior level executives did most of the
sales forecasting in organizations using the executive judgment approach. These
executives or managers use their past industry experience and knowledge to
50
determine what they thought the company’s sales could or should be (Mentzer &
Bienstock, 1998). This executive judgment method was seen as appropriate and
is believed to have worked well in the stable/ post-war economy of the United
States. As the economy has become more competitive and volatile, the executive
judgment method has been replaced in some organizations (Mentzer &
Bienstock, 1998).
However, as many as 86 percent of firms, in a recent study (Herbig,
Milwichz, & Golden, 1993) report, were still using the executive judgment
method for forecasting sales. A study of the wholesale industry ( Peterson &
Minjoon, 1999) reveals that almost all (99.6 percent) of the firms in this study
report were using managerial judgment as the primary sales forecasting tool.
This continued popularity of the executive judgment model is generally
attributed to the fact that it is a simple and inexpensive technique for forecasting
sales (Stanton & Spiro, 1999). The executive judgments made can be based on
any combination of objective and subjective data that is available to those
making the forecasting decisions (Gordon, 1997).
Some firms today utilize types of qualitative forecasting techniques.
Group discussions among members of a forecasting committee bring together
51
divergent viewpoints from different parts of a firm. Pooled individual estimates
from different functional areas of an organization are averaged to determine
what they think sales figures will be (Paley, 1994). The Delphi technique has
become an increasingly popular qualitative method. Forecasts are submitted in
writing to a forecasting team leader who feeds these estimates back to those who
submitted them with information about changes in the market place. Eventually
a consensus is reached based on the revisions being sent back and forth between
the team leader and team members (Paley, 1994). Another increasingly popular
qualitative technique is the composite of sales force opinion. Here the individual
sales representatives and/or their sales managers are asked to predict their sales
for the coming year. These individual predictions are aggregated to develop the
company’s sales forecast (Mentzer & Bienstock, 1998). The major advantage of
using qualitative forecasting techniques is that they have the ability to predict
changes that occur in an established sales pattern. Those involved in the day-to-
day operations of the company can anticipate and plan for these changes. This is
something quantitative approaches are unable to accomplish (Hogarth &
Makridakis, 1981).
52
Two major problems however exist when using qualitative techniques to
forecast sales. First is that qualitative forecasts are built on subjective
information. The opinion of executives who are predicting future sales are just
that — opinion. In addition, the manpower required to collect the data to create
qualitative forecasts can be very expensive (Mentzer & Bienstock, 1998).
QUANTITATIVE FORECASTING TECHNIQUES
It has also been reported that over the past 40 years, a large and varied array of
more sophisticated sales forecasting models have been developed. Mentzer and
Kahn (1995) reveal that while many firms are familiar with the quantitative
techniques available, most are still using less sophisticated models. Most of the
companies that are using quantitative techniques state that they are satisfied with
the results of these methods. Lawless (1997) argues that it is imperative for
firms to employ the quantitative methods available to them in forecasting sales.
The combination of an environment that is constantly changing, and the down-
sizing in organizations, makes reliance on the technology available more crucial.
It also creates more credibility for those performing the forecasting function.
Two popular quantitative sales forecasting techniques are: Time-series Analysis
and Regression Analysis. They are both fairly easily to apply (Chase, 1997).
53
Time-series techniques attempt to identify the patterns of the history of actual
sales. If a pattern can be established, the forecast can be generated. Time-series
relies exclusively on data generated within the organization itself (Mentzer &
Bienstock, 1998). Regression models have been found to be the most effective
forecasting technique. Regression model provide a framework that insures the
consistency of the sales forecasting process (chase, 1997).
In regression analysis, a set of variables that are believed by forecasters to best
predict the sales of a product, are chosen to generate the forecast. Forecasters are
looking for the variables that correlate with the sale of the product. The line that
best “fits” the relationship between sales and these other variables is used to do
the forecasting (Mentzer & Bienstock, 1998:81). Both time-series techniques
and regression analysis have weaknesses. They assume that errors in data are
independent, normally distributed with a zero mean, and have a constant
variance. Often, errors in past performances are difficult to detect, making it
complicated when building a time-series or regression model (Mentzer &
Bienstock, 1998). Causal forecasting is a model recommended by Lapide
(1999). This model is appropriate when the forecast is influenced by
controllable, temporary events such as promotions. It may also be used when
54
uncontrollable, temporary occurrences such as a sudden change in demand or a
series of on-going events influence the quantity of the product consumers
demand. Too much reliance on quantitative models can cause problems. A
reasonable combination of quantitative and qualitative techniques is required to
optimize the efficiency or that forecasting process. Knowing when to use each
type of model improves the accuracy of the model(s) chosen (Moon & Mentzer,
1998).
Bottom-up Forecasting Techniques
Another decision that a firm typically makes concerning the choice of a
sales forecasting model is whether to take a top-down or bottom-up approach to
the forecasting process. (Stanton & Spiro, 1999). Generally, bottom-up
approaches to sales forecasting are considered to be more accurate than top-
down approaches (Dunn, William, & Spiney, 1971). These bottom-up methods
use information generated by those closest to the consumer to generate the initial
data that is used to forecast sales for an organization. This data can be generated
by the salespeople or from data that is collected electronically at the point of sale
(Gordon & Morris, 1997). Critics of bottom-up methods agree that they work
55
well at predicting the sales of individual SKU’s at lower levels. However, most
bottom-up procedures do not take into account the overall effects of the
economy, seasonal trends, and other variables that can influence the sales of a
product (Kahn, 1998).
Top-Down Forecasting Techniques
Top-down forecasting techniques are seen as being more effective
forecasting at the macro or aggregate level. Thee models typically smooth lower
level information by accounting for the overall market conditions that can cause
variations when the data are added together (Kahn, 1998). Regressions and other
forms of correlation analysis were some of the first quantitative methods to be
used to forecast sales (Pindyck & Rubenfield, 1976). These are classified as top-
down methods as they start with the aggregate sales of a product. Time-series
analysis has the ability to smooth out some of the seasonal variations that are
generally seen as a weakness of bottom-up forecasting techniques (Kapoor,
Madhok, & Wu, 1981). Hybrid approaches are being developed that employ the
advantages of both the bottom-up and top-down techniques of forecasting.
56
While these approaches might produce better results in some situations, they are
much more time consuming to implement (Kahn, 1998).
One type of hybrid approach that is gaining some popularity in industrial
market is the simulated test market. Current and potential customers are exposed
to a company’s plans for new products and promotional campaigns. The
customers’ reactions are then compared to past reactions of consumers to predict
future sales. In addition to forecasting sales, the simulated test market can help
an organization alter its marketing mix variables for new products (Lancy &
Shulman, 1995).
2.6.3 SOURCES OF DATA FOR SALES FORECASTING
No matter which method is adopted, data to be used can be sourced from
several quarters. Some key sources of information to assist a firm’s sales
forecast efforts are: competitors neighbouring businesses, trade publications,
trade suppliers, downcast business associations, trade associations, trade
publications, trade directories, and national statistics (Canadian Business Service
Centre, 2003). Factors that can affect sales are categorically outlined by the
Canadian business Service Centre as follows:
57
Table 2.1 External and Internal factors Affecting Sales Forecasting.
External Internal Seasons holidays Special Events Competition, direct External labour events Productivity changes Family formations Births and deaths Fashions or styles Population changes Consumer earnings Political events Weather
Product changes, style, quality Service changes, type, quality Shortages, production capacity Promotional effort changes Sales motivation plans Price changes Shortages, inventory Shortages/working capacity Distribution methods used Credit policy changes Labour problems
Bolton (1994:69) equally itemized the various data necessary for accurate
forecasting as including sales volume, areas of sales volume, time sales volume,
price/sales volume, channels sales volume, order size statistics, cost data, sales
potential, sales force statistics, stock control data, and accounting ratios.
58
2.6.4 Time Series Analysis
Generally, two widely forecasting techniques are regression analysis and
correlation. This involves a function developed mathematically which expresses
the relationship between a dependent variable (in this case sales) and one or
more independent variables. Correlation is first designed to measure the
direction and intensity of this relationship, and only those variables showing a
significant level of correlation are subjected to regression.
According to patty and Hite (1988:95), the correlation-regression analysis
approach involves three major steps:
1. Determine the factors that seemingly affect the sales of the product for
which future sales are being forecasted;
2. Measure the degree of correlation in between product sales and the casual
factor;
3. Based on the independent variable, forecast product sales.
Whereas, step one can intuitively or judgmentally be determined by
examining the variables contained in table 2.1 and how they affect the issues at
hand, sep two follows a mathematically relationship of the variables,
demonstrated as:
59
r = n∑XY - (∑X) (∑Y)
√n∑X2Y - (∑X2)][(∑Y2) – (∑Y2)
Having determined that there is a fairly high correlation between sales and the
causal factor, simple regression formula may be used to make forecast of sales,
depending on the number of variables involved. This can be adopted based on
the following regression analysis model:
Yx = a +bx;
Where Y represents the variable to be forecasted, in this case, sales, X can be
any variable including time or an economic indicator; and a and b are estimated
and measured mathematically as follows:
Where b = ∑XY - ∑C)( ∑Y)/n ∑X2 - (∑X) 2/n and
a = ∑Y - b∑X n n The adoption of the above linear model depends on the number of independent
variable involved. Where the number of variables makes this inapplicable, then
the other versions of regression analysis may be adopted. In effect, there is the
multiple regression analysis- which is based on both the past values of the item
60
being forecast, and other variables that are thought to have a casual relationship.
There can also be a circumstance for the adoption of the econometric models-
which is a system of simultaneous regression equations which by their nature
depend upon each other and therefore their parameters need to be estimated in a
simultaneous manner. Estimating the sales for an item of product that is
influenced by more than one variable based on the multiple regression analysis
takes the following mathematics approach:
Y = a + b1 X1 + b2 X2 + ------bk Xk
Is the normal representation of multiple regression. This means that Y, the
dependent variable is a function of the variables x1, x2, x3, ------xK. In a situation
where there are only two variables x1 and x2 then Y = a + b1x1 + b2x2
In practice, sales forecasting takes a time-series analysis. As argued by
Chambers (1974), the essence of adopting the time-series analytical approach is
to help to identify and explain:
• Any regularity or systematic variation in the series of data because of
seasonality
• Cyclical patterns that repeat-for example every two to three years of more.
• Trends in the data
61
• Growth rates of these trends
• Inherent randomness in the data-that is, variations in the data-that cannot
be explained by statistical means.
According to Bovee and Thill (1992:96), the technique of time-series is the most
widely used, and is basically absed on the assumption that the past can be used
to predict the future. They went further to argue that this approach can be based
on the following:
i. Trend Analysis – that is the type that creates a equation to describe the
expected behaviour of sales in the future using sales data accumulated
over some period in the past;
ii. Moving Average forecasting methods that averages inside a moving
window of fixed duration;
iii. Exponential smoothing – one that assigns weights to the sales data used in
the forecast;
iv. Cycle Analysis – one that adjusts forecast for movements in the overall
economy;
62
v. Random factor analysis – one that follows an analysis of the unexpected
differences between predicted and actual sales behaviour, usually due to
such random occurrences such as strikes, wars and factory fires;
vi. Correlation Analysis – one that predicts the sales of an item on the basis
of the sales use, or availability of one or more other items.
Just as in the case of the linear regression method, the least square sum can be
used to solve for a,b1, and b2. the situation will be analogous to two dimensions
in which we need three axes Y, X1 and X2 and in which we try to fit a straight
line to the points located in the three dimensions. Such multiple regression
equations are generally difficult to solve but computer programs are available
for solving them.
2.6.5 Evaluating the Result of Sales Forecasting
Forecasts are evaluated in terms of accuracy – that is how close the forecast is to
actual sales (Wobtrub and Simpson, 1992:165). To find out the validity of the
results, Bovee and Thill (1992:97) posited that four key questions should be
asked and answered. These, according to them, include:
1. Are the assumptions on future events reliable?
63
2. Have consistent definitions been used?
3. Where did the data used come from?
4. Does the forecasting make sense?
In the words of Bolt (1994:41), good forecasting decisions must be such that is
based on good information sourced from the marketing research function of the
firm. This implies that the quality of the sales forecasting exercise necessarily
should depend on the quality of the marketing research outfit of the firm. Where
this is not the case, the result of the forecast is bound to be faulted and
unreliable. Variable methods are known to be applicable in valuing the result of
a sales forecasting exercise. These may include:
1. Variance Analysis – where graph of the forecast and sales values are
plotted, with a visual examination indicating any lead or lag effect;
2. Ranking Variances – where all the variables in their order of magnitude
are ranked; and
3. Ratios – where acceptable levels of ratios can be established by
calculating the ratio f the period sales to forecast, and allowing for seasonal and
medium-term cyclical factor.
64
To identify the effectiveness of the forecast result therefore, Davis (1988)
charted what he described as the forecast cycles. He demonstrated this
diagrammatically, indicating key questions that should be answer from the start
of the forecasting cycles. He demonstrated this diagrammatically, indicating
key questions that should be answe3r from the start of the forecasting process to
the end. As shown in figure 2.2, the questions are: How have we got here?
Where are we now? Where are we heading? Are we getting where we want to
go? Could we more profitably head in some other direction?
Figure 2.2. the forecasting Cycle.
In addition to the above qualitative analysis of the outcome of sales forecasting,
there is a quantitative model for finding out the margin of errors involved in the
forecast. This is most applicable when the quantitative techniques are adopted in
making the sales forecast. To this end, interest is geared towards determining
what is called standard Errors. According to Bolt (1994:3350, the forecasting
method giving the lowest standard error is taken to be most efficient method
How have we got here Where are we now Where are we heading
Could we more profitably head in some other
Are we getting where we want to go
65
during the period under consideration. Mathematically, the standard error is
determined a follows:
Standard Error = √[∑ (calculated sales – forecasted value]2
Number of Sales Periods
2.7 Summary of Literature
The conceptual applications of forecasting in businesses have changed
overtime. The recent trend now sees classification such as economic forecasting,
technological forecasting, market or industry forecsting, product/service/process
forecasting,. Sales forecasting, human resources forecsting, production
forecasting, and so on. Of these, sales forecasting is the most popular because it
uses data from virtually all areas of the organization. Modern sales forecasting,
in this regard, takes more of a scientific approach than the initial subjective
phenomenon.
The importance of sales forecasting lies in the fact that the success of other areas
of the organization revolves around the accuracy of the forecast exercise. Also,
profitability is strongly linked to firm’s ability to predict/estimate and manage
66
sales, especially as it enables the firm provide good and qualitative customer
services.
Structurally, sales forecasting takes four key dimensions in firms-functional
Integration, Approach, systems, and performance measurement. These are
combined to ensure efficiency in the forecasting process. On its own, the process
is a continuous one-starting with defining the purpose for the forecasting and
ending with evaluation of the outcome of the exercise.
In terms of the techniques, we saw used qualitative data (e.g. expert opinions)
and information about special events and might or might not take the past into
consideration. The second focused entirely on patterns and pattern changes and
thus, relied entirely on historical data. The third used highly refined information
about relationships between system elements. It was powerful enough to take
special events formally into account; and it also sued the past as important input.
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CHAPTER THREE
RESEARCH METHODOLOGY
3.1 INTRODUCTION
The purpose of the present chapter is to introduce the research methodology
used in the study, “Sales forecasting practices in Enugu manufacturing firms”.
The chapter includes population description and sampling procedure, method of
data collection, and method of data analysis.
3.2 POPULATION DESCRIPTION
The word ‘population’ is used to denote the aggregate from which a sample is
chosen. There are 461 manufacturing companies in Enugu. These comprised of
small-, medium-and large-scale firms. About 250 of these are located in Enugu
North Local Government Area- that is the main Enugu City.
3.3 sampling procedure
Using the Yaro Yameni Sample Size Determination formula, the following
procedure was adopted in selection the sample size from the 250 firms:
n = N
1+N(e)
Where, ‘n’ represents the sample size to be determined;
68
‘N’ represents the population size of 250
‘e’ represents the error margin (5 percent)
n = 250
250 (0.05)2 = 154
For the purpose of this study, 154 of the population were studied. This in effect
makes it more representative to use the data so collected as a general indication
of sales forecasting practice in Enugu Manufacturing firms.
3.4 method of data collection
Data for this study were taken from questionnaires administered to
sales/marketing managers of one hundred and fifty-four manufacturing firms
were those listed in the most recent Enugu state trade Directory 2004. location
of the company was done through three commissioned agents of the states Board
of internal Revenue.
Part of the questionnaires were administered indirectly by hand within some
major streets in Enugu Urban, whereas the three commissioned agents were used
to distribute the rest. The respondents wee required to either fill the
questionnaire in the presence of the researcher, or later in the absence of the
research- whichever was more convenient to the respondents. This provision
69
was necessary cognizant of the busy schedule of the respondents, and was
agreeable to them. Unfortunately, however, the provision was later to cause a lot
repeated visit often ending in disappointment. Nevertheless a sizeable number
were eventually collected.
The questionnaire design consisted mostly of questions adapted from the
questionnaire used by July Pan et al., though this was not without some
reasonable modifications. For instance, in place of their sentence completion
type question, a multi-choice one was used in order to encourage responses from
otherwise reluctant respondents and aid analysis. The questionnaire was
addressed to thirteen key points of information. Starting with questions on
annual revenue and age of firms--- the classification questions, to questions that
bordered on errors. In this way, it was possible to investigate the effect of age
and size of firms on forecasting practice. In addition to probing how accurate
sales forecasting has been in these companies. The sample questionnaire is
shown in the appendix.
Needed to be said that a dummy table was a statement of how the analysis
would be structured and conducted. It was complete in all respects except for
70
filling in the actual numbers that is it contained a title, headings, and specific
categories for the variables.
The researcher equally made use of Chi-Square to test the hypotheses set for the
study. The method was used in the analysis of the contingency tables
constructed. The formula adopted goes thus:
X2 = (Oi - Ei)2
Ei
Where, X2 = Chi-Square
Oi = Observed Values of frequencies; and
Oi = Expected Values of Frequencies;
The result of this was compared with the tabular value of X2 a, (R-1)(C-1).
Where a represents the 0.05 level of significance; and (R-1)(C-1) stands for the
degree of freedom.
DUMMY TABLES
Table 3.1 Importance of forecasting
RESPONSE NO. OF FIRMS PERCENTAGE
Very Important
Important but not critical
Some value
Limited value
X
X
X
X
X
X
X
X
71
Total X 100
Table 3.2 participants in the forecasting process
No of people NO. OF FIRMS PERCENTAGE
1 person only
2-4persons
5-10 persons
More than 10 persons
X
X
X
X
X
X
X
Total X 100
Using this methodology, the following nine areas were investigated.
1. Importance of sales forecasting
2. Participants in the forecasting process
3. frequency of preparation of forecasts by firms,
4. Frequency of comparisons of actual and forecast amounts.
5. longest and shortest period forecasts,
6. Variables employed in forecasting,
7. types of techniques employed,
8. Reasons for Increased or reduced errors by firms,
9. forecasting errors and the number of people involved.
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CHAPTER FOUR
DATA PRESENTATION AND ANALYSIS
4.1 INTRODUCTION
This chapter focused on the presentation and analysis of primary data generated
for the study. As indicated above, the population meant for study was 461. of
this number, about 250 firms located Enugu North were actually targeted, out of
which 100 were finally sampled. This gave rise to the distribution of 100 copies
of the research questionnaire. Of the 100 distributed, all were returned.
However, it was discovered that 25 firms wee not involved in any formal sales
forecasting procedure. The researcher deemed it unnecessary to use this group,
and decided to restrict the number useable for analysis to 75.
4.2 DATA PRESENTATION
Responses arising from the research questionnaire copies are presented in
this section as follows:
TABLE 4.1 RESPONSES ON THE RELIANCE OF SALES-
FORECASTING
RESPONSE NO. OF FIRMS PERCENTAGE
Very relevant 42 55.6
Relevant but not critical 25 33.3
73
Some value 8 11.1
Limited value 0 0
Total 75 100
It might be expected that in view of the fact Nigeria is largely regarded as a
sellers market, most respondents would downplay the importance/relevance of
sales forecasting to the success of their company. But results (table 4.10 showed
the opposite to be true. In this case 55.65 of the firms indicated that forecasting
was very relevant although 33.3% stated it was relevant but not critical. The
results were understandable, because the purpose of forecasts was to help reduce
the uncertainty of the future, while ensuring that opportunities of the future
would be tapped.
TABLE 4.2 RESPONSES ON THE PARTICIPANTS IN A
CASTING PROCESS
RESPONSE NO. OF FIRMS PERCENTAGE
2 to 4 persons 40 53.3
5 to 10 persons 23 31.7
Above 10 persons 5 6.7
Computer Aided 7 9.3
External consultants 0 0.0
74
Total 75 100.0
Table 4.2 above shows that 40 out of the 75 firms or 53.3 percent had the
practice of engaging between 2 to 4 persons in the sales forecasting process. At
the same time, only 23 firms or 31.7 percent engaged between 5 to 10 persons;
whereas just 5 firms or 6.7 percent engaged is above ten persons in the process.
That is to say that a team comprising of more than 10 persons was very rare
among the responding firms.
The survey findings also revealed that the sue of external consultancy agencies
were not practice among the sampled firms. Furthermore, only 7 firms or 9.3
percent of the total sample indicated an occasional use of computer to aid its
forecasting efforts, all other had never computers for this purpose.
TABLE 4.3: RESPONSES ON THE SET OF PARTICIPANTS WITH
THE HIGHEST RATE OF ANTICIPATED
FORECASTING ERRORS
RESPONSE NO. OF FIRMS PERCENTAGE
2 to 4 persons 39 52.0
5 to 10 persons 23 30.7
Above 10 persons 13 17.3
75
Total 7̀5 100.0
As indicated in table 4.3, a greater percentage of respondents – that 52.0 percent
– were of the opinion that the rate of forecasting errors was higher when 2 to 4
persons were involved. 30.7 percent showed that the rate of error was highest
under 5 to 10 persons. Only 17.3 answered that the error rat was highest when
above 10 persons were involved.
TABLE 4.4 RESPONSES OF THE FREQUENCY OF
PREPARATION OF SALES FORECASTING BY
FIRMS.
FREQUENCY NO. OF FIRMS PERCENTAGE
Weekly 0 0
Bi-weekly 0 0
Monthly 22 28.7
Quaterly 5 6.2
Semi-Annually 7 10.1
Annually 41 55.0
Total 75 100.0
The data presented in table 4.4 indicate that forecasts were most commonly
prepared by the affected firms on annual and monthly basis. However, most of
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the firms – that is about 55 percent of them- have the practice of preparing sales
forecast on annul basis. Whereas on 6.2 percent and 10.1 percent were
respectively in the practice of preparing forecast on quarterly and semi-annual
basis, none of the firms agreed to have engaged the weekly and bi-weekly
practice.
TABLES 4.5: RESPONSES ON THE FREQUENCY OF
COMPARISONS OF ACTUAL AND FORECAST
AMOUNT
FREQUENCY NO. OF FIRMS PERCENTAGE
Weekly 0 0
Bi-Weekly 0 0
Monthly 41 54.7
Quarterly 9 12.3
Semi-Annually 9 12.3
Annually 16 20.7
Total 75 100.0
Table 4.5. reveal that most of the firms involved used to carry out comparison f
forecast sales with actual sales on monthly basis. This was shown by the fact
that 41 out of the 75 firms or 54.7 percent held such stand. 16 firms or 20.7
percent maintained that their system of comparison was annually based; while 9
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firms (or 12.3 percent) each maintained that they were involved in quarterly and
semi-annual comparisons. None of the firms adopted weekly and bi-weekly
comparison approach.
TABLE 4.6 RESPONSES ON THE NATURE OF THE IMPACT OF
SOME IDENTIFIED FACTORS ON THE
NATURE/PROCESS OF SALES FORECASTING
Organizational
growth & Dev.
Sales Revenue Harsh operating
environment
Efficient Org.
Structure
Nature of
the Impact
No. % No. % No. % No. %
Positive 20 26.7 29 38.7 12 16.0 0 69.3
Negative 30 40.0 27 36.0 57 76.0 52 0.0
Neutral 25 33.3 19 25.3 6 8.0 23 30.7
total 75 100.0 75 100.0 75 100.0 75 100.0
Table 4.6 shows the various relationship between the nature and process of sales
forecasting on various factors identifiable with business operations and
existence. Whereas a simple majority of 40 percent were of the view that sales
forecsting negatively influenced overall business growth and development, a
slight majority of 38.7 percent held an otherwise position that sales forecsting
enhanced the level of sales revenue. About 76 percent of the total respondents
maintained that harsh operating environment was negatively related with sales
78
forecsting; while as much as 69.3 percent asserted that efficient organizational
structure could be a big boost to the process of sales forecsting.
TABLE 4.7: EVALUATION OF FORECSTING VARIABLES BY FIRMS
Very Import. Import. But
Critcal not
Import. And of
some Value
Of Ltd Important Total Variables
NO. % NO. % NO. % No. % No %
Past sales of
firm
85 76.7 13 16.7 4 5.7 0 0 75 100.nv
Projection of
Customers
attitude
53 70.1 22 29.9 0 0 0 75 100
Industry sales 29 38.2 16 21.1 13 17.5 17 23.2 75 100
Retail sales 32 42.5 12 15.7 23 31.0 8 10.8 75 100
Income 53 70.1 22 29.9 0 0 0 0 75 100
Population 3 16.7 2 11.1 0 0 0 0 75 100.0
Leading
indicators
Change in
inventory
65 86.7 10 13.3 0 0 0 0 75 100
Stock market 0 0 0 0 0 0 0 0 75 100
79
Firms were questioned about the importance of a number of variables in
forecasting sales. This was achieved by asking respondents to rate each variable
on a scale of importance, ranging from ‘very important’ to’ ‘of limited’.
As shown in table 4.7 as much as 76.6 recent of the 75 respondents rated their
respective past sales record as being of uppermost importance. This clearly
shows the importance of historic data in the sales forecsting process. The factor
which made up customers’ buying power index – such as income, population
and retail sales were rated as being of average importance in the forecsting
process. Again, change in inventory scored as much as 86.7 percent as being of
very important.
Table 8. forecsting techniques by usage
TECHNIQUES EMPLOYED NO. OF FIRMS PERCENTAGE Jury of Executive opinion 15 20.4
Industry survey 32 42.2
Trends Projections 4 5.6
Sales Force Composite 13 17.8
Moving Average 0 0.0
Regression 11 14.0
Exponential smoothing 0 0.0
Leading Index 0 0.0
80
Intension to buy survey 0 0.0
Total 75 100
Analysis of the methods used by respondents (as contained in table 4.8) revealed
that industry survey, jury of executive opinion, sales force composite and
regression, in that order, were the methods most often used. Of these three,
industry survey came first as the most popular with about 42.2 percent. This was
followed by jury of executive opinion with 20.4 percent; sales force composite
with 17.8 percent; and regression with 14.0 percent. Trend projections scored
5.6 percent. Clearly as shown above, none of the firms involved in the study
adopted the other identified techniques like Moving Average, Moving Average,
Leading index and intension to busy survey.
TABLE 4.9: RESPONSES ON WHETHER THERE WAS
INCREASING RATE OF FORECAST ERROR
Reason No. of firms Percentage
Yes 64 85.3
No 11 14.7
Total 75 100
As shown in table 4.9, 64 out of the 75 firms or 85.3 percent maintained that
there was increasing rate of forecasting errors in their respective firms. On the
81
other hand, 11 out of 75 or 14.7 percent indicated that the rate of error was on
the decrease in their firms.
TABLE 4.10: RESPONSES ON THE REASONS FOR THE
OCCURRENCE OF FORECASTING ERRORS
REASON NO. OF FIRMS PERCENTAGE
Unstable Business conditions 7 9.3
Inflation/fuel increase 7 9.3
Interruption in power supply 5 6.7
Combination of the above factors 54 72.0
Other problems 2 2.7
Total 75 100
As shown in table 4.10, about 72.0 percent of the total respondents indicated that
source of forecasting error is a combination of factors like unstable business
condition, inflation/fuel price increase, and interruption in power supply,
however, 9.3 percent each indicated that the key cause was singularity unstable
business and inflation/fuel increase.
TABLE 4.11: RESPONSES ON WHETHER THE NUMBER OF
INDIVIDUALS INVOLVED IN THE FORECSTING PROCESS
INFLUENCE THE RATE OF FORECSTING ERROR.
82
Response Percentage percentage
Yes 62 82.7
No 13 17.3
Total 75 100.0
As indicated in table 4.11, about 82.7 percent of the 75 respondents maintained
that there was a relationship between the number of individuals involved and the
rate of forecasting errors. Only 17.3 percent showed that there was no relation.
4.3 TEST OF RESEARCH HYPOTHESES
This section deals with the test of the four hypothesis set for the study. Each of
the hypotheses would be tested on accept or reject the respective statement using
shi-square model. In each case, the null hypothesis is matched with the related
alternative hypothesis.
Chi-square Decision Criterion: Using the appropriate degrees of freedom and
levels of significance, the null hypothesis is considered as having been rejected
if the calculated Chi-square greater than the critical chi-square. If otherwise, the
null hypothesis is considered as not rejected.
83
In the light of this study, a general level of significance of 5 percent of 0.05 is
considered for all the four hypotheses.
Test of Hypothesis one:
H0: Manufacturing firms’ operating environment does not have much impact
on the process and outcome of sales forecasting.
H1: Manufacturing firms’ operating environment has much impact on the
process and outcome of sales forecasting.
TABLE 4.12: CONTINGENCY TABLE OF OBSERVED AND
EXPECTED FREQUENCIES ON THE RELATIONSHIP
BETWEEN OUTCOME OF SALES FORECASTING AND
FORM’S OPERATING ENVIRONMENT.
Responses Sales forecasting &
Org. Growth/Dev.
Impact of Harsh Operating
Environment
Total
Oi Ei Oi Ei
Positive 20 16 12 16 32
Negative 30 43.5 57 43.5 87
Neutral 25 15.5 6 15.5 31
Total 75 75 75 75 150
84
Table 4.13: computational table for chi-square value
S/No Oi Ei Oi-Ei (Oi-Ei)2 (Oi-Ei)2
1 20 16 4 16 1.000
2 30 43.5 -13.5 182.25 4.190
3 25 15.5 9.5 90.25 5.823
4 12 16 -4 16 1.000
5 57 43.5 13.5 182.25 4.190
6 6 15.5 -9.5 90.25 5.823
X2 22.024
From table 4.13, it is clear that the calculated X2 is 22.024.
The level of significance = 0.05
The Degree of Freedom (DF) from table 4.12 is:
DF = (R-1)(C-1),
Where R is the number of rows and C is the number of columns.
DF = (3-1) (2-1) = 2x1 = 2
The critical value of X2 = 5.991 (From Chi-square table)
Conclusion: Since calculated X2 at 22.024 is greater than the critical X2 at 5.991,
reject the null hypothesis (Ho) and conclude that manufacturing firms’ operating
environment have much impact on the process and outcome of sales forecasting.
85
TEST OF HYPOTHESIS TWO:
H0: There is no relationship between a firm’s organizational structure and the
nature/outcome of its sales forecasting practice.
H1: There is a relationship between a firm’s organizational structure and the
nature/outcome of its sales forecasting practice.
Table 4.14: Contingency Table of Observed and Expected Frequencies in
the Relationship Between Outcome of sales Forecasting and firm’s
Organization Structure.
Table 4.17: computational table for chi-square value
Responses Sales Forecasting & Org.
Growth/Dev.
Impact of Efficient Org.
Structure
Total
Oi Ei Oi Ei
Positive 20 10 0 10 20
Negative 30 41.0 52 41.0 82
Neutral 25 24 23 24 48
Total 75 75 75 75 150
s/no Oi Ei Oi-Ei (oi-Ei)2 (Oi-Ei)2
Ei
1 20 16 4 16 1.000
2 30 43.5 -13.5 182.25 4.190
3 25 15.5 9.5 90.25 5.823
4 29 16 13 169 10.563
5 27 43.5 -16.5 272.25 6.259
86
From table 4. 17, it is clear that the calculated X2 is 28.624.
The level of significance = 0.05
The degree of freedom (DF) from table 4.16 is:
DF = (R-1) (C-1),
DF = (3-1)(2-1) = 2 x 1 = 2
The critical value of X2 = 5.991 ( From Chi-Square Table)
Conclusion: Since calculated X2 at 3.264 is less than the critical X2 at 5.991, we
fail to reject the null hypothesis (Ho) and conclude that the number of persons
involved in sales forecasting has no direct relationship with the frequency of
error occurrence in the process.
6 19 15.5 3.5 12.25 0.790
X2 28.624
87
CHAPTER FIVE
SUMMARY FINDINGS, CONCLUSION AND
RECOMMENDATIONS
5.1 SUMMARY OF RESEARCH FINDINGS
In this project the concern has been to find out whether manufacturing
firms practiced sales forecasting; and if so the nature and relevance of such
practice. Towards this end, data were collected from seventy-five firms which
were analysed in the last chapter. Arising from the above analysis are the
following major findings:
1. Firms operating environments impacts on the process and outcome of
sales forecasting. The indication was that the more the harshness of the
operating environment, the lesser the reliability of sales forecasting
outputs. This was the result of the test of hypothesis one, where the null
hypothesis was rejected. This result can be attributed to the fact that most
of the firms involved in the study had the practice of relying on historical
data (mainly past sales records) for the forecasting. In which case, the
changes normally associated with unstable operating environment
consequently go on to distort and alter the data upon which projections
have been made. This is also consequential to the finding that forecasting
88
errors were majorly caused by combined factors – such as economic
instability, inflation/fuel price increase and distruption in power supply.
2. There is a relationship between a firm’s organizational structure and the
process and outcome of its choice of sales forecasting practice. This was
the outcome of the test of the second hypothesis. The emphasis was that
the higher the level of efficiency associated with an organizational
structure, the more effective the process of sales forecasting and the better
the result of such exercise.
3. Sales forecasting practice has a direct impact on firms’ sales revenues and
market power. This again was evidenced in the test of hypothesis three of
this study. The indication was that a good practice of sales forecasting
resulted to increased sales revenue and market power.
4. The number of persons involved in sales forecasting does not influence
upwardly the rate of forecasting errors. This finding arose of the last of
hypothesis four of this study. However, there is an indication that the
higher the number of people involved in the exercise, the more efficient
the forecasting process becomes.
89
5. Firms use non-technical and less-mathematical techniques in sales
forecasting than the pure modeled statistical techniques like regression,
computer based, moving averages, smoothing, etc. Instead, the commonly
adopted methods are industry survey, jury of executive opinion and sales
force composite. Among the 75 firms involved in the study for instance, a
combined rate of 81.3 percent indicated preference for the above three
methods; with only 14 percent showing interest for regression.
The results of this study equally gave indication that manufacturing firms
understood the importance of and the need for the use of sales forecasting. This
can be demonstrated by the fact that a combined 88.9 percent of the firms
involved in the study acknowledged that the exercise was very essential in the
life of any manufacturing outfit. As shown above, the real problem hindering the
application was unstable business, which has continued to increase the size of
forecasting errors in recent times.
5.2 CONCLUSION
Evidently, sales forecasting is a favoured approach to sales management in those
manufacturing firms studied. Among other identifiable benefits, efficient
90
practice of sales forecasting has the capacity of helping a firm achieve high sales
revenue and enhance its market power.
In line with its set objectives, the research has clearly articulated the methods
and techniques used by manufacturing firms in Enugu. It notes that despite its
popularity, the application of sales forecasting is still subjected to less statistical
and more subjective approaches. Apart from the fact that the use of subjective
techniques can lead to high error rate, harsh operating environment, inflation,
incessant fuel price increase, erratic power supply, among other factors, have
joined to make the results of sales forecasting exercise very unreliable. In
addition, the nature of a firm’s organizational structure is equally found to be a
key contributor to the success or otherwise of a forecasting exercise. This then
means that increasing errors in forecasting may not just be as a result of
exogenous factors; but may arise of endogenous factors like weak organization
structure and lack of experience on the side of sales managers.
In this age of information technology, one expects firms to make good use of
computer-based techniques in projecting its turnover and sales revenue.
Ironically, this is yet to be embraced by virtually all the firms studied. As a
result, capturing and handling the complexities in the marketing environment
91
tends to be more and more difficult. As expected, very few persons are involved
in the forecasting process, thereby limiting the expert/knowledge contributions
in the whole process.
Generally, the results of this study have clearly established that a lot still needs
to be done to improve the nature and process of sales forecasting among
manufacturing firms in Nigeria. This study strived to identify such problem
areas, as well as the environmental hindrances to the success of forecasting in
the country.
5.3 RECOMMENDATIONS
Sales forecasting is a good planning tool for the success and growth of every
manufacturing firm. In this era of increasing uncertainty in the world of
business, a good understanding and application of this tool can provide a strong
competitive advantage and enhanced market power for firm.
In recognition of the above assertions, the research hereby proffered the
following recommendations:
1. Firms should adopt more scientific and proven statistical models in their
sales forecasting exercises;
92
2. The use of computer-based techniques in projecting sales volume, sales
revenue, and other consumer-related variables should be adopted by
manufacturing firms. This may serve to reduce the level of errors arising
from guess-works and subjective estimate of sales data by the firms.
3. The choice of sales forecasting techniques and methods should be made in
strict consideration of the nature of firms’ organizational structure. Where
the structure is weak, necessary restructuring and adjustments could be
made to allow for maximum benefits accruing from forecasting.
4. The measurement of the outcome of sales forecasting should be taken
very serious. To this effect, it is recommended that only techniques that
can allow for easy measurement of results should be chosen.
5. Only qualified and trained sales personnel should be allowed to manage
the sales forecasting process. However, inputs for forecasting should be
sort form all the sections/departemtns in the firm.
6. Regular consultations should also be made with marketing experts,
academics, and relevant professional marketing bodies to allow for the
discovery and adaptation of new techniques in forecasting
93
Finally, it is recommended that further research works in the area of sales
forecasting be geared towards: finding out the nature and process of sales
forecasting in other sections of the economy – service industry, retail firms and
the public goods industry; finding out the likely problems that may be involved
in using computer-based packages for sales forecasting; and finding out whether
the success and techniques of sales forecasting depend on the size of firms.
94
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APPENDIX RESEARCH QUESTIONNAIRE
Department of marketing University of Nigeria Enugu Campus.
Dear Sir/Madam, I am a postgraduate student of the University of Nigeria, Enugu Campus. I am carrying out a survey of sales forecasting practices of Manufacturing Companies in Enugu, Would you please help me by filling the attached questionnaire, if your company is engaged in formal sales forecasting practice? The information provided will be treated with utmost care and confidence. The exercise is purely academic and I count on your co-operation. Thank you. Yours sincerely, Ubani, Blessing PG/MBA
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Please indicate by filling or ticking as appropriate the following questions: 1. How old is your Company? a. Les than 2 years b. 2 – 5 years c. 6 – 10 years d. 11 – 20 years e. over 20 years 2. How relevance do you feel sales forecasting is to the success of your
company? a. Very relevance b. Relevance but not critical c. some value d. Limited value 3. How many people are in your sales forecasting group? a. 1 person only b. 2 – 4 persons c. 5 – 10 persons d. over 10 persons 4. How many times per year does scheduled preparation and revision of
forecast occurs? i. Weekly ii. Bi-Weekly iii. Monthly iv. Quarterly v. Semi-Annually vi. Annually 5. How regular does firm compare the outcomes of sales forecsting exercise? i. Weekly ii. Bi-Weekly iii. Monthly
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iv. Quarterly v. Semi-Annually vi. Annually 6. Which of these forecasting methods does your organization commonly
make use of? (see attached definitions). i. Jury of Executive Opinion ii. Industry Survey
iii. Trends Projections iv. Sales Force Composite v. Moving Average vi. Regression vii. Exponential smoothing viii. Intention to Buy Survey
What is the importance of the following variables in forcasting sales? v. Important Important
but not Critical
Important and of same value
Of some Limited Importance
Past of Firm Projection of: Customers Attitude Industry Sales Retail Sales Income Population Leading Indicators: Change in inventory Stock market 7. Indicate the kind of relationship that exists between the overall impact of
sales forecasting and any of the titled factors.
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Names of the impact
Organizational growth & Dev.
Sales Revenue
Harsh Operating Environment
Efficient Org. Structure
Positive Negative Neutral 8. Would you say that the rate of forecasting error has been on the increase
in your firm? Yes No 9. Which of the following reason do you think is responsible for your answer
in question thirteen? i. Unstable Business Conditions ii. Inflation/Fuel Increase iii. Interruption in power supply iv. Combination of the above Factors v. Other problems 10. Do you think the number of persons involved in a sales forecasting
process influences the rate of forecasting errors that may be encountered? Yes No 11. Among which set of participants do you thick forecasting error is most
prevalent? i. 2 to 4 Persons ii. 5 to 10 persons iii. Above 10 persons
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DEFINITION OF FORECASTING METHODS 1. JURY OF EXECUTIVE OPINION
Company forecast is a composite of estimates made by a selected group of managers
2. INDUSTRY SURVEY
Company forecast projected after contacting other firms in the industry 3. INTENTIONS TO BUY SURVEY
Customers are survey to determine how much of certain products they intend to buy or to derive an index measuring attitudes towards buying specific products.
4. TREND PROJECTIONS
This techniques fits an equation to a time series and then uses the equation to project into the future.
5. MOVING AVERAGE
Forecast is the arithmetic or weighted average of a number of consecutive periods in a time series.
6. SALES FORCE COMPOSITE
Company forecast is the sum of the individual estimates made by salesmen.
7. REGRESSION Relationship between sales and other independent variable (i.E. income, population) are used to build a forecasting equation. Estimates of independent variables for future time periods are plugged into the equation to give sales forecast.
8. EXPONENTIAL SMOOTHING
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Similar to moving average, except all time periods include, more recent time periods included. More recent periods receive more weight, more weights decline geometrically with the passage of time.
9. LEADING INDEX.
A time series measuring economic activity whose movement in a given direction proceeds the movement of the general economy and/company sales in the same direction.
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