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Public Policy and Administration Review 1(1); June 2013 pp. 0115 Wurim
© American Research Institute for Policy Development 1 www.aripd.org/ppar
Demand Forecasting and the Determination of Employee Requirements in Nigerian
Public Organisations
Wurim, Ben Pam Ph.D.
(Assistant Chief Accountant)
National Directorate of Employment
Plateau State, Nigeria.
Abstract
The right quality and quantity of human capital
(employed) is a measure of an organisation’s
strength and success. Where this optimum staff
mix is not maintained, imbalances in surpluses
or deficits of employees may arise leading to
unmanageable increases in personnel costs,
inefficiency, absenteeism, turnover and
productivity problems. The determination of this
optimum staff mix is subject to the application of
certain methods. The principal objective of this
paper is to assess the potency of demand
forecasting in the determination of employee
requirements. A hypothesis in line with this
objective is drawn and tested based on the data
generated through a questionnaire. The survey
investigation method was used in collecting the
primary data for the study. The sample
consisted of 349 top, middle and low levels
management staff of five public sector
organisations in Nigeria. The result shows that
demand forecasting is not a potent tool in the
estimation of employee requirements in
Nigerian public organisations. Based on the
aforementioned, the paper concluded that
although widely varying approaches to
forecasting the employees needs of an
organization exist, demand forecasting might
not predict with certainty the exact employee
needs of an organization. The paper
recommends that human capital planners
should adopt a strategy of combining both
qualitative and subjective methods in
forecasting the employee needs of an
organization;
Chief executive officers of organisations should
make it mandatory for human capital planners
to employ scientific methods in forecasting; and
the adoption of a combination of the technical
skills of experts from various fields in the
forecasting efforts.
Introduction
The further into the future it takes to plan
human capital, the greater will be the degree of
certainty of the number and types of employees
available for employment both within and
outside the organization. Effective workforce
planning for specific enterprises involves
determining which actions are needed to achieve
business objectives, identifying the types and
quantities of skills that are necessary to
accomplish those actions, determining how
those skills may vary from the skills that are
currently available, and developing strategies
for closing the gaps between today’s workforce
and the workforce needed to accomplish the
business objectives (Ward, 1996:1).
This brings to the fore the import of demand
forecasting. Armstrong (2003:371) defines
demand forecasting as the process of estimating
the future numbers of people required and the
likely skills and competences they will need.
Public Policy and Administration Review 1(1); June 2013 pp. 0115 Wurim
© American Research Institute for Policy Development 2 www.aripd.org/ppar
The ideal basis of the forecast is an annual
budget and longer term business plan, translated
into activity levels for each function and
department or decisions on downsizing. The
information gathered from external
environmental scanning and assessment of
internal strengths and weaknesses is used to
predict or forecast human capital supply and
demand in the light of organizational objectives
and strategies.
Forecasting uses information from the past and
present to identify expected future conditions.
Projections for the future are, of course, subject
to error. Changes in the conditions on which the
projections are based might even completely
invalidate them, which is the chance forecasters
take. Usually, though, experienced people are
able to forecast with enough accuracy to benefit
organizational longrange planning (Gerhart et
al, 2000:803).
But what is the costbenefit tradeoff of the
rigorous activities involved in demand
forecasting? Why should organisations concern
themselves with demand forecasting? There are
several good reasons to conduct demand
forecasting. It can help: (i) quantify the jobs
necessary for producing a given number of
goods, or offering a given amount of services,
(ii) determine what staffmix is desirable in the
future, (iii) assess appropriate staffing levels in
different parts of the organisation so as to avoid
unnecessary costs, (iv) prevent shortage of
people where and when they are needed most,
and (v) monitor compliance with legal
requirements with regard to reservation of jobs
(Aswathappa, 2005:74). In spite of the
numerous functions and advantages of demand
forecasting, most organisations are yet to take
advantage of this scientific way of estimating
employee needs.
The problem
The absence of effective and scientific demand
forecasting methods in most Nigerian public
organizations seems to be the main bane of
shortages and excesses in human resources
resulting to unmanageable and expensive
imbalances in the number and quality of
employees needed to optimally achieve
organizational objectives and plans. As a result,
public organizations’ scorecard has remained
excessive costs associated with excessive
turnover, absenteeism, stress, low morale, shift
work, healthcare services, low productivity and
internal market inefficiency.
Several demand forecasting techniques currently
exist. They vary from fairly simple qualitative
methods based on individual or group
judgements to highly complicated methods
involving sophisticated statistical
computerization. What is not yet very clear is
whether or not these forecasting methods are
used and further still which particular
techniques are used and what is the result of
such efforts? Are there benefits derivable from
such exercises?
Objectives of the study
The principal objective of the paper therefore, is
to assess the potency of demand forecasting in
the determination of employee requirements.
Specifically the paper seeks to assess the extent
to which demand forecasting techniques are
used in the determination of employee
requirements and to find out the degree to which
demand forecasting leads to the determination
of employee requirements.
Methodology
The research design used for the study is the
survey research method.
Public Policy and Administration Review 1(1); June 2013 pp. 0115 Wurim
© American Research Institute for Policy Development 3 www.aripd.org/ppar
Primary data for the study were sourced from
five public sector organisations namely:
National Directorate of Employment (NDE),
Power Holding Company of Nigeria (PHCN),
Plateau State Water Board (PSWB),
Federal Ministry of Finance (FMF) and
Nigerian National Petroleum Corporation
(NNPC). The population of the study includes
all the 10,127 top, middle and lower
management staff of the five organisations.
Given that the population of the study is finite,
the Taro Yamane (1964) statistical formula for
selecting a sample was applied.
The formula is given as:
n = N
1 + N (e)2
Where: n = Sample size; N = Population; e = level of significance (or limit of tolerance error) in this
case 0.05; 1 = Constant value
This gives a sample size of 385.
For its data collection, a suitable Likert Scale (5
Points) questionnaire was designed and
developed. The data so collected was then
analyzed using the chisquare (x2) test statistic.
Theoretical considerations
Methods for forecasting human resources range
from a manager’s best guess to a rigorous and
complex computer simulation. Simple
assumptions may be sufficient in certain
instances, but complex models may be
necessary for others. However, it has been
observed that despite the availability of
sophisticated mathematical models and
techniques, forecasting is still a combination of
quantitative methods and subjective judgement.
In theory or in practice, the most commonly
used techniques in forecasting the demand for
human resources include the following:
Management / Executive Judgement
The simplest approach to manpower forecasting
is to prepare estimates of future needs based on
the individual opinions of departmental or line
managers. The technique may involve a bottom
up approach by asking junior managers to sit
down, think about their future workloads and
decide how many people they need.
Alternatively, a “top downward” approach can
be used, in which company and departmental
forecasts are prepared by top management,
possibly based on the advice/information
available from the personnel, and organisation
and methods departments. The suggested
forecasts are circulated downwards for
discussions and therefore reviewed and agreed
with departmental managers (Sen, 2007:135).
Aswathappa (2005:74) indicates that in
“bottomup” and “topdown’’ approaches,
departmental heads are provided with broad
guidelines. Armed with such guidelines, and in
consultation with the human resource section in
the human resource management department,
departmental managers can prepare forecasts for
their respective departments. Simultaneously,
top human resource managers prepare company
forecasts. A committee comprising departmental
managers and human resource managers will
review the two sets of forecasts; arrive at a
unanimity, which is then presented to top
managers for their approval. Needless to say,
this technique is used in smaller organisations or
in those companies where sufficient database is
not available.
Public Policy and Administration Review 1(1); June 2013 pp. 0115 Wurim
© American Research Institute for Policy Development 4 www.aripd.org/ppar
Work Study Techniques
Work Study is as old as industry itself. In the
opinion of Currie (1972:22), work study is the
study of human work in the deepest sense and
dignity of the word, and not merely in the
special and more restricted meaning used in the
physical sciences. Even today it is not limited to
the shop floor, or even to manufacturing
industry. In one or another form it can be used
in any situation wherein human work is
performed. In the book “Introduction to work
study” (1983:29), the International Labour
Organisation (ILO) defines “work study” as a
generic term for such techniques, particularly,
method study and work measurement, as are
used in the examination of human work in all its
contexts and which leads systematically to the
investigation of all the factors that affect the
efficiency and economy of the situation being
reviewed in order to effect improvement”.
Work study, therefore, has a direct relationship
with productivity. It is most frequently used to
increase the amount produced from a given
quantity of resources with little or no further
capital investment.
Work study technique can be used when it is
possible to apply work measurement to calculate
how long operations should take and the number
of people required. Work study techniques for
direct workers can be combined with ratio trend
analysis to calculate the number of indirect
workers needed (Armstrong, 2003).
The starting point in a manufacturing company
is the production budget, prepared in terms of
volumes of saleable products of the company as
a whole, or volumes of output for individual
departments. The budgets of productive hours
are then compiled using standard hours for
direct labour. The standard hours per unit of
output are then multiplied by the planned
volume of units to give the total number of
planned hours for the period. This is then
divided by the number of actual working hours
for an individual operator to show the number of
operators required. Allowance will have to be
made for absenteeism and idle time. As already
stated, work study techniques for direct workers
can be combined with ratiotrend analysis to
forecast for indirect workers, establishing the
ratios between the two categories. The same
logic can be extended to any other category of
employees.
Ratio Trend Analysis
This is the quickest forecasting technique. The
technique involves studying past ratios, like the
number of workers involved in direct production
(direct hours) and sales in an organisation and
forecasting future ratios, making some
allowance for changes in the organisation or its
methods. Table1 shows how an analysis of
actual and forecast ratios, between the number
of routine proposals to be processed by an
insurance company underwriting department
and the number of underwriters employed could
be used to forecast future requirement
(Armstrong, 1988:209).
Public Policy and Administration Review 1(1); June 2013 pp. 0115 Wurim
© American Research Institute for Policy Development 5 www.aripd.org/ppar
Table 1: Demand Forecast – Inspectors
Year No. of Employees Ratio
Inspectors :
Production
Production
Inspectors
Actual  3
 2 1500 150
1800 180 1 : 10
1 : 10
Last year
Next year 2000 180
2200* 200** 1 : 11
1 : 11
Forecast
+ 2
+ 3 2500* 210**
2750 230** 1 : 12
1 : 12
* Calculated by reference to forecast activity levels
**Calculated by applying forecast ratio to forecast activity levels.
Source: Armstrong (1988:209), A Hand book of Personnel Management, New Delhi: Kogan Page.
Delphi Technique – Expert Forecast
Named after the ancient Greek Oracle at the city
of Delphi, the Delphi technique is a subjective
method used to predict future personnel needs
of an organisation by “integrating the
independent opinions of experts”.
Schuler (1983:48) explains that the technique
involves a large number of experts who take
turns at presenting their forecasts and
assumptions. An intermediary passes each
expert’s forecast and assumptions to the others,
who then make revisions in their forecasts. This
process continues until a final forecast emerges.
The final forecast may represent specific
projections or a range of projections depending
on the position of the experts. Originally
developed as a method to facilitate group
decision making, it has also been used in
workforce forecasting. Experts are chosen on
the basis of their knowledge of internal factors
that might affect a business (e.g. projected
retirements), their knowledge of the general
business plans of the organisation, or their
knowledge of external factors that might affect
demand for the firm’s product or service and
hence its internal demand for labour.
Experts may range from firstline supervisors to
toplevel managers. Sometimes experts internal
to the firm are used, but if the required expertise
is not available internally, then one or more
outside experts may be brought in to contribute
their opinions. To estimate the level of future
demand for labour, an organisation might select
as experts, for example, managers from
corporate planning, human resources,
marketing, production and sales department.
Facetoface group discussion is avoided since
differences in job status among group members
may lead some individuals to avoid criticizing
others and to compromise on their good ideas.
To avoid these problems, an intermediary is
used. The intermediary’s job is to pool,
summarize, and then feed back to the experts
the information generated independently by all
the other experts during the first round of
forecasting. The cycle is then repeated, so that
the experts are given the opportunity to revise
their forecasts and the reasons behind their
revised forecasts. Successive rounds usually
lead to a convergence of expert opinion within
three to five rounds.
Public Policy and Administration Review 1(1); June 2013 pp. 0115 Wurim
© American Research Institute for Policy Development 6 www.aripd.org/ppar
Process Analysis
The widespread interest in reengineering
activities has produced a hypothetical approach
to workforce demand forecasting based on
process analysis. Some articles on the topic
suggest businesses should develop a detailed
analysis of process components of work
activities and that predictive ratios could then be
designed to forecast the associated workload for
each unit level of process output (Ward,
1996:2). Data collection and analysis phase of a
process analysis approach according to Ward is
similar to the traditional historical ratio
approach.
Process steps are substituted for work activity
steps, so that the analysis is done at an
organizational level rather than a work group.
Ward goes further to observe that the bench
mark analysis showed some reengineered
companies have developed the traditional
historical ratio analysis described in section 3,
and have then adjusted those ratios for their
assumed productivity gains to be achieved via
process improvements. In theory, the positive
and negative aspects of this process would
mirror those described for historical ratios.
The concept seems fundamentally sound, but
the benchmarking efforts do not seem to find a
single case where this concept has been
translated into an operational model. In order for
the process to work as hypothesized, the work
load analysis should be incorporated within a
reengineering study. It might fairly be
questioned whether the extensive level of
analysis should become part of an annual
planning cycle or should only be done in
conjunction with a major reengineering effort.
Flow Model
Flow models are very frequently associated with
forecasting personnel needs. The simplest one is
called the Markov model. In this technique,
Rothwell (1988:175) outlines the activities to be
carried out by the forecasters as follows:
1. Determine the time that should be covered.
Shorter lengths of time are generally more
accurate than longer ones. However, the time
horizon depends on the length of the human
resource plan which, in turn, is determined by
the strategic plan of the organisation.
2. Establish categories, also called “states” to
which employees can be assigned. These
categories must not overlap and must take into
account every possible category to which an
individual can be assigned. The number of states
can neither be too large nor too small.
3. Count annual movements (also called
‘flows’) among states for several time periods.
These states are defined as ‘absorbing’ (gains or
losses to the company) or ‘nonabsorbing’
(change in position levels or employment
status). Losses include death or disability,
absences, resignations and retirements. Gains
include hiring, retirements, transfer and
movement by position level.
4. Estimate the probability of transitions from
one state to another based on past trends.
Demand is a function of replacing those who
make a transition.
There are alternatives to the simple Markov
model. One, called ‘SemiMarkov’, takes into
account not just the category but also the tenure
of individuals in each category. After all,
likelihood of movement increases with tenure.
Another method is called the ‘Vacancy model’
which predicts probabilities of movement and
number of vacancies.
While the SemiMarkov model helps estimate
movement among those whose situations and
tenure are similar, the vacancy model produces
the best results for an organisation. Markov
analysis is advantageous because it makes sense
to decision makers. They can easily understand
its underlying assumptions. They are therefore,
likely to accept results.
Public Policy and Administration Review 1(1); June 2013 pp. 0115 Wurim
© American Research Institute for Policy Development 7 www.aripd.org/ppar
The disadvantages include: (i) heavy reliance on
past oriented data, which may not be accurate in
periods of turbulent change, and (ii) accuracy in
forecasts about individuals is sacrificed to
achieve accuracy across groups.
Statistical techniques
The most commonly used statistical approaches
to human capital forecasting range from
methods of simple scatter diagram through
regression or correlation analysis, to economic
models. All of these methods depend, for their
validity, on the assumption that developments in
the future will exhibit some continuity with the
past. Simple extrapolation assumes that past
trends will continue, regression analysis
assumes that particular relationships will hold
firm and econometric models assume that the
basic interrelationship between a whole range
of variables will be carried on into the future.
Regression and Correlation
This method seeks to provide a measure of the
extent to which movements in the values of two
or more variables – as for example, labour input
and sales are related (or correlated) with each
other. The aim is to predict changes in one
variable by reference to changes in the other or
others, where the future value of these other (or
explanatory) variables are already postulated.
Regression therefore, is a technique used to
describe a relationship between two or more
variables, in mathematical terms. Francis
(2004:173) asserts that regression is concerned
with obtaining a mathematical equation which
describes the relationship between two
variables. The equation can be used therefore
for comparison or estimation purposes. The
process of obtaining a linear regression
relationship for a given set of (bivariate) data is
often referred to as fitting a regression line.
Francis (2004:174) asserts that there are three
methods commonly used to fit a regression line
to a given set of bivariate data.
(a) Inspection
This method is the simplest and consists of
plotting a scatter diagram for the relevant data
and then drawing in the line that most suitably
fits the data. The main disadvantage of this
method is that different people would probably
draw different lines using the same data. It
sometimes helps to plot the mean point of the
data (that is, the mean of the x’s and y’s
respectively) and ensure the regression line
passes through this. In Figure 1, possible
relationships are examined to see whether they
might prove useful for forecasting. Francis goes
further to explain that for any set of bivariate
data, there are two regression lines which can be
obtained viz: i) The y on x regression line – that
regression line which is used for estimating y
given a value of x and ii) the x on y regression
line – that regression line which is used for
estimating x given a value of y. The two
regression lines are quite distinct.
(b) Semi – averages
The method of semiaverages according to
Francis is for obtaining the y on x regression
line using the following steps: STEP 1 – Sort
the (bivariate) data into size order by xvalue.
STEP 2 – Split the data up into equal groups, a
lower half and an upper half (if there is an odd
number of items, ignore the central one). STEP
3 – Calculate the mean point for each group.
STEP 4 – Plot the above mean point on a graph
within suitably scaled. This method is
considered superior to the method of inspection.
However, a major drawback of the semi average
technique for obtaining a regression line is the
fact that it relies on only two points, both means
of the two respective data groups. If there are
extreme values present either or both of the
means are easily distorted, thus so is the
regression line.
Public Policy and Administration Review 1(1); June 2013 pp. 0115 Wurim
© American Research Institute for Policy Development 8 www.aripd.org/ppar
200 

180 

160 

140 

120 

100 

80 
18 20 22 24 26 28
Mid Point
Regression line
(by inspection
)
Sales
(’000’ N)
E
m
p
l
o
y
e
e
s
i
z
e
x
y
5
52
x
x
xxxx
xxx
Figure 2.8 Regression line relationships between sales and employment size.
Source: Francis, A (2004:176), Business Mathematics and Statistics, London Bedford Row: Thomson
Learning.
As already mentioned, an important use of regression lines is for estimating the value of one variable
given a value of the other.
(c) Conventional Statistical Technique –
Simple Linear Regression and Multiple
Regression (Least Square Method).
The least squares regression method can be used
to forecast direct labour employment needs of
an organisation. In simple linear regression
(least square method), a forecast of future
human capital demand is based on past
relationship between employment level and a
variable related to employment.
For example, the number of beneficiaries
supervised (x) determines the number of persons
needed for employment (i.e. internal demand),
y.
The least square method is considered to be the
standard method of obtaining a regression line.
The derivation of the technique has
mathematical base which involves all values and
is thus considered to be superior. (Francis,
2004:173174).
Berenson et al (1985:587) assert that the
computation is represented by two
simultaneously solved equations given as:
Public Policy and Administration Review 1(1); June 2013 pp. 0115 Wurim
© American Research Institute for Policy Development 9 www.aripd.org/ppar
n n n
n∑ Xi Yi  ( ∑ Xi) ( ∑Yi)
bi = i – 1 i =1 i = 1___
n n
n∑ Xi 2  ( ∑ Xi )2
i=1 i = 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
bo = Y  bi X . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Where: bo = Coefficient of y intercept, bi = the slope used in predicting Y, X=
number of beneficiaries selected, trained, placed and monitored/
Supervised ( i.e. the work load). Y = Manpower demand (number of
persons needed for employment), yi = Actual value of y for observation,
xi = Actual value of x,
n n
Y = ∑ yi; and X = ∑ xi
i = 1 i = 1
n n
Examining the above equations, it is observed that there are five quantities that must be calculated in
order to determine bo and bi. These are n, the sample size; ∑Xi, the sum of the X values;
i = 1
n n
∑ yi, sum of the Y values, ∑ Xi2 , the sum of the square of X values,
i = 1 i = 1
n
and ∑ Xi Yi, the sum of the cross product of X and Y.
i = 1
Where there are more than one independent
variables to be used for example, number of
beneficiaries, productivity, turnover,
absenteeism, etc, this method becomes
ineffective and gives room to “MULIPLE
REGRESSION MODEL” – One which could
utilize several explanatory variables (xi ,
x2,…………., xn) to predict a quantitative
dependent variable (y). If the least squares
method is utilized to compute the sample
regression coefficient (bo, b1 and b2) we will
have the following three normal equations
(Berenson, et al, 1985:650):
n n n
∑ yi = n bo + b1 ∑ x, i + b2 ∑ x2 i ………………………..3
i = 1 i = 1 i = 1
n n n n
∑x1i yi = bo ∑x1 i + b1 ∑ x2 i + b2 ∑ x1 i x2 i ................................4
i = 1 i = 1 i = 1 i = 1
n n n n
∑ x2 i yi = bo ∑x2 i + b1 ∑ x1 i x2 i ∑x2 i ………………………5
i = 1 i = 1 i = 1 i = 1
Public Policy and Administration Review 1(1); June 2013 pp. 0115 Wurim
© American Research Institute for Policy Development 10 www.aripd.org/ppar
Standard Error of the Estimate
Although the least squares method results in the
line that fits the data with the minimum amount
of variation, the regression equation is not a
perfect predictor, especially when samples are
taken from a population, unless all the observed
data points fall within the predicted regression
line. Thus, the regression line serves only as an
approximate predictor of a y curve, for a given
value of x.
Therefore, the measure, of variability around the line of regression is called the standard error of the
estimate and is given by the symbol Syx and defined as:
n ^
∑ ( Yi  Yi )2
Syx = i = 1_____________
n – 2
Where: Yi = Actual value of Y for a given Xi,
^
Y = Predicted value of Y for a given Xi.
In the final analysis, the standard error of the estimate Syx can thus be obtained using the following
computational formula:
n n n
∑ Yi 2  bo ∑ Yi  b1 ∑ X i Y i
Syx i = 1 i = 1 i = 1
n – 2
Computer Simulations and Modeling
The most common packages available for use
when developing regression models for business
application are the statistical analysis system
(SAS) (Reference11 and 18), the statistical
package for the social sciences (SPSS)
(Reference 20), and Minitab (references 12 and
17) (Berenson, et al, 1985:711). Using any of
the three packages, the values of the three
sample regression coefficients in equations 3, 4
and 5 may be obtained. That is to say that the
computer can be used to effectively forecast
internal manpower demand even when there are
many dependent variables. To improve human
capital decision making, an organisation must
also be concerned with its external demand
conditions especially within the industry and in
the economy as a whole.
This can also be predicted by the use of the
Delphi and conventional statistical techniques,
and estimating the needs of other organisations
in the same industry, and the mass media.
Time Series Analysis
It is necessary to analyse past trends in human
capital activities and sift the significant points
while preparing a forecast. This requires an
understanding of the concept of the time series.
Francis (2004:214) asserts that a time series is
the name given to the values of some statistical
variables measured over a uniform set of time
points. A time series therefore, is a name given
to numerous data that is described over a
uniform set of time points’data classified
chronologically’ for example, monthly
absenteeism rates.
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The recording of such casual relationships
between different variables for example, is there
a positive correlation between absence and age
or length of service, or with prediction of
future?
Depending on the nature, complexity and extent
of the analysis required, there are various types
of models that can be used to describe time
series data. They include two models called
simple additive and multiplicative models. The
components that go to make up each value of a
time series are described in the following
definitions:
The time series additive model
y = t + s + r
Where: y = a given time series value, t = the trend component, s = the seasonal component and r =
the residual component.
The time series multiplicative model
y = t x s x R
Where: y = a given time series value, t = the trend component
s is the seasonal component, R is the residual component (Francis, 2004:215).
Trend (t) is the underlying, longterm tendency
of the data. Seasonal variations are shortterm
cyclic fluctuations in the data about the trend
which take their name from standard business
quarters of the year. Season, however, can have
many different meanings, for example, daily,
monthly or quarterly seasons. Residual
variations include other factors not included in
trend or seasonal factors. Time series therefore,
is an alternative method that can be used to
analyse employment levels over a time and used
as a basis for forecasting human capital levels.
This means projecting the past into the future
and then allowing for any foreseen changes
resulting in a change in use of capital and
machinery, change in external economic
climate, internal problems within the
organisation and emergence of competitors.
Contrary to the three factors mentioned by
Francis, Lynch (1982:72) mentions four factors
or movements to be revealed by an analysis of a
time series as: (a) a longterm (basic) trend; (b)
seasonal fluctuations; (c) catastrophic
(abnormal) movement; and (d) residual (chance
movement).
Results
The questionnaire was distributed to 385 top,
middle and lower levels staff of the five selected
organisations but only 349 completed and
returned the questionnaire yielding an overall
response rate of 92%. We set out to provide the
necessary lead for empirical examination of the
degree to which demand forecasting leads to the
determination of employee requirements in
organisations. For this reason, hypothesis one
was formulated thus:
H1: Demand forecasting positively affect the
determination of employee requirements. Table
1 shows that 57.54% of the respondents agreed
that scientific calculation of the quality and
quantity of staff before recruitment leads to
proper estimations while 42.46% responded to
the contrary; 62% of the respondents agreed that
job analysis is made before a ‘fit’ person is
employed and that it leads to accurate
estimation of employee needs but 38%
disagreed.
Public Policy and Administration Review 1(1); June 2013 pp. 0115 Wurim
© American Research Institute for Policy Development 12 www.aripd.org/ppar
Also, 66% of the respondents affirmed that the
total number and quality of workers in their
organisations is estimated based on their
organisation’s policies and objectives which
leads to accurate manpower needs
determination, while 34% disagreed; 64%
affirmed that the total number and quality of
workers is estimated based on their
organisation’s workload/sales or production
targets
Table 1: Opinion of Respondents on the Impact of Demand Forecasting on the Estimation of
Employee Requirements
S/no Description Response Frequency %
1 Scientific calculation and
evaluation of staff before
recruitment leads to proper
estimation
Agreement category 145
Disagreement category 107
57.54%
42.46
2 A clear analysis of the needs,
experience and expectations
of a particular job before
recruitment leads to proper
estimation
Agreement category 166
Disagreement category 100
62.41
37.59
3 Number and quality of
workers is estimated based on
my organization’s policies
and objectives
Agreement category 177
Disagreement category 91 66.05
33.95
4 Total number and quality of
workers is estimated based on
my organization’s workload/
sales or production targets an
Agreement category 185
Disagreement category 106
63.57
36.43
5 Subvention from Government
and /or internally generated
income determines the right
number and quality of
workers employed
Agreement category 204
Disagreement category 95 68.23
31.77
Source: Field Survey, 2012
Which leads to the accurate estimation of
personnel needs while 36% disagreed. Lastly,
68% of the respondents agreed that subvention
from government and internally generated
revenue determines the right number and quality
of workers employed but 34% of the
respondents disagreed.
The Chisquare (x2) test statistic was used to test
the hypothesis (H1). The theoretical frequency
for each cell in Table 1 was computed using the
formula: nRnc /n as shown in Table 2. The X2t 4
under 0.05 = 9.49 while the calculated X2c =
7.29.
Public Policy and Administration Review 1(1); June 2013 pp. 0115 Wurim
© American Research Institute for Policy Development 13 www.aripd.org/ppar
Table 2  Chisquare (X2) Table for Testing Hypothesis H1
Cell fo ft fo – ft (fo – ft)
2
f
t
1 145 161 –16 1.60
2 107 91 16 2.40
3 166 170 –4 0.09
4 100 96 4 0.17
5 177 171 6 0.20
6 91 97 –6 0.37
7 185 185 0 0
8 106 106 0 0
9 204 191 13 0.89
10 95 108 –13 1.57
Total 1376 1376 0 7.29
Source: Field survey, 2012
d.f. = (r – 1)(c – 1) = (5 – 1)(2 – 1) = (4)(1) = 4
X2t 4 under 0.05 = 9.49. But calculated Chisquare (X2c) = 7.29
Statistical Decision
Level of significance = 0.05, Sample size (n) =
349; Test statistic = x2. Decision rule: Accept
Ho if calculated value (X2c) Chisquare (X2t),
if otherwise, reject the Ho and accept H1. Since
the calculated Chisquare (x2c) value falls within
the acceptance region (i.e. x2c = 7.29 < x2t =
9.49), we accepted the null hypothesis and
rejected the alternate and we thus concluded that
demand forecasting is not a potent tool in the
estimation of employee requirements in Nigeria
public organisations.
Discussion and Implications of Findings
Result of the test of the hypothesis indicate that
demand forecasting does not significantly affect
the estimation of employee needs in Nigerian
public organisations (α = 0.05, x2c = 7.29 < x2t =
9.49), we thus conclude that the two variables
are not associated: The result is contrary to
Karen Legge theory which states that demand
forecasting is a very potent tool in human
capital forecasting that yields accurate or precise
estimation of employee requirement in terms of
number and quality (Legge 1989: 36).
The result is also contrary to the findings of a
survey of 115 large organisations conducted
jointly by the American Management
Association and Creasp, McCormick and Paget
– which indicated that some firms particularly,
in stable businesses like utilities or insurance,
simply perfected requirements on the basis of
past growth or sales forecasts or company
budgets. The result is precision in the estimation
of employee requirements (Sen, 1987:20).
]
The weakness in the relationship between
human capital forecasting and employee
requirements as witnessed in the Nigerian
Public Sector Organisations could be as a result
of the lack of proper knowledge and expertise.
Bartholomew (1976: 67) asserts that human
capital forecasting requires the combined
technical skills of statisticians, economists and
behavioral scientist, managers and planners.
It is also possible that the inability of public
organisations in Nigeria to forecast with
precision its employee requirements could be as
a result of forecasting in isolation from other
sectors or departments of the organisation.
Public Policy and Administration Review 1(1); June 2013 pp. 0115 Wurim
© American Research Institute for Policy Development 14 www.aripd.org/ppar
Bramham (1982: 22 – 23) strongly belief that
human capital forecasting cannot be done in
isolation from forecasting in other domains. To
him, having established a fund of knowledge on
all aspects of the firm’s business, it is possible
to move at attempts to indicate in which
direction human capital is going and the
direction it should take to meet organisational
objectives.
Also, the finding is unlike results from a
research conducted on some selected Indian
Public Sector organisations where human
capital forecasting gave precise estimation of
employee need requirements (Sen, 2005: 129 –
163). However, Sen goes on to conclude that
forecasting could be right or wrong.
Conclusion
Widely varying approaches to forecasting the
employee needs of an organization exist and
effective forecasting requires a combination of
quantitative methods and subjective judgement.
Where demand forecasting is conscientiously
pursued, imbalances in surpluses or deficits of
employees can be detected and handled before
they become unmanageable leading to decrease
in personnel costs.
Also, demand forecasting is an interdisciplinary
activity which requires the combined technical
skills of statisticians, economists, behavioural
scientists, together with the practical knowledge
of human capital managers. Lastly, forecasting
cannot tell what will happen, but only what
might happen under given conditions and
circumstances.
Recommendation
In view of the findings and conclusion above,
the following recommendations are hereby
submitted:
1. Human capital managers should adopt a
strategy of combining both quantitative methods
and subjective judgement in forecasting the
employee needs of organization.
2. Chief executive officers of public
organisations should make it mandatory for
human capital planners to employ scientific
methods in forecasting. This is with a view to
reducing personnel cost, accurate estimation of
employee requirements and the achievement of
organisational effectiveness and employee
productivity.
3. Since forecasting is an interdisciplinary
activity, there should be a combination of the
technical skills of statisticians, economists,
behavioural scientists and human capital
managers in planning the human capital in
public organisations.
Public Policy and Administration Review 1(1); June 2013 pp. 0115 Wurim
© American Research Institute for Policy Development 15 www.aripd.org/ppar
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