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Evaluation of the Effect of Cost Volume Profit on the Profitability of Manufacturing Firms
in Enugu State Nigeria
1Emmanuel C. Ebe, 2Moses O. Ede, 1Virginia N. Onyeka, 3Jeremiah C. Aleke, 4John J. Agah, 5Ugo C. Amauchechukwu,
and 6Francisca N. Ogba
1Department of Accounting, Michael Okpara University of Agriculture, Umudike, Abia State, Nigeria
2Department of Educational Foundations, University of Nigeria, Nsukka, Nigeria
3National Teachers Institute, Kaduna South East Zonal Office, Enugu State, Nigeria
4Department of Science Education, University of Nigeria, Nsukka, Nigeria
5Post-Primary School Management Board, Enugu, Nigeria
6Department of Educational Foundations, Alex Ekwueme Federal University, Ndufu-Alike Ebonyi State, Nigeria
Key words: Effect, profit, profitability, manufacturing,
OLS, tool
Corresponding Author:
John J. Agah
Department of Science Education, University of Nigeria,
Nsukka, Nigeria
Page No.: 2199-2211
Volume: 15, Issue 10, 2020
ISSN: 1816-949x
Journal of Engineering and Applied Sciences
Copy Right: Medwell Publications
Abstract: This study sought to evaluate the effect of cost
volume profit on the profitability of manufacturing firms
in Enugu state. The specific objectives of the study were
to examine the effect of cost volume profit analysis on
profitability, cost of production and sales of
manufacturing firms. The study adopted the ex-post facto
research design and data were obtained from the annual
reports and accounts of two manufacturing firms for the
period 2003-2012. The Ordinary Least Squares
Regression (OLS) were used to test the hypotheses stated.
The result emanating from the hypotheses tested were
mixed for sampled firms (Innoson Industrial and
Technical Company and Emenite Ltd. It was revealed that
while contribution margin ratio had positive and
significant effect on profitability it had positive and
non-significant effect on cost of production and sales. The
implication of these findings is that CVP application
among manufacturing firms in Nigeria varies and this
cloud be as a result of lack of awareness, low education
levels and technical knowhow on the parts of directors.
The study therefore concludes that as an analytical tool,
it is useful as it enables the firm to determine the quantum
of sales that will assist the firm not only to make profit
but to break-even. The research therefore recommended
that relevant directors should be sent on in-services
training and short term courses on CVP application
technique. Moreover, government should put a standard
that would guide CVP application on manufacturing
firms, so as to curtail price fluctuation’s among them in
Nigeria business environments.
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J. Eng. Applied Sci., 15 (10): 2199-2211, 2020
INTRODUCTION
The cost volume profit analysis is management
acceptable tool for decision making process which is
applicable in almost all economic sectors such as
manufacturing sector and other business activities. CVP
is a tool employed by business managers that will enable
them to control business operations effectively why
because it gives them an idea on how to concentrate on
the relationship among revenue, costs, volume of
production, taxes and also profit. This uses a
mathematical concept to check the cost behaviour model
of accounting ensuring proper representation of a plan to
unveil internal laws of variable cost, sales volume,
profits, fixed cost and variable cost. Financial information
necessary for accounting prediction, planning and
decision making are provided with the use of cost volume
profit.
Cost Volume Profit (CVP) analysis is an essential
technique for planning, organising and decision making.
Cost Volume Profit examines the inter-relationship that
exists between output levels, variable costs, fixed costs
and the target profits. The ease at which cost volume
profit is used all over the world has given it an added
advantage compared to the other similar analytical tools.
Jerold assert that cost volume profit analysis is an
instrument used by the management in their business
operations which enables them to measure or ascertain the
effect of changes or variation that is occurred in the areas
of production volume, cost of production, prices of goods
and materials, the product mix if any and above all the
company’s target, i.e., profit making. In fact, the variables
are interrelated and each one of them is affected by a
number of internal and external factors. Contributing
further, he added that costs may vary due to the choice of
the project, scale of operation, extent of automation and
new technology, management and workers efficiency.
Externally, costs are affected by the market forces.
Edward and Chem are also of the opinion that cost-
volume-profit analysis is regarded as the marginal costing
and that it is a profit planning technique used in studying
the relationship between the volume of costs, price and
profit for proper decision making in an organization.
They also added that the cost volume profit analysis
educates the management of a firm with in-depth analysis
on the overall effect on the revenue and the cost of all
kinds of both long and short-run changes in financial
position that might occur in the use of cost volume profit
in checking the volume of production, associated costs
and the benefits to be derived.
Cost volume profit analysis which serves as a starting
point in the planning and managing the level of profit
helps management to ascertain the level of sales volume
that would be made, so as to reduce losses, minimize cost
and maximize profit in the organisation. Edward and
Chem stress that it helps the management to seek
profitable combination of cost and volume. Cost volume
profit analysis is not a new phenomenon to the business
organizations; rather, it is a dynamic management tool in
decision making process that involves planning for profit
making. It is therefore, decision-making and profit
planning technique that helps the management to forecast
and examine the possible effect on both long and short
run decision on Variable cost, volume, fixed cost and the
selling price to ensure maximization of profit[1].
The future of any organization largely depends on its
profit planning and the evaluation of volume of sales to
ensure that the organizational goal is achieved. The cost
volume profit analysis is of immense importance to the
management because it enables them to have in-depth
knowledge on the implementation and inter-relationship
of the various factors which causes changes in the profit
level of firms. Based on the relevance of cost volume
profit analysis accountants would be able to quantitatively
present their accuracy reports without the use of the rule
of thumb. Pictures and diagrams such as charts would also
be made possible in their annual reports and account
which enabled quick decision and action that would
promote company’s growth.
Cost volume profit analysis is a method or tool for
measuring potential changes in the company’s revenues,
costs and prices[2, 3]. They further assert that CVP is used
in manufacturing companies to determine how many units
of a particular product must be sold in order to break
even. The application of this principle is relatively straight
forward with the unit selling price being subtracted from
the variable cost per unit to arrive at the contribution
margin. The total fixed costs are then divided by the
contribution margin in order to arrive at the number of
break even units required.
They described that this application allows managers
to see the behaviour of the cost prior to making a solid
commitment or final decision on a specific order. CVP
according to them may also be used by managers when
considering if a product should be bought or made. This
assertion was in agreement with Nweze[4] which points
out the same avenue for manufacturing firms to strive. In
support of the above scholar Bruto by Alleyne and
Weekes-Marshall[3] stressed that cost volume profit
analysis appears to be a practice that is strongly used by
manufacturing companies in food business. However
CVP should not only be limited to food business because
it is a practical tool for management decision making and
planning technique for profitability.
Cost Volume Profit analysis (CVP) is one of the
financial management tools that provide an efficient
overview of the possible impact of wide range of strategic
decisions. Such strategic decision involves such things
like product mix, market contractions and expansions,
outsourcing, considerable process planning among others.
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Application of cost volume profit in our contemporary
society and its simplicity considering the usage cannot be
overemphasized. Importance of application of cost
volume profit in manufacturing firms examines these
basic cost concepts thus; cost per unit, fixed cost, variable
cost and further analyzes the result that potentially affects
the basic nature of the firm. Basically cost volume profit
application allows management to set a predetermined
profit and work towards it. This would enable them to
determine the relationship between it and other vital
income statement variables. Essentially management
would want to know the volume or quantity of goods to
be sold in relation to the cost, so as to ascertain what
amount of profit would be made over the period.
Selling price as a profit determinant ought not to be
commonly known owing to the fact that volume of
production may be regarded as market related issue and
not management decision variable only. Additionally,
because selling price and volume often directly related
and certain costs are considered fixed in this note
therefore managers should use CVP to determine how
variable cost may be and also still allow the firm in
producing the desired amount of profit. Variable cost may
be affected by modifying product specification or material
quality or by being more efficient or effective in the
production, service and /or distribution processes. Profit
as the major player in the market economy in determining
the through position of the firm may either state variable
or fixed amount recorded by the firm before tax matters
are taking into consideration.
Statement of the problem: Over the years, there has
been poor application of cost volume profit analysis in
manufacturing organization in Nigeria and this has
become increasingly noticeable Busan and Dina[5]. This is
seen as a major impediment affecting production activity
of manufacturing organizations; thus, jeopardizing the
profitability and business proficiency of the
organisations[6].
According to Nwagboso[7], cost volume profit
analysis aids manufacturing organisations on the right
direction to increase production, sales and the profit
volume in return increase the organisation’s profit which
enables the firm to meet the organisational objectives.
However, the reverse is the case due to poor or inadequate
application of cost volume profit analysis in these
manufacturing organisations.
This not with standing cost volume profit analysis is
associated with other factors that bring constraints to this
inadequate application thus; Inadequate information, it is
obvious that those sampled manufacturing firms (Innoson
Industrial and Technical Company and Emenite Ltd.) in
Enugu state lack innovations and application techniques
of modern sophisticated machines that boast the
production are not applied. This lack of awareness is very
critical because it lowers production volume and also
affects negatively other economic activities of these
manufacturing firms. This in a nutshell leads to recession
and affects profitability of the firms.
Most manufacturing firms in Nigeria do not apply
CVP in their production unit due to low level of education
and technical know-how Agbadudu[8] . Because of this, no
emphasis is placed on the application and hence, leading
to low rate of returns. It is also observed that
manufacturing firms in Enugu state has suffered serious
setbacks owing to low rate of returns and profitability due
to inadequate application of cost volume profit on her
production.
Firms are established for the purpose of profit
making. This notwithstanding affects other variables
which ought to assist in profit making; these firms as a
result of inadequate information, also staff are not
engaged in training and re-training which will enable
them gain meaningful experience on the application of
CVP that will help to sustain the economic base of the
firm and in turn improve the entire economy. Industry
characteristics can also affect the application and
effectiveness of CVP. Certain features such as;
profitability, sales, volume of production cost depends
equally on firm size, nature of the firm and its associated
business operation.
The application of cost volume profit analysis ought
to be a priority in the manufacturing organisations
because it predicts and examine the effect of both long
and short run decision that concern their product volume,
prices, its level of output and the variable costs of those
products, the company’s fixed costs and above all the
target profits. On the other hand, a proper application of
CVP by manufacturing organisations pre-supposes that
the laid down objective (target profits) are in conformity
with plans (production volume) as well as break-even and
optimum utilization of resources.
In spite of the benefits derived from the
application of CVP analysis, its application to the
evaluation of profitability of manufacturing firms in
Nigeria are yet to be fully empirically determined. It was
based on the forgoing that this study seeks to investigate
the extent to which manufacturing firms in Enugu
state apply CVP.
Objectives of the study: The general objective of the
study is to investigate the effect of cost volume profit
analysis on the profitability of manufacturing firms in
Enugu state:
CSpecific objectives of the study include
CTo find the extent to which CVP application affects
the profit after tax of manufacturing firms
CTo evaluate the effect of CVP on production cost of
manufacturing firms
CTo find the effect of CVP on sales volume of
manufacturing firms
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Research questions: To achieve these objectives of the
study, the following research questions are formulated
and their answers were sought in the course of the
study:
CTo what extent does cost volume profit application
affect the profit after tax of manufacturing firms?
CHow far does cost volume profit affect cost of
production of manufacturing firms?
CTo what extent does cost volume profit affect the
sales volume of manufacturing firms?
Hypothesis: The hypothesis that guided the study are
stated in null as follows:
CCost volume profit applications do not have positive
and significant effect on the profit after tax of
manufacturing firms
CCost volume profits do not have positive and
significant effect on the cost of production of
manufacturing firms
CCost volume profits do not have positive and
significant effect on the sales volume of
manufacturing firms
Scope of the study: This study covers the period
2003-2012. The choice of 2003 as the base year for this
study is hinged on availability of data for both firms
(Innoson Industrial and Technical Company and Emenite
Ltd. The researcher because of cost and time constraints
limited the study to the evaluation of the effect of CVP on
profitability of manufacturing firms in Enugu state.
MATERIALS AND METHODS
Research design: The study used ex-post facto research
design. Kerlinger[9] defined ex-post facto research as that
in which the independent variable have already occurred
and in which the researcher starts with the observation of
the independent variable or variables. While Onwumere[10]
posit that the ex-post facto research design establishes a
causal link between them. Thus, the ex-post facto
research design is adopted to determine the casual
relationship CVP and other independent variables
introduced in this study.
Population: The population of this study comprised of all
manufacturing firms in Enugu state. This includes firms
registered with manufacturers association of Nigeria,
Enugu chapter.
Sample techniques: Purposive sampling as the non-
probability method was used to choose the sample size of
the study. The selection was owing to the fact that
researcher considered annual reports and accounts that
will be accessible those that have high rate of return
as to compare with others and also those that have
stayed for >10 years. In a nutshell Emenite Ltd. and
Innoson Technical Manufacturing Ltd. were selected.
Method of data analysis: The model specification of the
study was linked to that of production function.
Production function is the technical relationship between
input and output. In the study, the relationship that will be
modeled was that of appslication of cost volume profit
analysis and production functions of Emenite and Innoson
Ltd.In other to establish the application of cost volume
profit analysis in manufacturing organizations, data
generated was collated and analyzed using such statistical
tool (OLS) model of regression analysis.
Model specification: In this study model was specified in
line with the hypothesis stated in line with works by
Luther and O’Donovan. Thus, for hypothesis one which
states that Cost volume profit application does not have
effect on the profitability of manufacturing firms. It was
represented as:
01 1
CVP b + b Log PAT +
Where:
CVP : Cost Volume Profit
b0: Constant of the equation
b1 : Coefficient of independent variable
Log PAT : Profit After Tax
Σ1: Error term
The natural Logarithm of Profit After Tax (PAT) was
used to normalize the data. For hypothesis two which
states that cost volume profit analysis does not have effect
on the cost of production of manufacturing firms. It
was represented as:
01 1
CVP b + b LogPrCo+
Where:
CVP : Cost Volume Profit
b0: Constant of the Equation
b1 : Coefficient of independent variable
LogPrCo: Cost of production
Σ1: Error term
The natural logarithm of cost of Production (PrCo)
was used to normalize the data. Lastly, for hypothesis
three which states that cost volume profit application does
not have effect on the sales of manufacturing firms. It was
represented as:
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01 1
CVP b + b LogSales +
Where:
CVP : Cost Volume Profit
b0: Constant of the equation
b1 : Coefficient of independent variable
LogSales : Sales volume
Σ1: Error term
The natural logarithm of sales volume (Sales) was
used to normalize the data.
Description of variables
Dependent variable = cost volume profit analysis: Cost
Volume Profit (CVP) for the purpose of this study is taken
to be the dependent variable. In other to determine, the
CVP of manufacturing firms the contribution margin ratio
was used as proxy. The contribution margin ratio is the
ratio that establishes the relationship between revenue and
variable cost revenue. The contribution margin ratio for
the manufacturing firms was computed from the financial
statements of the two firms (Emenite and Innoson).
Independent variables
Profit after tax: The net profit of the two manufacturing
firms was used as proxy for profitability. The figure was
collated and computed from the financial statements of
the two firms. The natural log of the profit after tax was
adopted.
Cost of production: The cost of production of the
sampled manufacturing firms was used. The figure was
handpicked from the financial statements of the two firms
and the natural log of the cost of production was used to
normalize the variable.
Sales volume: the quantum of sales volume of the
manufacturing firms was used. The figure was collated
and computed from the financial statements of the two
firms and the natural log of the sales was adopted.
Data analysis: In achieving the objectives of this study,
data was collected from the financial statements of
Emenite and Innoson Ltd. The data for the period
2003-2012 (10 years) were collected, presented and
analysed in this chapter. This section is subsequently
housing two sections. The first section is the data
presentation while in the second section the hypotheses
stated were tested. The natural log of the model proxies
are depicted in Table 1. These data were used to test the
hypothesis stated in this study as well the analyses. The
movement of the trend is analysed using the descriptive
statistics presented in Table 2.
As indicated from Table 3, the mean value of the
LogCVP within the period of this study was 5.4690 while
the standard deviation was 0.28623. LogCVP was highest
in 2012 when the value was 6.40 while the year with the
minimum value was in 2007 when the value was 4.80. As
indicated by the Kurtosis which was 2.916<3 which is the
normal value indicates that the degree of peakedness
within the period of this study were not normally
distributed as most of the values was moving away from
the mean. The mean value of the LogPAT within the
period of this study was 6.5569 while the standard
deviation was 0.13916. LogPAT was highest in 2004
when the value was 6.70 while the year with the minimum
value was in 2007 when the value was 6.25. As indicated
by the Kurtosis which was 1.702<3 which is the normal
value indicates that the degree of peakedness within the
period of this study were not normally distributed as most
of the values was moving away from the mean
(Table 4 and 3).
From Table 5, the mean value of the LogPrCo within
the period of this study was 6.3314 while the standard
deviation was 0.03577. LogPrCo was highest in 2004
when the value was 5.71 while the year with the minimum
value was in 2007 when the value was 6.24. As indicated
by the Kurtosis which was -0.281<3 which is the normal
value indicates that the degree of peakedness within the
period of this study were not normally distributed as most
of the values was moving away from the mean. Lastly, the
mean value of the LogSales within the period of this study
was 7.3260 while the standard deviation was 0.22101.
LogSales was highest in 2010 when the value was 7.65
while the year with the minimum value was in 2006 when
the value was 7.05. As indicated by the Kurtosis which
Table 1: Extract of innoson and emenite financial statement
Years CVP (N) Profit (N) Prod Cost (N) Sales (N) CVP (N) Profit (N) Prod cost (N) Sale (N)
2003 131,861 1,480,262 1,623,211 8,726,711 725,488 2,548,887 1,235615 10988720
2004 35, 974 4,849,394 1,419,748.90 11,267,190 961,884 322,371 1011726 10957700
2005 401.376 45,92,742 1,962,887.30 141,716,171 67,116 127,093 601719 1705548
2006 45,268 3,773,835 1,646,176.60 10,261,719 194,149 236,838 726508.30 17714269
2007 28,677 1,359,035 1,696,760.80 10,867,600 67,716 816,732 596293.40 2926347
2008 355,247 4,840,356 1,755,000 25,275,671 194,165 303,759 568217.40 4048205
2009 289,307 2,884,068 1675,639.10 18,667,561 20,477 326,188 715218.50 4131390
2010 271,163 4,008,089 1,889,900 31,256,771 91,727 66,789 732169.20 3751546
2011 340,625 3,839,360 1,968,648.90 29,556,716 53,098 380,44 540908.30 26980495
2012 285,080 3,141,751 2,238,070 30,611,796 56,583 480,249 552716.14 27471801
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Table 2: Average data of the two sample companies
Years CVP PAT Prod Co Sales
2003 494,605 2,754,706 2,241,019 14,221,071
2004 516,916 5,010,580 1,925,612 16,746,040
2005 434,934 4,656,289 2,263,747 15,568,945
2006 142,343 3,892,254 2,009,431 11,118,854
2007 62,535 1,767,401 1,994,908 12,330,774
2008 452,330 4,992,236 2,039,109 27,299,774
2009 299,546 3,047,162 2,033,248 20,733,256
2010 317,027 4,041,484 2,255,985 33,132,544
2011 367,174 4,029,582 2,239,103 43,046,964
2012 313,372 3,381,876 2,514,428 44,347,697
Extracted Annual Reports and Accounts of Emenite and Innoson
Table 3: Descriptive statistics
Correlations N statistic Ic Minimum statistic Maximum statistic Mean statistic SD statistic Kurtosis statistic Statist SE
LogCVP 10 4.80 5.71 5.4690 0.28623 2.916 1.334
LogPAT 10 6.25 6.70 6.5569 0.13916 1.702 1.334
LogPrCo 10 6.28 6.40 6.3314 0.03577 -0.281 1.334
LogSales 10 7.05 7.65 7.3260 0.22101 -1.441 1.334
Valid N (listwise) 10
Researcher’s computation: audited financial statements of the sampled firms
Table 4: Mode proxy data used for regression
Years Log CVP Log PAT Log Pr Co Log sales
2003 5.694259 6.440075 6.35044544 7.152932
2004 5.71342 6.699888 6.28456876 7.223912
2005 5.638423 6.66804 6.35482785 7.192259
2006 5.153335 6.590201 6.30307304 7.04606
2007 4.796123 6.247335 6.29992276 7.09099
2008 5.655455 6.698295 6.30944038 7.436159
2009 5.476463 6.483896 6.30819043 7.316668
2010 5.501096 6.606541 6.35333613 7.520255
2011 5.564872 6.60526 6.35007408 7.633943
2012 5.496059 6.529158 6.40043922 7.646871
Researcher’s Excel Computation
Table 5: Presents the correlation statistics for the model proxies.
Models Correlations Log CVP Log PAT Log PrCo Log sales
LogCVP Pearson correlation 1
Sig. (2-tailed)
N10
LogPAT Pearson correlation 0.716(*) 1
Sig. (2-tailed) 0.020
N1010
LogPrCo Pearson correlation 0.293 0.026 1
Sig. (2-tailed) 0.411 0.944
N101010
LogSales Pearson correlation 0.395 0.311 0.613 1
Sig. (2-tailed) 0.259 0.382 0.059
N10101010
*Correlation is significant at the 0.05 level (2-tailed) Researcher’s SPSS Result: audited financial statements of the sampled firms
was -1.441<3 which is the normal value indicates that the
degree of peakedness within the period of this study were
not normally distributed as most of the values was moving
away from the mean.
Correlation statistics: As revealed from Table 5, there
was a positive correlation between LogCVP and LogPAT
(R = 0.716). This indicates that a percentage increase in
LogCVP increases Log PAT by 0.716 for a two tail test
and significant at 2%. There was also a positive
correlation between LogCVP and LogPrCo (R =
0.293).This also reveals that a percentage increase in
LogCVP increases LogPrCo by 29% for a two tail test.
LogCVP also had a positive correlation with LogSales
(R = 0.395). It shows that a percentage increase in
LogCVP increases LogSales by 40% for a two tail test.
Again as indicated from Table 6, there was a positive
correlation between LogPAT and LogPrCo (R = 0.026)
indicating a percentage increase in LogPAT increases
LogPrCo by 2% for two tail test. Also, LogPAT had
positive correlation with LogSales (R = 0.311) revealing
that an a percentage increase in LogPAT increases
LogPrCo by 31%. Lastly, LogPrCo had positive
correlation with LogSales (R = 0.613) revealing that a
percentage increase in LogPrCo increases LogSales
by 61%.
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Table 6: Presents the results of hypothesis one models
Regression result of hypothesis one
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Unstandardized coefficients Standardized coefficients
------------------------------------------ -------------------------------
Models B SE Beta t-values Sig.
(Constant) 4.654 0.657 7.080 0.000
LogCVP 0.348 0.120 0.716 2.898 0.020
aDependent Variable: LogPAT Researcher’s SPSS Regression result
Table 7: ANOVA result of hypothesis one
Models Sum of squares df Mean square F Sig.
Regression 0.089 1 0.089 8.399 0.020(a)
Residual 0.085 8 0.011
Total 0.174 9
a Predictors: (Constant), LogCVP; b. Dependent Variable: LogPAT; Researcher’s ANOVA result
Table 8: Regression result of hypothesis two
Unstandardized coefficients Standardized coefficients
--------------------------------------- --------------------------------
Models B SE Beta t-values Sig.
(Constant) 6.131 0.231 26.508 0.000
LogCVP 0.037 0.042 0.293 0.868 0.411
a Dependent variable: LogPrCo; Researcher’s SPSS Regression result
Test of hypothesis: This section (4.3) deals with the test
of researcher’s hypotheses. Four steps were utilized in the
exercise. The steps involved first, restating the hypotheses
in null and alternate forms; second, stating the decision
rules; third, interpreting the results of the estimated
models and fourthly using the decision criteria to accept
or reject the null/ alternate hypothesis as relevant.
Test of hypothesis one
Step one: restatement of hypotheses in null and
alternate forms
CH01:Cost volume profit applications do not have any
effect on the profitability of manufacturing firms
CHa1:Cost volume profit applications have any effect
on the profitability of manufacturing firms
Step two: decision rule: Accept Ha and reject Ho where
the coefficient estimate of the independent variable (is
positively signed and statistically significant (<0.05).
Accept Ho and reject Ha where the coefficient
estimate of the independent variable (is positively signed
and statistically significant (>0.05).
Step three: Interpretation of results of estimated
models: As revealed from Table 7, LogCVP has positive
and significant impact on LogPAT (coefficient of
LogCVP = 0.348, t-value = 2.898). The probability value
of 0.02 <0.05 confirms the significance of the result. The
coefficient of determination which measures the goodness
fit of the model as revealed by (R2) indicates that 71.6%
of the variations observed in the dependent variable were
explained by variations in the dependent variable.
Table 7 shows the output of the ANOVA analysis
and whether we have a statistically significant difference
between LogCVP and LogPAT. It was observed that the
significance level is 0.021 (p = .020) which is below 0.05.
Therefore, there is a statistically significant difference in
the mean of LogCVP and LogPAT. Othe calculated result
includes the F-variable which follows the F-distribution
was significant.
Step four: decision: The null hypothesis which states that
cost volume profit applications have positive and
significant effect on the profitability of manufacturing
firms is rejected while the alternate hypothesis which
states that cost volume profit applications have positive
and significant effect on the profitability of manufacturing
firms is accepted.
Test of hypothesis two
Step one: restatement of hypotheses in null and
alternate forms
CH02: Cost volume profits do not have effect on the
cost of production of manufacturing firms
CHa2: Cost volume profits have effect on the cost of
production of manufacturing firms
Step two: decision rule: Accept Ha and reject Ho where
the coefficient estimate of the independent variable (is
positively signed and statistically significant (<0.05)
Accept Ho and reject Ha where the coefficient
estimate of the independent variable (is positively signed
and statistically significant (>0.05)
Step three: Interpretation of results of estimated
models: Table 8 presents the results of hypothesis two
models. As revealed from Table 8, LogCVP has positive
and non-significant impact on LogPrCo (coefficient of
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Table 8: Regression result of hypothesis two
Unstandardized coefficients Standardized coefficients
--------------------------------------- -------------------------------
Models B SE Beta t-values Sig.
(Constant) 6.131 0.231 26.508 0.000
LogCVP 0.037 0.042 0.293 0.868 0.411
a Dependent variable: LogPrCo; Researcher’s SPSS Regression result
Table 9: ANOVA result of hypothesis two
Models Sum of squares df Mean square F Sig.
Regression 0.001 1 0.001 0.753 0.411a
Residual 0.011 8 0.001
Total 0.012 9
aPredictors: (Constant), LogCVP b dependent variable: LogPrCo Researcher’s ANOVA result
Table 10: Regression result of hypothesis three
Unstandardized coefficients Standardized coefficients
-------------------------------------------- -------------------------------
Models B SE Beta t-values Sig.
(Constant) 5.659 1.373 4.120 0.003
LogCVP 0.305 0.251 0.395 1.216 0.259
aDependent Variable: LogSales; Researcher’s SPSS Regression Result
Table 11: ANOVA result of hypothesis three
Models Sum of squares df Mean square F Sig.
Regression 0.069 1 0.069 1.478 0.259a
Residual 0.371 8 0.046
Total 0.440 9
aPredictors: (Constant), LogCVP; b Dependent variable: LogSales Researcher’s ANOVA result
LogCVP = 0.037, t = 0.868). The probability value of
0.411>0.05 confirms the non-significance of the result.
The coefficient of determination which measures the
goodness fit of the model as revealed by (R2) indicates
that 29.3% of the variations observed in the dependent
variable were explained by variations in the dependent
variable.
Table 9 shows the output of the ANOVA analysis
and whether we have a statistically non-significant
difference between LogCVP and LogPrCo. It was
observed that the non-significance level is 0.411 (p =
0.411) which is above 0.05. Therefore, there is a
statistically non-significant difference in the mean of
LogCVP and LogPrCo.
Step four: decision: The null hypothesis which states that
cost volume profits do not have positive and significant
effect on the cost of production of manufacturing firmsis
rejected while the alternate hypothesis which states that
cost volume profits have positive and significant effect on
the cost of production of manufacturing firmsis accepted
although it is non-significant.
Test of hypothesis three
Step one: restatement of hypothesis in null and
alternate forms
CHo3: Cost volume profits do not have effect on the
sales volume of manufacturing firms
CHa3: Cost volume profits have effect on the sales
volume of manufacturing firms
Step two: decision rule: Accept Ha and reject Ho where
the coefficient estimate of the independent variable (is
positively signed and statistically significant (< 0.05).
Accept Ho and reject Ha where the coefficient
estimate of the independent variable (is positively signed
and statistically significant (>0.05).
Step three: interpretation of results of estimated
models: As revealed from Table 10 LogCVP has positive
and non-significant impact on LogSales (coefficient of
LogCVP = 0.305, t = 1.216). The probability value of
0.259 >0.05 confirms the non-significance of the result.
The coefficient of determination which measures the
goodness fit of the model as revealed by (R2) indicates
that 39.5% of the variations observed in the dependent
variable were explained by variations in the dependent
variable.
Table 11 It shows the output of the ANOVA analysis
and whether we have a statistically non-significant
difference between LogCVP and LogSales. It was
observed that the non-significance level is 0.259 (p =
0.259) which is above 0.05. Therefore, there is a
statistically non-significant difference in the mean of
LogCVP and LogSales.
Step four: decision: The null hypothesis which states that
cost volume profits do not have any effect on the sales
volume of manufacturing firms is rejected while the
alternate hypothesis which states that cost volume profits
do not have effect on the sales volume of manufacturing
firms is accepted although it is non-significant.
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DISCUSSION
Hypothesis one: To achieve objectives one of this study,
the collated values were obtained from the financial
statement of those sampled manufacturing firms in Enugu
state. A cursory look at the data, indicated that the
minimum values within the period of this study was 4.80
while log CVP was highest in 2004 when it was 5.71342
while the year with the least log CVP was 2007 and 2009
when the value was 4.796123. As was revealed by the
kurtosis of statistics there was a negative kurtosis
indicating that the means of the distribution is negative
that overall, there was an inconsistent increase
contribution margin ration from 2003-2012. Though as
indicated by the kurtosis which was 2.92<3 which is the
normal value indicted that the degree of peakedeness
within the period of this study were not normally
distributed as most of this the values was moving away
from the means.
The findings revealed that the means value of these
manufacturing profitability ration was 6.556% while the
maximum value was 6.70 the profitability ratio was
highest in 2004 when the value was 6.699888, ratio
indicating that the degree of departure from the mean of
distribution is positive revealing that overall, there was a
constant increase profitability ratio from 2003-2012.
Through as i9ndicated by the kurtosis which was 2.916<3
which is the normal value indicated that the degree of
peakedness. Within the period of this study were normally
distributed as most of the values hover around the mean.
From the hypothesis tested, the coefficient of profitability
ratio is positive and also has a non-Significant effect on
profitability of this manufacturing firm in Enugu state
from 2013-2012. For this result therefore, it shows that as
profit ability ratio varies over the gear of the study, it
recorded highest in the year, 2004 when it was 6.699888
increases by 47 % unit with a profit ability of obtaining at
value of 2.898 >80% thus in significant at 0.05 critical
value. The 22 the regression result line fits the data
goodness of a fit. From the model above the R2 value of
0.716 means that 70.3% in cheats that there was a
percentage increas e in the dependent variable (ILWP) was
explained by the independent variable and the remaining
29.7% was also explained by variable not included within
the model.
The adjusted R2 tell us that after taking account of the
number of other un-included repressors, profitably ratio
still explained by the same variation with the other
variable in the profitability of those manufacturing
firms. The f-values (2.853) (p-0.02) at a critical values of
0.05. This therefore implies that the entire model is
significant.
Also as the hypothesis tested for the sampled
manufacturing firms the coefficient of contribution
margin ratio is positive and again has a significant effect
on profitability of the firm from 2003-2012. For this result
therefore, it shows that as contribution varies across time
by one unit, the log of profitability obtained at a value
77.700 significant at 0.05 critical values. From the above
model, the R2 value of 0.716 mean that 71.8% percentage
variable in the dependent variable was explained by the
independent variables.
For research hypothesis one cost volume profit
applications have effect on the profitability of
manufacturing firms. The findings are consistent with
works by Henry[11] who describes the relationship that
exists between CVP analysis and profit maximization
theory. They submit that the maximization of the normal
profit of the firm should be relative to cost incurred in the
production. This assumption equips the management in
line of profit making which enables them to determine the
difference that exist among the revenue and costs this
regard. It is therefore, the management’s prediction and
planning process to ensure that the most profitable course
of action is taken vehemently.
Agbadudu[8] Malomo and Henry[11] were of the view
that decision making process has to look at the blueprint
of the available resources and the associated cost to incur,
this guides manager’s action on the right direction and
assumption on best approach to maximize profit this is
line with findings of hypothesis one.
Hypothesis two: To achieve objective two, the figures
generated from the financial statement of the two firms
indicate that the mean value for the sampled firms cost of
production ratio within the period of this study was 7.14
while the medium value was 7.39. The cost of production
ratio was highest in 2011 when the value was 7.54 while
the year with the least cost of production ratio was in
2010 when the value was 6.51. As revealed by the
skewness of cost of production ratio, there was a negative
skewness of the cost of production ratio indicating that the
degree of departure from the mean of the distribution is
negative revealing that overall; there was an inconsistent
decrease in cost of production ratio from 2003-2012.
Though as indicated by the Kurtosis which was 1.36<3
which is the normal value indicates that the degree of
peakedness within the period of this study were not
normally distributed as most of the values moved away
from the mean.
Again for the sampled manufacturing firms, the mean
value of cost of production ratio within the period of this
study was 6.83 while the medium value was 6.91. The
cost of production ratio was highest in 2006 when the
value was 7.02 while the year with the least cost of
production ratio was in 2003 when the value was 6.18. As
revealed by the skewness of cost of production ratio, there
was a negative skewness of the cost of production ratio
indicating that the degree of departure from the mean of
the distribution is negative revealing that overall; there
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was an inconsistent decrease in cost of production ratio
from 2003-2012. Though as indicated by the Kurtosis
which was 6.58>3 which is the normal value indicates
that the degree of peakedness within the period of this
study were normally distributed as most of the values
hover away from the mean.
To achieve objective two, the hypothesis tested
revealed that the coefficient of contribution margin ratio
is negative and also has a non-significant effect on cost of
production of those firms from 2003-2012. For this result
therefore, it shows that as contribution margin ratio varies
across time by one unit, cost of production of the sampled
firm’s decreases by 0.768 units with a probability of
obtaining a t-0.806 insignificant at 0.05 critical values.
The R2 is a summary measure of how well a sample
regression line fits the data (goodness of fit). From the
model above, the R2 = 0.775 means that 77.5%
percentage variations in the dependent variable was
explained by the independent variables and the remaining
23.5% was explained by variables not included in the
model. The adjusted R2 tells us that after taking account
of the number of other unenclosed repressors, contribution
margin ratio still explains 64.0% variation in cost of
production of Innoson Ltd. The (F-650.0) which follows
the F distribution was significant (p-0.003) at a critical
value of 0.05. This implies that the entire model is
significant.
Also, for it was observed that the coefficient of
contribution margin ratio is positive and also has a
insignificant effect on cost of production of the firms from
2003-2012. For this result, therefore, it shows that as
contribution margin ratio varies across time by one unit,
cost of production of Emenite Ltd. increases by 0.196
units with a probability of obtaining a t value of 0.814
insignificant at 0.05 critical values. The R2 is a summary
measure of how well a sample regression line fits the data
(goodness of fit). From the model above, the R2 value of
0.876 means that 87.6% percentage variations in the
dependent variable was explained by the independent
variables and the remaining 12.4% was explained by
variables not included in the model. The adjusted R2 tells
us that after taking account of the number of other
un-included repressors, contribution margin ratio still
explains 83.8% variation in cost of production of Emenite
Ltd. The F-value (663.0) which follows the F distribution
was significant (p-value of 0.01) at a critical value of
0.05. This implies that the entire model is significant. The
researcher concluded that contribution margin ratio had
negative and non-significant effect on cost of production
of Innoson Ltd; however, contribution margin ratio had
positive and non-significant impact on cost of production
of the firms within the period of this study.
Hall demonstrated the use of CVP Model
incorporating the cost of capital and if properly done will
be used in computing the breakeven sales in quantity
which measures the range of activity of the firms. In
the same vein it shows the discounted income statement
regarding the unit change in the sales volume recorded.
Haven identified the importance of CVP Model in-
cooperating the cost of capital to a product cost function,
facilitates the measures to take in alternative arrangement
in terms of cost structures. It helps in estimating the
impact of product’s profitability in checking the level of
improvement recorded in the firm’s process of developing
programme relating to the production.
Also, James results that the implication of cost
volume profit analysis as a gateway to decision-making
under uncertainty in the manufactur ing organizations. The
study discovered that using the model correlation analysis
as a research design, the strength of the company and the
volume of production solely depend on the application of
cost volume profit analysis. In review of this related
literature, the cost volume profit analysis application
questions were developed and solved on the forgoing to
determine the relationship between the volume costs and
profits which the results on the recent performance
of the company were discussed. The study concludes and
recommends that for effective management decision-
making cost volume profit analysis should be applied.
This was true with findings of hypothesis two for the
sampled firms.
For hypothesis two it was revealed that cost volume
profits have positive and significant effect on the cost
of production of manufacturing firms, although it is
non-significant. This is consistent with the findings on
hypothesis two Hall demonstrated the CVP model
incorporating the cost of capital, so as to have a clear
picture on the computation of product’s breakeven sales
quantity in order to measure the range of product’s
income with respect to a unit change in sales. The CVP
model incorporating the cost of capital facilitates
measures the trade-off in alternative investments and cost
structures, as estimating the impact upon a product’s
profitability from a programme of process improvement.
Also, James, results that the implication of cost
volume profit analysis as a gateway to decision-making
under uncertainty in the manufacturing organizations. The
study discovered that using the model correlation analysis
as a research design, the strength of the company and the
volume of production solely depend on the application of
cost volume profit analysis. In review of this related
literature, the cost volume profit analysis application
questions were developed and solved on the forgoing to
determine the relationship between the volume costs and
profits which the results on the recent performance
of the company were discussed. The study concludes
and recommends that for effective management
decision-making cost volume profit analysis should be
applied. This was true with findings of hypothesis two for
manufacturing firms in Nigeria.
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Hypothesis three: For objective three, the mean value of
the manufacturing firm’s sales volume ratio within the
period of this study was 6.67 while the medium value was
6.59. The sales volume ratio was highest in 2011 when
the value was 7.44 while the year with the least sales
volume ratio was in 2005 when the value was 6.23. As
revealed by the skewness of sales volume ratio, there was
a positive skewness of the sales volume ratio indicating
that the degree of departure from the mean of the
distribution is positive revealing that overall; there was a
consistent increase in sales volume ratio from 2003 to
2012. Though as indicated by the Kurtosis which was
2.54<3 which is the normal value indicates that the degree
of peakedness within the period of this study were not
normally distributed as most of the values moved away
from the mean.
The mean value of Emenite Ltd. sales volume ratio
within the period of this study was 6.34 while the medium
value was 6.33. The sales volume ratio was highest in
2006 when the value was 6.98 while the year with the
least sales volume ratio was in 2010 when the value was
5.67. As revealed by the skewness of sales volume ratio,
there was a negative skewness of the sales volume ratio
indicating that the degree of departure from the mean of
the distribution is negative revealing that overall; there
was an inconsistent decrease in sales volume ratio from
2003-2012. Though as indicated by the Kurtosis which
was 1.07 <3 which is the normal value indicates that the
degree of peakedness within the period of this study were
not normally distributed as most of the values moved
away from the mean.
As observed the test of hypothesis, the coefficient of
contribution margin ratio is positive and also has a
significant effect on sales of the firms from 2003-2012.
For this result therefore, it shows that as contribution
margin ratio varies across time by one unit, sales of the
sampled firms increases by 1.81 units with a probability
of obtaining a t value of 2.75 significant at 0.05 critical
values. The R2 is a summary measure of how well a
sample regression line fits the data (goodness of fit). From
the model above, the R2 = 0.886 means that 88.6%
percentage variations in the dependent variable was
explained by the independent variables and the remaining
11.4% was explained by variables not included in the
model. The adjusted R2 tells us that after taking account
of the number of other unenclosed repressors, contribution
margin ratio still explains 82.2% variation in sales of the
firms. The (F = 757.1) which follows the F distribution
was significant (p = 0.02) at a critical value of 0.05. This
implies that the entire model is significant. This suggests
that in line with works by Louis in that using correlation
coefficient as research design states that the extent of the
individual production contributions to volume, sales and
profit inrelation to the cost of production. In review of
this, the cost volume profit analysis application developed
and solved the problem on the ground and determined the
effective contributions of individual product line in
relation to cost volume profit.
As it was observed that the coefficient of contribution
margin ratio is positive and also has a significant effect on
sales of the firms from 2003-2012. From this result
therefore, it shows that as contribution margin ratio varies
across time by one unit, sales of the sampled
manufacturing firms increases by 1.392 units with a
probability of obtaining at value of 4.06 significant at 0.05
critical values. The R2 is a summary measure of how well
a sample regression line fits the data (goodness of fit).
From the model above, the R2 = 0.673 means that 67.3%
percentage variations in the dependent variable was
explained by the independent variables and the remaining
32.7% was explained by variables not included in the
model. The adjusted R2 tells us that after taking account
of the number of other un-included repressors,
contribution margin ratio still explains 83.8% variation in
sales of the firms. The (F-1650.0) which follows the F
distribution was significant (p = 0.003) at a critical value
of 0.05. This implies that the entire model is significant.
The researcher concludes that contribution margin ratio
had positive and significant effect on sales of the two
manufacturing firms within the period of this study.
They concluded and recommend that the purpose of
cost volume profit is to show the sensitivity of profits to
changes in volume. Cost volume profit analysis
emphasises maximizing contribution and focuses on the
difference between costs that vary output and costs that
remains constant as observed from the works by
Kee[12] (2001), Haring and Smith, Blocher and Chen[13],
Stefan[14].
For hypothesis three it was shown that cost volume
profits have positive and significant effect on the
sales volume of manufacturing firms, although, it is
non-significant. Again, This result was buttress by Luther
and O’Donovan found out that application of cost volume
profit analysis in order to determine the constant level of
fixed unit of the product likewise selling price and that of
variable costs would indicates the level of activity that
would display sales curve intersecting with the cost curve
as an indication of firm’s prosperity. They concluded and
recommend that the purpose of cost volume profit is
essentially to analysis the profit sensitivity to changes that
occur in respect of volume of the output. Cost volume
profit further emphasises condition of some costs by
saying that maximizing contribution implies that there
would be a focus among those costs that varies because of
volume in spite of volume of output. Cost volume profit
analysis emphasizes in maximizing contribution on profit
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J. Eng. Applied Sci., 15 (10): 2199-2211, 2020
and focuses on the existing difference among production
cost that will vary with level o output and those costs that
remain constant as observed from the works by Kee[12]
Bloch[13] and Stefan[14].
CONCLUSION
This uses a mathematical concept to check the cost
behaviour model of accounting ensuring proper
representation of a plan to unveil internal laws of valuable
cost, sales volume, profits, fixed cost and variable cost.
Financial information necessary for accounting prediction,
planning and decision making are provided with the use
of cost volume profit.
Cost Volume Profit (CVP) analysis is an essential
technique for planning, organising and decision making.
Cost Volume Profit examines the inter-relationship that
exists between output levels, variable costs, fixed costs
and the target profits. The ease at which cost volume
Profit is used all over the world has given it an added
advantage compared to the other similar analytical tools.
Cost Volume Profit (CVP) analysis is a widely used tool
for the managerial planning and decision making and
according to Jerold (1995), it infers that cost volume
profit analysis is an attempt to measure the effects of
changes in the volume, cost, price and the product mix on
profits. Also Edward and Chem (2004) are also of the
opinion that cost-volume-profit analysis is regarded as the
marginal costing and that it is a profit planning technique
used in studying the relationship between the volume of
costs, price and profit for proper decision making in an
organization. They also added that the cost volume profit
analysis provides the management with comprehensive
overview of the effect on the revenue and the cost of all
kinds of short-run financial changes that might occur in
the use of cost volume profit; in checking the volume of
production, associated costs and the benefits to be
derived.
This study examined the effect of cost volume profit
on profitability, cost of production and sales of
manufacturing firms in Nigeria the result was mixed for
both firms. As indicated while contribution margin ratio
had any effect on profitability of manufacturing firms in
Nigeria, it was found to have positive and non-significant
impact on cost of production and Lastly, contribution
margin ratio had any effect on sales manufacturing firms
in Nigeria the researcher concluded that as an analytical
tool, it is useful as it enables the firm to determine the
quantum of sales that will assist the firm not only to make
profit but to break-even.
RECOMMENDATIONS
The following are recommended. These are: Cost
volume profit analysis should be used as a major decision
making process that will involve planning for profit
making in the organisation. Managers should be engaged
in training and re-training exercise to ensure usage of
modern machines in her production.
Cost and management accountants should be trained
to acquire the required skills for proper application of
CVP in ensuring that management actions and decisions
are made in order to promote the company’s growth. This
enable all stakeholders to fully understand the cost
elements involved in the business. Directors of
manufacturing firms should place emphasis on the
application of CVP in order to increase sales volume so as
to sustain the economic base of the firm.
Areas of further research: The researcher is of the
opinion that there are numerous variables that can be used
as proxy for application of CVP in manufacturing
organizations and therefore challenges contemporary
researchers to investigate into those proxies that will
improve the application of CVP by Nigerian
manufacturers.
Again, the study concentrated on 2 manufacturing
organizations. Emenite and Innoson; there is need to
venture into (5) manufacturing organizations. These
constitute major limitations to this study, calling for
further investigation on:
CThe analysis of CVP applications in manufacturing
organizations
CThe analysis of CVP application as a basis for post
industrial society in manufacturing organizations in
Nigeria
CThe effect of poor application of CVP in
manufacturing organization to her productivity
CAssessment of application of CVP in a
manufacturing environment focusing on
incorporating cost of capital into a product areas of
interest are desire for further investigations for
contemporary scholars.
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