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ABSTRACT
The collapse of many large companies around the world and the current
manufacturing environment where fixed manufacturing cost has gained its
significance have awakened the absorption versus variable costing debate. It has long
been acknowledged that absorption costing allows management to increase or
decrease net income being reported by overproducing or under-producing than the
level of production actually needed for sales. Raising funds in globally connected
capital markets and a higher level of capital intensity as indicated by proportion of
fixed manufacturing overhead to total manufacturing costs may have presented an
incentive and opportunity for management to artificially increase or decrease net
incomes being reported under absorption costing. However, just-in-time (JIT)
production system, which has been widely adopted since the early 1980s, tends to
minimize such opportunity to manage net income being reported. When JIT
philosophy has a greater impact than capital intensity, the two product costing
methods would yield net incomes that are not much different. However, when this is
not the case, product costing choices do have impacts on net incomes being reported
and the quality of net incomes may be questionable. This could threaten creditability
of the company and is harmful to capital market development. It is now, therefore,
interesting and important to examine whether the recent changes in operating and
manufacturing environment have provided enough incentive and opportunities for
management to manage net income being reported under absorption costing by
varying production levels in different periods.
This study aims to examine the effects of product costing choices on
profitability in the current operating environment by 1) examining whether fixed
manufacturing overhead has increasingly become a greater portion of total
manufacturing costs; 2) examining whether JIT has impacts on Stock Exchange of
Thailand iSF T) listed manufacturing companies; 3) examining impacts of product
costing choices on profitability of the sample companies over the period studied in
terms of both direction and significance of inventory adjustments; 4) examining
relative impact of capital intensity and JIT on profitability differentials in aggregate
over the nine-year period studied; and also 5) examining relative impacts of capital
intensity and JIT on profitability differentials reported under the two methods in each
of the 9 years studied to observe trends of the impact each of the two factors over
time. The examination was conducted on manufacturing companies listed in the
Stock Exchange of Thailand in three sectors: Agro & Food Industry, Consumer
Products, and Industrials.
This study has provided many important findings. Firstly, capital intensity of
the SET listed manufacturing companies have not increased significantly over the
period studied. Secondly, it has shown that JIT system has not had consistent impacts
on SET listed manufacturing companies. Thirdly, Average differences between net
incomes reported under the two product costing methods in aggregate for all the three
industries fluctuate in the range between 2.11 and 4.67 million Bahts. Average
differences between net incomes under the two product costing methods as a
percentage of net incomes under absorption costing in aggregate for all the three
industries range from 1.78.% to 6.42%. Average difference between profit margin
ratios under the two product costing methods for all the three industries ranges from
iii
0.12% to 0.32%. Finally, the OLS analysis of panel data shows that the extent of net
income differentials and net income differentials as a percentage of sales are driven
more by the capital intensity than the degree companies embraced JIT philosophy.
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V
TABLE OF CONTENTS
Page
Chapter 1: Introduction
1.1 Background to the Problem
1.2 Purposes of the Study 3
1.3 Hypotheses 4
1.4 Scope of Research 5
1.5 Definition of Technical Terms 6
1.6 Contributions of the Study 7
Chapter 2: Literature Review 9
2.1 Manufacturing Costs and Product Costs 10
2.2 Absorption Costing vs. Variable Costing 11
2.3 Impacts of Product Costing Choices on Net Income Being Reported... 12
2.4 Debate on which method to use 13
2.5 Accounting for Manufacturing Overhead 15
2.6 Possible Solutions 17
2.7 Just in Time System 19
2.8 Prior Research Examining Effects of using Absorption Costing as a
Basis for Product Costing in Externally Reported Financial Statements..... 23
Chapter 3: Research Methodology 26
3.1 Sample Selection and Data Collection 26
3.2 Measurement of Variables 27
3.2.1 Capital Intensity 27
3.2.2 JIT Philosophy 29
3.2.3 Profitability Differentials 30
3.2.4 Profitability Differentials Under the Two Product Costing
Choices 30
3.2.4.1 Directions of Inventory Adjustments 31
vi
3.2.4.2 Significance of Inventory Adjustments 31
3.2.4.3 Impacts of Inventory Adjustments on Profitability 32
3.2.4.4 Profitability Differentials 34
3.3 Hypotheses Development and Testing 35
3.3.1 Fixed Manufacturing Overhead Trend 35
3.3.2 Effect of JIT Philosophy 35
3.3.3 The Impact of Capital Intensity and .111 Philosophy on
Profitability Differentials over Time 36
3.3.4 The Impact of Capital Intensity and JIT Philosophy on
Profitability Differentials in Each Year 38
Chapter 4: Results of the Study 39
4.1 Samples 39
4.2 Descriptive Statistics of Variables of Interest 39
4.2.1 The Proportion of Fixed Manufacturing Costs to Total
Manufacturing Costs 40
4.2.2 Inventory to 42
4.2.3 Directions of Inventory Adjustments 44
4.2.4 Significance of Inventory Adjustments 45
4.2.5 Impacts of Inventory Adjustments on Profitability 46
4.2.6 Average Annual Inventory Adjustment as a Percentage of Net
Income under Absorption Costing 48
4.2.7 Average Annual Inventory Adjustment as a Percentage of
Sales 50
4.2.8 Average Net Income Differential 51
4.2.9 Average Net Income Differential as a Percentage of Net
Incomes Under Absorption Costing 53
4.2.10 Average Net Income Differential as a Percentage of Sales..... 54
4.3 Hypotheses Testing Results 55
4.3.1 Capital Intensity. 55
4.3.2 Inventory to Sales 57
4.3.3 The Impacts of Capital Intensity and the JIT Philosophy over
Vii
Time 59
4.3.4 The Impacts of Capital Intensity and the Jff Philosophy in
Each Year 62
Chapter 5: Conclusions 66
5.1 Summary of the Results and Implications 66
5.2 Limitations and Future Research 68
References 69
Vitae 75
viii
LISTS OF TABLES
Page
Table 4.1: Industries of the Samples 39
Table 4.2: Descriptive Statistics of the Proportion of Fixed Manufacturing
Costs to Total Manufacturing Costs 41
Table 4.3: Descriptive Statistics of Inventory to Sales 44
Table 4.4: Number of Companies Having Negative Annual Stock Adjustment 45
Table 4.5: Descriptive Statistics of Average Annual Inventory Adjustment as a
Percentage of Beginning Inventories 46
Table 4.6: Descriptive Statistics of Average Inventory Adjustments 47
Table 4.7: Descriptive Statistics of Average Annual Inventory Adjustment as a
Percentage of Earnings Before Interest and Taxes 49
Table 4.8: Descriptive Statistics of Average Annual Inventory Adjustrner.4, as a
Percentage of Sales 51
Table 4.9: Descriptive Statistics of Net Income Differential 52
Table 4.10: Descriptive Statistics of Net Income Differential as a Percentage of
Net Income under Absorption Costing 54
Table 4.11: Descriptive Statistics of Net Income Differential as a Percentage of
Sales 55
Table 4.12: Correlations and Paired-Sample T-Test Results of 2-Year Average
Proportion of Fixed Manufacturing Costs to Total Manufacturing Costs of the
Total Sample and That of Each Industry at the Beginning and at the End of the
Period Studied 56
Table 4.13: Correlations and Paired-Sample T-Test Results of 2-Year Average
Inventory to Sales of the Total Sample and That of Each Industry at the
Beginning and at the End of the Period Studied 58
Table 4.14: Results of Panel Data Analysis 61
Table 4.15: Coefficients of Independent Variables in Model 1 62
Table 4.16: Coefficients of Independent Variables in Model 2 63
Table 4.17: Coefficients of Independent Variables in Model 3 65
ix
LISTS OF CRAPI-IS
Page
Graph 4.1: "[rends of Avergoe Sales, Average Costs of Goods Sold, Average
Depreciation, and Average 'Iota] Nlanufacturing Costs 42
Graph 4.2: Average Sales of Each Industry 48
Graph 4.3: Coefficients of CI and JIT in Model I Comparison 63
Graph 4.4: Coefficients of CI and JET in Model 2 Comparison 64
Graph 4.5: Coefficients of CI and JIT in Model 3 Comparison 65
CHAPTER 1
INTRODUCTION
1.1 Background to the Problem
Two product costing methods that manufacturing companies generally used
are: absorption costing (or full costing) and variable costing (or direct costing). The
difference between the two methods is that absorption costing treats fixed
manufacturing overhead as part of product cost while variable costing treats it as
period cost. The appropriateness of treating fixed manufacturing overhead as part of
product cost was one of the controversial areas debated extensively in accounting
journals during the 1950s and 1960s (Baxendale, Boyd, & Gupta, 2006). The debates
have been resolved in favor of absorption costing. Absorption costing is used for
external financial reporting in many countries.
However, the collapses of many large companies around the world and the
current manufacturing environment where fixed manufacturing cost has gained its
significance have awakened the absorption versus variable costing debate. It has long
been acknowledged that absorption costing allows management to increase or
decrease net income being reported by overproducing or under-producing than the
level of production actually needed for sales. In the past when direct labor made up a
large portion of total manufacturing costs, managing net income through increasing or
decreasing inventories would not have a significant impact. However, the
manufacturing environment has changed drastically in the last two decades as a result
of advances in technology and global competition (Weygandt, Kimmel, & Kieso,
2
2012). 'Ihe manufacturing process has become increasingly automated. Direct labour
component in the manufacturing cost structure has decreased dramatically while fixed
manufitcturing overhead has become a significant portion of total manufacturing
costs. Raising funds from a large number of investors especially in globally
connected capital markets may provide incentives for management to show that the
financial performance of their companies meet investors' expectations while the
greater portion of fixed manufacturing overhead in total manufacturing costs may
have made opportunities to manage income through production and inventory
decisions to actually affect net incomes being reported as intended. It is now,
therefore, interesting and important to examine whether the recent changes in
operating and manufacturing environment have provided enough incentive and
opportunities for management to manage net income being reported under absorption
costing by varying production levels in different periods. This will be evidenced by
large inventory adjustment in each period and thereby shifting fixed manufacturing
overhead between periods.
However, since the early 1980s, just-in-time (JIT) system has been widely
adopted (Huson & Nanda, 1995) and has been one of improvement programs being
taught in most business school. JIT system and other improvement programs can
vastly affect how companies are managed such that the current business environment
cannot be properly understood without some appreciation of what these improvement
programs attempt to accomplish (Garrison, Noreen, & Brewer, 2008). While JIT
system can be used in both merchandising and manufacturing companies, it has
greater profound effects on the operations of manufacturing companies (Garrison et
al., 2008). In JIT system, inventories are kept to a minimum or even at zero. With
3
little or no inventories, changes in inventories will be small and fixed manufacturing
overhead costs shifted between periods under absorption costing will be trivial.
Under JIT system, the distortions of income that can occur under absorption costing
will largely disappear (Garrison et al., 2008). The two product costing methods
would yield net incomes that are not much different.
From the discussion above, it can be seen that raising funds in globally
connected capital markets and a greater capital intensity as indicated by proportion of
fixed manufacturing overhead to total manufacturing costs may have presented an
incentive and opportunity for management to artificially increase or decrease net
incomes being reported under absorption costing while JIT system tends to minimize
such opportunity to manage net income being reported. However, when this is not the
case, product costing choices do have impacts on net incomes being reported and the
quality of net incomes may be questionable. This could threaten creditability of the
company and is harmful to capital market development.
1.2 Purposes of the Study
This study aims to examine the effects of product costing choices on
profitability in the current operating environment by 1) examining whether fixed
manufacturing overhead has increasingly become a greater portion of total
manufacturing costs; 2) examining whether JIT system has impacts on Stock
Exchange of Thailand (SET) listed manufacturing companies; 3) examining impacts
of product costing choices on profitability of the sample companies over the period
studied in terms of both direction and significance of inventory adjustments; 4)
4
examining relative impact of capital intensity and the degree to which companies
embraced ilT philosophy on profitability differentials in aggregate over the nine-year
period studied; and also 5) examining relative impacts of capital intensity and the
degree to which companies embraced JIT philosophy on profitability differentials
reported under the two methods in each of the 9 years studied to observe trends of the
impact each of the two factors over time.
1.3 Hypotheses
The introduction of advanced manufacturing technologies has changed the
environment in which companies operate. This should have implications on
accounting methods. It is important to examine the usefulness and impacts of some of
accounting methods. One accounting method that may have been impacted by this
changing operating environment is inventory valuation choices. Therefore, it is
important to examine effects of using absorption costing as the basis for inventory
valuation in financial statements on profitability being reported.
Companies are now increasingly implementing new manufacturing
technology. A paired t-test will be conducted to test whether the average proportion
of fixed manufacturing costs to total manufacturing costs at the beginning of the
period studied is significantly different from that at the end of the period studied at the
five percent level of confidence.
Also, it is expected that ending inventories to sales would decline throughout
the period as a result of J1T philosophy. A paired t-test will conducted to test whether
average ending inventory to sales at the beginning of the period studied is
5
significantly different from that at the end of the period studied at the five percent
level of confidence
Lastly, a greater portion of fixed manufacturing overhead in total
manufacturing costs may have made production and inventory decisions an effective
scheme to manage net incomes being reported under absorption costing. However,
JIT system may have mitigated the impact of the choices of product costing.
Regression analysis was conducted to test relative impacts of the two factors using the
following equation:
Profitability Differential = a + b1 (CI) + b2 (JIT) + Sales + e
Where CI is measured by the proportion of fixed manufacturing costs to total
manufacturing costs. JIT is measured by ending inventory to sales.
It was hypothesized that:
H„: bi = 0
Ha: bi 0
1.4 Scope of Research
The examinations will be conducted on SET listed manufacturing companies
that remained listed throughout the period from 2003 to 2011 in three sectors: Agro &
Food Industry, Consumer Products, and Industrials. Data will be collected from
manufacturing companies that remain Stock Exchange of Thailand listed from 2003
to 2011 so that availability of the data in each year in the period is as much
comparable as possible and the length is long enough to observe potential reversal of
inventory adjustments. It is important to note that the period studied is shorter than
that initially proposed in the research proposal. This was due to the fact that trends of
6
variables studied obtained from the sample companies in the year 2003 to 2011
indicate no further benefits to be gained from collecting additional data in the year
2001 and 2002. It was observed that trends of variables of interest obtained from the
data of nine years are unlikely to be different from those will be obtained if the data
were collected from 2001 to 2011. Furthermore, if data were collected from 2001 to
2011 as initially proposed, usable sample companies would be lower than 100
companies. It was deemed that collecting data of another two years will not change
trends of data observed from what has been obtained from the data collected over the
nine years studied and will only reduce the sample size even further. Therefore, data
collection was stopped when the sample size approached 100 companies.
1.5 Definition of Technical Terms
There are some technical terms encountered in this study. The definitions
shown below are based on those presented in Garrison, Noreen, & Brewer (2008).
Manufacturing costs: Costs that are incurred in the manufacturing processes.
They comprise direct materials, direct labor, and manufacturing overhead.
Direct materials: materials that become an integral part of the finished product
and that can be directly traced to it.
Direct labor: labor costs that can be directly traced to the finished product.
Labor costs that cannot be traced easily are classified as indirect labor, which is part
of manufacturing overhead.
Manufacturing overhead: all costs other than direct materials and direct labor
that are incurred in manufacturing processes.
7
Product costs: Costs that are deemed to be the costs that are involved in
acquiring or making a product. Initially, they are recorded as inventory on the
balance sheet. When the products are sold, costs of the products sold are then
expensed as cost of goods sold.
Period costs: all the costs that are not included in product costs. These costs
are expensed on the income statement in the period in which they are incurred.
Fixed costs: Costs that remain constant in total, regardless of changes in the
level of activity.
Variable costs: Costs that vary in total in direct proportion to changes in the
levels of activity.
Absorption costing: an inventory valuation method that treats both variable
and fixed manufacturing costs as product costs.
Variable costing: an inventory valuation method that treats only variable
manufacturing costs as product costs.
1.6 Contributions of the Study
The results of this study provide several important contributions. Firstly, it
provides evidence as to the trend of fixed manufacturing overhead, inventory levels,
inventory adjustments, and the impacts of product costing choices on profitability
being reported of SET listed manufacturing companies from the year 2003 to the year
2011. Secondly, it sheds light on effects of proportion of fixed manufacturing
overhead to total manufacturing costs and the degree to which companies embraced
JIT philosophy on profitability differentials over the nine-year period. This provides
evidence as to whether increase in fixed manufacturing overhead has made production
8
and inventory decision an effective strategy to influence net income being reported
under absorption costing; or this influence has been mitigated by JIT philosophy
embraced by the companies. Finally, it also sheds light on changes in relative impacts
of the two factors from one year to the next.
The remainder of this paper firstly presents literature review. Then, research
methodology used in this study was outlined, followed by results of the study.
Finally, it discusses the results and implications of the study.
CHAPTER 2
LITERATURE REVIEW
This chapter provides a review of literature related to the study. It firstly
describes the concept of manufacturing costs and the two product costing methods to
gain an understanding of each product costing method and how the difference in the
treatment of fixed manufacturing overhead of both methods can result in different net
incomes measured. In this section, debates on the merits of variable versus absorption
costing in the past are also summarized with a conclusion that absorption costing is
the accepted method for external financial reporting in many countries based
predominantly on matching principle. This is followed by a discussion of alternatives
of capacity that can be used in the allocation of overhead to the product manufactured
under absorption costing, issues surrounding each alternative, and a review of a
variety of actions recently proposed in the literature in an attempt to obtain a more
meaningful and higher quality of net income. Next, a brief description of JIT system
is introduced to shed lights on how JIT system may contribute to the discussion in the
literature. Towards the end of literature review section, prior research examining
effects of product costing based on absorption costing for external reporting purposes
was reviewed.
10
2.1 Manufacturing Costs and Product Costs
Manufacturing refers to activities and processes that convert ra \\ materials into
finished products (Reeve & Warren, 2008; Weygandt et al., 2012). Manufacturing
costs are classified into three components as direct materials. direct Likair, and
manufacturing overhead. For the external financial reporting purpose. costs arc
classified as either product costs or period costs. Product costs may not be expensed
in the period in which they are incurred. This is due to the fact that they are expensed
gradually as units of finished products are sold. In contrast, period costs are deducted
from revenues in the period in which they are incurred. Selling and administrative
expenses, which are non-manufacturing costs, are period costs.
Both direct materials and direct labour costs can be traced directly to specific
product and increase as production volume increases. As such, allocating direct
material and direct labour to products is straight forward. l-lo \\ever, manufacturing
overhead is indirect to products and must be allocated to products on the basis of
some allocation base that reflects the consumption of manufacturing overhead costs
such as direct labour hours, machine hours, direct material costs, and number of
activities performed. To identify allocation bases that reflect consumption of
manufacturing overhead, the relationship between allocation bases and manufacturing
overhead costs must be determined. In order to facilitate management planning and
control, manufacturing overhead costs can be separated into variable and fixed
manufacturing overhead costs. The variable manufacturing overhead costs comprise
costs that vary in proportion to changes in the level of activity. The fixed
manufacturing overhead costs comprise costs that remain the same in total as the level
of activity changes within the relevant range. There is general agreement that variable
tt
manufacturing overhead along with direct material and direct labour should be treated
as product costs. However, it is debatable that fixed manufacturing overhead should
be treated as product costs. The next section describes two product costing methods
representing these divergent ideas of how fixed manufacturing overhead should be
treated in product costing.
2.2 Absorption Costing vs. Variable Costing
There are two commonly used product costing methods: absorption costing
and variable costing. Absorption costing is the accepted alternative used for
determining product costs and cost of goods sold for external financial reporting
purposes. All manufacturing costs, regardless of whether they are variable or fixed
costs, are treated as product costs. Along with direct materials and direct labour costs,
both variable and fixed manufacturing overhead costs are allocated to products
manufactured. These costs are expensed when the products are sold; otherwise, they
are reported as a current asset on the statement of financial positions. Period costs
include only selling and administrative expenses. Therefore, similar to other
manufacturing costs, fixed manufacturing overhead costs are expensed proportionally
to the number of units of products sold.
Under variable costing, only variable manufacturing costs are treated as
product costs. Fixed manufacturing overhead is treated as a period cost, similar to
selling and administrative expenses. Fixed manufacturing overhead is charged off in
its entirety against revenue in the period in which they occurred. The main difference
between absorption costing and variable costing lies in how they treat fixed
12
manufacturing overhead costs. All fixed manufacturing, overhead costs are expensed
immediately. This is in contrast to absorption costing under which fixed
manufacturing overhead costs are charged against revenues gradually as units of
products are sold.
2.3 Impacts of Product Costing Choices on Net Income Being Reported
The differences in the treatment of fixed manufacturing overhead may lead to
different incomes reported under absorption costing and variable costing.
Net income under variable costing is equal to net income under absorption
costing' when units manufactured equal units sold. Net income under absorption
costing will be greater than net income under variable costing when the number of
units manufactured exceeds the number of units sold. This is due to the fact that some
of the products manufactured during the period have not been sold. Part of fixed
manufacturing overhead incurred during the period remains in ending inventories,
which are presented in the statement of financial position. In contrast, net income
under absorption costing will be less than net income under variable costing when the
number of units manufactured is less than the number of units sold. This is due to the
fact that some of the products manufactured in prior periods were sold in the current
period. Fixed manufacturing overhead in the beginning inventories was, therefore,
expensed in the current period in addition to that in the products manufactured and
sold during the period. Therefore, inclusion of fixed manufacturing overhead costs in
1 But in the case when different denominator capacity levels are used in each year, net income
under variable costing may be different from that under absorption costing even when the units manufactured equals the units sold (see, Ajinkya, Atiase, & Bamber, 1986; King, 2006; Sopariwala, 2007, p. 44).
13
the costs of products manufactured may encourage management to build up
inventories, rather than minimizing them as underlies JIT philosophy.
2.4 Debate on which method to use
As indicated, absorption and variable costing can result in different net
incomes being reported. This has created the long debate about the different
treatments of fixed manufacturing overhead under the two methods. Prior research
(e.g., Fess & Ferrera, 1961; Fremgen, 1962; Littleton, 1953; Paton & Littleton, 1940)
has stated that absorption costing allows all production costs to be properly matched
to revenues (Foster & 13axendale, 2008; Pong & Mitchell, 2006). According to
matching principle, sales revenues and the costs of generating that revenues must be
matched to determine net incomes Wong & Mitchell, 2006). All production costs,
both fixed and variable costs, are costs involved in manufacturing products; they,
therefore, should be treated as an asset until the product is sold (Fess & Ferrera, 1961;
Pong & Mitchell, 2006). Brummet (1955, p. 441) indicated that absorption costing
results in a more meaningful income measurement and product costing. It is also
particularly suitable for long-run decisions such as pricing and choosing a product mix
because companies must be able to recover all costs in the long run if they are to survive
(Fremgen, 1964; Horngren, Datar, Foster, Rajan, & Ittner, 2009; Matz, Curry, &
Frank, 1962; Neuner, 1962; Pong & Mitchell, 2006; Reeve & Warren, 2008).
In contrast, advocates of variable costing have provided a number of
arguments in justifying the method. Neilsen (1954) indicated that there was
subjectivity involved in the allocation of fixed manufacturing overhead to products; and
variable costing should be used for external financial reporting. The allocation of fixed
14
manufacturing overhead requires estimation of both fixed manufacturing cost for the
period and allocation base, which may involve measurement of impairment losses and
estimation 01- useful life, residual value of assets, and denominator capacity level
(I lomgren et al., 2_009). This introduces a degree of subjectivity, which compromises
the reliability and comparability of the financial statements (Thomas, 1969, 1974).
Seiler (I 959, p. 63) further substantiated variable costing by arguing that the
value of inventory in balance sheets should represent working capital of the company tied
up in unsold products. Fixed manufacturing costs represent resources that provide
production capacity and remain intact in the short run. Therefore, only variable
manufacturing costs reflect actual working capital tied up in inventory. Seiler (1959, p.
65) indicated that presenting separately the effect of heavy investments in fixed costs and
changes in such controllable costs as materials, labor, and variable manufacturing
expenses make variable costing a better accounting method for external reporting.
Similarly, I lorngren and Sorter (1961) and Sorter and Horngren (1962) argued
that future economic benefits could be gained from future costs that will be saved in
the future, rather than the value obtained through sales. Fixed manufacturing
overhead does not change, irrespective of the production level in each period.
Therefore, only variable production costs shall be treated as an asset until the products
are sold.
Furthermore, when production capacity is not fully utilized, costs of idle
capacity present. It was widely recognized that the costs of idle capacity should not
be attached to products manufactured (See, Clark, 1923, as cited in Aranoff, 2011;
Fess & Ferrara, 1961). However, in traditional cost accounting, manufacturing
overhead costs are allocated to products using predetermined overhead rate, which is
15
calculated by dividing budgeted overhead costs by budgeted activity. This results in
allocating costs of idle capacity to products manufactured (Ajinkya et al., 1986;
Bettinghaus, Debruine, & Sopariwala, 2012). Bettinghaus et al. (2012) (see also,
Bruggen, Krishnan, & Sedatole, 2011) indicates that the accounting system that assign
excess capacity costs to product manufactured, together with an incentive system that
rewards short-term decision-making, induces companies to produce in excess of what
they can sell.
The debates have resolved in favor of absorption costing on the basis of
matching principle as a "conceptually superior" method of valuing inventory and
measuring net income for external financial reporting purposes (Baxendale et al.,
2006; Foster & Baxendale, 2008; Pong & Mitchell, 2006). However, separating fixed
costs from variable costs has made variable costing an appealing method for cost
volume profit analysis, management planning and control, incremental analysis
(NAA, 1961; Dixon, 1966; Rickwood & Piper, 1980; Horngren et al., 2009; Pow:4 &
Mitchell, 2006) and evaluating a company's operating leverage (Sopariwala, 2007).
2.5 Accounting for Manufacturing Overhead
As indicated above, manufacturing overhead costs are indirectly related to
products and are allocated to products manufactured using a predetermined
manufacturing overhead rate or multiple predetermined manufacturing overhead rates,
depending on the allocation methods used.
The calculation of predetermined variable manufacturing overhead cost rate
can be done (Horngren et al., 2009, pp. 288-289) by determining the period to be used
for the budget; selecting and estimating the cost-allocation bases for the production
16
volume budgeted; identify and total all the budgeted variable manufacturing overhead
costs associated with each cost allocation base for the period; and dividing the total
budgeted variable manufacturing overhead costs by the estimated cost-allocation base.
The calculation of predetermined fixed manufacturing overhead cost rate can he done
in a similar manner as outlined for the calculation of predetermined variable
manufacturing overhead cost rate. At the end of the period, in case actual production
is different from the denominator capacity level, over-applied or under-applied
manufacturing overhead will occur. The over-applied or under-applied manufacturing
overhead is transferred to cost of goods sold or allocated among the work-in-process,
finished goods, and cost of goods sold at the end of the period.
However, special considerations must be exercised when choosing the
capacity level used in the calculation of predetermined fixed manufacturing overhead
cost rate. There are four types of capacity levels that can be used in the calculation of
predetermined fixed manufacturing overhead rate, or the denominator capacity level:
expected actual capacity, normal capacity, practical capacity, or theoretical capacity.
The choice of capacity level used as the denominator capacity level can affect (have
implications on) information reported in income statement (see, Ajinkya et al., 1986).
Fixed manufacturing overhead costs represent the costs to acquire production
capacity. Some such as those advocate product costing based on variable costing see
that fixed manufacturing overhead should not be treated as product costs. On the
contrary, absorption costing advocates consider that fixed manufacturing overhead
costs should be treated as product costs. Traditionally, normal capacity is used as the
denominator capacity level in the calculation of fixed manufacturing overhead rate,
resulting in all the costs of capacity acquired being allocated to products. This treats
17
the costs of idle capacity as product costs, which are aggregated in cost of goods sold
and ending inventories in traditional financial statements.
2.6 Possible Solutions
In light of income smoothing among large companies in the last decade, it is
questionable whether absorption costing is still an appropriate method for external
financial reporting purposes. A variety of actions have been proposed in preventing
income smoothing through varying production levels. Recently, there have been calls
(e.g., Aranoff, 2011; Baxendale, et al., 2006; Baxendale & Foster, 2010; Garry, 1991;
Sopariwala, 2009) for accounting rule makers' consideration to mandate variable
costing for external financial reporting purposes. However, Sopariwala (2007)
indicated that this is not sufficient to eliminate all inventory-related incentives to
manipulate earnings. Prior research (Ajinkya et al., 1986; King, 2006; Sopariwala,
2007) has shown that a company can still have different absorption and variable
costing incomes by using different denominator capacity levels even when the
company has level or no inventories. This means that different denominator capacity
levels can be used to manipulate absorption costing income (Sopariwala, 2007). To
remove all inventory-related incentives for earnings manipulation, Sopariwala (2007)
has suggested that theoretical capacity or practical capacity should be used as the
denominator capacity level. This also allows for the separation of the cost of capacity
used and the cost of idle capacity. It has been widely acknowledged that charging the
cost of idle capacity to products is inappropriate (Gantt, 1994; Sopariwala, 2007).
Prior research (Baxendale & Foster, 2010; Sopariwala, 2007, 2009) has suggested that
the cost of capacity supplied should be decomposed into the cost of capacity used and
18
the cost of idle capacity; and the cost of idle capacity should be written off separately.
Sopariwala (2007, 2009) has proposed an absorption costing income statement that
isolates the cost of idle capacity and reports it separately from the cost of producing
products. This differs from traditional absorption costing income statements that treat
the cost of idle capacity as product costs and are aggregated in the cost of goods sold
and ending inventories (Baxendale & Foster, 2010). Sales revenue is matched against
the cost of capacity used to earn the sales revenue in calculating operating income.
The cost of idle capacity is, then, deducted from operating income in determining
absorption costing income, so that the operating income was unaffected by varying
production levels. Hence, the cost of idle capacity is not treated as a product cost and
is not aggregated in the cost of goods sold, which are consistent with Clark (1923, as
cited in Aranoff, 2011), Fess and Ferrara (1961), and McNair and Vangenneersch
(1998).
However, mandating actions suggested in the literature such as variable
costing income statement, the use of theoretical capacity or the disclosure of the cost
of idle capacity for external financial reporting purposes is deemed to be unlikely,
mainly due to competitiveness and self-interest reasons. In addition, the International
Accounting Standard and the U.S. GAAP require product costing and net income
measurement to be based on absorption costing for external financial reporting
purposes. Variable costing may be used for internal reporting, especially when the
production and sales of the company vary considerably from period to period and the
amount of fixed manufacturing overhead is quite large. But if there is little variation
in sales and production from period to period, the choice between absorption costing
and variable costing will not make much difference. In such case, either method will
19
result in approximately the same net income. Furthermore, even if variable costing is
not used for external reporting purposes, it is always possible to identify fixed and
variable costs of the company for management planning, control, and decisions. The
question remains whether it is necessary to disclose such information to shareholders
when management is the one who makes decisions, instead of information for
evaluating overall performance of management. Baxendale and Foster (2010) refine
Sopariwala's (2009) absorption costing income statement by using multiple activity
rates instead of a single overhead rate. However, the complication involved,
investment and time required in obtaining data for preparation, the necessity to
disclose such information to shareholders, and the increased reliability and
comparability of income statement remain issues to be concerned about before the
income statement proposed by Baxendale and Foster (2010) can be adopted.
In summary, with all the recent developments relating to possibility to
mandate the variable costing income statement or the newly proposed income
statements discussed above, it is unlikely to happen. Absorption costing will remain
the mandated method for external reporting in many countries. However, this
assessment will have a greater support, if there is empirical evidence to show that
management has not really exploited the opportunity to manipulate net income by
over-producing or under-producing that the quantity they actually need for sales.
2.7 Just in Time System
Many companies have recognized the need to produce products and services
with greater flexibility, responsiveness, low cost, and high quality (Horngren et al.,
2009, p. 740; Reeve & Warren, 2008, p. 473). JIT system was introduced at the
20
Toyota Motor plant in the mid-1970s (Biggart & Gargeya, 2002). There has been a
lack of consensus concerning what JIT system means (Ramarapu, Mehra, & Frolick,
1995). JIT system has been referred to by many names and can be viewed as either a
philosophy or a disciplined method of production (Biggart & Gargeya, 2002). Many
companies would seem to be implementing various aspects of JIT system on an ad
hoc basis while few are applying JIT system as part of an integrated manufacturing
policy (Voss & Robinson, 1987). Despite this, JIT system is generally referred to as a
manufacturing system for achieving excellence through continuous improvements in
productivity and elimination of waste (Bigart & Grageya, 2002; Fullerton &
McWatters, 2001). JIT system, together with manufacturing automation, allow
companies to enhance flexibility and responsiveness of their manufacturing processes
to simultaneously meet customer demand in a timely manner with high quality
products at the lowest possible total costs (Dugdale, 1990; Horngren et al., 2009). JIT
system focuses on reducing time, cost, and poor quality in both manufacturing and
non-manufacturing processes. But it has a greater effect in manufacturing processes.
This study limited its sample to manufacturing companies. Therefore, this section
only describes JIT system within manufacturing setting (see, Hilton, 2008; Horngren
et al., 2009; Mowen & Hansen, 2007; Reeve & Warren, 2008).
Traditionally, inventories are carried for a variety reasons: to balance ordering
or setup costs and carrying costs; to satisfy customer demand either when demand is
greater than expected or when production is lower due to production inefficiency or
machine breakdowns; to prevent shut downs from late deliveries, defective parts, and
machine breakdowns; to take advantage of quantity discounts (Mowen & Hansen,
2007). In contrast, JIT views inventory as wastes that hide these underlying
21
production problems because inventory is needed as buffers to satisfy sales in case of
machine breakdowns, manufacturing schedule changes, and unexpected rework or to
prevent shutdown in case of late deliveries (Reeve & Warren, 2008). JIT system
focuses on removing these production problems so that inventory can be kept to a
minimum (Reeve & Warren, 2008).
In JIT system, manufacturing activity at any particular workstation is
conducted as soon as the workstation's output is needed by the next workstation
(Hilton, 2008; Horngren et al., 2009). In such a demand-pull production system,
defects arising at one workstation inevitably affect other workstations in the
production line (Horngren et al., 2009). This creates urgency for tracing the problems
to and solving the problems at workstation where the problems are likely to originate
(I lorngren et al., 2009). Therefore, workers in JIT production system were granted
with responsibility and authority to make decisions about operations. They are
trained to be multi-skilled and capable of performing any operation within the
manufacturing cell (Horngren et al., 2009). They learn how to operate several
machines and perform routine equipment maintenance and quality control within their
manufacturing cell, rather than rely on centralized service departments (Reeve &
Warren, 2008).
In order to reduce inventory level, the speed of production needs to be
improved. This can be achieved through reducing setup time — the time required to
get production line ready to start the production of a product (Horngren et al., 2009).
When setup time is long, products will be manufactured in large batches and
inventory level will be high (Reeve & Warren, 2008; Horngren et al., 2009).
Therefore, reducing setup time enables products to be manufactured in smaller
22
batches, which reduces inventory level and enhances the company's responsiveness n)
uncertainty in demand (I lorngren et al., 2009). The speed of production can also be
improved through reducing lead time — the time from when an order is received b\
manufacturing until it becomes a finished product. To reduce lead time, total lead
time must be divided into value added and non-value added time (Reeve & Warren.
2008). Value-added lead time is the time required to actually manufacture a unit of
product while non-value-added lead time is the time that a unit of product sits in
inventories or moves unnecessary (Reeve & Warren, 2008). JIT system aims to
minimize non-value-added lead time by organizing production in manufacturing cells,
which group together all the different types of equipment used to make a given
product and operations are sequenced closely (Reeve & Warren, 2008). Production
manufacturing cells, therefore, can minimize unnecessary movement between
operations and material handling costs, thereby reducing lead time, and work in
process, and production costs (Reeve & Warren, 2008; Horngren et al., 2009).
In JIT system, it is important that raw materials are available when they are
needed for production without carrying inventories (Mowen & Hansen, 2007; Hilton,
2008). This can be done by negotiating long-term contracts with suppliers, which are
selected on the basis of their ability to deliver quality materials when needed (Mowen
& Hansen, 2007).
As such, JIT system enables companies to lower carrying costs of inventory,
improve quality by identifying and prevent the cause of defects and rework, and
reducing lead time. In addition, the use of manufacturing cells also makes some costs
usually classified as indirect costs directly traceable to specific products (Mowen &
Hansen, 2007). For example, the costs of setup, maintenance, and quality inspection
23
become direct costs in JIT system, thereby reducing manufacturing overhead. It is
expected that the greater the degree to which companies embraced JIT philosophy, the
less the level of inventory and manufacturing overhead will be. Since inventories in
companies using JIT system are minimal, changes in inventories from period to
period is unlikely to be significant. Therefore, differences between net income under
absorption costing and net income under variable costing will be immaterial, so does
earnings manipulation by over-producing or under-producing.
2.8 Prior Research Examining Effects of using Absorption Costing as a Basis for
Product Costing in Externally Reported Financial Statements
Accounting literature has widely acknowledged that absorption costing for
product costing provides an opportunity for management to smooth net income by
managing production and inventory dated back when variable versus absorption
costing was extensively debated. However, effects of using absorption costing as a
basis for product costing in the externally reported financial statements on net
incomes being reported may have not been documented. No single prior research
examining the effects in those days has been found. This may be due to the fact that,
during the time, fixed manufacturing overhead was a small portion of total
manufacturing overhead and technology and communication systems were not as
advanced as those today. Additionally, the collapse of many large companies has
recently drawn a greater attention to the quality of net income being reported.
Until recently, Pong and Mitchell (2006) explored the impact of absorption
costing as a basis for product costing on the reported profitability of UK
manufacturing companies from 1988 to 2002 by assessing the sensitivity of these
)4
companies' net incomes to the adoption of variable costing. they found that the
selection of absorption or variable costing as a basis for product costing has a
potential important impact on UK manufacturing companies' profitability. Foster and
Baxendale (2008) revisited the debate and examined whether the current operating
environment has increased the potential for net income smoothing through production
and inventory decisions. However, they did this by examining the effect of capital
intensity on fixed manufacturing overhead in ending inventories of thousands of
actively and inactive publicly held U.S. and Canadian companies over a 47-year
period from the year 1960 to the year 2005. They found that while the sample
companies had become capital intensive at a higher level and ill system might have
had some impacts on them from 1986 to 2005, increased capital intensity has a greater
impact than JIT system. However, it is important to note that management's efforts to
manage earnings through production decisions can be observed clearer by focusing on
their impact on resulting net income, not ending inventories.
This study aims to examine the effects of product costino, choices on
profitability in the current operating environment by 1) examining whether fixed
manufacturing overhead has increasingly become a greater portion of total
manufacturing costs; 2) examining whether JIT system has impacts on Stock
Exchange of Thailand (SET) listed manufacturing companies; 3) examining impacts
of product costing choices on profitability of the sample companies over the period
studied in terms of both direction and significance of inventory adjustments; 4)
examining relative impact of capital intensity and the degree to which companies
embraced JIT philosophy on profitability differentials in aggregate over the nine-year
period studied; and also 5) examining relative impacts of capital intensity and the
degree to which companies embraced ill philosophy on profitability differentials
reported under the two methods in each of the 9 years studied to observe trends of the
impact each of the two factors over time. This study aims to examine the competing
diverse effects of capital intensity and in' philosophy on profitability reported in the
financial statements, which uses absorption costing as a basis for the preparation.
This, therefore, differs from Pong and Mitchell (2006) that mainly examined whether
net incomes under absorption costing would have been higher or lower than net
incomes under variable costing at different levels of fixed to total costs ratios derived
from existing literature. This study also differs from Foster and Baxendale (2008) in
that it will examine relative impact of the two competing factors directly on
profitability differentials reported under the two methods over the nine-year period
studied and also examine changes in relative impacts of the two factors from one year
to the next.
There is no known similar study in Thailand. There are several studies that
examined management accounting practices including absorption costing such as
Ruttanapon, Komaratat, Cheniam, and Bailes (2000), Ruttanapon (2005), and
Komaratat and Boonyanet (2008). These studies mainly conducted surveys on the
extent to which sample companies in Thailand used various management accounting
techniques.
CHAPTER 3
RESEARCH NlETIIODOLOGY
3.1 Sample Selection and Data Collection
This study aims to examine the effects of product costing choices on
profitability in the current operating environment by 1) examining whether fixed
manufacturing overhead has increasingly become a greater portion of total
manufacturing costs; 2) examining whether JIT system has effects on Stock Exchange
of Thailand listed manufacturing companies; 3) examining impacts of product costing
choices on profitability of the sample companies over the period studied in terms of
both direction and significance of inventory adjustments; 4) examining relative impact
of capital intensity and the degree to which companies embraced JIT philosophy on
profitability differentials in aggregate over the nine-year period studied; and also 5)
examining, relative impacts of capital intensity and the degree to which companies
embraced JIT philosophy on profitability differentials reported under the two methods
in each of the 9 years studied to observe trends of the impact each of the two factors
over time. Since absorption costing and variable costing are methods of product
costing for manufacturing companies, the samples in this study comprised
manufacturing companies listed in the Stock Exchange of Thailand in three sectors:
Agro & Food Industry, Consumer Products, and Industrials.
At the end of the year 2011, there were 41, 39, and 78 companies in Agro &
Food Industry, Consumer Products, and Industrials sectors, respectively. Only
manufacturing companies in those three sectors that remained listed throughout the
27
period studied were included in the final sample. The final sample companies
comprise 33, 30, and 41 companies in Agro & Food Industry, Consumer Products,
and Industrials sectors, respectively. The data was collected from financial statements
in the SETSMART Advance database developed by the SET.
3.2 Measurement of Variables
3.2.1 Capital Intensity
The capital intensity is assessed by the proportion of fixed manufacturing
costs to total manufacturing costs instead of the proportion of fixed manufacturing
costs to costs of goods sold that was used in Foster and Baxendale (2008). The
proportion of fixed manufacturing costs to total manufacturing costs incurred in the
manufacturing process is deemed to give a clearer indication of capital intensity as it
is not affected by the timing of selling goods like the proportion of fixed
manufacturing costs to costs of goods sold. Costs of goods sold are costs that are
realized as expenses in the period which goods are sold and may be greater or lower
than total manufacturing costs incurred during the period. When costs that are
realized as expenses in the period (i.e., costs of goods sold) is lower (higher) than total
manufacturing costs incurred during the period, the proportion of fixed manufacturing
costs to costs of goods sold would be higher (lower) than the proportion of fixed
manufacturing costs to total manufacturing costs. The extent of the differences
between costs of goods sold and total manufacturing costs has a direct impact on the
differences between the proportion of fixed manufacturing costs to costs of goods sold
and the proportion of fixed manufacturing costs to total manufacturing costs. The
28
proportion of fixed manufacturing costs to costs of goods sold may exhibit an
increasing (or decreasing) trend, which gives the readers an impression that capital
intensity of sample companies is increasing (or decreasing), while in fact the
proportion of Fixed manufacturing costs to total manufacturing costs remains the
same. Also, the proportion of non-current assets to sales may be thought intuitively as
another proxy for capital intensity. However, it is deemed to provide a rougher
estimate of capital intensity than the proportion of fixed manufacturing overhead to
total manufacturing costs because it can be affected by noncurrent assets other than
property, plant, equipments, and other factors affecting sales. Therefore, the
proportion of non-current assets to sales does not provide a measure of capital
intensity as good as the proportion fixed manufacturing overhead to total
manufacturing costs. This study, therefore, measures capital intensity by the
proportion of fixed manufacturing costs to total manufacturing costs.
The data used for the calculation of the proportion of fixed manufacturing
costs to total manufacturing costs are not directly available in external financial
statements. The fixed and variable portion of total manufacturing costs is not
information disclosed in the public domain. Consequently, fixed manufacturing costs
of the samples were estimated. Depreciation relating to manufacturing should be used
as a proxy for fixed manufacturing costs. While depreciation is not the only fixed
manufacturing costs, it is the major component of fixed manufacturing costs and other
fixed manufacturing costs are not generally reported. However, not all companies
reported depreciation specific to manufacturing. Therefore, total depreciation, which
comprises depreciation relating to manufacturing and selling and administrative
activities, is used instead to represent the major portion of fixed manufacturing costs.
29
It is acknowledged that a limitation of this study is that the actual fixed manufacturing
costs of the samples are not known. Total manufacturing costs during the period are
calculated from the following equations:
COGS
And,
COGM
Hence,
COGS
and
TMC
FGB + COGM - FGE
WIPB + TMC -- WIPE
FGB + WIPE + TMC - WIPE - FGE
COGS + WIPE + FGE - WIPE - FGB
Where COGS is the costs of goods sold during the period; WIPE is ending
work-in-process; FGE is ending finished goods; WIPE is beginning work-in-process;
FGE is beginning finished goods; TMC is total manufacturing costs during the period,
which comprise direct material, direct labour, and manufacturing overhead incurred
during the period; and COGM is the costs of goods manufactured during the period.
3.2.2 JIT Philosophy
The degree to which a company embraced JIT philosophy is measured by
ending inventory to sales. Ending inventory may be increasingly held to support
changing sales in each period. To measure whether ending inventory is managed to
be just enough to support sales, ending inventory must be measured relative to sales.
The data collected, therefore, are beginning and ending inventories and sales for each
of the nine years studied. It is important to note that only are finished goods and
work-in-process used to represent inventory in the calculation of ending inventory to
30
sales and inventory turnover. Raw materials are excluded because the level of raw
materials companies hold may he impacted by other factors such as purchasing lead
time and discounts being offered.
3.2.3 Profitability Differentials
The differences between net income under absorption costing and net income
under variable costing can be calculated using a one-line adjustment proposed by
Solomons (1965, p. 111-112) as follows:
NI v,,, NI (BINV t - EIN V ,) * X ,
Y t, t
Where NI v, , is net income before tax of company i at time t under variable
costing; NI A, t, is net income before tax of company i at time t, under absorption
costing; BINV , is beginning inventory based on absorption costing of company i at
time t; E1NV , is ending inventory based on absorption costing of company i at time
t; X , is fixed manufacturing costs of company i at time t; and Y is total
manufacturing costs of company i at time t. NI A, t, BINV ,, and EINV , are all
available in financial statements in the databases. The proportions of fixed
manufacturing costs to total manufacturing costs are estimated as discussed earlier.
3.2.4 Profitability Differentials Under the Two Product Costing Choices
Profitability differentials were examined in terms of both directions and
significance of the inventory adjustments as outlined below.
31
3.2.4.1 Directions of Inventory Adjustments
The prevalence of positive or negative differences between beginning
inventories and ending inventories for each period (i.e., annual inventory adjustment)
can indicate whether net incomes under absorption costing of the companies studied
were lower or higher than those would have been under variable costing for most
companies. If there were more negative adjustments, this indicates that more
companies reported net incomes under absorption costing that were higher than those
that would have been under variable costing. In contrast, if there were with more
positive annual inventory adjustments, this indicates that more companies reported net
incomes under absorption costing that were lower than those that would have been
under variable costing. The number of positive and negative annual inventory
adjustments in each year will be counted and compared.
3.2.4.2 Significance of Inventory Adjustments
To visualize significance of inventory adjustments from one year to the next,
average inventory adjustment as a percentage of beginning inventories will be
calculated for each year as follows:
Average inventory adjustment as a percentage of beginning inventories
(BINV i,t- EINV i,t)
BINV i,t 1=
Where BINV , is beginning inventory based on absorption costing of
company i at time t and EINV , is ending inventory based on absorption costing of
company i at time t.
32
3.2.4.3 Impacts of Inventory Adjustments on Profitability
From the relationship between net income under absorption costing and net
income under variable costing in the one-line adjustment proposed by Solomons
(1965, p. 111-112) illustrated earlier and reiterated in equation (1) below, it can be
observed that inventory adjustments and proportion of fixed manufacturing costs to
total manufacturing costs are the two factors that explain the difference between net
incomes under the two product costing methods. Therefore, inventory adjustment can
provide an indication of the extent to which net income being reported and profit
margin ratio can be influenced by the two product costing choices given the capital
intensity of the company in each year.
From equation (I) below, dividing both sides by earnings before interest and
taxes under absorption costing and sales will result in equation (2) and (3),
respectively.
NI f NI A, (BINV - EINV ,) * X t
Y
NI v,t, t =
NI A, t (BINV 1, 1- EINV * X ...(2)
NI t
NI A, t NI A, , YE,,
NI v, t = NI A, (BINV - ENV * X •-(3)
Sales ,,
Sales , Sales ,
Where BINV , is beginning inventory based on absorption costing of
company i at time t and EINV , is ending inventory based on absorption costing of
company i at time t. NI A is earnings before interest and taxes under absorption
costing of company i at time t. NI v, , is earnings before interest and taxes under
variable costing of company i at time t. Sales , are sales of company i at time t.
33
Hence, the extent to which net income being reported and profit margin ratio
can be influenced by the two product costing choices can be assessed by average
annual inventory adjustments, average inventory adjustment as a percentage of
earnings before interest and taxes, and average inventory adjustment as a percentage
of sales. They are calculated for each year as follows:
Average annual inventory adjustment
" = l(BINV — EINV t) I n
Average inventory adjustment as a percentage of earnings before interest and
I (BINV i,t- EINV i,t) I
NI A,i,t
Average inventory adjustment as a percentage of sales
(BINV i,t- EINV i,t)
Sales i,t 2=
However, it should be noted that average annual inventory adjustment is
deemed to give an unclear indication of its relative impact on profitability differentials
when comparing average annual inventory adjustments of different industries and
over time because the sample companies are of different sizes and their sizes change
over time. A better comparison can be achieved through comparing inventory
adjustment to net income being reported and inventory adjustment to sales. The
greater the ratio of inventory adjustment to net income under absorption costing and
inventory adjustment to sales, the greater the impact of the two product costing
choices on net income being reported and profit margin ratio.
taxes
n
34
3.2.4.4 Profitability Differentials
Impact of product costing choices examined solely from inventory
adjustments in the previous section has not taken the capital intensity into account.
Profitability differentials in this section take both capital intensity and annual
inventory adjustments into account. This allows an examination of relative impacts of
capital intensity and JIT philosophy that may affect the annual inventory adjustment,
which in turn affect profitability differentials. This shed lights on appropriateness of
product costing choices within the current operating context where fixed
manufacturing costs have become a greater portion of total manufacturing costs and
where JIT philosophy has become a prominent management concept. Profitability
differentials are the adjustment portion of the equation (1), (2), and (3) illustrated
earlier, which are reiterated below.
NI v NI A, z, (BINV — El-NV t) * X , t •--(1)
NI v r=
NI A, t (BINV — EINV r) * X ---(2)
NI A,
NI V, t, t =
NI A, i, NI A, I, / Y ;. ,
NI A, t - EINV * X .43)
Sales , Sales Sales
Y t
The adjustment portion in equation (1) is the difference between net incomes
reported under the two methods, while those in equation (2) and (3) are the difference
between net incomes reported under the two methods calculated as a percentage of net
income under the absorption costing and as a percentage of sales, respectively.
35
3.3 Hypotheses Development and Testing
3.3.1 Fixed Manufacturing Overhead Trend
As described before, companies are now increasingly implementing new
manufacturing technology. This has seen an increase in investment in advanced
manufacturing technology and indirect costs supporting automation (Drury, 1989). It
is also expected that fixed manufacturing overhead has become a greater portion of
total manufacturing costs in SET listed manufacturing companies. To assess whether
the sample companies have became more (or less) capital intensive, a paired t-test will
be conducted to test whether the average proportion of fixed manufacturing costs to
total manufacturing costs at the beginning of the period studied is significantly
different from that at the end of the period studied at the five percent level of
confidence when examined in aggregate for the total sample and separately for each
industry_ It was hypothesized that:
Ho: Capital Intensity beginning — Capital Intensity ending — 0
Ha: Capital Intensity beginning — Capital Intensity ending t 0
3.3.2 Effect of JIT Philosophy
In JIT production system, finished products are manufactured when they are
needed. Embracing JIT philosophy at a greater degree should result in a decline in
finished goods and work-in-process level. Hence, it is expected that ending
inventories to sales would decline throughout the period. To assess whether the
sample companies have been increasingly embracing JIT philosophy, a paired t-test
will conducted to test whether average ending inventory to sales at the beginning of
36
the period studied is significantly different from that at the end of the period studied at
the five percent level of confidence when examined in aggregate for the total sample
and separately for each industry. It was hypothesized that:
I l: Inventory to Sales Inventory to Sales beginning ending ---
lia:
Inventory to Sales beginning — Inventory to Sales ending 0
3.3.3 The Impact of Capital Intensity and the Degree to Which Companies
Embraced JIT Philosophy on Profitability Differentials over Time
As indicated earlier, fixed manufacturing overhead has become a greater
portion of total manufacturing costs. And this may have made production and
inventory decisions an effective scheme to manage net incomes being reported under
absorption costing. However, JIT system is prominence management concept in the
past two decades. Embracing JIT philosophy should result in minimal inventory
levels and inventory adjustments, thereby mitigating the impact of the choices of
product costing. Management should be well aware of the effect of high inventory
level and tend to decrease inventory to the levels necessary for sales rather than
building up inventory to manage net incomes. The greater the level of capital
intensity of companies within the same period, the greater the incentive for
management to manage earnings by over-producing or under-producing than the
levels actually needed for sales. This results in greater net income differentials. In
contrast, the greater degree to which companies embraced JIT philosophy into their
operations in each period, the smaller net income differentials will be. Therefore, it is
interesting and timely to examine relative impact of the two factors on profitability
37
differentials over time. To do this, data of nine years were pooled and analyzed by
OLS method using the following regression equation:
Profitability Differential = a + b, (CI) + h2 (JIT) + Sales + e
Where CI is measured by the proportion of fixed manufacturing costs to total
manufacturing costs. JIT is measured by ending inventory to sales. Profitability
differential is measured by each of the following three proxies:
1) the adjustment portion in Solomons' (1965) equation;
(BINV „ — EINV „ t) * X , 1
Y1,
2) the adjustment in item 1) above divided by absorption costing income as
follows;
(BINV „ — EINV „ * t
NI A, E, t
3) the adjustment in item 1) above divided by sales;
(BINV „ t — ENV * X t
Sales t Y,. r
It was hypothesized that:
Ho: hi = 0
Ha: b, 0
38
3.3.4 The Impact of capital Intensity and the Degree to Which companies
Embraced JIT Philosophy on Profitability Differentials in Each Year
In addition to examine relative impacts of the two factors over time, the
impacts of the two factors on profitability differentials in each of the nine years will
also be examined. This study also examines which of the two factors has a greater
influence on the differences in profitability measures in each period using the same
regression model. The purpose of this analysis is to examine trend of impacts of the
two factors over time. It was hypothesized that:
Ho: = 0
Ha: bi # 0
CHAPTER 4
RESULTS OF THE STUDY
4.1 Samples
At the end of the year 2011, there were 158 SFT listed companies in the three
sectors used as samples of this study: 41, 39, and 78 companies in Agro & Food
Industry, Consumer Products, and Industrials, respectively. Non-manufacturing
companies, those did not remain listed throughout the period studied, and one
company in Industrials that was deemed to be an outlier of the data set were excluded,
leaving 104 companies in the final sample. Table 4.1 shows the number of sample
companies in each industry.
Table 4.1: Industries of the Samples
Industry Number of Companies
Agro & Food Industry 33
Consumer Products 30
Industrials 41
Total 104
4.2 Descriptive Statistics of Variables of Interest
This section provides descriptive statistics of variables used in the examination of this
study, which include the proportion of fixed manufacturing costs to total
manufacturing costs, inventory to sales, direction of inventory adjustment, inventory
adjustment to beginning inventory, inventory adjustment to sales, inventory
40
adjustment to net incomes under absorption costing, net incomes differentials, net
income differentials as a percentage of net incomes under absorption costing, and net
income differentials as a percentage of sales.
4.2.1 The Proportion of Fixed Manufacturing Costs to Total Manufacturing Costs
From Table 4.2, the average proportion of fixed manufacturing costs to total
manufacturing costs of the total sample decreases consistently from 5.38% in the year
2003 to 4.69% in the year 2008. Then, it rises up to 5.81% in the year 2009, but it
declines over the next two years to 4.38% in the year 2011. The trends of medians
resemble to those of average. Similar trends of average proportion of fixed
manufacturing costs to total manufacturing costs can also be observed for Agro &
Food and Industrials industries. Average proportion of fixed manufacturing costs to
total manufacturing costs for Consumer Products varies at a greater extent than the
other two industries over time with a similar rise in the year 2009.
The trend over the nine-year period is in contrary to what has been expected.
This is likely to be due to the fact that the nine-year period is too short to observe
significant changes in manufacturing technology that causes manufacturing overhead
costs to increase and that sample companies may have implemented new
manufacturing technology before the year 2003 which is the beginning of the period
studied.
41
Table 4.2: Descriptive Statistics of the Proportion of Fixed Manufacturing Costs
to Total Manufacturing Costs
Year Average (Median) (%) Standard
Deviation
(%)
Agro & Food
Industry
Consumer Products Industrials Total
2003 4M4 (3.46) 5.27 (4.76) 6.54 (6.14) 5.38 (4.77) 3.56
2004 3.98 (3.34) 5.09 (4.38) 5.46 (5.30) 4.88 (4.29) 3.12
2005 4.29 (3.07) 5.09 (4.32) 4.95 (4.62) 4.78 (4.20) 3.21
2006 4.03 (3.17) 4.83 (4.11) 4.88 (4.03) 4.60 (3.85) 3.10
2007 4.11 (3.01) 5.10 (4.77) 5.00 (4.31) 4.74 (4.05) 3.13
2008 3.87 (3.58) 5.54 (4.29) 4.73 (3.93) 4.69 (3.97) 3.28
2009 4.63 (3.45) 6.53 (5.98) 6.24 (5.13) 5.81 (4.71) 4.21
2010 4.04 (3.59) 5.63 (4.32) 5.21 (4.16) 4.96 (3.94) 3.95
2011 3,40 (3.02) 5.51 (4.48) 4.34 (3.98) 4.38 (3.40) 3.97
Standard Deviation
(%)
3.17 4.13 3.24 3.54
There are two possible reasons for the increase in the proportion of fixed
manufacturing costs to total manufacturing costs (i.e., level of capital intensity) in the
year 2009. Firstly, it may result from the fact that depreciation grows at a greater rate
than total manufacturing costs (and also costs of goods sold and sales), which can
result from greater new investments to tap new opportunities or a simple decline in
sales. Secondly, it can result from greater investments to implement new
manufacturing technology. To examine possible reason for the increase of proportion
of fixed manufacturing costs to total manufacturing costs of the total sample and that
of each industry in the year 2009, average sales, average costs of goods sold, average
depreciation, and average total manufacturing costs were calculated to observe the
42
trends of these variables (as shown in Graph 4.1). It can be observed that average
sales, average costs of goods sold, and average total manufacturing costs all decreased
in the year 2009 while average depreciation grew marginally throughout the nine-year
period studied. Therefore, the increase in the proportions of fixed manufacturing
costs to total manufacturing costs of the total sample and that of each industry in the
year 2009 is likely to result from the former reason.
7,000.00
6,000.00
5,000.00
4,000.00
3,000.00
2,000.00
1,000.00
—4— Sales
—a—Cost of Goods Sold
Depreciation
—4E—Total Manufacturing Costs 1
, A , A A A A
2003 2004 2005 2006 2007 2008 2009 2010 2011
Graph 4.1: Trends of Average Sales, Average Costs of Goods Sold, Average
Depreciation, and Average Total Manufacturing Costs
4.2.2 Inventory to Sales
From Table 4.3, while average inventory to sales of the total sample vary over
the nine-year period. Similar trends can also be observed from the medians of
inventory to sales of the total sample and those of each industry. When examining
average inventory to sales of each industry, average inventory to sales of Agro &
Food Industry and Industrials decrease in the majority of the nine years studied while
43
that of Consumer Products gradually increases in the majority of the nine years
studied.
It is likely to he the case that JIT philosophy has an inconsistent impact on
SET listed manufacturing companies. This is in line with what happened in the U.K.
reported in Pong and Mitchell (2006, p. 141). This may be attributable to the nature
products of the industries. Products of Agro & Foods Industry have shorter lives than
those of other industries. This, therefore, creates pressure onto the Agro & Food
manufacturers to keep inventories just enough to support sales as much as possible.
Similarly, products of Industrials tend to capture a significant amount of working
capital and demand for products is more difficult to forecast than that of Consumer
Products. This also creates a greater pressure on manufacturers in this industry than
manufacturers in Consumer Products to try to lower inventory levels. Although it is
relatively easier to forecast demands for consumer products and make it easier to
implement JIT philosophy in the Consumer Products, functionality of the products,
economies of scale to be achieved in the supply chain, and lost sales opportunity
make it unwise to produce product in a make-to order fashion. Additionally, an easier
to forecast demands for the products contributes to smaller annual inventory
adjustments relative to the other two industries.
44
Table 4.3: Descriptive Statistics of Inventory to Sales
Year Average (Median)(%) Standard
Deviation
(%)
Agro & Food
Industry
Consumer Products Industrials Total
2003 10.32 (7.27) 13.93 (11.24) 9.85 (6.14) 11.17 (7.61) 13.38
2004 10.61 (7.92) 12.92 (10.88) 10.60 (6.52) 11.28 (8.16) 12.33
2005 11.05 (7.48) 14.22 (11.07) 11.86 (8.93) 12.28 (9.15) 14.34
2006 9.69 (7.75) 15.87 (9.40) 11.24 (8.04) 12.08 (8.21) 14.35
2007 9.70 (6.71) 17.53 (11.79) 10.48 (7.68) 12.27 (8.34) 15.55
2008 9.29 (5.43) 16.56 (12.24) 10.42 (7.18) 11.83 (7.64) 13.67
2009 9.34 (6.38) 16.97 (13.55) 12.47 (7.79) 12.78 (8.30) 13.14
2010 7.71 (5.55) 16.15 (13.12) 8.73 (5.13) 10.55 (6.75) 11.32
2011 7.31 (5.73) 17.17 (14.22) 9.07 (6.09) 10.85 (7.53) 11.15
Standard Deviation
(%)
10.67 16.09 12.39 13.28
4.2.3 Directions of Inventory Adjustments
From Table 4.4, there are a greater number of companies having negative
annual stock adjustments in six to seven out of the nine years studied for both the total
sample and each industry. This means that there is a greater number of companies
having negative annual inventory adjustments in the majority of the years studied
regardless of whether it is examined in aggregate or by industry. In other words,
companies reporting net incomes under absorption costing that are higher than those
which would have been under variable costing are more pervasive.
45
Table 4.4: Number of Companies Having Negative Annual Stock Adjustment
Industry Total 2003 2004 2005 2006 2007 2008 2009 2010 2011
A gro & Food 33 21 23 17 20 14* 22 10* 15* 22
Consumer
Products
30 12* 21 19 15 14* 15 10* 21 23
Industrials 41 27 33 21 23 13* 25 9* 26 31
Total 104 60 77 57 58 41* 62 29* 62 76
,ess thanhalt of total number of companies in each category.
4.2.4 Significance of Inventory Adjustments
Average annual inventory adjustment as a percentage of beginning inventories
was calculated to assess the significance of inventory adjustments. From Table 4.5,
ignoring the direction of inventory adjustments, average annual inventory adjustment
as a percentage of beginning inventories of the total sample varies from one year to
the next, ranging between 27.40% and 52.20% over the nine-year period. Hence,
annual inventory adjustments are considered to be large and vary greatly. When
examining average annual inventory adjustment as a percentage of beginning
inventories of each industry, it was found that average annual inventory adjustment as
a percentage of beginning inventories of Agro & Foods Industry varies at a greater
extent than that of the other two industries. This is also consistent with the findings
reported earlier that inventory to sales declines marginally and shows unclear impacts
of JIT philosophy.
46
Table 4.5: Descriptive Statistics of Average Annual Inventory Adjustment as a
Percentage of Beginning Inventories
Year Average (Median) (%) Standard
Deviation Agro & Food Consumer
Products
Industrials Total
2003 92.52 (29.76) 24.38 (11.37) 23.58 (19.52) 45.90 (20.59) 144.10
2004 41.74 (24.85) 33.74 (18.28) 49.29 (33.91) 42.41 (27.24) 48.54
2005 37.96 (20.39) 19.90 (18.62) 40.42 (21.64) 33.72 (20.70) 43.63
2006 34.10 (26.03) 23.90 (18.89) 29.49 (11.26) 29.34 (18.88) 33.77
2007 31.44 (25.62) 16.76 (11.70) 31.94 (19.44) 27.40 (17.80) 33.07
2008 41.11 (26.16) 33.46 (18.14) 36.93 (27.45) 37.25 (25.24) 40.06
2009 23.01 (14.94) 37.45 (20.81) 25.45 (22.45) 28.14 (18.96) 57.72
2010 40.36 (29.10) 34.65 (22.15) 26.43 (21.58) 33.22 (24.51) 33.07
2011 78.08 (30.98) 20.14 (12.80) 54.83 (28.53) 52.20 (23.40) 95.63
Standard Deviation
(%)
102.42 45.21 43.74 68.62
4.2.5 Impacts of Inventory Adjustments on Profitability
From Table 4.6, ignoring the direction of inventory adjustments, average
annual inventory adjustment of the total sample and that of each industry are quite
large. However, average annual inventory adjustment of Consumer Products is
smallest in the majority of the years studied. This is in line with what has been shown
by relatively smaller significance of inventory adjustment of the industry. However,
comparing average inventory adjustments across all industries must be exercised with
care as sample companies in each industry are of different sizes as indicated earlier.
47
Table 4.6: Descriptive Statistics of Average Inventory Adjustments
Year Average (Median) ( Million Bahts) Standard
Deviation Agro & Food
Industry
Consumer
Products
Industrials Total
2003 98.54 (53.07) 39.54 (22.18) 46.88 (16.59) 61.15 (25.19) 108.40
2004 122.47 (30.09) 40.83 (23.71) 103.69 (44.06) 91.52 (32.81) 197.37
2005 204.53 (34.63) 43.47 (29.06) 265.85 (39.12) 182.24 (33.40) 914.09
2006 163.44 (45.50) 40.73 (27.30) 181.42 (31.59) 135.13 (32.40) 505.13
2007 117.70 (47.05) 31.08 (26.08) 167.77 (21.60) 112.45 (34.51) 452.32
2008 116.77 (39.77) 77.55 (44.28) 166.81 (24.66) 125.18 (35.26) 308.53
2009 127.83 (33.68) 43.28 (22.23) 128.06 (40.61) 103.53 (29.55) 315.62
2010 187.20 (38.95) 87.16 (22.16) 59.14 (27.84) 107.85 (28.60) 285.32
2011 209.02 (59.73) 48.69 (22.98) 87.98 (51.85) 115.05 (40.23) 211.77
Standard
Deviation
373.10 87.63 582.29 429.66
Not only can the level of inventories a company maintains be affected by the
degree to which that that particular company embraced JIT philosophy, but it is also
inevitably related to the level of sales. Everything else remains the same, companies
with lower level of sales tends to maintain lower level of inventory. With lower level
of inventory, inventory adjustments tend to be small. Therefore, the smaller annual
inventory adjustments of Consumer Products can be the result of either a greater
degree to which the companies in this industry embraced JIT philosophy or its
average sales that may be lower than that of the other two industries.
From Table 4.3 discussed earlier, the inventory to sales indicates that
Consumer Products maintains a gradually increasing level of inventory, while the
other two industries' inventory to sales vary across the nine-year studied with a lower
10,000
8,000
6,000
4,000
2,000
--4—Agro & Foods Industry
—IN— Consumer Products
Industrials
0
2003 2004 2005 2006 2007 2008 2009 2010 2011
48
level towards the end of the period studied. This shows unclear impacts of JIT
philosophy on the three industries. Additionally, when examining average sales of
each industry, average sales of Consumer Products is lower than that of the other two
industries (see Graph 4.2). Therefore, the smaller annual inventory adjustments of
Consumer Products are likely to be driven by relatively lower level of sales of the
industry.
Graph 4.2: Average Sales of Each Industry
4.2.6 Average Annual Inventory Adjustment as a Percentage of Net Income under
Absorption Costing
Ignoring the direction of inventory adjustment, average annual inventory
adjustment as a percentage of net income under absorption costing can indicate the
approximate impact of the two product costing choices as a percentage of net income
being reported. From Table 4.7, it is observed that average annual inventory
adjustment as a percentage of net income under absorption costing of the total sample
and that of each industry vary greatly. Consumer Products has the smallest inventory
adjustment as a percentage of net income under absorption costing in five years, while
49
the other two industries have those in two years. That is, Consumer Products has the
smallest inventory adjustment as a percentage of net income under absorption costing
in the majority of the year studied. However, inventory adjustments as a percentage
of net income under absorption costing over time of the total sample and that of the
Agro & Foods Industry do, in fact, decrease. This is in contrast to what can be
observed from the average annual inventory adjustments. It is important to note that
the variations of average annual inventory adjustment as a percentage of net income
under absorption costing can be driven by those of net income under absorption
costing since the average annual inventory adjustment to beginning inventory does
not vary as much.
Table 4.7: Descriptive Statistics of Average Annual Inventory Adjustment as a _
Percentage of Net Income under Absorption Costing
Year Average (Median) (%) Standard
Deviation Agro & Food
Industry
Consumer
Products
Industrials Total
2003 280.49 (22.36) 30.22 (13.84) 36.84 (10.86) 112.24 (13.89) 558.60
2004 84.53 (27.32) 80.77 (18.37) 51.33 (23.94) 70.36 (21.23) 174.09
2005 102.79 (28.98) 58.09 (20.37) 98.70 (18.30) 88.28 (20.65) 220.51
2006 76.08 (26.48) 37.70 (28.29) 104.39 (20.67) 76.17 (24.59) 176.40
2007 108.61 (24.83) 58.66 (21.67) 66.19 (17.74) 77.48 (20.81) 145.88
2008 36.88 (22.71) 212.60 (40.51) 74.97 (26.41) 102.58 (26.55) 278.52
2009 30.00 (12.80) 150.09 (23.58) 105.86 (15.67) 94.55 (17.16) 343.67
2010 151.14 (18.27) 78.64 (26.60) 17.84 (8.89) 77.67 (15.86) 328.05
2011 41.68 (21.42) 33.47 (17.35) 75.11 (19.68) 52.49 (19.94) 129.87
Standard Deviation 399.05 247.85 204.95 290.52
50
4.2.7 Average Annual Inventory Adjustment as a Percentage of Sales
Given the level of capital intensity of each company, average annual inventory
adjustment as a percentage of sales indicates the impact of product costing choices on
the profit margin ratio. From Table 4.8, ignoring the direction of inventory
adjustment, it is observed that average annual inventory adjustment as a percentage of
sales of the total sample and that of each industry increase slightly. The average
annual inventory adjustment as a percentage of sales of the total sample ranges from
2.42% to 3.73%, while that of Agro & Foods Industry and that of Industrials range
from 2.46 to 3.53% and from 2.43% to 4.80%, respectively. However, the average
annual inventory adjustment as a percentage of sales for Consumer Products varies at
the greatest extent, comparing to the other two industries. It ranges from 2.15% to
5.67%. The inventory to sales indicates that Consumer Products has maintained a
gradually increasing level of inventory throughout the nine-year period studied. The
larger inventory adjustment as a percentage of sales in later years is likely to result
from higher inventory adjustments in the year 2008 and 2010. Furthermore, it is
observed that the average annual inventory adjustment as a percentage of sales for
Agro & Foods Industry and Industrials do not fluctuate as much as the average annual
inventory adjustment as a percentage of beginning inventories of the two industries.
This implies that inventory adjustments of these two industries vary more with sales
than with the beginning inventories. In other words, inventory adjustments of these
two industries may be affected by fluctuations in sales.
In addition, Consumer Products has the smallest average annual inventory
adjustment as a percentage of sales in four years, while the Agro & Foods Industry
has it in five years. That is, inventory adjustments as a percentage of sales of the
51
Agro & Foods Industry and Consumer Products are relatively smaller in the majority
of the years studied. However, inventory adjustments as a percentage of sales do not
increase as much as average annual inventory adjustments do.
Table 4.8: Descriptive Statistics of Average Annual Inventory Adjustment as a
Percentage of Sales
Year Average (Median) (%) Standard
Deviation Agro & Food
Industry
Consumer
Products
Industrials Total
2003 2.65 (1.34) 2.15 (1.35) 2.43 (0.96) 2.42 (1.29) 2.99
2004 3.53 (1.42) 2.50 (1.71) 3.10 (2.50) 3.06 (2.02) 4.63
2005 2.88 (1.72) 2.40 (1.62) 3.87 (1.24) 3.13 (1.56) 4.26
2006 2.67 (1.88) 3.75 (1.70) 3.46 (0.96) 3.29 (1.53) 5.93
2007 2.56 (1.73) 3.85 (1.20) 3.20 (1.18) 3.18 (1.29) 7.14
2008 2.52 (1.86) 5.67 (2.46) 3.29 (1.33) 3.73 (1.91) 6.89
2009 2.46 (0.88) 3.50 (1.37) 4.80 (1.27) 3.68 (1.26) 7.39
2010 2.87 (1.27) 5.26 (2.03) 2.99 (1.08) 3.60 (1.38) 7.68
2011 2.89 (1.37) 2.73 (2.12) 2.89 (1.65) 2.85 (1.63) 3.72
Standard Deviation 4.04 6.90 6.30 5.85
4.2.8 Average Net Income Differential
Table 4.9 shows descriptive statistics of average net income differential. From
Table 4.9, it can be observed that average net income differential of the total sample
and that of each industry increase over time. Also, average net income differential of
Industrials is the largest and that of Consumer Products is the smallest in the majority
of nine years studied.
52
Table 4.9: Descriptive Statistics of Net Income Differential
Year Average (Median) (Millions Baht) Standard
Deviation
(Millions Baht)
2.50
6.36
Agro & Food
Industry
Consumer
Products
Industrials Total
2003 2.10 (1.14) 1.61 (0.99) 2.50 (1.30) 2.12 (1.17)
2004 3.85 (0.77) 2.58 (1.25) 3.98 (1.80) 3.53 (1.35)
2005 5.95 (1.42) 2.24 (1.52) 5.65 (1.55) 4.76 (1.52) 16.77
2006 4.61 (1.20) 1.80 (1.11) 5.18 (1.26) 4.02 (1.19) 11.13
2007 3.38 (1.37) 1.70 (0.63) 4.66 (0.83) 3.40 (1.01) 9.78
2008 2.66 (1.41) 2.90 (1.56) 4.00 (0.93) 3.26 (1.40) 4.89
2009 2.52 (0.92) 3.20 (0.84) 4.42 (1.53) 3.46 (1.04) 5.73
2010 3.56 (1.06) 4.73 (0.85) 3.52 (1.21) 3.85 (1.05) 8.26
2011 4.52 (1.23) 2.25 (0.89) 3.66 (2.49) 3.50 (1.05) 6.30
Standard Deviation
(Millions Baht)
10.30 4.40 9.99
_
8.88
It was expected that net income differentials be larger in Consumer Products
due to its increasing degree of capital intensity observed in Table 4.2. however, net
income differentials of the Consumer Products is the smallest comparing to that of the
other two industries in the majority of the years studied. This is likely to result from
the fact that the average annual inventory adjustment of Consumer Products is the
smallest comparing to that of the other two industries, which was driven by lower
average sales of the industry. However, the smaller size of average net income
differential in Consumer Products does not necessarily indicate that the impact of
product costing on Consumer Products is relatively smaller than the other two
industries as sample companies are of different sizes.
53
4.2.9 Average Net Income Differential as a Percentage of Net Incomes under
Absorption Costing
Taking into account the effect of company site, the impact of the two product
costing methods was calculated as that as a percentage of net income under absorption
costing. Table 4.10 shows descriptive statistics of average net income differential as a
percentage of net income under absorption costing. From Table 4.10, average
difference between net incomes under the two product costing methods as a
percentage of net income under absorption costing of the total sample ranges from
1.78.% to 6.42%. Except for the year 2008 and 2011, the impact of the two product
costing methods as a percentage of net income under absorption costing varies
slightly. Average difference between net incomes under the two product costing
methods as a percentage of net income under absorption costing of Consumer
Products varies at the greatest extent, followed by that of Agro & Food Industry and
Industrials, respectively. In addition, each industry has the largest average net income
differential as a percentage of net income under absorption costing in three years.
while Agro & Food Industry has the smallest average net income differential as a
percentage of net income under absorption costing in the majority of the nine years
studied.
54
Table 4.10: Descriptive Statistics of Net Income Differential as a Percentage of
Net Income under Absorption Costing
Year Average (Median) Standard
Deviation Agro & Food
Industry
Consumer
Products
Industrials Total
2003 6.23 (0.73) 1.36 (0.56) 1.93 (0.54) 3.13 (0.59) 13.68
2004 2.94 (0.78) 8.21 (0.55) 3.06 (1.25) 4.51 (0.83) 19.09
2005 2.91 (0.74) 3.14 (0.76) 3.60 (0.96) 3.25 (0.83) 7.26
2006 2.48 (0.67) 1.83 (0.89) 5.73 (0.77) 3.57 (0.78) 11.17
2007 3.01 (0.56) 2.76 (1.08) 2.57 (1.20) 2.77 (1.03) 4.39
2008 1.16 (0.41) 15.39 (2.18) 4.09 (1.08) 6.42 (0.88) 22.93
2009 0.91 (0.23) 9.30 (1.32) 3.61 (0.97) 4.39 (0.88) 12.23
2010 7.32 (0.50) 4.79 (1.02) 0.97 (0.52) 4.09 (0.55) 19.29
2011 1.05 (0.38) 1.54 (0.63) 2.55 (0.77) 1.78 (0.58) 3.99
Standard Deviation
(%)
- 14.12 19.26 8.76 14.18
4.2.10 Average Net Income Differential as a Percentage of Sales
Taking into account the effect of company size, the impacts of two product
costing methods were calculated as a percentage of sales. This also indicates the
impacts of the two product costing methods on profit margin ratio. Table 4.11 shows
descriptive statistics of average net income differentials as a percentage of sales.
From Table 4.11, average differences between profit margin ratio under the two
product costing methods for all the three industries range from 0.12% to 0.32%.
Comparing average differences between profit margin ratio under the two product
costing methods of each industry, that of Agro & Foods Industry and that of
Industrials vary in a narrower range than that of Consumer Products. It can be
55
observed that the impact of the two product costing methods in Consumer Products is
now the largest and that in Agro & Foods Industry k the smallest in the majority of
nine years studied. The impact of the two product costing methods on profit margin
ratio of the total sample changes slightly over the period studied, except that in year
2008 to 2010.
Table 4.11: Descriptive Statistics of Net Income Differential as a Percentage of
Sales
Year Average (Median) Standard
Deviation
(%)
Agro & Food
Industry
Consumer
Products
Industrials Total
2003 0.11 (0.04) 0.09 (0.05) 0.16 (0.05) 0.12 (0.05) 0.19
2004 0.15 (0.03) 0.16 (0.05) 0.21 (0.11) 0.18 (0.07) 0.37
2005 0.11 (0.03) 0.14 (0.06) 0.16 (0.09) 0.14 (0.07) 0.20
2006 0.11 (0.03) 0.24 (0.06) 0.15 (0.05) 0.16 (0.05) 0.50
2007 0.08 (0.06) 0.25 (0.06) 0.11 (0.07) 0.14 (0.06) 0.50
2008 0.11 (0.03) 0.48 (0.11) 0.12 (0.05) 0.22(0.05) 0.96
2009 0.21 (0.03) 0.27 (0.09) 0.32 (0.06) 0.27 (0.06) 0.78
2010 0.14 (0.03) 0.44 (0.07) 0.38 (0.05) 0.32 (0.05) 1.38
2011 0.09 (0.03) 0.17 (0.06) 0.12 (0.08) 0.12 (0.05) 0.25
Standard Deviation
(%)
0.35 0.86 0.75 0.68
4.3 Hypotheses Testing Results
4.3.1 Capital Intensity
Table 4.2 has shown that the proportion of fixed manufacturing costs to total
manufacturing costs, a proxy of capital intensity, declines over the nine years studied,
56
which is contrary to what has been expected. In order to determine whether there is
any significant change in capital intensity over the period studied and reduce the
impact of one-year anomalies, averages proportion of fixed manufacturing costs to
total manufacturing costs were calculated for the 2-year period at the beginning of the
period studied (2003 and 2004) and at the end of the period studied (2010 and 2011).
A paired t-test was conducted to test whether the 2-year average proportion of fixed
manufacturing costs to total manufacturing costs at the beginning of the period
studied are significantly different from that at the end of the period studied in
aggregate and by industry. Table 4.12 shows the correlations and paired-sample t-test
results of 2-year average proportion of fixed manufacturing costs to total
manufacturing costs of the total sample and that of each industry at the beginning and
at the end of the period studied.
Table 4.12: Correlations and Paired-Sample T-Test Results of 2-Year Average
Proportion of Fixed Manufacturing Costs to Total Manufacturing Costs of the
Total Sample and That of Each Industry at the Beginning and at the End of the
Period Studied
Year Average
Agro & Food
Industry
Consumer
Products
Industrials Total
C12003-2004 4.01% 5.18% 6.00% 5.13%
Cl2o10-20i1 3.72% 5.57% 4.78% 4.67%
Correlation of CI 2003-2004 and Cl2o10-2011 0.846* 0.337 0.560* 0.532*
Cl2oo3-zoo4 - Clzoio-2011 0.28% 0.39% 1.22% 0.46%
T Value 1.008 -0.439 2.698* 1.406
*significantly different at the 5% level of confidence.
57
The correlations between 2003-2004 average capital intensity and 2010-2011
average capital intensity both in aggregate and by industry are all positive and
significant. This indicates that sample companies which have higher/lower capital
intensity continue to have higher/lower capital intensity. In addition, the result shows
that the 2-year average proportion of fixed manufacturing costs to total manufacturing
costs of the total sample at the beginning of the period studied is not significantly
different from that at the end of the period studied at the five percent level of
confidence. When examining the 2-year average proportion of fixed manufacturing
costs to total manufacturing costs of each industry, only Industrials exhibits a
significant decrease in capital intensity. As indicated earlier, this may be attributable
to the fact that the nine-year period is too short to observe significant changes in
manufacturing technology that causes manufacturing overhead costs to increase and
that sample companies may have implemented new manufacturing technology before
the year 2003 which is the beginning of the period studied.
4.3.2 Inventory to Sales
From Table 4.3, inventory to sales of the total sample, Agro & Foods Industry
and Industrials decline, while that of Consumer Products increases over the nine-year
period studied. In order to determine any significant change in the inventory levels
maintained to support sales over the period studied and reduce the impact of one-year
anomalies, average inventory to sales was calculated for the 2-year period at the
beginning of the period studied (2003 and 2004) and at the end of the period studied
(2010 and 2011). A paired t-test was conducted to test whether the 2-year average
58
inventory to sales at the beginning of the period studied are significantly different
from that at the end of the period studied in aggregate and by industry. Table 4.13
shows the correlations and paired-sample t-test results of 2-year average inventory to
sales of the total sample and that of each industry at the beginning and at the end of
the period studied.
Table 4.13: Correlations and Paired-Sample T-Test Results of 2-Year Average
Inventory to Sales of the Total Sample and That of Each Industry at the
Beginning and at the End of the Period Studied
Year Average
Agro & Food
Industry
Consumer
Products
Industrials Total
INV/S2o03-2004 10.47% 13.43% 10.22% 1 L22% -
INV/S20102011 7.51% 16.66% 8.90% 10.70%
Correlation of
INV/S2oo3-too4 and IN WS2010-2011
0.725* 0.414* 0.778* 0.583*
INV/S.2003-2004 — INWSzoio-mii 2.96% -3.23% 1.32% 0.5.3%
T Value 1.817 -1.190 1.092 0.494
*significantly different at the 5% level of confidence.
The correlations between 2003-2004 average inventory to sales and 2010-
2011 average inventory to sales both in aggregate and by industry are all positive and
significant. This indicates that sample companies which maintain higher/lower
inventory levels to support sales continue to maintain higher/lower inventory levels to
support sales. However, the results of paired t-tests show that the 2-year average
inventory to sales of the total sample is not significantly different from that at the end
59
of the period studied at the five percent level of confidence. When examining the 2-
year average inventory to sales of each industry, no industry exhibits a significant
change in inventory levels maintained to support sales at the live percent level of
confidence. As indicated earlier, it shows inconsistent impacts of JIT philosophy.
This is consistent with what happened in the U.K. reported in Pong and Mitchell
(2006, p.141). Although there is a greater pressure on manufacturers in Agro & Food
Industry and Industrials to embraced JIT philosophy, there still is a significant work to
be done.
4.3.3 The Impacts of Capital Intensity and the Degree to Which Companies
Embraced JIT Philosophy over Time
As indicated earlier, capital intensity and JIT system tend to have diverse
impacts on inventory adjustments and profitability differentials. To test the relative
impacts of capital intensity and the degree to which companies embraced JIT
philosophy on profitability differentials under the two product costing approaches
over the nine-year period studied, the following multiple regression analysis was
conducted on a panel of nine-year data of 104 companies.
Profitability Differential = a + 111 (CI) + b2 (JIT) + Sales + e
Where CI is measured by the proportion of fixed manufacturing costs to total
manufacturing costs; the degree to which companies embraced JIT philosophy is
measured by ending inventory to sales; and Profitability Differential measured by
each of the following three proxies:
1) the adjustment portion in Solomons' (1965) equation, which has been
referred to earlier as net income differential:
60
I (BINV — EINV 01* X,,
V,.,
2) the adjustment in item 1) above divided by absorption costing income,
which has been referred to earlier as net income differential as a
percentage of net incomes under absorption costing:
— EINV 1.1)1* X,.,
NI A, t Y ,, ,
3) the adjustment in item 1) above divided by sales, which has been referred
to earlier as net income differential as a percentage of sales:
(BINV — EINV * X,,
Sales
This results in three equations with the same set of independent variables but
different dependent variables. Table 4.14 shows results of panel data analysis when
net income differentials, net income differentials as a percentage of net incomes under
absorption costing, and net income differentials as a percentage of sales were used as
dependent variables, which will be referred to as Model 1, Model 2, and Model 3,
respectively.
61
Table 4.14: Results of Panel Data Analysis
Coefficient
Independent Variable Model 1 Model 2 Model 3
C -0.289215* -0.015964 -0.005389*
CI 4.747518* 0.760335* 0.082353*
JIT 1.437697* 0.160359* 0.026605*
Income 0.609240* -0.003049 0.000563
AR(1) 0.449454* 0.000292 0.262428*
Adjusted R-squared 0.433970 0.064011 0.410688
Durbin Watson Statistic 2.064493 1.829581 2.347106
*significantly different at the 5% level of confidence.
The results show that both CI and JIT have significant impact on all three
dependent variables and capital intensity does have a greater impact on profitability
being reported than inventory to sales, which was used as a proxy of the degree to
which companies embraced JIT philosophy. This may be due to the fact that JIT
philosophy has an inconsistent effect on the sample companies. Annual inventory
adjustments still do vary a great deal during the period studied. It can be observed
that adjusted R-square for model 2 is quite low, which indicates that model 2 explains
only 6.40% of the variability in dependent variable can be explained by the estimated
multiple regression equation. This is likely due to the fact that there may be other
variables that affect net incomes used in the calculation of dependent variable in the
model. However, the result is consistent with the rest. The impact of capital intensity
on profitability being reported in every model is around four times as much of that of
the degree to which companies embraced JIT philosophy.
62
4.3.4 The Impacts of Capital Intensity and the Degree to Which Companies
Embraced JIT Philosophy in Each Year
In addition to the analyses conducted on a panel of nine-year data of 104
companies, similar analyses were also conducted using data of 104 companies in each
year. The purpose was to examine whether patterns of changes in coefficients of CI
and JIT can be observed. Table 4.15 show coefficients of independent variables in
each of the three models from year by year analysis.
Table 4.15: Coefficients of Independent Variables in Model 1
Year Coefficients Adjusted R-Square
a CI JIT Income
2003 -0.042 2.378* 0.250 0.333* 0.333
2004 -0.367.* 5.639* 1.919* 0.684* 0.391
2005 -0.949* 7.280* 1.608* 2.378* 0.710
2006 -0.604* 5.402' 1.786* 1.340* 0.644
2007 -0.362* 3.535 0.997* 0.968* 0.422
2008 -0.030 2.122 0.686' 0.371* 0.312
2009 -0.151 4.629* 0.649 0.347* 0.246
2010 -0.444* 7.731* 2.690* 0.330* 0.404
2011 0.068 2.470** 0.061 0.300* 0.249
*significantly different at the 5% level of confidence.
It should be noted that coefficients of CI and JIT are significantly different
from zero at the 5% level of significance in most years and CI has a greater impact on
net income differentials than JIT in every year as shown in Graph 4.3.
2003 2004 2005 2006 2007 2008 2009 2010 2011
63
Graph 4.3: Coefficients of CI and JIT in Model 1 Comparison
Table 4.16 shows coefficients of independent variables in model 2 from year
by year analysis.
Table 4.16: Coefficients of Independent Variables in Model 2
Year Coefficients Adjusted R-Square
a CI JIT Income
2003 0.845 -2.633 3.522 -1.060 -0.021
2004 -0.072 1.817* 0.268 -0.003 0.099
2005 -0.0.17 0.495* 0.174* 0.014 0.140
2006 -0.013 0.753* 0.129 -0.003 0.052
2007 0.010 0.097 0.0109* -0.001 0.137
2008 -0.039 2.021* 0.123 -0.013 0.079
2009 -0.022 0.876* 0.143 -0.007 0.113
2010 -0.008 0.355 0.318 -0.003 0.024
2011 0.010 -0.012 0.086* -0.002 0.037
*significantly different at the 5% level of confidence.
-4t- CI
-JIT 2004 2005 2006 2007 2008 2009 2010 2011
-1
2
3
64
It should be noted that coefficients of Cl and JIT are significantly different
from zero at the 5% level of significance only in some years and adjusted R-square is
extremely low. The results must be used with care. however, CI has a greater impact
on net income differentials than JIT only in years that coefficient of CI is significantly
different from zero as shown in Graph 4.4.
Graph 4.4: Coefficients of CI and JIT in Model 2 Comparison
Table 4.17 shows coefficients of independent variables in model 3 from year
by year analysis.
2003 2004 2005 2006 2007 2008 2009 2010 2011
65
Table 4.17: Coefficients of Independent Variables in Model 3
Year Coefficients Adjusted 12-Square
a CI JIT Income
2003 -0.000 0.022* 0.003* -0.000 0.205
2004 -0.002* 0.055* 0.013* 0.000 0.396
2005 -0.001* 0.035* 0.005* 0.000 0.455
2006 -0.002* 0.042* 0.018* 0.000 0.366
2007 -0.002* 0.022* 0.023* 0.000 0.560
2008 -0.007* 0.100* 0.040* 0.001 0.492
2009 -0.005* 0.074* 0.027* 0.000 0.403
2010 -0.011* 0.140* 0.066* 0.001 0.605
2011 -0.001* 0.010* 0.016* 0.000 0.561
*significantly different at the 5% level of confidence.
Coefficients of CI and JIT are significantly different from -zero At the 5% level
of significance in every year. Consistent with Model 1, CI has a greater impact on net
income margin differentials than JIT in every year as shown in Graph 4.5.
Graph 4.5: Coefficients of CI and JIT in Model 3 Comparison
CHAPTER 5
CONCLUSION
5.1 Summary of the Results and Implications
This study has provided many important findings. Firstly, capital intensity of
SET listed manufacturing companies have not increased significantly over the period
studied. As indicated earlier, this may be due to the fact that the nine-year period is
too short to observed significant changes in manufacturing technology or that new
manufacturing technology may have been implemented prior to the beginning of the
period studied. Secondly, it has shown that JIT system has not had consistent impacts
on SET listed manufacturing companies. This is likely to be attributable to nature of
products of the industries as discussed before.
Thirdly, the directions of annual inventory adjustments show that there are a
greater number of companies reporting higher net incomes under absorption costing
than those which would have been under variable costing in the majority of the
periods studied. Average inventory adjustments of the total sample vary at a great
extent, ranging between 27.40% and 52.20% of beginning inventory over the nine-
year period studied. This indicates that inventory adjustment retains its significance.
This study examined the impact of product costing methods from both annual
inventory adjustment and net income differentials. Annual inventory adjustment was
examined in this study because it can provide an indication of the extent to which net
income being reported and profit margin ratio can be influenced by the two product
costing methods given the capital intensity of the company in each year (Pong and
Mitchell, 2006). Therefore, the differences in net incomes under the two product
67
costing methods, resulting from inventory and production decisions, can be examined
from annual inventory adjustments. Average inv entory adjustment consistently
increases from 61.15 million Bahts in the year 2003 to I 15.05 million l3ahts in the
year 2011. However, when taking into account the effect of company size, average
inventory adjustment as a percentage of net incomes under absorption costing and
average inventory adjustment as a percentage of sales do not change much over the
nine years studied. This suggests that recent changes in operating environment have
not elevated earnings management through inventory or production decisions.
Average net income differential, which indicates the actual impact of the two
product costing methods, of the total sample and that of each industry increase over
the nine year studied. When taking into account of the company size, average net
income differential as a percentage of net income under absorption costing and that as
a percentage of sales vary slightly. This indicates that the current operating
environment has not driven net income differentials to he greater than what they have
been in the past.
Lastly, with the insignificant change in capital intensity and the degree to
which companies embrace JIT philosophy, it is unclear whether net income
differential was driven more by which of the two factors. This study, therefore,
examined the relative impact of the two factors. The OLS analysis of panel data
shows that while both capital intensity and the degree companies embraced JIT
philosophy do have significant impacts on profitability being reported, capital
intensity has a greater impact than the degree companies embraced JIT philosophy
over the nine-year period studied. The analysis was also conducted on data for each
year. It also shows that both capital intensity and the degree companies embraced JIT
68
philosophy do have significant impacts on profitability being reported in most years.
Also, capital intensity consistently has a greater impact than the degree companies
embraced JIT philosophy in most years. The result indicates that net income
differential is driven by both capital intensity and the degree to which companies
embrace ET philosophy. However, capital intensity has a greater impact and is
affected by long-term investment decisions. Additionally, there are many other ways
to influence net income being reported. This should deter management from
managing net incomes through inventory and production decisions. The evidence in
this study, therefore, provides further supports for accounting standard setters in
mandating product costing choices on theoretical and logical ground.
5.2 Limitations and Future Research
This study has several limitations. Firstly, it is acknowledged that there are
many other ways to manage net incomes. This study focuses on one possible way to
influence net incomes figures. Secondly, some of the variables used in this study arc
not directly available in the public domain and have been estimated. Lastly, due to
availability of data, the number of years studied may be not large enough to observe
the impact of new manufacturing technology implemented on fixed manufacturing
costs and the impact of JIT system. Future research may be conducted in other
countries with a longer period of time studied where the impact of capital intensity
and JIT system can be clearly observed and see if the findings of this study can be
generalized when using different sets of data.
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