an association between cassava pledging scheme and the...
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Ref. code: 25595822040100EWT
AN ASSOCIATION BETWEEN CASSAVA PLEDGING SCHEME AND THE
FINANCIAL PERFORMANCE OF CASSAVA PRODUCT MANUFACTURER
BY
TRINUJ VONGSOMTAKUL
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF MASTER OF
ENGINEERING (LOGISTICS AND SUPPLY CHAIN SYSTEMS
ENGINEERING)
SIRINDHORN INTERNATIONAL INSTITUTE OF TECHNOLOGY
THAMMASAT UNIVERSITY
ACADEMIC YEAR 2016
Ref. code: 25595822040100EWT
AN ASSOCIATION BETWEEN CASSAVA PLEDGING SCHEME AND THE
FINANCIAL PERFORMANCE OF CASSAVA PRODUCT MANUFACTURER
BY
TRINUJ VONGSOMTAKUL
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF MASTER OF
ENGINEERING (LOGISTICS AND SUPPLY CHIAN SYSTEMS
ENGINEERING)
SIRINDHORN INTERNATIONAL INSTITUTE OF TECHNOLOGY
THAMMASAT UNIVERSITY
ACADEMIC YEAR 2016
Ref. code: 25595822040100EWT
ii
Acknowledgements
First, I would like express my sincere gratitude to my advisor, Assoc. Prof. Dr.
Jirachai Buddhakulsomsiri for his encouragement, sacrifice, patience and dedication in
guiding, teaching, and giving me advice throughout my master degree at SIIT.
Besides my advisor, I would like to thank Assoc. Prof. Dr.Parthana Parthanadee
who suggested me the thesis topic, provided me an access to essential source of data as
well as supporting me through my entire research thesis.
Furthermore, I would like to thank my thesis committees, Assist. Prof. Dr.
Morrakot Raweewan and Assoc. Prof. Dr. Tanachote Boonvorachote who constantly
guided me and gave me suggestions to improve my thesis results.
Last but not least, I would like to thank my friends at SIIT who endlessly gave
me encouragement and practical support throughout the study at SIIT. Most
importantly, I would like to thank my family who is a big support behind my education
success.
Ref. code: 25595822040100EWT
iii
Abstract
[AN ASSOCIATION BETWEEN CASSAVA PLEDGING SCHEME AND THE
FINANCIAL PERFORMANCE OF CASSAVA PRODUCT MANUFACTURER]
by
TRINUJ VONGSOMTAKUL
Bachelor of Engineering. (Industrial Engineering), Sirindhorn International Institute
of Technology, Thammasat University, 2015.
Master of Engineering (Logistics and Supply Chain Systems), Sirindhorn
International Institute of Technology, Thammasat University, 2017.
Abstract
This paper involves a study that investigates the association between government
pledging program and the financial performance of cassava product manufacturers in
Thailand using multiple linear regression modelling. Financial performance in terms of
return on equity was the response variable obtained from financial statement of the year
after the pledging program. The list of manufacturers who joined the pledging program
is the key research variable. Internal factors and financial statement of 24 samples of
starch manufacturers and 12 chip manufacturers were collected and treated as control
variables. The results indicate that there is a positive association between
manufacturers’ financial performance and participating in the pledging program. The
study also investigated characteristics of companies that joined the pledging program
using binary logistics regression.
Keywords: Association, pledging scheme, financial performance, cassava product
manufacturers
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Table of Contents
Chapter Title Page
Signature Page i
Acknowledgements ii
Abstract iii
Table of Contents iv
List of Figures vi
List of Tables vii
1 Introduction 1
1.1 Research Overview 1
1.2 Problem Statements 3
1.3 Objectives 3
2 Literature Review 4
2.1 Background of the Study 4
2.2 Financial Performance Indicator 5
2.3 Company’s Success Factors 5
3 Methodology 12
3.1 Sample and Data Collection 12
3.2 Research Questions 12
3.3 Regression Modelling 13
4 Results and Discussions 17
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4.1 Sample Demographic 17
4.2 Multiple Regression to Evaluate an Association between 18
Participating in the Pledging Scheme and Financial Performance
4.3 Binary Logistics Regression to Determine the Characteristics of 21
the Manufacturers who joined the Pledging Scheme.
5 Conclusions and Recommendations 24
References 25
Appendices 28
Appendix A 29
Appendix B 32
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List of Figures
Figures Page
4.1 Residual plot of return on equity 21
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List of Tables
Tables Page
2.1. Literature review of banking industry with the response variables and 7
independent variables
2.2. Literature review of manufacturing industries with the response variables 9
and the independent variables.
2.3. Literature review of service industries with the response variables and the 10
independent variables.
2.4. Study using control variables 11
3.1. List of response variable, research variable and control variable 14
4.1 Regression model results 17
4.2 Regression model results 18
4.3 Odds ratios for continuous predictors 18
4.4 Binary logistic regression: deviance table 21
4.5 Odds ratios for continuous predictors 22
4.6 Goodness of fit tests 22
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Chapter 1
Introduction
1.1 Research Overview
Cassava is the third most important economic crops of Thailand after rice and
para rubber. In 2015, Thailand is the world’s largest exporter of cassava (2,771 USD),
followed by Vietnam (1,139 million USD), Costa Rica (72 million USD), Peru (29
million USD) and China (25 million USD). The world’s largest importer of cassava is
China (3,246 million USD), Japan (418 million USD), US (363 million USD), and
Germany (326 million USD). Cassava can be converted into several forms including
cassava chips, tapioca starch, sago, and pallets. In 2015, Thailand export values of these
products were 51,868.82 million thb for cassava chips, 41,166.70 million thb for
tapioca, 771.04 million thb for sago, and 293.6 million thb for pellets (NSTDA, 2017).
Due to the Thai cassava farmer’s poverty, the Thai government had launched
the price intervention policies in some harvest years to secure farmer’s income. The
Thai government started launching the price intervention policies during 1999/2000 to
2008/2009 cassava seasons (Parthanadee et al., 2016). From 2011-2017, Thailand faced
an economic loss of approximately 5.8 billion thb as the government set price 50%
higher than the market price. Thai cassava farmers gained little benefits from the
pledging program, while most benefit went to cassava exporters, manufacturers, and
cassava yards (TDRI, 2011). The government pledging program was set again in
2011/2012 and 2012/2013 seasons (Public Warehouse Organization, 2013; North
Eastern Tapioca Trade Association, 2013). Some researcher suggested that the Thai
government should promote the market oriented policy instead of launching the
pledging program (Laiprakobsup, 2014). Interestingly, another researcher found out
that the Thai cassava farmers preferred the price guarantee policy over the pledging
program (Poramacom et al., 2013).
Net income of cassava manufacturers may depend on both internal and external
factors. Internal factors may include how they manage their asset, liability, and owner
equity. It may also include how they manage their production processes, human
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2
resource, and supplier and customer relationships. External factors may include the
government intervention policies, cassava production from farmers (supply), domestic
and oversea demand, cassava root and cassava product prices, alternative crop price,
and so on.
This paper focuses on investigating whether the government cassava pledging
program has an association with the cassava product manufacturers’ financial
performance. The financial performance data of starch and chip manufacturers were
collected during the year 2013 when pledging programs were in effect. The list of
cassava product manufacturers who participated in the pledging program in the
2012/2013 season were collected and used as the key research variable. Also, the
research would like to identify the internal factors that influence the financial
performance of cassava product manufacturers. In addition, the characteristics of those
manufacturers who participated in the program were identified using binary logistics
regression.
Past research that studied the benefits received by the farmers from government
pledging policies involves the rice pledging program. The rice pledging program, which
was implemented during 2011-2014, had severely damaged the Thai rice milling
business in the long term. However, the benefits from the pledging program that
actually went to the farmers might be much less than hoped for. The rice might have
exposed to corruptions at every stage, ranging from farmers, collectors, surveyors,
yards, manufacturers, to government officials, as evidence from many stakeholders are
under investigation by the current government. (TDRI, 2014). Moreover, the rice
pledging program was conducted without the conditions on the rice quality. Many
researchers (Laiprakobsup, 2014; Permani and Vanzetti, 2015; Attavanich, 2016;
Laiprakobsup, 2014; and Attavanich, 2016) concluded that the Thai rice pledging
program was set up merely for the government to win the election. They also suggested
that the Thai government should instead focus on the long term benefits such as
developing the Thai rice quality, warehousing, fertilizers, harvesting tools, providing
low interest loan for farmers and even educating the farmers to produce higher rice
standard.
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1.2 Problem Statement
Net income of cassava manufacturers may depend on both internal and external
factors. Internal factors may include how they manage their asset, liability, and owner
equity. It may also include how they manage their production process, human capital
and their downstream industries. The external factors may include the government
intervention policies, cassava yield from farmers (supply), demand from China,
alternative crop price, and so on. In this study, the researcher would like to determine
if the financial performance of the cassava product manufacturers is influenced
specifically by the Thai government cassava pledging policy. Also, the research would
like to determine what internal factors that influence the financial performance of
cassava manufacturers.
1.3 Objectives
To determine an association between the cassava pledging policy and the
financial performance of cassava manufacturers.
To determine factors that affect the financial performance of cassava
manufacturers.
To determine the characteristics for the manufacturers who participated in
the pledging policy.
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Chapter 2
Literature Review
2.1 Background of the Study
Several research studies focused on the impact of the agricultural policy on the
benefits of the farmers and on economic impact of the nation as a whole in developing
countries. Due to the farmer poverty, government had to intervene to assist farmers in
increasing crop price. The papers reviewed will be based on the government
intervention policy on rice and palm oil and the benefits received by the farmers and
other related consequences of such policy.
Government intervention policy is intensive in the developing countries. During
2011-2014, Thai government was heavily criticized for its massive public spending of
$3.78 billion for the rice pledging policy which incurred a loss of $16 billion. Despite
large spending, the benefit gained was less than expected as the program aimed to
improve the net income of the farmers. Price guarantee is the more popular among the
short term subsidy program. Not only that the pledging scheme did not improve the
economic viability in rice farming, it also has a negative effect on the national politics
and economy in the long term.
Rice pledging scheme had damaged the Thai rice milling business, particularly the
rice millers that did not participate in the program, as the government sets the price
much higher than the market price. The program was also exposed to corruption at
every stage. The government had to keep large stock of rice and sold them at a loss.
Permani and Vanzetti (2016) pointed out that out of 18 million ton of rice accumulated
from the pledging scheme, only 10% were on standard quality thus government had to
stock rice for more than five years considering the rice spoilage.
In addition, it encouraged the farmers to sell their rice without assessing the rice
quality, thus lowering Thai rice standard. The study concluded that the pledging scheme
did not improve the yield of farmers as it did not give any incentive to improve and
increase rice productivity. The program was set up merely for government to win the
election. Laiprakobsup (2014), Permani and Vanzetti (2015), Attavanich (2016),
Laiprakobsup (2014) and Attavanich (2016) suggested that government should instead
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assist farmers in the long run such as providing supports in terms of fertilizers,
harvesting tools, rice variety, warehouse and even providing low interest loan for
farmers.
Castiblanco, et al. (2015) investigated the impact of subsidy policy and blending
mandates for the palm oil producers and biodiesel producers in Columbia. Similar to
the Thai rice pledging scheme, the program benefits the crude palm oil producers in a
short term only. However, in a long term, biodiesel producers gained the most benefits
from the subsidy program. Moreover, the efficiency loss and deadweight loss are
insignificant in the short term but become important in the long term. The study
suggested that the subsidy policy alone was not effective and thus should be combined
with the blending mandates. Integrating the subsidy policy and the blending mandates
led to positive effect on the biodiesel producers as they increased productivity.
2.2 Financial Performance Indicator
From the literature review, no research has studied the relationship between the
government intervention policy and financial performance of cassava product
manufacturers. This study aims to fill this research gap. Regarding financial
performance, one of the most widely used measures of business performance is return
on equity (ROE). ROE measures how much return is received from an investment.
Research studies that focused on ROE are Sufian and Habibullah, 2010 in Banking
industry; Dietrich and Wanzenried, 2011; Gul et al., 2011; and Shubita and Alsawalhah,
2012.
2.3 Company’s Success Factors
Much research studied the factors that contribute to company’s success. The
company’s success may include return on asset (ROA), return on equity (ROE), return
on investment (ROI), export performance, turnover, productivity, and profitability.
Research that studied the factors to company’s success were mostly found in the
banking industries, followed by manufacturing and service industries. Table 2.1 shows
that most researchers prefer using ROA and ROE as profitability indicator in banking
industry. Most banking industries were found using capital adequacy, size in terms of
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asset, GDP and inflation as the independent variables in explaining the profitability.
Some used loan to customer (measure of liquidity), credit risk (loan loss provisions/total
loans), and a few used cost to income ratio, age of the bank, market concentration,
market capitalization, and ownership (public bank if public sector runs more than 50%
of market share). From table 2.2, most researchers used ROA and ROE as profitability
indicator and used as size (in terms of asset) and debt ratio as the common independent
variable in the manufacturing companies. Agiomirgianakis et al. (2006) used size, age,
management efficiency, and debt leverage to predict profitability. Kim and Hemmert,
(2016) used export, managerial skills, human resource, technological resources,
marketing resources, strength of customer relationship and number of network ties as
the Table 2.3 shows that the common variables used in the service industry are size,
age and location of the business. Gursoy and Swanger (2007) used human resource,
technological resource, marketing resource, and strength of customer relationship in
testing the profitability and ROI. Aissa and Goaied (2016) used the size, age, debt,
affiliation, location, management efficiency and managerial skills to explain the return
on asset (ROA).
Company’s characteristics, management personal characteristics, strategic internal
factors (human resource), and financial conditions will be used for setting up the
hypothesis in this study. Many studies use control variables to help explain the
variability in the response variable. Table 2.4 shows that firm size, inflation, and sales
growth were widely used as control variables in banking industry (Athanasoglou et al.,
2008; Shubita and Alsawalhah, 2012; Kanasa et al., 2012; Djalilov and Piesse, 2016
and Hamid et al., 2015). In addition, Antoniou et al. (2008) used equity premium, term-
structure of interest rate, laws and regulations, ownership concentration, creditor rights
and anti-director rights as control variables.
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Tab
le 2.1
Literatu
re review
of b
ankin
g in
du
stry with
the resp
onse v
ariables an
d in
dep
enden
t variab
les
Stu
dy
Auth
ors, y
ear
Resp
onse
variable
Cap
ital
adeq
uecy
Size
(Aset)
Loan
Cost:Inco
me
Ratio
Age
GD
PInflatio
nC
oncentratio
n
Cred
it
risk
Mark
et
capitalizatio
nO
wnership
Ban
k-sp
ecific
,
industry
-specific
and m
acro
eco
no
mic
dete
rmin
ants o
f
ban
k p
rofitab
ility
(Sufian
and
Hab
ibullah
, 20
10
)R
OE
or R
OA
xx
xx
xx
Fac
tors in
fluencin
g
the p
rofitab
ility o
f
do
mestic
and
fore
ign c
om
merc
ial
ban
ks in
the
Euro
pean
Unio
n
(Pasio
uras an
d
Ko
smid
ou, 2
00
7)
RO
Ax
xx
xx
xx
Dete
rmin
ants o
f
ban
k p
rofitab
ility
befo
re an
d d
urin
g
the
crisis: E
vidence
from
Sw
itzerlan
d
(Die
trich an
d
Wan
zenrie
d, 2
01
1)
RO
Ex
xx
xx
x
Fac
tors A
ffectin
g
Ban
k
Pro
fitability
in
Pak
istan
(Gul e
t al., 20
11
)R
OE
, RO
Ax
xx
xx
x
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Tab
le 2.1
(contin
ued
)D
ete
rmin
ants o
f
ban
k p
rofitab
ility in
Pak
istan: In
tern
al
facto
r analy
sis
(Javaid e
t al., 20
11
)R
OA
xx
x
The R
elatio
nsh
ip
betw
een C
apital
Stru
ctu
re an
d
Pro
fitability
(Shubita an
d
Alsaw
alhah
, 20
12
)R
OE
, RO
Ax
Dete
rmin
ants o
f
ban
ks’ p
rofitab
ility:
evid
ence fro
m E
U
27
ban
kin
g sy
stem
s
(Petria e
t al., 20
15
)R
OE
, RO
Ax
xx
xx
Revisitin
g b
ank
pro
fitability
:
A se
mi-p
arametric
appro
ach
(Kan
asa et al.,
20
12
)R
OA
, RO
Ex
xx
Dete
rmin
ants o
f
ban
k p
rofitab
ility in
transitio
n c
ountrie
s:
What m
atters m
ost?
(Djalilo
v and
Pie
sse, 2
01
6)
RO
Ax
xx
xx
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9
Tab
le 2.2
Literatu
re review o
f man
ufactu
ring in
du
stries with
the resp
on
se variables an
d th
e ind
epen
den
t variables.
Stu
dy
Auth
ors, y
earR
espo
nse
Size
Age
Man
agem
ent
efficien
cy
Exp
ort
Man
agerial
skills
Hum
an
resourc
e
Tec
hno
logic
al
resourc
es
Mark
eting
resourc
es
Stren
gth
of
Custo
mer
relatiosn
hip
Num
ber
of
Netw
ork
ties
Deb
t
ratio
Fin
ancial fac
tors affec
ting
pro
fitability
and
emp
loym
ent g
row
th: th
e
case o
f Greek
man
ufac
turin
g
(Agio
mirg
ianak
is
et al., 2006)
Pro
fitability
xx
xx
x
What d
rives th
e exp
ort
perfo
rman
ce o
f small an
d
med
ium
-sized
sub
co
ntrac
ting firm
s? A
stud
y o
f Ko
rean
man
ufac
turers
(Kim
and
Hem
mert, 2
016)
Exp
ort
perfo
rman
c
e
xx
xx
xx
x
A p
anel d
ata analy
sis of
pro
fitability
determ
inan
ts
(Prath
eepan
,
2014)
RO
Ax
x
Fac
tors d
etermin
ing
Pro
fitability
:
A S
tud
y o
f Selec
ted
Man
ufac
turin
g C
om
pan
ies
listed o
n C
olo
mb
o S
tock
Exchan
ge in
Sri L
anka
(Siv
athaasan
et al., 2013)
RO
E,R
OA
xx
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Tab
le 2.3
Literatu
re review o
f service ind
ustries w
ith th
e respo
nse variab
les and
the in
dep
end
ent variab
les.
Stu
dy
Auth
ors, y
ear
Industry
Resp
onse
variable
Size
Age
Inflation
Deb
tA
ffiliation
locatio
nM
anagement
efficiency
Managerial
skills
Hum
an
resource
Techno
logical
resources
Mark
eting
resources
Strength o
f
Custo
mer
relationship
Mark
et
segment
Pro
duct
develo
pm
ent
Service
Perfo
rman
ce-
enhan
cin
g in
tern
al
strategic
facto
rs and
co
mpete
ncie
s:
Impac
ts on fin
ancial
success
(Gurso
y an
d
Sw
anger, 2
00
7)
Servic
e
Pro
fitability
,
RO
I
xx
xx
xx
Strate
gic
Ho
tel
Deve
lopm
ent an
d
Po
sitionin
g:
The E
ffects o
f
Reve
nue D
rivers o
n
Pro
fitability
(O’n
eill
and M
attila,
20
06
)
Ho
tel
Net
operatin
g
inco
me
perc
entag
e
xx
xx
Dete
rmin
ants o
f
Tunisian
ho
tel
pro
fitability
:
The ro
le o
f
man
agerial
effic
iency
(Aissa an
d
Go
aied, 2
01
6)
Ho
tel
RO
Ax
xx
xx
xx
x
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Tab
le 2.4
Stu
dies u
sing co
ntro
l variab
les
Stud
yA
uth
ors, ye
arIn
du
stryFirm
Age
SizeG
DP
Inflatio
nSale
s
grow
th
Inte
rest
rateC
apital
Finan
cial
structu
reC
ost
Equ
ity
pre
miu
mO
wn
ersh
ipC
on
cen
tration
cred
it rights
&an
ti
cred
it rights
Laws an
d
regu
lation
s
Ind
ustry
type
Cyclical
ou
tpu
t
Ban
k-spe
cific, ind
ustry-sp
ecific
and
macro
eco
no
mic
de
term
inan
ts
of b
ank p
rofitab
ility.
(Ath
anaso
glou
et al., 2008)
Ban
kx
xx
xx
The
Re
lation
ship
be
twe
en
Cap
ital
Structu
re an
d P
rofitab
ility.
(Shu
bita an
d
Alsaw
alhah
,
2012)
Ban
kx
x
Wh
at drive
s the
exp
ort
pe
rform
ance
of sm
all and
me
diu
m-size
d su
bco
ntractin
g
firms? A
stud
y of K
ore
an
man
ufactu
rers.
(Kim
and
He
mm
ert,
2016)
Man
u
facturin
gx
x
Re
visiting b
ank p
rofitab
ility:
A se
mi-p
arame
tric app
roach
.
(Kan
asa et al.,
2012)B
ank
xx
x
De
term
inan
ts of b
ank
pro
fitability in
transitio
n co
un
tries: W
hat
matte
rs mo
st?
(Djalilo
v and
Pie
sse, 2016)
Ban
kx
xx
The
De
term
inan
ts of C
apital
Structu
re: C
apital M
arket-
Orie
nte
d ve
rsus B
ank-O
rien
ted
Institu
tion
s.
An
ton
iou
et al. (2008)
Ban
k
x
xx
xx
x
Cap
ital Structu
re an
d
Pro
fitability in
Family an
d N
on
-
Family Firm
s: Malaysian
evid
en
ce.
Ham
id
et al. (2015)
Ban
kx
xx
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CHAPTER 3
METHODOLOGY
3.1 Sample and Data Collection
The research used panel data from financial statements of starch manufacturers and
cassava chip manufacturers in the year 2013 that were submitted to the Department of
Business Development, Ministry of Commerce, Thailand. Additional data about the
companies’ internal factors were collected through in-depth interviews using
questionnaires by phone, e-mails and by post. The input data contain a total of 24 starch
manufacturers and 12 chips manufacturers that had complete information of both
financial statement and internal factors. Moreover, an important piece of information is
the list of cassava product manufacturers who participated in the pledging scheme in
2013, which was obtained from the Public Warehouse Organization, Ministry of
Commerce, Thailand.
3.2 Research Questions
There are two research questions. The key research question is whether or not there
is a relationship between government intervention policy, specifically, the pledging
scheme, and financial performance of cassava product manufacturers. The second
hypothesis is to identify the characteristics of those manufacturers who joined the
pledging policies. The hypothesis can be stated as follows.
Hypothesis 1: There is a significant association between cassava product
manufacturers’ financial performance in terms of ROE and pledging scheme
participation.
Hypothesis 2: What are the characteristics of those manufacturers who joined the
pledging policies?
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In addition, the third research questions are to identify the companies’ internal factors
that are highly associated with their financial performance, and thus, can help describe
the variability in the financial performance, while evaluating the primary research
question. The following hypotheses can be stated.
Hypothesis 3: There are associations between companies’ internal factors and ROE of
cassava product manufacturers, where internal factors include:
(1) managerial capability in terms of work experience (years), level of education, and
gender of manager;
(2) company characteristics in terms of company age, size, percentage of company’s
sales in domestic market, level of workforce, amount and topic of training, type of
customers’ industry, standards and certifications that company has, production
capacity, sources of cassava, yield of cassava (ton/ha), minimum, average and
maximum inventory days of finished goods, and number of months that production
process is in operation;
(3) supplier relationship in terms of number of farmers that are regular suppliers, having
knowledge sharing among farmers, and having regular meetings with farmers;
(4) customer relationship in terms of having purchase contracts, having knowledge
sharing with customers and percentage of return customers.
3.3 Regression Modelling.
This study used the return on equity (ROE) in 2013 as the response variable.
The key research variable is a variable that indicates whether or not a manufacturer
joined the pledging scheme in 2013. The control variables are from the manufacturers’
financial performance data, including cash ratio, inventory turnover, fixed assets
turnover, total assets turnover, debt ratio, account payable turnover, payable deferral
period, and debt to equity ratio in 2013. Another set of control variable is the firm’s
internal factors including managerial capability, company characteristics, supplier
relationship, and customer relationship as listed previously.
General form of the regression model that relates the response and the research variables
is as shown below.
ROE = β0 + β1PS + β2CR + … +
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where the variables are listed in Table 3.1 and is the error term.
Table 3.1 List of response variable, research variable and control variable
Variables Definition and formula
Response variable
ROE Return on equity
Research variable
PS Indicator variable, which takes on the value of 1 if a
manufacturer joined the pledging program; or 0
otherwise.
Control variable
CR Cash ratio = cash / current liability
INV Inventory turnover = Cost of goods sold / average
inventory
Fixed Fixed assets turnover = Net sales / fixed asset
TAT Total assets turnover = Sales / total assets
DR Debt ratio = Total debts / total assets
APT Account payables turnover = Cost of goods sold /
average payable
PDP Payable deferral period = Payables / (cost of goods
sold/365)
DER Debt to equity ratio = Total debts / total assets
Company characteristics
Age The age at which the company registered with the
Department of Business Development
RegCap Company’s registered capital
PT Product type starch or pallet
DS Percentage of company’s sales in domestic market
Labor No. of labor in production process
TR No. days of training/year/person
Topic Topic of training: safety, production, managerial
skill, and software skill
AvgC, MaxC Average and maximum capacity in ton per day
Cert Standard certification: GMP, HACCP, Halal,
Kosher, ISO 9000, ISO 9001, etc.
Cus_d Type of customers’ in domestic industry: native
starch, modified starch, sweetener, glue, paper
Cus_e Type of customers’ in export industry: Modified
starch, food, glue, paper
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Source
Sources of cassava: buying in front of factory,
middle merchant, or factory farm
Area Planting area (hectare) of a company
Yield Yield of cassava (ton per hectare)
Lead Manufacturing lead time (min)
Min_invt, Avg_invt,
Max_invt
Minimum, average and maximum inventory days of
finished goods
Month_manu Number of months that production process is in
operation
Relationship to farmers
(supplier)
Mem Supplier relationship in terms of number of farmers
that are regular suppliers
TF* Sharing agricultural knowledge to farmers
Manufacturer meeting
with farmers (MF)*
Strengthening relationship with farmers
Buying contract (BuyC)* Having buying contract?
Sharing manufacturing
knowledge (ShareC)*
Is there an exchange of information, giving
specification
% Old customer (Old.c) Percentage of old customer
Managerial capability
Gender* Male/Female
Edu* Primary, secondary, bachelor, or master degree
Exp Number of years of experience of manager
*Categorical variable
The final model is fitted such that it contains only significant variables with p-value
being less than 0.05. However, other variables having p-value more than 0.05 were kept
in order to help explain the variability in the model.
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3.4 Binary Logistics Regression
Logistics regression is commonly used for predicting the probability of occurrence of
two or more binary dependent variable(s) using one or more quantitative or categorical
variables.
𝑝(𝑥)
1 − 𝑝(𝑥)= 𝑒𝛽0+𝛽1𝑋
P(Y) = exp(Y')/(1 + exp(Y'))
𝑦 = {1, 𝛽0 + 𝛽1𝑋 + > 0
0, 𝑒𝑙𝑠𝑒
The binary logistic regression can be explained using the odd ratio. The nominator
represents the probability of success and the denominator represents the probability of
failure. The expression on the left is the “odd”. Similar to the simple linear regression,
increasing one unit of x will change the slope of β accordingly.
For this study, the researcher would like to determine the characteristics of the
manufactures who joined the pledging policies. Thus, the dependent variables are the
list of manufacturers who participated in the pledging policy. The independent variables
are a mixture of categorical and quantitative variable based on the questionnaire.
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Chapter 4
Results and Discussions
4.1 Sample Demographic
The sample of processing factories where internal factor data were collected
were obtained from personal interview, phone interview, e-mail, and mail using
questionnaire form (see Appendices A and B). A total of 36 responses were obtained,
24 of which are starch manufacturers and 12 are chips manufacturers. Note that there
are a total of 87 starch manufacturers and 335 chip manufacturers in Thailand (NSTDA,
2017). Tables 3.2-3.3 provides geographic data of the response by region and by
province, respectively. According to the Revenue Department, the small, medium, and
large companies were distinguished by size of fixed asset. The small enterprise would
possess the fixed asset being less than 50 million baht, the medium enterprise would
possess the fixed asset between 51-200 million baht and the large enterprise would have
the fixed asset more than 200 million baht (Department of Revenue, 2017).
Table 4.1 Number of samples collected from each region in Thailand
Region Large Medium Small
NE 11 2 2
C 4 2 1
W 2 0 0
E 3 2 2
Total 20 6 5
There are more number of starch and cassava chips factories in the northeast of
Thailand. Therefore, most samples were collected from Nakhon Ratchasima in the
northeast region. The second most sample collected is from the central region. Even
though only 36 samples were collected, many samples were from the largest chain of
manufacturers, Eiam group. Eiam group consists of Eiamheng and Eiamburapa group.
Eiamheng group includes Eiamheng Topica Starch Industry Co., Ltd.; Eiam E-San
Topica Starch Co., Ltd.; Eiamheng Modified Starch Co., Ltd.; and Eiamrungruang
Industry Co., Ltd. Eiamburapa Group includes Eiamsiri Starch Co., Ltd.; Eiamburapa
Starch Co., Ltd.; and Eiam Ubon Co., Ltd. Samples collected covered all companies in
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the chain except Eiam Ubon Co., Ltd. as the company is in ethanol industry, which is
out of the scope of this study.
4.2 Multiple Regression to Evaluate an Association between Participating in the
Pledging Scheme and Financial Performance
The regression model results are shown in Table 4.1, followed by the regression
equations.
Table 4.2 Regression model results.
Source DF Adj Dev Adj Mean Chi-Square P-value
Regression 7 18.0139 2.5734 20.51 0.000
Min_invt 1 0.4314 0.4314 3.44 0.074
Fixed 1 1.0052 1.0052 8.01 0.008
DER 1 12.1741 12.1741 97.05 0.000
TP 1 1.9539 1.9539 15.58 0.000
HACCP 1 0.4765 0.4765 3.8 0.061
Exporter2 1 0.7216 0.7216 5.75 0.023
PS 1 1.0264 1.0264 8.18 0.008
Error 28 3.5124 0.1254
Total 33 21.5263
Table 4.3 Regression equation.
HACCP Exporter2 PS TP
0 0 0 1
ROE
= -0.612 + 0.000985 Min_invt
+ 0.0462 Fixed - 0.06249 DER
0 0 0 2
ROE
= -0.034
+ 0.000985 Min_invt
+ 0.0462 Fixed - 0.06249 DER
0 0 1 1
ROE
= -0.221 + 0.000985 Min_invt
+ 0.0462 Fixed - 0.06249 DER
0 0 1 2
ROE
= 0.357 + 0.000985 Min_invt
+ 0.0462 Fixed - 0.06249 DER
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0 1 0 1
ROE
= -0.252 + 0.000985 Min_invt
+ 0.0462 Fixed - 0.06249 DER
0 1 0 2
ROE
= 0.326 + 0.000985 Min_invt
+ 0.0462 Fixed - 0.06249 DER
0 1 1 1
ROE
= 0.139
+ 0.000985 Min_invt
+ 0.0462 Fixed - 0.06249 DER
0 1 1 2
ROE
= 0.717 + 0.000985 Min_invt
+ 0.0462 Fixed - 0.06249 DER
1 0 0 1
ROE
= -0.332 + 0.000985 Min_invt
+ 0.0462 Fixed + 0.000985 Min_invt
From figure 1 below, the model assumption is conformed. The residuals are
normally distributed and independent of one another. The research variable of interest,
PS, is significant with p-value of 0.000. Its positive coefficient of 0.0463 indicates that
the government pledging scheme is positively associated with ROE for both tapioca and
ethanol manufacturers that participated in the program, even though the average
pledging price in 2013 is slightly lower than 2012. Thus, a null hypothesis can be
rejected. The significant factors that affect ROE can be explained below.
1. Minimum inventory days (Min_Inv)
This is the amount of inventory that the company keeps before releasing to the market.
From Table 4.2, p-value is significant being 0.018 and the coefficient is positively
correlated to the ROE. For those who keep their stock more than 1 day, ROE is 25%
higher than those who release stock immediately after production since manufacturers
would wait for price to rise before selling.
2. Fixed asset turnover (Fixed)
Fixed asset turnover is significant with p-value being 0.001. Fixed asset turnover is
sales over total asset. Thus, the higher the sales, the more profitability in terms of ROE.
3. Type of product (TP_1)
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Type of product 1 represents starch industry. It is positively correlated to ROE which
means that starch industry could gain more profitability in terms of ROE than the chip-
pallet industry.
4. HACCP
Manufacturers who have HACCP have more creditability in terms of food safety and
hazard control. The manufacturers having HACCP could gain more profitability than
others by 40% than those who do not have HACCP certificate.
5. Exporter2
Exporter2 is the outbound industry in which the manufacturer export their product to
the exporter and the exporter sell the product to another exporter. The manufacturers
who export their product to the exporter has significantly higher ROE than those who
do not by 97% on average.
6. DER
Debt to equity ratio is negatively correlated to return to equity ratio. Several studies
found the negative correlation between leverage and profitability. Shubita and
Maroofalsawalhah (2012); Pedro Proença, Raul M. S. Proença, Laureano, and
Laureano (2014); Hamid, Abdullah and Kamaruzzaman (2015). However, some
researchers found a positive correlation between leverage and profitability (MacKay
and Phillips, 2001 and Gaud et. all, 2007).
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Figure 4.1 Residual plots of return on equity
4.3 Binary Logistics Regression to Determine the Characteristics of the
Manufacturers who joined the Pledging Scheme.
Since, PS is significant, a researcher would like to determine the characteristics of the
manufacturers who participated in the pledging scheme using logistics regression.
Table 4.4 shows the binary logistic regression result. Table 4.3 presents the odd ratios.
Table 4.4 Binary logistic regression: deviance table
Source DF Adj Dev Adj Mean Chi-Square P-value
Regression 10 21.9477 2.19477 21.95 0.015
Age 1 8.0164 8.01636 8.02 0.005
Member 1 7.1453 7.14532 7.15 0.008
Labor 1 2.6637 2.66374 2.66 0.103
RC 1 6.0594 6.05944 6.06 0.014
Avg_cap 1 0.9444 0.94439 0.94 0.331
Yield 1 6.2199 6.21985 6.22 0.013
Lead 1 5.4491 5.44911 5.45 0.020
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Month_manu 1 7.9799 7.97994 7.98 0.005
Min_invt 1 3.0296 3.02960 3.03 0.082
TR 1 0.0404 0.04039 0.04 0.841
Error 23 25.1863 1.09506
Total 33 47.1340
Table 4.5 Odds ratios for continuous predictors
Variable Odd Ratio 95% CI
Age 1.1760 (1.0151, 1.3624)
Member 1.0050 (0.9998, 1.0103)
Labor 1.0086 (0.9938, 1.0237)
RC 1.000 (1.0000, 1.0000)
Avg_cap 0.9969 (0.9905, 1.0033)
Yield 0.4164 (0.1394, 1.2435)
Lead 0.9638 (0.9269, 1.0021)
Month_manu 0.3741 (0.1612, 0.8682)
Min_Invt 1.0105 (0.9834, 1.0383)
TR 0.9287 (0.4384, 1.9674)
Regression Equation
P(yes) = exp(Y')/(1 + exp(Y'))
Y' = 4.39 + 0.1621 Age + 0.00503 Member + 0.00859 Labor + 0.000000 RC
- 0.00311 Avg_cap - 0.876 Yield - 0.0369 Lead - 0.983 Month_manu
+ 0.0104 Min_invt - 0.074 TR
Table 4.6 Goodness-of-Fit tests
Test DF Chi-square P-value
Deviance 23 25.19 0.341
Pearson 23 32.13 0.098
Hosmer-Lemeshow 8 5.93 0.655
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According to Table 4.5, the significant variables are not be included in the 95%
confidence interval. Hosmer-Lemeshow shows a p-value of 0.655 which indicates that
the model is not lack of fit. From the binary logistic regression result in Table 4.4, the
company that participated in the pledging policy tends to be the company that has been
established for a longer period of time than others, p-value of age is 0.005. These
companies tend to have a large registered capital and large number of labors. Moreover,
these companies have shorter manufacturing lead time and operate for a shorter month
period in a year which show that they can operate at a higher efficiency than others.
They also keep their inventory for longer time and release them to the market when
price increases. This behavior can be done for those who have considerably large
amount of stock in which some part is sold directly into the market and some is kept
for price to increase. Yield is significant with p-value of 0.013 and the coefficient is
negative. Since, only 9 companies out of 36 companies grow their own cassava farm,
thus it cannot be concluded that manufacturers who tend to join the pledging scheme
will have low yield of their cassava product. To conclude, these characteristics show
that the companies that tend to join the cassava pledging policy are those who are big
companies. This research result may indicate that the SME may not benefit from the
cassava pledging policy since the pledging policy did not motivate the SME to
participate.
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Chapter 5
Conclusion and Recommendations
The study investigates the relationship between the government market intervention
policy and the financial performance of the cassava product manufacturers measured in
terms of return on equity. The model results suggested that ROE was positively
associated with the manufacturers participating in the program. This suggested that the
pledging program, which aimed at elevating the farmers’ financial situation, also had a
positive impact on the cassava chip and starch manufacturers’ financial performance.
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Appendices
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Appendix A
Questionnaire form for cassava starch manufacturers
แบบสอบถามส าหรบผประกอบการอตสาหกรรมแปงมนส าปะหลงดบ / แปงดดแปร
วนทใหขอมล
สวนท 1 ขอมลทวไปเกยวกบสถานประกอบการ
1) ชอผใหขอมล
ชอสถานประกอบการ
เลขท หมท ซอย ถนน
ต าบล/แขวง อ าเภอ/เขต จงหวด
รหสไปรษณย โทรศพท โทรศพทมอถอ
โทรสาร เวบไซต
เรมตนกจการเมอ หรอระบอายของกจการ ป
ปจจบนทานด ารงต าแหนง □ เจาของกจการ □ ผบรหาร □ หนสวนกจการ □ พนกงาน □ อนๆ โปรดระบ
เพศ □ชาย □ หญง ประสบการณในต าแหนงปจจบน ป วฒการศกษา
2) ประเภทอตสาหกรรมและก าลงการผลต (ตอบไดมากกวา 1 ขอ) อตสาหกรรม ก าลงการผลตรวมสงสด ก าลงการผลตทใชโดยเฉลย
□ แปงดบ (ตนตอวน) (ตนตอวน)
□ แปงดดแปร (ตนตอวน) (ตนตอวน)
3) ตลาดและลกคาหลกของกจการ □ ในประเทศ % โปรดระบประเภทอตสาหกรรมของลกคา
□ แปงมนดบ □ แปงมนดดแปร □ เอทานอล □ ผงชรส □ อาหารสตว □ แปงเปยก □ กระดาษ
□ ยา □ บะหมกงส าเรจรป □ สาค □ กาว □ กรดมะนาว □ ซอสปรงรส □ เครองดม
□ ไมอด □ สารความหวาน □ สงทอ □ ซกรด □ ผสงออก □ อนๆ โปรดระบ
□ ตางประเทศ % โปรดระบประเภทอตสาหกรรมของลกคา □ แปงมนดดแปร □ เอทานอล □ ผงชรส □ อาหารสตว □ แปงเปยก □ กระดาษ □ เครองดม
□ ยา □ บะหมกงส าเรจรป □ สาค □ กาว □ กรดมะนาว □ ซอสปรงรส □ ไมอด
□ สงทอ □ ซกรด □ สารความหวาน □ อนๆ โปรดระบ
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ระบปลายทางในประเทศ ทาเรอ □ กรงเทพ □ แหลมฉบง □ อยธยา □ อนๆ โปรดระบ
4) บรษทไดรบ ใบรบรองมาตรฐานอะไรบาง □ ISO
□GMP □ HACCP □ TISI □ HALAL □ อนๆ โปรดระบ
5) ความสมพนธกบเกษตรกร □ มการจดอบรม/ใหความรกบเกษตรกรดานการเพาะปลก □ มเกษตรกรเปนสมาชก ราย □ อนๆ โปรดระบ
6) ความสมพนธกบลกคา ลกคาใหม (นอยกวา 1 ป) % ลกคาเกา %
□ มการตกลงซอขายลวงหนา (ทไมใชการซอขายตามความตองการของลกคาหรอ Spot market) %
□ มการใหความรดานการผลต ปจจยการผลต และเทคโนโลยจากลกคา
สวนท 2 การส ารวจและวเคราะหสภาพขอเทจจรง (As-Is analysis)
การจดหาวตถดบ
7) รปแบบการจดหาหวมนส าปะหลง □ รบซอหนาโรงงาน □ ซอทไรของเกษตรกร □ ซอจากสหกรณการเกษตร
□ รบซอผานพอคาคนกลาง □ น าเขาจากประเทศ
□ เพาะปลกในไรของบรษท พนทเพาะปลก ไร ผลผลตตอไร ตน/ไร
การผลต
8) จ านวนแรงงานทใชในสายการผลต คน อตราคาจางแรงงาน บาท ตอ
9) เวลาทท าการผลต ชวโมง/วน ท าการผลตรวมทงสน วนตอเดอน เดอนตอป
10) ระยะเวลาทในการแปรรปหวมนสดจนเปนแปง ชม.
11) กระบวนการผลตเปนแบบ □ Semi-automation □ Automation
การฝกอบรม
12) จ านวนพนกงานทไดรบการฝกอบรม คนตอป จากทงหมด คน
13) ระยะเวลาการฝกอบรม วนตอคนตอป
14) หวขอการฝกอบรม
15) □ ความปลอดภย □ กระบวนการผลต □ การเพมทกษะการบรหารการจดการ
16) □ การใชเครองจกร □ อนๆ
การจดเกบสนคา การขนสง และการสงออก
17) เวลาทใชในการจดเกบสนคา โดยเฉลย วน นอยทสด วน มากทสด วน
18) รปแบบการขนสงผลตภณฑมนส าปะหลงทใช (ตอบไดมากกวา 1 ขอ)
รปแบบการขนสง ปรมาณ ระยะเวลาเดนทางไปกลบ (ช.ม.)
คาใชจาย
(บาท/เทยว)
ของบรษท ผานผใหบรการขนสง
Ref. code: 25595822040100EWT
31
(ตน/
เทยว)
ในประเทศ
รถบรรทก 6 ลอ □ □
รถบรรทก 10 ลอ □ □
รถพวง □ □
รถหวลาก Trailer □ □
อนๆ □ □
สงออก
รถบรรทก 6 ลอ □ □
รถบรรทก 10 ลอ □ □
รถพวง □ □
รถหวลาก □ □
สวนท 3 ความคดเหนและขอเสนอแนะ
19) โครงการ การใหความชวยเหลอผประกอบการ ป 2557/58 ป 2558/59
□ การเพมสภาพคลองทางการคาผประกอบการ □ การยกระดบมาตรฐานการแปรรปมนส าปะหลง
□ โครงการสนบสนนสนเชอเพอรวบรวมและสรางมลคาเพมมนส าปะหลงแกสหกรณภาคการเกษตร กลมเกษตรกร และกลมวสาหกจชมชนทเกยวของ เพอเปนเงนทนหมนเวยนในการรบซอผลผลตมนส าปะหลงจากเกษตรกร
โครงการอนๆทเขารวม โปรดระบปทเขารวม
20) ขอเสนอแนะตอทางภาครฐ
ผวจยขอขอบคณทานเปนอยางยงทใหความอนเคราะหในการใหขอมล
Ref. code: 25595822040100EWT
32
Appendix B
Questionnaire form for chip-pellet industry
แบบสอบถามส าหรบผประกอบการอตสาหกรรมมนเสน
สวนท 1 ขอมลทวไปเกยวกบสถานประกอบการ
1) ชอผใหขอมล
ชอสถานประกอบการ
เลขท หมท ซอย ถนน
ต าบล/แขวง อ าเภอ/เขต จงหวด
รหสไปรษณย โทรศพท โทรศพทมอถอ
โทรสาร เวบไซต
เรมตนกจการเมอ ทนจดทะเบยน บาท
ปจจบนทานด ารงต าแหนง □ เจาของกจการ □ ผบรหาร □ หนสวนกจการ □ พนกงาน □ อนๆ โปรดระบ
เพศ □ชาย □ หญง ประสบการณในต าแหนงปจจบน ป วฒการศกษา
2) ประเภทอตสาหกรรม (ตอบไดมากกวา 1 ขอ) อตสาหกรรม ก าลงการผลตสงสด ก าลงการผลตทใชโดยเฉลย
□ มนเสน (ตนตอวน) (ตนตอวน)
3) ตลาดและลกคาหลกของกจการ (ตอบไดมากกวา 1 ขอ) □ ในประเทศ % ระบประเภทอตสาหกรรม
□ เอทานอล □ อาหารสตว □ แอลกอฮอล □ ผรวบรวมเพอสงออก
□ อนๆ โปรดระบ
□ ตางประเทศ %
ระบปลายทางในประเทศ ทาเรอ □ กรงเทพ □ แหลมฉบง □ อยธยา □ อนๆ โปรดระบ
4) ความสมพนธกบลกคา ลกคาใหม (นอยกวา 1 ป) % ลกคาเกา %
สวนท 2 การส ารวจและวเคราะหสภาพขอเทจจรง (As-Is analysis)
การจดหาวตถดบ
5) รปแบบการจดหาหวมนส าปะหลง □ รบซอหนาโรงงาน % □ ซอทไรของเกษตรกร %
Ref. code: 25595822040100EWT
33
□ ซอจากสหกรณการเกษตร %
□ รบซอผานพอคาคนกลาง % □ น าเขาจากประเทศ %
□ เพาะปลกในไรของบรษท % พนทเพาะปลก ไร ผลผลตตอไร ตน/ไร
การผลต
6) จ านวนแรงงานทใชในสายการผลต คน อตราคาจางแรงงาน บาท ตอ
7) เวลาทท าการผลต ชวโมง/วน ท าการผลตรวมทงสน วนตอเดอน เดอนตอป
8) เวลาน าในการผลต วน
การฝกอบรม
9) จ านวนพนกงานทไดรบการฝกอบรม คนตอป จากทงหมด คน
10) ระยะเวลาการฝกอบรม วนตอคนตอป
11) หวขอการฝกอบรม
□ กฎระเบยบขอบงคบ ขอกฎหมายเกยวกบการน าเขาสงออก หรอภาษการคา □ การเพมทกษะการบรหารการจดการ □ การใช Software
การจดเกบสนคา การขนสง และการสงออก เวลาทใชในการจดเกบสนคา โดยเฉลย เดอน นอยทสด เดอน มากทสด วน
12) รปแบบการขนสงผลตภณฑมนส าปะหลงทใช (ตอบไดมากกวา 1 ขอ)
รปแบบการขนสง ปรมาณ
(ตน/เทยว)
ระยะเวลาเดนทางไปกลบ
(ช.ม.)
คาใชจาย
(บาท/เทยว)
ของบรษท ผานผใหบรการขนสง
ในประเทศ
รถบรรทก 6 ลอ □ □
รถบรรทก 10 ลอ □ □
รถพวง □ □
รถหวลาก Trailer □ □
สงออก
รถบรรทก 10 ลอ □ □
Ref. code: 25595822040100EWT
34
รถพวง □ □
รถหวลาก □ □
สวนท 3 ความคดเหนและขอเสนอแนะ 13) โครงการ การใหความชวยเหลอผประกอบการ
ป 2557/58 ป 2558/59
□การเพมสภาพคลองทางการคาผประกอบการ □การยกระดบมาตรฐานการผลตและแปรรปมนส าปะหลง
□สงเสรมโครงการสนบสนนสนเชอเพอรวบรวมและสรางมลคาเพมมนส าปะหลง
โครงการอนๆทเขารวม โปรดระบปทเขารวม
คณะวจยขอขอบคณทานเปนอยางยงทใหความอนเคราะหในการใหขอมล