1. DSG: A Decision Support System for Garment Industry
Benjapuk Jongmuanwai, Thara Angskun, Jitimon Angskun
{benjapuk@gmail.com, angskun@sut.ac.th, jitimon@sut.ac.th}
ABSTRACT decrease cost in order to obtain the maximum profit and
decision support for executive manager.
The garment industry is one of the most important Additionally, the problem is to process production
industries in Thailand. This industry profit is worth more for garment industry and overview intra-organization for
than six billion Baht per year or five percentage of GDP. garment industry (e.g., customized pattern, manufacturing
Unfortunately, most businesses in the industry are still process, selling process, etc), software tools utilized were a
lacking of using information technology in their production spreadsheet-based forecasting and inventory management
and management. This paper presents a design and activities analysis process must be opened model and which
implementation of a decision support system for garment executive manager should be save budget for organization
industry called DSG. The DSG has potential to help [1]. The Agent-based simulations have been developed to
executive managers in their decision by providing goal-seek model the decision-making process in the supply chain.
and scenario (“what-if”) analysis. This paper also discusses Consumer trait, preferences, constraints on purchase
mathematical models used in DSG which analyze costs, decisions and the products could be modeled to offer the
incomes and their relationships of each department in order executive a tool for exploring consumer response [2]. The
to obtain the maximum profit for the organization. business process model has a relation between garment
Additionally, it also evaluates the model in term of accuracy industries of a DSS this research for studies ratio money.
by comparing with the real business data. The results show Therefore, model has implemented by object oriented
that the model provides more than 94% accuracy. programming. Modeling implementation for decision
support system several investments, planning production,
Keywords— Garment Industry, Decision Support ratio money, goods money production and material for
System, Profit Prediction Model supplier in the system. It can be overview affecting occurred
facilitate in the year. (E.g. average profit, maximize loan,
1. INTRODUCTION average loan etc.) For business process industry [3].
Additionally, case studies life cycle assessment the depart of
The Garment Industry has well known problem, production for Garment industry different issue. Therefore,
which many researchers have paid attention for several garment industry of the environment in Thailand and
years. Since 2000, many different cases of the garment usability, used in for garment industry in Sweden and three
industry for problems have been researchers by many pairs methods for depart of fixed goal and process analyzer
and many methods have been proposed to solve the inventory; assess effecting total systems [4]. Garment
problems. Unfortunately, the applications of Decision industry and process activities have a proposed business
Support System (DSS) aren’t widely spread among of profit model for organization of the medium and process
garment industry systems in Thailand. Thus, every production in England of theory control the activities.
organization has to system but hasn’t tool for decision- Therefore, activities force of cost for the management the
making. The organization of garment industry has problem activities an implementation the total cost organization [5].
for cost management and minimum profit. Therefore, we Model economic with of clause looking for strategy has to
want to development DSG system can be divided into increase profit and the wasting process. In the part member
several problem depend on assumed restrictions for of calculate number production to customer, finally
organization the garment industry. The executive manager immediately data delivery many variables need to be
and department manager can be kind of problem is a special considered during the process model [6]. Garment industry
case of cost minimize in which each department. In the past, firms to use foreign-invested enterprises to obtain direct
we are process management each factors in department for access to overseas purchasing networks, and deciding on
organization from exactly five departments. Therefore, DSG appropriate investment in industrial/manufacturing ventures
system has been development for repairing problem can be difficult [7]. The activity-based management (ABM)
was mainly developed to serve the accountancy function not

2. the needs of the decision makers in the firm, which enables the second section explains the method of approach and the
the financial information in the firm to be used for active characteristic of the problem. The third section describes the
decision-making. This is activity-based management characteristic of a proposed model and also explains how
(ABM). – Thereby reducing cost, through for example, total to apply the concept of cost minimize to solve the problem.
quality management (TQM), just-in-time (JIT) or process The forth section deals with another algorithm approach
re-engineering. Similarly activities will be identified which which has been constructed to compare the result with the
are core to the firm, and those that are simply supporting. proposed algorithm. The conclusion and recommendation
ABC/ABM enables firms to focus on its activities and for this paper are finally presented in the fifth section.
products; it traces cost-to-cost drivers, for example, the
number of machinists needed to produce trousers. The 2. DSG FRAMEWORK
business then understands; its business processes in detail;
the cost of process failures; the relationship of processes to A Decision Support System for Garment Industry
customers; the profitability of customer segments; and the (DSG) has unlimited cost and can supply all requirements
affordable amount that can be spent on influencing the for department manager or executives manager. In this
benefits from the improved insights resulting from an ABC paper, the process of decision support system included
analysis”[8]. At the initial development stage of the garment delivery data local-organization has not included the
industry, more than 90 percent of the Sri Lankan delivery data across process data infra-organization.
entrepreneurs managed their enterprises as family Improving creativity in decision support system. In addition,
businesses. Most of the factory activities, purchasing and having observed the data fact that based on the profit centre
higher level management were conducted by themselves and strategy that the company or organization applies at the
amongst many enterprises it continues to be the case even moment, this causes an organization independent
today. There is an evident lack of professionalism in the management and no linkage between internal organization
industry as most entrepreneurs are unwilling to invest in the garment industry should be acknowledge the pattern
human resources to manage the various functions of their about the forecasting details from department manager.
business professionally [9]. In the case of garments, this may Moreover the DSG system should be to verify authentication
mean that design takes place in London or New York, fabric user department. Therefore, we are design framework for
is sourced from China, trim and other inputs are made in organization garment industry the best optimal.
India, and assembly takes place in Mauritius. While the DSG is a problem which industry about where and
manual concentrates on the garment industry, the how many organizations must be opened and which manager
methodology can also be used to investigate other industries. should be assigned to each opened organization in such a
The manual’s style was intended to make it accessible to way that the total cost is minimized when a set of potential
those whose training is not in research. Yet we believe that organization and a set of demand executive manager are
its general approach, its discussion of the issues, and the given. The DSG has potential to help executive managers in
explanation of methodology should make it useful also to their decision by providing goal-seek and scenario
academic researchers venturing into this field for the first (“what-if”) analysis of proposed model concerns between
time [10]. However, the most problems in Thailand garment the relationships of infrastructure organization. The total
manufactures have been in existence for only a relatively are cost consists of cost of establishing garment industry, cost of
small in size because of the incomplete adoption of the assigning department manager or cost production (depend
decision support system, restricted access to information and on the cost analysis between executive manager and
unfair competition. These smaller manufacturers remain department manager and amount of several month in the
dependent on traditional business model, and as a result, year process in to system) and uncover demand cost
their operating costs have remained very high and the economy. The proposed model considers a capacity of each
importance of garment industry in Thailand has not organization as constraint, each cost will be calculated in
remained still for decision support system. Therefore, in this term of cost analysis which result in the maximize profit or
paper we have proposed an algorithm into mathematical higher total cost. Furthermore, an effective method, which
models with, in addition, consideration of the cost of Decision Support System (DSS) has been proposed to solve
uncovered demand. The experiments have been conducted the model. This method has shown an efficient solution
to find the accuracy of the proposed algorithm in terms of within a reasonable calculation cost analyzer and income
both performance measure and calculation. analyzer for giving the solution. More details of the ideal
This paper consists of five sections. It includes DSG are described in the discussion below of Figure 1.
problem statement of the paper. The first section deals with
a framework of decision support system for garment industry
called DSG. The framework describes the model and also
explains three categories of the DSG problems, cost
analyzer, income analyzer, Estimation Profit. Next,

3. Figure 1: DSG FRAMEWORK: (1) Authorization; (2) Department Manager; (3) Profit Prediction
Model; (4) Knowledge Inference Engine; (5) Estimation Plan
2.1 Authorization between organization and department manager in the
system. Executive manager and department manager
Authorization refers to external schemas, which usually demand at each cost analysis are independent, identically
are also in terms of the data model of the DBMS, allow decision support system process with a normal model cost
data access to be customized (and authorized) at the level analysis and income analysis identify at any unit of baht,
of individual users or groups of users [11]. Essentially, hour, or one people a month- provided that all the system.
design is guided by end user requirements. Each external
schema consists of a collection of one or more views and 2.3 Profit Prediction Model
relation from the conceptual schema. This can be done by
defining the following view: Table login (id_login: string, This proposed model has been assumed to have
user_id: string, user_password: string, user_name: string) database management systems which not vary the demand
The database of department manager (e.g. an accounting department manager due to the relational database.
manager or a financial manager center) receives data from Furthermore, the time horizon for this model has been
database each transaction. considered as a dynamic model in which optimize the
system performance for one representative a month or per
2.2 Executive Manager and Department Manager years. Profit Prediction Model is calculated from linear
regression type goal-seek the of each month potential to
The Department Manager of overviews physical the years of each cost of department manager. The fixed
schema specifies additional storage details. We must costs at each factor involve the cost of constructing
decide what file organizations to use to store the relation garment industries, cost of materials, labors, etc. Income
and create auxiliary data structures, to speed up data Analyzer and Cost Analyzer, the calculation of program
retrieval operations. Each department manager has been costs is the first step in establishing the cost benefit
served from exactly one data and the demand of manager model, and is usually reduced to a cost each department
will not violate the relation. The Executive Manager has that corresponds to the reporting for management.
been considered as the first member in the system. If management requires a quarterly accounting of the
The Executive Manager demands occurring at the facility program benefit, then the program prediction are
has been randomly generated based on the normal calculated for a typical 3-month period, and included in
database. The model has concerned about the relationship the income benefit analysis. Therefore, profit prediction

4. model by providing algorithm calculate for organization 2.3.2 Income Estimating
of maximize profit. 2.3.2.1. Methodology
The step following after the data extraction and the
2.3.1 Cost Estimating building model is income data analyzer. Initially,
2.3.1.1. Methodology (Selecting an appropriate estimating method) some
The step following after the data extraction and the preparation activities in order to organization for data
building model is cost data analyzer. Initially, (Selecting income. Therefore, income is used to situation; such as
an appropriate estimating method) some preparation income, sales expense, capital and loan. The estimating
activities in order to organization for data and facilitate data is compared with the income numbers of month is
their manipulation. For instance, garment industries that equal to past the year. Specifically, actual income each
do not reveal actual usage information are removed and factors referred money, income of the organization (e.g.
missing data are completed. Then follows the application selling expenses). In some cases, proposed and income
of statistical and data analysis techniques in order to design changes can be tabled or cancelled based on new
detect interesting patterns in the pre-processed data. The information generated by executive manager for
most well known techniques used for data analysis include organization. These are generally one-time reductions, but
clustering, classification and association rules the savings can be significant. Therefore, income
organization. Therefore, actual cost each factors referred estimating can choose each of factor this system
to as the “hard” money, direct cost savings are the calculated income by increasing or reducing factor for
backbone of the organization (e.g. financial benefit for a income.
predictive maintenance program). In some cases, 2.3.2.2. Encoding and Calculating Income Analyzer
proposed and cost design changes can be tabled or The encoding for income analyzer into table
cancelled based on new information generated by factors. This core value is modified as additional income
executive manager for organization. These are generally for the activities department, or as a program of
one-time reductions, but the savings can be significant. continuous provides additional reductions in income each
One methodology a core value for direct cost savings is factor. Occasionally, diagnostic data indicates the need for
developed based on the reduction and the removed increased income, and in those cases, that must be
preventive activities and in those cases, which must be subtracted from the factors assigned for reducing. The
subtracted from the benefits assigned for reduced cost. calculating income for each factor several total numbered
Therefore, cost estimating can choose some of factor this in table for DSG defined as:
system calculated cost by increasing or reducing each
factor. n n
Income = ∑ loic + ∑ saic
2.3.1.2. Encoding and Calculating Cost Analyzer i =1 i =1
The encoding for cost analyzer into table factors.
This core value is modified as additional cost for the n = number of month or year each for factors
activities department, or as a program of continuous i = 1 ; n = number of month factors
provides additional reductions in cost each factor. n = 1 ; loic = means number are equal of factor
Occasionally, diagnostic data indicates the need for
increased cost, and in those cases, that must be subtracted n = 1 ; saic = means number are equal of factor
from the factors assigned for reducing. The calculating
cost for each factor several total numbered in table for 2.3.3 Profit Estimating
DSG defined as: 2.3.3.1. Methodology
The step following after the data extraction and the
n building model is income data analyzer and cost analyzer.
Cost = avg (∑ Fi ) Therefore, profit estimating is used to calculating every
i =1 factor. The estimating data is compared with the income
and cost numbers for average of month is equal to past the
year. Specifically, actual income and cost each factors
avg = calculation of factor per month or per year
referred money, income and cost of the organization (e.g.
n = number of month or year for factors selling expenses, machine, and hour of work). In some
i = 1 ; n = number of month factors cases, proposed profit estimating for executive manager
n = 1 ; Fi = means number are equal of factor can be design changes or cancelled based on new
information generated data for organization. These are
department
generally one-time reductions, but the savings can be
significant. Therefore, profit estimating can choose

5. estimate of minimize or maximize profit. This system framework system a hypertext preprocessor (PHP)
calculated income and cost by increasing or reducing relational DBMS is used as the database platform.
factor for profit maximize.
2.3.3.2. Encoding and Calculating Profit Analyzer 2.5 Estimation Plan
The encoding for profit analyzer into system.
This core value is modified as calculating between income The estimates plan has been present in the next month
and cost for the activities department, or as a program of or next year. There DSG will be used in the organization
continuous provides additional reductions in income and for garment industry and most organization not estimate
cost each factor. The calculating income for each factor planning cost, income for maximize profit of each year.
several total numbered in table for DSG defined as: Therefore, estimation plan can be push up economy
organization to continue and the DSG use in for executive
 n n
  n  manager. Internet has been use of the organization
profit =  ∑ loic + ∑ saic  −  ∑ Fi   Or industry will be used for several to manager. Thus, DSG
 i =1 i =1   i =1   can be linked between executive manager and department
manager for decision planning. Additionally, cause of
factors of production increase or decrease. Therefore,
profit = income − cos t management must estimation plan of cost and income for
estimation minimize cost or estimation increase income.
n = number of month or year for factors Furthermore, may be directly affected by the
i = 1 ; n = number of month factors fluctuation pricing in which when there is a change in the
n = 1 ; Fi = means number are equal of factor market and customer will be unavoidable affected.
Therefore, proposed model has been developed and added
department
some variables into the model which are the ideal
n = 1 ; loic = means number are equal of factor estimating for scenario of DSG. The research is studying
n = 1 ; saic = means number are equal of factor about calculation model for the estimating profitable that
should be plan cost and income each of factor to make the
best profit and accordingly to the ideal. This calculation
model will be use as the calculation method for DSG
2.4 Knowledge Inference Engine planning development. Therefore, this paper presents a
decision support system for garment industry called DSG.
The knowledge inference engine which is developed on
databases using DBMS technology to manage facts and
rules. The DSG system or known as knowledge-based 3. PREDICTION MODEL
system comprises of knowledge base and inference engine
which their knowledge base is used to represent expertise Any method of fitting equation to a set could be called
knowledge as data and algorithm. The knowledge can be regression [12]. Such equations are valuable for at least
called up on when needed to solve problem by inference two reasons, i.e. analyzing the trend and making
engine. In large system the knowledge-based can be predictions. Of the various methods of performing
represent using framework approach called framework regression, least square fitting is the most widely used.
system or DSG. Many systems have the ability to connect In this section, five equations used compare models:
to external databases. Facts stored in databases can be
loaded into data system’s knowledge base and inference is 3.1 Linear Regression
performed by the inference engine of the DSG system.
In many cases, such external facts are required several Linear regression is a basic regression model where
system for each inference. Thus, a lot of communication there is only one explanatory variable [12]. The regression
traffic takes place. This research presents the design and function is linear and the two parameters, slope term m
implementation of a framework relational database system and intercept term b, of that straight line have to be found
which has a tight coupling between the intra-organization such that the sum of percentage prediction errors, where
 diff 
system and the external knowledge base. The external % error=  cos t  × 100 is minimum. The model can be stated as

 ,
knowledge base also use frame as its knowledge follows:
representation. Moreover, it has its own inference engine y = mx + b (1)
so that inference can be perform on the knowledge base
side and the results, not only simple facts, are sent back to y − b
the expert system for further inference. The DSG x = (2)
consultation system is used as an illustrated example
m
y = Prediction cost for using/ forecasting profit or

6. ∑ ( x − x )( y − y )
n
Dependent variable and i i (7)
m = Number of slope line b= i =1
∑( x − x)
n 2
i
i =1
b = Number of point x
x = Number of month for factors or Independent Where x is the explanatory variable in terms of month, y
variable and y are the dependent variable and its predicted value
Therefore, variables m, b the mathematical will be respectively in terms of number of requests.
presented as following equation (3) and (4) [13], [14], and
[15]: 3.3 Polynomial Regression
n∑(xy) − ∑x∑y
m= (3)
n∑x2 −( ∑x)
2 Polynomial regression assumes that the predicted
trend is a polynomial function. The polynomial function is
linear and the three parameters, slope term c1, c2 and
∑ ( x − x )( y − y )
n
i i
(4) intercept term b, of that straight line have to be found such
b= i =1
that the sum of percentage prediction errors, where
∑ ( x − x)
n 2
i =1
i % error= cos t  × 100 is minimum. The model can be stated as

diff
 ,
 
follows:
n = Number of data, (n = 1, 2, …, n)
y = c1 x + c2 x 2 + b (8)
y = Dependent variable, (y = 1, 2, …, n)
m = c1 x + c2 x 2
(9)
x =Independent variable, (x = 1, 2, …,n)
and
∑ ( x − x )( y − y )
n
i i
x = Average of independent variable, there are sum b= i =1 (10)
∑( x − x)
n 2
independent variable mod number data, (x = 1, 2, …,n)
i
i =1
y = Average of dependent variable, there are sum
dependent variable mod number data, (y = 1, 2, …,n) Where x is the explanatory variable in terms of month, y
and y are the dependent variable and its predicted value
Where x is the explanatory variable in terms of month, y
respectively in terms of number of requests.
and y are the dependent variable and its predicted value
respectively in terms of number of requests. 3.4 Power Regression
3.2 Logarithmic Regression Another predicted used model is the power regression,
which expressed in terms of a power function. The model
Logarithmic regression assumes that the predicted considers the situation where heavy tail exists in the trend.
trend is a linear function. The linear function is In contrast with exponential function, power function
logarithmic and the three parameters, slope term c, ln(x) experiences a rapid initial drop and gradual final decrease.
and intercept term b, of that straight line have to be found Compared with the exponential one, power regression can
such that the sum of percentage prediction errors, where obtain a shaper initial drop and a flatter tail. By
% error=  cos t  × 100 is minimum. The model can be stated as

diff
 , substituting x=log x and y=log y, one form of this
 
follows: regression model is
y = ( cLn( x) + b )
(5) y = cxb (11)
and hence
1
cLn ( x ) = c ( ) (6) (12)
x log y = log c + b log x
c = Fixed rate ∑ ( x − x )( y − y )
n
(13)
where i i
b= i =1
∑( x − x)
n 2
ln = Natural logarithm or log x i
i =1
and log c = y-bx (14)

7. which slop term and intercept term of log y against log x and log c = y-bx (18)
now becomes b and log c respectively. e = 2.7182818
3.5 Exponential Regression which slop term and intercept term of log y against log x
now becomes b and log c respectively.
Another predicted used model is the exponential
regression, assumes that the predicted trend is an
4. MODEL EVALUATION
exponential function where there is only single
explanatory variable. It is similar to the linear regression This model evaluation used to Garment Industry
after taking an anti-logarithm on both sides. By has been case study in Thailand. Therefore, cost number
substituting y=log y, the equation of linear regression may of three year since 2006-2008. The well known evaluation
also be applied. The regression model can be expressed as of testing data is compared with the categorized result
from the mathematical. The performance (%) error
(15)
y = cebx discussed in this section. One most important value is
accuracy estimated by a testing model. The known class of
and hence
testing data is evaluation process is used to assess the
performance of groups in the table such as Exponential,
log y = log c + bx
(16) Polynomial and Logarithmic of the Trend. Finally, we are
∑ ( x − x )( y − y )
n
i i presented the percentage linear regression for average
with b= i =1
(17) department in pie and table 3 present compare models as:
∑( x − x)
n 2
i
i =1
Table 1. The cost numbers of month is equal to 36 and vary the number for since 2006-2008: (%) model compare predicted
(%) Error
Factors Lin Log Poly Power Expo Best
Marketing Department
Advertising expense 4.08 5.15 2.01 5.96 4.99 Poly
Selling expense 9.31 11.69 9.31 11.50 24.49 Poly, Lin
Average wage marketing / month 17.40 47.26 17.40 9.16 14.42 Power
Employees marketing / sales 2.43 0.43 0.43 0.41 2.41 Power
Average wage marketing / person / month 10.06 10.21 11.65 10.23 9.92 Expo
Average Marketing Department 8.66 14.95 8.16 7.45 11.25 Power
Production Department
Number of production employees. 3.50 11.78 17.46 13.47 5.27 Lin
Hours of work. 1.33 32.89 32.04 26.53 0.52 Expo
Machine 0.56 0.56 0.56 0.56 0.56 Log
The average wage of production / month 29.76 12.37 29.76 27.58 26.91 Log
The average wage of production / person / month 20.32 7.38 44.86 3.78 18.07 Power
Average Production Department 11.10 13.00 24.94 14.38 10.26 Expo
Personal Department
Employees of a person 1.41 1.54 0.68 1.53 1.40 Poly
Average wage of persons / person / month 5.93 8.10 0.84 7.92 5.83 Poly
Wage employees of all persons / month 0.69 11.30 10.02 14.34 1.59 Lin
Average Personal Department 2.68 6.98 3.85 7.93 2.94 Lin
Accounting Department
Number of employees, accounting / inventory. 3.65 5.11 6.49 4.99 2.84 Expo
The average wage of account / person / month 2.90 9.93 7.30 11.08 3.80 Lin
Wages of all employees account / month 4.31 4.36 4.01 4.92 4.69 Poly
Average Accounting Department 3.62 6.47 5.93 7.00 3.77 Lin
Financial Department
Employees of financial 0.00 0.00 0.00 0.00 0.00 All
Capital, loan 0.00 0.00 0.00 0.00 0.10 All
The average wage of financial / person / month 0.00 0.00 0.00 0.00 0.00 All
Pay all employees of financial / month 0.00 0.00 0.00 0.00 0.00 All
Average Financial Department 0.00 9.00 9.74 7.70 6.39 All
All Department 5.88 9.00 9.74 7.70 6.39 Lin

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