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  • 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
  • 8. 5. CONCLUSION AND FUTURE [7] Chien-Hsun Chen, and Hui-Tzu Shih, “The impact of WORK WTO accession on the Chinese garment industry,”. Journal of fashion Marketing and Management, (2004), pp.221-229. In this paper, the designs of prediction model for [8] Andrew Hughes, “ABC/ABM - activity-based forecasting based on previous theoretical arguments costing and activity-based management: A profitability explaining the intention to use of linear regression model, model for SMEs manufacturing clothing and textiles in various factors are tested. Our results show that some the UK,”. Journal of Fashion Marketing and factors like context table 2 and 3. The design choices are Management, (2005), pp. 8-19. explained in details focus on linear regression. The result [9] Gopal Joshi, “Garment industry in South Asia Rags or of accuracy more than 94 % because of error minimum. riches?Competitiveness, productivity and job quality Furthermore, many of the lessons learned during the in the post-MFA environment,”. International Labour project commissioning are described. Though all Organization, (2002), pp.197-241. algorithm have been accepted by spread-sheet developers [10] Dorothy McCormick, and Hubert Schmitz, “manual as a great technique, they were not originally designed for for value chain research on homeworkers in the prediction model but can be calculate trend. Most existing garment industry,”. Journal of Supply Chain experiments confirm that the use of linear regression best Management, (2001), pp. 1-64. model of equation achieves good results. [11] Ramakrishnan, and Raghu, Database management Future research should focus on further validating this systems (3rd ed.), (2006), ISBN 0-07-246563-8. study. Recent advances in development planning scenario [12] Ting-Cheng Chang, Kun-Li Wen, and Mei-Li You, for maximize profit. In order to evaluate the DSG, it was “The study of regression based on grey system compared to a system with non-DSS and to an automated theory,”. IEEE International Conference on System, system, in terms of the performance. These technologies (1998), pp. 4302-4311. can be used to feature computational platforms including [13] George B, and Mukund N, Linear programming, the accommodation of real-time interaction between (2003), ISSN 1406-8190. distributed agents (executive manager, department [14] Jen-Tzung Chien, Member, and Robbins S.P., manager and software). Therefore, we are implementation “Linear Regression Based Bayesian Predictive system and testing in garment industry for case study. Classification for Speech Recognition,”. IEEE Transactions on speech and audio processing, 6. REFERENCES (2003), No.2. [15] Danny M.P. Ng , Eric W.M. Wong2, K. T.KO, and [1] Chandra Charu, and Sameer Kumar, “An Application K.S. Tang, “Trend Analysis and Prediction in of a System Analysis Methodology to Manage Multimedia-on-Demand Systems,”. IEEE Logistics in a Textile Supply Chain,”. Department of Electronic Engineering, (2001), Journal of Supply Chain Management, (2000), pp. 1292-1298. pp. 234-245. [2] Brannon, Ulrich, Anderson, and Presley, “Optimal reorder decision-making in the agent-based apparel supply chain,”. Journal of Supply Chain Management, (2000), [3] Sepulveda and Akin, “Modeling A Garment Manufacturer’s Cash Flow Using Object-Oriented Simulation,”. Proceedings of the 36th conference on Winter Simulation, (2004), pp. 1176-1183. [4] Lisbeth dahllof, “LCA Methodology Issues for Textile Products,”. (2004), ISSN 1404-8167. [5] Chung Yeh and Hung-Cheng Yang, “A cost model for determining dyeing postponement in garment supply chain,”. Department of Industrial Engineering, (2003), pp. 134-140. [6] Glenn O. Allgood, Wayne W. Manges, and Oak Ridge, “Modeling Manufacturing Process to Mitigate Technological Risk,”. Proceeding of the 1st Mechanical Working and Steel Processing Conference, Baltimore, (1999).

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