Professor of Industrial Engineering, Production Engineering Department, Alexandria University,

Transcription

1 A HIERARCHICAL PRODUCTION PLANNING FRAMEWORK FOR THE TETILE INDUSTRY WITH MAKE TO ORDER AND MAKE TO STOCK CONSIDERATIONS W. Kotayet 1, A.B. Eltawl 2, M.N. Fors 3 1 Graduate student, Alexandra Unversty, Egypt, emal: walaakotayet@gmal.com 2 Assocate Professor, Industral Engneerng and Systems Management, School of Innovatve Desgn Engneerng, Egypt-Japan Unversty of Scence and Technology (E-JUST), P.O. Box 179, New Borg el Arab, Egypt, emal: eltawl@ejust.edu.eg 3 Professor of Industral Engneerng, Producton Engneerng Department, Alexandra Unversty, emal: fors@dataxprs.com.eg Abstract: The am of ths paper s to ntroduce a proposed framework for Herarchcal Producton Plannng (HPP) n the textle ndustry. A three level HPP s adopted; at the frst level, the decouplng pont concept s used to splt the full producton lne nto smaller producton stages, followed by solvng the problem of where to hold stock to provde a hgh level of servce. Usng Theory of Constrants (TOC), the bottleneck stage has been dentfed n the textle producton cycle. In the second level of the HPP, the decson of determnng whch products to manufacture to-order and whch products to manufacture to-stock n the bottleneck stage s done usng a cluster analyss approach and demand varablty analyss, n the form of Relatve Demand Volatlty (RDV). At the thrd level, the requred demand and the avalable capacty at the bottleneck stage are balanced and the decson of allocatng the producton orders to the plannng perods s made. Keywords: Textle ndustry, herarchcal producton plannng, customer order decouplng pont, theory of constrants, relatve demand volatlty, master producton schedule Introducton Accordng to the Unted Natons conference on trade and development [1], durng the last two decades textles and clothng were the second most dynamc products n world trade. The sector s mportant for developng countres, whch supply about 50 % of the world market for textles and over 60 % of the world market for clothng. On the other hand, dversfcaton nto export categores wth greater value added than tradtonal agrcultural exports remans a major objectve for many low-ncome developng countres. A textle producton unt s characterzed by a mult-stage process wth multple producton unts per stage (.e., parallel machnes). Textle manufacturng systems nvolves more than one stage wth each stage yeldng a product that s ether pulled as fnshed product or further reprocessed n the next stage. A dfferent producton plannng method may be used for each producton stage are requred [2]. Fbers are the raw materals for all fabrcs. Some fbers are natural that come from plants, anmals, and mnerals. Other fbers are synthetc that are made from petrochemcals. From the natural fbers n Egypt, cotton s the largest crop produced and processed n the textle ndustry. Ltrature Revew Bowers and Jarvs [3] proposed a herarchcal producton plannng and schedulng model encompassng an apparel producton plannng system. Adamopoulos et al. [4] presented a decson support system dealng wth the producton plannng and schedulng n the textle ndustry. Ford and Rager [5] descrbed an expert system developed to support a decson scenaro n the textle ndustry. Karacaplds and Papps [2] presented an nteractve model based system for the management of producton n textle producton systems. Shroma and Nemeyer [6] presented the detals of the mplementaton of an evolutonary algorthm that made use of doman-dependent nformaton to generate producton schedules for a manufacturer of women's clothng. Tucc and Rnald [7] presentd the experence of applyng the tabu search metaheurstc to producton schedulng n weavng. De Ton and Meneghett [8] mproved producton plannng process n a tme-based logc for a network of frms n the textle-apparel ndustry. Wu and Chang [9] descrbed the method and procedure for optmzng a textle dyeng manufacturng. Ferrars and Morn [10]

2 bult a smulaton of producton schedulng at spnnng stage. Laoboonlur [11] solved the dyeng and fnshng schedulng problem by usng the Vrtual Factory plus famly schedulng. Mohafqul et al. [12] dealt wth a decson makng of apparel export-orented frm regardng ther capacty utlzaton durng producton. Vassleva [13] descrbed an applcaton of a software system at the spnnng department n a textle enterprse. Most research n the area of producton plannng n textle ndustry concentrate on studyng the operatonal level and the sequencng of the jobs. The tactcal level plannng seldom has been tackled as nput for the studed operatonal level. Moreover, hybrd make-to-order and make-tostock polcy dd not take enough attenton n producton management lterature on textle ndustry, although t s an mportant ssue n producton plannng and n product-mx decsons as t wll be further llustrated n ths work. The textle ndustry s well known for unque characterstcs when compared to ndustres n ether process or dscrete manufacturng [2]. Both dscrete and contnuous operatons are present n textle ndustry. Spnnng, weavng and dyeng stage n textle are contnuous operatons, but, the entre system ncludes both contnuous operaton and dscontnuous operatons [14]. The mxed character of a textle producton system, whch les between job-shop and flow-shop, makes producton management qute complex. Herarchcal producton plannng approach s best suted for such ndustry. The essental dea of havng such herarchcal approach s the partton of a large problem nto smaller, more manageable sub-problems. Ths paper presents a HPP framework that asssts the decson maker n handlng the complexty of the plannng process n textle ndustry. Usng the proposed framework, the potental stockng ponts can be located and the bottleneck stage s to be dentfed. Producton and nventory polces wll be assgned for the bottleneck stage. Fnally, the bottleneck stage wll be optmzed to acheve a proper nventory and producton decsons. The Proposed Framework In order to reduce the complexty of the producton plannng process n textle ndustry, a three levels Herarchcal Producton Plannng (HPP) approach s proposed, as shown n fgure 1. At the frst level of the HPP, the decouplng pont concept s used to splt the full producton lne nto smaller producton stages, then, the problem of where to hold stock s solved to provde a hgh servce level. Moreover, the Customer Order Decouplng Pont (CODP) concept suggests a qualtatve way to answer the MTO/MTS queston. Usng the Theory of Constrants (TOC), the bottleneck stage s dentfed. At the second level of the HPP, the decson of determnng whch products to manufacture toorder, and whch products to manufacture to-stock n the bottleneck stage s done usng a cluster analyss approach and demand varablty analyss, n the form of Relatve Demand Volatlty (RDV). Usng cluster analyss, the products can be categorzed nto four clusters: hgh volume, low varablty; hgh volume, hgh varablty; low volume, low varablty ; and low volume, hgh varablty. Accordngly, the products can be classfed nto two groups: hgh prorty products, whch have large orders, and low prorty products, whch have small orders. These two groups wll be handled separately n terms of responsveness. Ths categorzaton wll help to explot the bottleneck n a better way. Due to the large product mx n textle ndustry, determnng whch product to make-to-order versus make-to-stock s an mportant ssue n the producton plannng and n the product-mx decson. The objectve of the product-mx decson n the overall producton plan s to fnd the product mx and the producton program that mnmzes the cost subject to constrants mposed by resource lmtatons, market demand, and sales forecast (the costs are producton cost, nventory holdng cost, and penalty of unsatsfed demand). Such decson mples utlzng lmted resources to maxmze the net value of the output from the producton facltes.

3 At the thrd level of the HPP, the requred demand and the avalable capacty are balanced and the decsons of allocatng the producton orders to the plannng perods are made as follows: The frst cluster s to develop a Master Producton Schedule (MPS) model that can be mplemented n the bottleneck stage. The products for the second cluster wll follow make-to-order polcy, however, suffcent safety stock should be held to meet the customer servce objectve. Snce clusters three and four have low volume demand, they are more lkely to be fulflled wthn the delvery due data. Consequently, t s reasonable to produce them to-order. Moreover, the fabrcs for these two clusters are classfed as low prorty fabrcs, so backorders may be allowed. Rough-cut capacty plannng s done for all the products at all clusters; ths roughcapacty evaluaton s combned wth the MPS model. " ' % & " '% & ( " " # # % % ')* ' $ Applyng the HPP Framework n cotton Based Textle Industry Level One: Potental Stockng Ponts and the Bottleneck Identfcaton (1) The applcaton of CODP Theoretcally, seven potental decouplng ponts exst at the producton process: the stock of raw materals, the stock of yarns, the stock of warped yarns, the stock of starched warp, the storage of weaved fabrc, the storage of the dyed and fnshed cloth, and the storage of the sewed end product, as shown n Fgure 2. Fgure 1. The proposed herarchcal producton plannng framework Fgure 2. Potental decouplng ponts wthn the stages of cotton based textle ndustry (2) Identfyng the bottleneck stage usng TOC (a) Identfyng the prmary constrant: The weavng process s a bottleneck n the entre producton cycle of cotton based textle plants. It s characterzed by hgh setup tme, hgh producton tme,

4 hgh product mx, and lmted capacty. Ths s not the case when applyng TOC on other types of textle ndustres such as synthetc or slk based ones. (b) Explotng the constrants: () In order to ncrease the throughput gven the exstng capacty lmtatons, the products that have the least nventory and operaton costs and sell more, are gven hgh prorty n producton schedulng. () These products are canddates to have nventory stocked to produce them (raw materal or processed product), n order to mprove ther assocated servce level. (c) Subordnatng everythng else to the decsons made n the prevous step: Explotng the constrant does not ensure that the materals needed by the constrant stage wll always be avalable on tme. Ths s often because these materals are watng n queue at a non-constrant stage that s runnng a job whle the constrant stage does not need t yet. Inventory should be used carefully so that the bottleneck stage s never starved for materals, the flow of products should equal the market demands. (d) Elevatng the constrant: By elmnatng all dle tmes, mprovng mantenance and coordnatng mantenance plans and reducng setup tmes, the constran stage capacty can be ncreased. Level two: takng MTO vs. MTS decsons at the bottleneck stage Usng the Theory of Constrants (TOC), t s found that the weavng stage s a bottleneck n the cotton based textle producton cycle. Therefore, n the second level, the decson of determnng whch products to manufacture to-order and whch products to manufacture to-stock n the weavng stage s done usng a cluster analyss approach and the demand varablty analyss n the form of Relatve Demand Volatlty (RDV). (1) MTO and MTS decsons at weavng stage A demand varablty analyss, n the form of Relatve Demand Volatlty (RDV), as suggested n Olhager [15] and Van Donk et al [16], could be followed. In ths approach, RDV and average demand volume are used to categorze products nto MTO and MTS. The RDV s defned as the coeffcent of varaton,.e. the rato between standard devaton of demand and the average demand. RDV = σ, where σ = Standard devaton for the demand of product ( Av. Demand) Clusterng s the parttonng of a data set nto subsets (clusters), so that the data n each subset share some common trat. The computatonal task of classfyng the data set nto k clusters s often referred to as K-clusterng. The K-means algorthm assgns each pont to the cluster whose center s nearest. Usng the Cluster Analyss, the products would be categorzed nto four clusters: (1) Hgh volume, low varablty, (2) Hgh volume, hgh varablty, (3) Low volume, low varablty and (4) Low volume, hgh varablty, ths categorzaton depends on the average monthly demand and the coeffcent of varaton. Fgure 3 llustrates an example of the products classfcaton clusters for a typcal case. (2) Explotaton of the bottleneck stage In order to acheve better explotaton to the bottleneck stage, a soluton s proposed for fndng sutable producton polcy for each cluster. (a) Classfcaton of the products: The fabrcs are classfed nto two groups: hgh prorty fabrcs, whch have large orders volume, and low prorty fabrcs, whch have small orders volume. The due date of hgh prorty fabrcs should be treated more strctly than the one of low prorty fabrcs. Moreover, the hgh prorty products customers should be provded wth a hgh degree of responsveness. (b) Fabrcs wth hgh volume and low varablty: In order to facltate fast response to the hgh prorty fabrcs customers, the hgh volume tems wth low varablty wll be made-to-stock.

5 (c) Fabrcs wth hgh volume and hgh varablty: If the RDV value s hgh, t s not reasonable to use an MTS polcy, snce ths would lead to excessve nventores. Therefore, the products for ths cluster wll follow make-to-order polcy. However, suffcent safety stock wll be held to meet the customer servce objectve. Safety stock (SS) s held as a hedge aganst uncertanty. Ths stock wll be used only n the case n whch the delvery lead tme happened to be less than the producton lead tme. (d) Fabrcs wth low volume and hgh / low varablty: These two clusters have low volume demand. Therefore, they are more lkely to be fulflled wthn the due date. Consequently, t s reasonable to produce them to-order. Moreover, the fabrcs of these two clusters are classfed as low prorty fabrcs so backorder maybe allowed. In the next level, a master producton schedule model s developed to be mplemented n the weavng stage. Fgure 3. An example for the resultng products' classfcaton clusters Level three: balancng the demand and the avalable capacty At the thrd level of the HPP, the requred demand and the avalable capacty are balanced and the decsons of allocatng the producton orders to the plannng perods are made. (1) The proposed Master Producton Schedule The Master Producton Schedule (MPS) s a delvery plan for the manufacturng organzaton. It ncludes exact amounts and delvery tmng for each end product. It s derved from demand estmates, but t s not necessary equal to them. When developng the MPS, a major manufacturng constrant s the producton capacty. Therefore, to check feasblty of the MPS, an ntal capacty evaluaton s made usng Rough-Cut Capacty Plannng (RCCP). (a) The MPS problem defnton The weavng producton system under study s a flexble flow shop made up of dentcal parallel machnes of known capacty. A number of products are to be produced for customers, havng known determnstc demand n each perod. The requred output s (1) For cluster one products, determnng how much to produce from each product at each perod. (2) For cluster two products, a rough-cut capacty evaluaton for the current perod, takng nto account the avalablty of safety stock. (3) For cluster three and four products, rough-cut capacty evaluaton at the current perod s to be done. Ths should be determned wth the objectve of mnmzng the overall costs ncludng the costs of nventory, producton and the penalty of unsatsfed demand. Also, the capacty evaluaton should consder the lmted capacty assgned for each cluster, and the avalable raw materals (yarn). The proposed master producton schedule s updated va the use of a three months rollng horzon schedule. For the products of cluster one, the producton of the current perod s used to fulfll the

6 forecasted demand of the next perod, n other words, the demand s produced for one perod ahead. (b) The MPS model assumptons and constrants Model Assumptons Set-up costs are sgnfcant at the lowest operatonal level when the ndvdual products are sequenced. At the monthly level, however, producton costs domnate change-over costs. Hence, set-up costs are not ncluded n the analyss. For cluster one: Actual demand for tems s assumed to be known for each perod of the plannng horzon. Backorders are not allowed and holdng nventory s allowed For cluster two: The demand s derved from the confrmed orders and backorders are not allowed. For clusters three and four: The demand s derved from the confrmed orders and backorders maybe allowed. Decson varables: t = Number of unts produced of tem at perod t (meter) V t = Number of unts at end nventory of product at perod t (meter) J t = Number unsatsfed producton unts of tem at perod t (meter) Parameters: D t = demand of product at perod t (meter). C tu = capacty, meters/perod t, avalable for products of cluster u (meter). S = the value of begnnng nventory (meter). Y ktu = total number of unts avalable of yarn of type k at perod t for cluster u (meter). F k = number of unts requred from yarn type k to produce one unt of product (meter). L = unsatsfed producton penalty / unt of tem (L.E.) H = nventory holdng cost /unt of tem / perod (L.E.). P = producton cost /unt of tem (L.E.) PL = producton lead tme (day) DL= delvery lead tme (day) At cluster two, the stock wll be used only n the case n whch the delvery lead tme happened to be less than producton lead tme. V 0 S f DL < PL = 0 otherwse The objectve functon The objectve s to mnmze the total costs, for the frst cluster, along the plannng horzon (typcally three months). For the second, thrd and fourth clusters the mnmzaton s done for the frst/current perod. These costs nclude the total nventory holdng cost (whch s calculated based on the remanng stock from the prevous perod) tme bucket two weeks, total unsatsfed producton penalty (whch s a combnaton of the cost per unt short of an tem and cost assocated wth the mportance of the fabrc), and the total producton cost. Mn t J t L + Vt H t P (1)

7 Subject to:. Balance constrants: For clusters one, the end nventory for tem at end of perod t equals the sum of end nventory of perod t-1, n addton to the quantty produced of tem at perod t, and the quantty of unsatsfed producton unts of tem at perod t, mnus the demand of at t. For cluster two, the end nventory for tem at the end of the current perod (t=1) equals the sum of end nventory of the last perod (t=0), n addton to the quantty produced of tem at the current perod t, and the quantty of unsatsfed producton unts of tem at the current perod, mnus the demand of the current perod. For clusters three and four, the sum of the quantty produced of tem durng the current perod t and the quantty of unsatsfed producton unts of tem durng the current perod t equals to the demand of at tme perod t. { u 1} V ( t 1) + t + Jt Dt = Vt t, t = 1,2,..., T = (2) { 2} V 0 + t + Jt Dt = Vt t = 1, u = (3) { 3, 4} t + J t = Dt t = 1 u = (4). Inventory constrants: The ntal quanttes of the nventory on hand from each product at the end of the frst perod equal value S. { 2} V 0 = S u = 1, (5). Capacty constrants: They ensure that the total producton quantty n unts from all ndvdual products per cluster cannot exceed the capacty allocated for that cluster. { t Ctu t u = 1 (6) 1 1 } { u = 2,3, 4} t Ctu t = 1 (7) v. Raw materal constrants: They ensure that the total yarn of type k that s consumed at perod t cannot exceed the total avalable amount of yarn type k at perod t. Fk t Yktu t,, k t = 1,2,... T, u = 1 (8) 1 Fk t Yktu t,, k t = 1, u = 2,3,4 (9) 1 v. Non negatvty constrant: J,, V 0 t (10) t t t, { { } } (c) Model testng and verfcaton To verfy the MPS model, some scenaros were solved usng hypothetcal data. These scenaros were prepared so that the output can be expected before solvng the model, n order to prove that the model yelds rght results. The model was mplemented and solved usng LINGO optmzaton package. LINGO uses exact algorthms to solve lnear programmng models.

8 Conclusons In ths paper, we employed hybrd approach make-to-order and make-to-stock at a herarchcal producton plannng framework. Ths framework tres to fll the gap n the research of studyng the tactcal level at textle ndustry. When applyng the framework n one of the leadng companes n the area of textle ndustry, the model takes nto account the effect of products segregaton to MTO and MTS products. Moreover, t gves dfferent prortes that depend on the amount of demand the product occupy, compared wth the total demand. The proposed model's results were superor compared to the company's performance n terms of cost, nventory and unsatsfed demand reducton. References 1. Trade and development board, Report of the expert meetng on strengthenng partcpaton of developng countres n dynamc and new sectors of world trade, Unted Natons conference on trade and development, Geneva,7-9 February, Karacaplds N, Papps C. Producton Plannng and Control n Textle Industry: A Case Study. Computers n Industry, 1996; 30 : Bowers M, Agarwal A. Herarchcal Producton Plannng: Schedulng n the Apparel Industry. Internatonal Journal of Clothng Scence and Technology. 1993, 5,4 : Adamopoulos G, Karacaplds N, Pantazopoulos S. Producton management n the textle ndustry usng the YFADI decson support system. European Symposum on Computer Aded Process Engneerng, 1994; 18 : S577-S Ford F, Rager J. Expert System Support n the Textle Industry: End Product Producton Plannng Decsons. Expert Systems wth Applcatons. 1995; 9,2 : Shroma P, Nemeyer G. Producton Schedulng n the Textle Industry: Practcal Approach Usng Evolutonary Algorthms wth Doman-Dependent Informaton. IEEE: Industral Electronc Socety IECON 98 conference. 1998; 1 : Tucc M, Rnald R. From Theory to Applcaton: Tabu Search In Textle Producton Schedulng. Producton Plannng and Control. 1999; 10, 4: De Ton A, Meneghett A. The producton plannng process for a network of frms n the textleapparel ndustry. Internatonal Journal of Producton Economcs, Vol. 65, pp , Wu C, Chang N. Global Strategy for Optmzng Textle Dyeng Manufacturng Process Va GAbased Gray Nonlnear Integer Programmng. Computers and Chemcal Engneerng. 2003; 27: Ferrars G, Morn M. Smulaton n The Textle Industry: Producton Plannng Optmzaton. WOA workshop, Torno, Italy Laoboonlur P (2004). Producton Schedulng n Kntted Fabrc Dyeng and Fnshng: A Case Approach. Ph.D. Thess, North Carolna State Unversty, USA, Mohafqul K, Narta H, Chen L, Fujmoto H. Allocatng Capacty for Export-Orented Apparel Producton n a Developng Country Usng Mult-Item Newsboy Problem. JSME Internatonal Journal Seres C. 2005; 48 : Vassleva M. Operatve Plannng of the Producton Program n a Textle Enterprse wth the Help of MKO-1 Software System. Cybernetc and Informaton Technologes. 2006; 1 : Esfandyar A, Osama M, Tahrr F, Redel R. Lean Producton Tools And Technques Applcaton Toward More Productvty In Textle Industres 37th Internatonal Computers and Industral Engneerng, Alexandra, Egypt ; Olhager J. Strategc postonng of the order penetraton pont. Internatonal Journal of Producton Economc. 2003; 85 : Van Donk D, Soman C, Gaalman G. A Decson ad for make-to-order and make-to-stock classfcaton n food processng ndustres. Proceedng of the EurOMA Internatonal Conference on Operatons and Global Compettveness, Budapest, Hungary, 2005.