ORDER RELEASE PLANNING UNDER UNCERTAINTY

Transcription

1 T.C. BAHÇEŞEHİR UNIVERSITY ORDER RELEASE PLANNING UNDER UNCERTAINTY M.S. Thesis Emre TÜRKBEN Isanbul, 2011

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3 T.C. Bahçeşehir Unıversıy Insiue Of Science Indusrial Engineering ORDER RELEASE PLANNING UNDER UNCERTAINTY M.S. Thesis Emre TÜRKBEN Supervisor: Assis. Prof. Barış Selçuk Isanbul, 2011

4 T.C. BAHÇEŞEHİR UNIVERSITY INSTITUTE OF SCIENCE INDUSTRIAL ENGINEERING Tile of he Maser s Thesis : Order Release Planning Under Uncerainy Name/Las Name of he Suden : Emre TÜRKBEN Dae of Thesis Defense : The hesis has been approved by he Graduae School of Naural and Applied Sciences. Ass. Prof. F. Tunç BOZBURA Acing Direcor This is o cerify ha we have read his hesis and ha we find i fully adequae in scope, qualiy and conen, as a hesis for he degree of Maser of Science Examining Commiee Members: Assis. Prof. Assis. Prof. Assis. Prof. : Barış SELÇUK : Erkan BAYRAKTAR : Osman Mura ANLI

5 ACKNOWLEDGEMENT Foremos, I would like o express my sincere graiude o my advisor Assis. Prof. Barış SELÇUK for he coninuous suppor of my maser hesis sudy and research, for his paience, moivaion, enhusiasm, and immense knowledge. His guidance helped me in all he ime of research and wriing of his hesis. I could no have imagined having a beer advisor and menor for my sudies. Besides my advisor, I would like o hank o my eseemed colleague Barış ERDOĞAN who devoed his ime, experience and knowledge o my hesis during he simulaion coding. My sincere hanks also goes o my colleagues Mehap ĠNCE, Doğan AYDIN, Beül ERDOĞDU and Feryal ÇUBUKÇU who devoed heir ime, suppor and effors o my sudies. Las bu no he leas, I would like o hank my family: my parens Mesu TÜRKBEN, Aliye TÜRKBEN and my siser Ayşe BÜYÜKBAHÇECĠ, supporing me hroughou my life and showing grea paience, care and love. Emre TÜRKBEN ii

6 ABSTRACT ORDER RELEASE PLANNING UNDER UNCERTAINTY TÜRKBEN, Emre Indusrial Engineering Supervisor: Assis. Prof. Barış SELÇUK (June, 2011) XVI There are a lo of differen producion conrol principles and conceps in manufacuring environmen. The main idea of all hese conceps is minimize he oal cos of producion sysems and planning hem easy. As a handicap of hese conceps, hese conceps are no close o real-life cases because here exis alo of surprising condiions in real-life manufacuring environmens. The uncerainies and variaions in producion sysems are he main reasons which make producion conrolling and planning hard. Uncerainy represens he usual and random changes in producion lines, bu variaions represen unusual and rapid changes in he producion sysem. In manufacuring environmens, hese wo facors have he mos imporan roles in increasing oal coss of producion sysems. In lieraure, here exis he idea of changing he main producion conrol parameers adapively according o unsable changes of producion and demand condiions. In his maser hesis, a comparision of a radiional Kanban sysem wih a flexible kanban sysem which can adap o unsable changes have maken. The flexible kanban sysem has modeled by adapively changing he number of kanban cards in producion cenre according o invenory levels. Manufacuring process has designed as a mulisage CONWIP sysem and performance analysis of his adapive kanban conrolled producion sysem have been made according o he differen characerisics of his sysem via simulaion. Keywords: Kanban, CONWIP, Adapive kanban conrolled producion sysems, JIT, Pull producion sysems iii

7 ÖZET BELĠRSĠZLĠK ALTINDA ÜRETĠM PLANLAMA TÜRKBEN, Emre Endüsri Mühendisliği Danışman: Yrd. Doç. Barış SELÇUK (Haziran, 2011) XVI Üreim çevrelerinde kullanılmaka olan birçok üreim konrol uygulaması mevcuur. Büün bu uygulamaların emel hedefi üreimin planlanmasını kolaylaşırmak ve oplam maliyeleri minimize emekir. Ancak gerçek hayaa birçok sürpriz koşul bulunması nedeniyle, kullanılmaka olan üreim poliikaları gerçek üreim koşullarına yakın değildir bu da mevcu üreim planlama ekniklerinin bir handikapı olarak değerlendirilebilir. Üreim sisemlerindeki belirsizlikler ve değişkenlikler üreimin planlanması ve konrolunü zorlaşıran emel ekenlerdir. Belirsizlik; üreim haındaki ve alepeki olağan ve rassal değişimleri, değişkenlik ise bu sisemdeki olağan olmayan ve ani değişiklikleri emsil eder. Günümüz karmaşık iş yapış şekillerinde her iki fakör de maliyelerin armasında önemli rol oynamakadır. Değişen üreim ve alep koşullarına göre emel üreim konrol paramerelerinin adapif bir şekilde değişirilmesi fikri lieraürde mecvuur. Bu çalışmada belirsizliklere ve sabil olmayan değişikliklere karşı esnek davranış göserebilen bir Kanban siseminin, geleneksel Kanban sisemine göre ne gibi farkları olduğu araşırılmışır. Esnek kanban sisemi üreim merkezindeki kanban sayısı sok seviyesine bağlı adapif bir şekilde değişirilerek modellenmişir. Üreim merkezi çok seviyeli bir CONWIP sisemi olarak düşünülmüşür ve adapif kanban konrol modelinin bu sisemin farklı özelliklerine göre performans değerlendirmesi, bilgisayarda benzeim yazılımı kullanılarak gerçekleşirilmişir. Anahar Kelimeler: Kanban, CONWIP, Adapif Kanban Konrol Sisemleri, Tam Zamanlı Üreim, Çekme Üreim Sisemleri iv

8 CONTENTS ACKNOWLEDGEMENT... ii ABSTRACT... iii ÖZET... iv LIST OF TABLES... vii LIST OF FIGURES... x LIST OF SYMBOLS... xiii LIST OF ABBREVIATIONS... xiv 1. INTRODUCTION INTRODUCTION OBJECTIVE ORGANIZATION OF THE DISSERTATION LITERATURE REVIEW PROBLEM DESCRIPTION AND MODEL PROBLEM AND SYSTEM DESCRIPTION ASSUMPTIONS METHODOLOGY AND MATHEMATICAL MODEL EXPERİMENTAL CASES AND RESULTS EXPERİMENTAL CASES Case Case 1-a Case 1-b Case 1-c Case 1-d Case 2-Boleneck Case for saion Case 2-a Case 2-b Case 2-c Case 2-d Case Case 3-a Case 3-b Case 3-c Case 3-d Case Case 4-a Case 4-b Case 4-c Case 4-d v

9 5. CONCLUSION REFERENCES AUTOBIOGRAPHY vi

10 LIST OF TABLES Table 4.1: Trial values of Case 1-a Table 4.2: Trial values of Case 1-b Table 4.3: WIP levels a each sage for Case 1-b Table 4.4: Uilizaion raes of Case 1-b a each sage for each rial Table 4.5: Number of backordered demands and invenory levels for Case 1-b Table 4.6: Toal cos values of Case 1-b a each sage for each rial Table 4.7: Trial values of Case 1-c Table 4.8: WIP levels a each sage for Case 1-c Table 4.9: Uilizaion raes a each sage for Case 1-c Table 4.10: Backorders and invenory levels for Case 1-c Table 4.11: Toal cos of sysem for Case 1-c Table 4.12: The values of Case 1-d Table 4.13: WIP levels a each sage for Case 1-d Table 4.14: Uilizaion raes a each sage for Case 1-d Table 4.15: Backorders and invenory levels for Case 1-d Table 4.16: Toal coss of sysems for Case 1-d Table 4.17: The rial values of Case 2-a Table 4.18: WIP levels a each saion for Case 1-d Table 4.19: Uilizaion raes a each saion for Case 1-d Table 4.20: Backorders and invenory levels of sysem for Case 2-a Table 4.21: Toal coss of sysem for Case 2-a Table 4.22: Trial values of Case 2-b Table 4.23: WIP levels a each sage for Case 2-b Table 4.24: Uilizaion raes a each sage for Case 2-b Table 4.25: Backorders and invenory levels of Case 2-b Table 4.26: Toal coss of sysems for Case 2-b Table 4.27: The rial values for Case2-c Table 4.28: WIP levels a each sage for Case 2-c Table 4.29: Uilizaion raes of Case 2-c a each sage for each rial vii

11 Table 4.30: Backorders and invenory levels of Case 2-c Table 4.31: Toal coss of sysems in Case 2-c Table 4.32: The rial values for Case2-d Table 4.33: WIP levels a each sage for Case 2-d Table 4.34: Uilizaion raes a each sage for Case 2-d Table 4.35: Backorders and invenory levels of Case 2-d Table 4.36: Toal coss of sysems for Case 2-d Table 4.37: The rial values for Case3-a Table 4.38: WIP levels a each sage for Case 3-a Table 4.39: Uilizaion raes a each sage for Case 3-a Table 4.40: Backorders and invenory levels of Case 3-a Table 4.41: Toal coss of sysems in Case 3-a Table 4.42: The rial values of Case 3-b Table 4.43: WIP levels a each saion for Case 3-b Table 4.44: Uilizaion raes a each saion for Case 3-b Table 4.45: Backorders and invenory levels of Case 3-b Table 4.46: Toal coss of sysems in Case 3-b Table 4.47: The rial values for Case 3-c Table 4.48: WIP levels a each saion for Case 3-c Table 4.49: Uilizaion raes a each saion for Case 3-c Table 4.50: Backorders and invenory levels of Case 3-c Table 4.51: Toal coss of sysems in Case 3-c Table 4.52: The rial values for Case 3-d Table 4.53: levels a each saion for Case 3-d Table 4.54: Uilizaion raes a each saion for Case 3-d Table 4.55: Backorders and invenory levels of Case 3-d Table 4.56: Toal coss of sysems in Case 3-d Table 4.57: The rial values for Case 4-a Table 4.58: WIP levels a each saion for Case 4-a Table 4.59: Uilizaion raes a each saion for Case 4-a Table 4.60: Backorders and invenory levels of sysem for Case 4-a Table 4.61: Toal coss of sysems for Case 4-a viii

12 Table 4.62: The rial values for Case 4-b Table 4.63: WIP levels a each saion for Case 4-b Table 4.64: Uilizaion raes a each saion for Case 4-b Table 4.65: Backorders and invenory levels of sysem for Case 4-b Table 4.66: Toal coss of sysem for Case 4-b Table 4.67: The rial values for Case 4-c Table 4.68: WIP levels a each saion for Case 4-c Table 4.69: Uilizaion raes a each saion for Case 4-c Table 4.70: Backorders and invenory levels of Case 4-c Table 4.71: Toal coss of sysems in Case 4-c Table 4.72: The rial values for Case 4-d Table 4.73: WIP levels a each saion for Case 4-d Table 4.74: Uilizaion raes a each saion for Case 4-d Table 4.75: Backorders and invenory levels of Case 4-d Table 4.76: Toal coss of sysems in Case 4-d ix

13 LIST OF FIGURES Figure 1.1: A picure of Kanban card... 3 Figure 1.2: Schemaic working mehod of a classical Kanban sysem... 3 Figure 1.3: Schemaic model of a sample CONWIP sysem... 5 Figure 2.1: Single-card kanban, MRP, ROP, and he coninuous sysem, in a coninuoum. As i becomes harder o associae pars and end produc demands, invenories likely increase-from heoriical zero on he exreme lef o monhs worh on he exreme righ Figure 2.2: Decomposiion of he original sysem ino r single-produc subsysems Figure 2.3: Descripion Scheme of an e-kanban sysem Figure 3.1: Algorihm of adapive kanban sysem s processing principles Figure 3.2: Schemaic model of a coninuous ime Markov Chain Figure 3.3: A screensho of he simulaion model inerface Figure 3.4: Warm-Up Figure For WIP Levels Figure 3.5: Relaion Curve beween opimal kanban number and oal cos in a kanban conrolled producion sysem Figure 4.1: WIP Levels variaions a each sage for Case 1-a Figure 4.2: Average backorder and invenory variaion for Case 1-a Figure 4.3: Toal coss variaions of sysem for Case 1-a Figure 4.4: WIP levels variaion for Case 1-b Figure 4.5: Average backorder and invenory variaion for Case 1-b Figure 4.6: Toal Coss variaions of sysem for Case 1-b Figure 4.7: WIP levels variaions a each saion for Case 1-c Figure 4.8: Average backorder and invenory levels variaions for Case 1-c Figure 4.9: Toal coss variaions of sysem for Case 1-c Figure 4.10: WIP levels variaions a each saion for Case 1-d x

14 Figure 4.11: Average backorder and invenory variaion for Case 1-d Figure 4.12: Toal coss variaions of sysem for Case 1-d Figure 4.13: WIP levels variaions a each saion for Case 2-a Figure 4.14: Average backorder and invenory variaion for Case 2-a Figure 4.15: Toal coss variaion of sysem for Case 2-a Figure 4.16: WIP levels variaions a each saion for Case 2-b Figure 4.17: Average backorder and invenory variaion for Case 2-b Figure 4.18: Toal coss variaions of sysem for differen rial values of Case 2-b 68 Figure 4.19: WIP levels variaions a each saion for Case 2-c Figure 4.20: Average backorder and invenory variaion for Case 2-c Figure 4.21: Toal coss variaions of sysem for Case 2-c Figure 4.22: WIP levels variaions a each saion for Case 2-d Figure 4.23: Average backorder and invenory variaion for Case 2-d Figure 4.24: Toal coss variaions of sysem for Case 2-d Figure 4.25: WIP levels variaions a each saion for Case 3-a Figure 4.26: Average backorder and invenory variaion for Case 3-a Figure 4.27: Toal coss variaions of sysem for Case 3-a Figure 4.28: WIP levels variaions a each saion for Case 3-b Figure 4.29: Average backorder and invenory variaion for Case 3-b Figure 4.30: Toal coss variaions of sysem for Case 3-b Figure 4.31: WIP levels variaions a each saion for Case 3-c Figure 4.32: Average backorder and invenory variaion for Case 3-c Figure 4.33: Toal coss variaions of sysem for Case 3-c Figure 4.34: WIP levels variaions a each saion for Case 3-d Figure 4.35: Average backorder and invenory variaions for Case 3-d Figure 4.36: Toal coss variaions of sysem for Case 3-d xi

15 Figure 4.37: WIP levels variaions a each saion for Case 4-a Figure 4.38: Average backorder and invenory variaions for Case 4-a Figure 4.39: Toal coss variaions of sysem for Case 4-a Figure 4.40: WIP levels variaions a each saion for Case 4-b Figure 4.41: Average backorder and invenory variaion for Case 4-b Figure 4.42: Toal coss variaions of sysem for Case 4-b Figure 4.43: WIP levels variaions a each saion for Case 4-c Figure 4.44: Average backorder and invenory variaion for Case 4-c Figure 4.45: Toal coss variaions of sysem for Case 4-c Figure 4.46: WIP levels variaions a each saion for Case 4-d Figure 4.47: Average backorder and invenory variaions for Case 4-d Figure 4.48: Toal coss variaions of sysem for Case 4-d xii

16 LIST OF SYMBOLS Producion Raes a each sage : λ P1, λ P2, λ P3, λ P4 Demand arrival rae : λ D Producion rae of boleneck worksaion : r b Random variae : U Quanile funcion : F -1 Number of kanban cards : K Saring value of kanban cards : K * Number of exra kanban cards : E Release hreshold value : R Capure hreshold value : C Number of kanbans in process a ime : N() Number of exra kanbans in process a ime : X() Demand arrival ime for i h demand : D i Producion process ime a j h saion for k h order : P jk Invenory Levels : I Penaly cos of average backordered demands : p Average cos of work-in-process levels : WIPC Average cos of invenory levels : IC Toal cos of sysem : TC Toal ime of simulaion : T Uilizaion Rae : u Filled i h worksaion : FilledWSi Work In Process levels a each saion : WIP1, WIP2, WIP3, WIP4 Number of backorders : BO xiii

17 LIST OF ABBREVIATIONS Jus-In-Time : JIT Consan work in process : CONWIP Worksaions : WS1, WS2, WS3, WS4 Backorder Queue : BO Demand Queue : D xiv

18 1. INTRODUCTION 1.1 INTRODUCTION In manufacuring environmen, here are differen ypes of producion conrol mechanisms. One of hem is Jus In Time (JIT) producion sysems which use demands as a signal for he producion sysem. The main concep of JIT is o produce he produc when i is needed and according o he requesed amoun. JIT sysems help us o reduce seup imes, improve he flow of producs from warehouse o shelves and ake advanage of employees more effecively. Since he producion process is relaed o demand in JIT sysems, if here is no demand here will be no producion. Furhermore, JIT helps us o improve he imporance of he relaionship wih he supplier. The expecaion from JIT sysem is avoiding wase producs and invenory, bu especially decreasing he amoun of invenory o zero, which is physically and pracically impossible for producion sysems. Even hough he JIT sysems have benefis, here are some missing links beween heory and pracice of JIT seings. JIT sysems are designed for perfec condiions wih sable demands, consan and balanced processing imes, very low uncerainies and no breakdowns, bu in real life cases here happen oo much problems in he producion process such as processing imes variaions, unexpeced breakdowns, demand uncerainies. Many manufacuring companies which use JIT sysems, are rying o avoid hese uncerainies and increase efficiency of heir producion sysems. Considering he definiion of JIT sysems, we call he sysem pull producion sysems since he sysems use he demands as signal. Pull sysem is a kind of manufacuring mehod which conrols he flow of resources by only using wha has been demanded from sysem. In he pull sysems, consumers reques produc and pull i hrough he delivery channels. In hese sysems, he producion process sars from he las sage, any demand sars he producion process. The main characerisic of he pull sysems is ha producion and disribuions are demand-driven, and his enables producers o decrease lead imes; however, pull sysems are difficul o implemen. Implemening

19 manufacuring sysems is based on producion conrol policies. In a manufacuring sysem a he shop-floor level, hese conrol policies help o idenify when o sar and sop producing a produc and when o swich from one produc o anoher (Alıok 1996, pg.274). The single echnique mos closely associaed wih he JIT pracices of he Japanese is he pull sysem known as kanban developed a Toyoa (Hopp, J. W. and Spearman, M. L.,2008, pg.168). Kanban sysem is a kind of producion conrol sysem and Kanban means card in Japanese. Kanban sysem is also known as Toyoa s producion conrol sysem. This sysem is no an invenory conrol sysem, i is a sysem which ells manufacurers wha o produce, when o produce and how much o produce by using he informaion on he cards. Using Kanban, manufacurers handle wih he produc and informaion flow ogeher. There is no need for exra sock managemen. A Kanban card used in a facory is shown in Figure 1.1. As we see from he picure here is much informaion and daa on he card. The informaion on a ypical kanban card is as follows; he sage where card is used he number of componen he name of componen he definiion of componen he kanban number he name or he code number of he box which kanban card is regularly pu in he worksaion adress where Kanban card will be released. (he code number or he name). 2

20 Figure 1.1: A picure of Kanban card There are differen ypes of Kanban sysems in a manufacuring environmen. Main Kanban concep is he classical Kanban sysem. In classical concep, when a demand eners he sysem, a par is removed from he sysem s invenory poin, hen he worksaion which feeds he invenory poin sends an auhorizaion signal o replace he par which was removed from invenory poin. Then, each worksaion does he same hing. Auhorizaion signals are represened by Kanban cards. In he Kanban sysem, an operaor requires boh pars and an auhorizaion signal (kanban) o work. A schemaic working mehod of classical kanban sysem is shown in Figure 1.2. Raw Maerial Invenory Assembly Kanban Signals Worksaion Maerial Flow Figure 1.2: Schemaic working mehod of a classical Kanban sysem According o figure 1.2, we can ell ha here is one auhorizaion signal only for he produc bu in Toyoa s kanban sysem, hey make use of wo ypes of cards o 3

21 auhorize producion and movemen of produc. Toyoa s wo-card Kanban sysem s schemaic model is shown in Figure 1.3. As we menioned before, classical kanban is designed for favorie condiions in a manufacuring environmen. However, here exis sysems which have uncerainies in sysems performance measures such as demand or producion process imes. To avoid hese uncerainies, many researchers, muanufacuring companies, academicians have ried o adap he kanban sysems o hese uncerainies. In adapive kanban sysems, seing up he required kanban levels o avoid fill raes and relaed coss is complicaed. Tha s why, he aim of mos of researches abou JIT sysems is o define he opimal soluions and measures for uncerainies in JIT sysems and o specify how o design, monior, and conrol kanban levels according o changes in demand, producion capaciy, and uncerainy levels o improve fill rae performance. In genereal, he radiional kanban sysem is he mos famous implemenaion of pull sysems in which WIP levels are conrolled a each saion via cards bu i s no he simples way for implemening he pull sysems. To implemen i easier, here is a varian which is named of kanban sysem which named Consan Work-in-process (CONWIP). CONWIP is a kind of single-sage kanban sysem which is easier o implemen and adjus because in a radiional kanban sysem he producion line uses cards for each produc bu in CONWIP he producion line uses a se of cards for managing all he sysem. For sample, in a radiional kanban sysem o produce a finished iem in he sysem here is a card for each par of he iem, bu in CONWIP sysem here is only one card which auhorizes all pars of produc. In figure 1.4, a scheme which shows a sample CONWIP sysem. 4

22 Sock Poin Auhorizaion signals Figure 1.3: Schemaic model of a sample CONWIP sysem In manufacuring environmen all companies which are using Kanban sysems as producion conrolling policy ry o adjus heir sysem o real-life condiions. In ha way academicians, researchers and companies do researches on producion conroling sysems. 1.2 OBJECTIVE The objecive of his research is o develop a mehodology o be used during he design of Kanban sysems under uncerain condiions such as demand, lead ime uncerainies. In his research a muli-sage, single-produc CONWIP sysem is modeled and analyzed via simulaion. The expecaion is o decide how we can adjus a CONWIP kanban sysem o he uncerain condiions. Using exra kanban cards can help us o adjus he sysem a he righ ime or we can sabilize he producion process agains uncerainies, which helps us o save he cos of he sysem. 1.3 ORGANIZATION OF THE DISSERTATION This research focuses on he effecs of uncerainies on a single-card, single-produc CONWIP based Kanban conrolled producion sysem. Chaper 2 represens a deailed lieraure review wih he previous works which are focused on producion conrol sysems and policies. Chaper 3 describes he algorihms and formulas which are used for developing an sample model for analyzing he sysem. Chaper 4 represens he 5

23 simulaion model, experimenal cases and resuls which are analyzed via simulaion. Chaper 5 represens he conclusion of all he resuls. 6

24 2. LITERATURE REVIEW As we menioned in Chaper 1, here are a lo of alernaive producion sysems and conrol policies which are used in manufacuring environmen. General alernaives are pull and push sysems. Pull sysems can be implemened in several ways. Kanban sysem is he mos popular pull sysem in manufacuring environmen, bu kanban sysems show heir bes performance under he ideal condiions such as sable demand, sable lead imes, sable iner-arrival imes beween demands ha creae a missing link beween heoriical and pracical implemenaions of kanban sysems. Due o his reason many researchers, academics or companies made a lo of research abou how o implemen kanban sysems under he real-life condiions or how o reduce missing links beween heoriical and pracical implemenaions of a kanban sysem. Also here are differen conrol policies such as base sock policy which is very easy o implemen C. Duri e al. (2000). From he poin ha here exis a lo of sudies abou Kanban sysems wih differen algorihms and formulas Akurk & Erhun,(1999) made a lieraure review and classified differen ways of deermining design parameers and kanban sequences echniques for jus in ime sysems. The imporan poin is o sae he relaionships beween design parameers which are number of kanbans, kanban sizes and scheduling decisions. The auhors saed he relaionships beween parameers in a muli-iem, muli-sage and muli-horizon kanban sysem. A model has been developed by auhors o make some experimens for evaluaing he impac of operaional issues, like sequencing rules and acual lead imes on design parameers. Mehods which are used o deermine design parameers have some seps such as model developmen, soluion approaches, defining decision variables, defining performance measures, objecives of sysem, sysem s configuraion, ype of kanban and he assumpions of model. The sample models are presened for sequencing producion kanbans a each sage. Under differen experimenal condiions, sample models are analyzed. Analysis shows ha none of he exising models of JIT considers he impac of operaional issues on design parameers. There is a lack of differen experimenal kanban models for elaboraion on scheduling kanban sysems, hese models have o work under differen experimenal condiions. Also, analysis which is done under differen experimenal condiions show ha mos 7

25 commonly used combinaion of Firs Come Firs Serve (FCFS) rule wih insananeous kanban wihdrawal mechanism may no be a good policy all he ime. FCFS rule performs beer when he wihdrawal cycle lenghs are long enough for jusifying seup imes. By using four commonly used sequencing rules in lieraure Akurk & Erhun (1999) analyzed he impac of operaional issues on design parameers. As Akurk & Erhun (1999) menioned in heir deailed lieraure review, here are los of differen implemenaions of producion conrol mechanisms. C. Duri e al. (2000) handled hree differen implemenaion ypes of producion conrol sysems and compared hem wih each oher. They worked on make o sock pull conrol mechanisms such as kanban policy, base sock policy and generalized kanban policy which includes special cases of classical kanban and base sock policy. Auhors noiced ha he bes known pull sysem is he kanban policy. This policy conains one design parameer per sage and for each ype of produc: he number of kanbans in sage. This parameer limis he maximum level of work-in-process (WIP) and finished pars invenory. The second policy is he base sock policy which includes one design parameer a each sage of sysem and for each ype of produc. Also base sock policy is very reacive. To show he characerisics of hese policies wih samples, auhors made a quaniaive comparison of hese hree conrol policies. These samples are analyzed wih analyical mehods for esimaing he sysems performance measures which depend on manufacuring processes, arrival process of exernal demands and parameers of differen sages. For comparing hree policies hey designed hree sysems for each one of he policies. The auhors noiced ha opimizaion mehods are no enough o analyze he generalized kanban sysem which is defined wih design crieria of heir work. These design crieria can be used by base sock and classical kanban because hese sysems are a kind of special case of generalized kanban sysem. They aimed a making a quaniaive and qualiiaive comparison of hree differen pull mechanism producion conrol sysems, o choose he bes policy o implemen for conrolling a producion sysem and give pracical rules o be used for choosing he sysem. Generally, heir selecion crieria is sysems cos performances for he same producion qualiies under he same condiions. They show ha if here is no delay in filling orders, all hree policies have similar coss. However, if here is a delay in filling 8

26 orders, generalized kanban sysems and base sock sysems yield close o opimal coss ha are lower han he coss of kanban sysem for he same producion qualiy. However, Duri e al. (2000) compared and analyzed differen conrol policies such as kanban, generalized kanban and base sock sysems. They did no consider he fac ha here are differen applicaions of producion conrol policies. Schonberger Richard J. (1983) handled wih differen applicaions of kanban sysems such as single-card and dual-card kanban sysems. As we menioned before, here are differen implemenaions of kanban sysems, one is single-card kanban sysem and he oher one is dual-card kanban sysem which is known as Toyoa producion sysem. Schonberger defines kanban, push, and pull sysems, and gives informaion abou general characerisics of hese sysems such as where hey are used, he ease of associaing hese sysems, and any weak or powerful sides of heirs. Also, he showed a schemaic comparison of differen producion conrol sysems characerisics. In Figure 2.1, we can see he schemaic model of hese characerisics. Figure 2.1: Single-card kanban, MRP, ROP, and he coninuous sysem, in a coninuoum. As i becomes harder o associae pars and end produc demands, invenories likely increase-from heoriical zero on he exreme lef o monhs worh on he exreme righ. 9

27 Source: Rıchard J. Schonberger, (1983), Applicaions Of Single-Card And Dual-Card Kanban, Producion Scheduling, Maerials Handling, 65. He shows and makes criicism of differen producion conrol mechanisms and defines heir characerisics in his work. Alhough here are differen ypes of kanban in manufacuring environmen, generally firms use classical kanban sysem, which can be single-produc or muli-produc kanban sysems. This can creae a difference beween sysems. As we can see here are differen pull producion sysems which are conrolled by kanban cards in manufacuring environmen. Woodruff e al. (1990) described a new pull based kanban sysem called CONWIP which means Consan Work-in-process. They compared he new sysem wih classic kanban and push based producion conrol of a single producion line. They ell ha CONWIP differs from Kanban in hree main ways. These ways are as follows; The use of backlog helps dicae he par number sequence, cards are associaed wih all pars produced on a line raher han individual par numbers, and jobs are pushed beween worksaions in series once hey have auhorized by a card o sar a he beginning of he line. They analyzed he new sysem by developing a simulaed sysem. They have conduced numerous simulaions o make comparisons beween CONWIP and push based sysems. They discuss he resuls of simulaion sudy ha illusraes some of advanages of CONWIP over a push based sysem. The sysem does offer some disinc advanages over kanban. For example, CONWIP can be used in some producion environmens where using classical kanban is no effecive and pracical because of oo many par numbers or because of significan seups. CONWIP concep has been used in differen sudies for solving differen problems in pull producion sysems. Sarah M. Ryan e al. (1998) conduced a research on conrolling a job shop seing wihin he concep of CONWIP. They ried o solve he problem of deermining fixed overall WIP level o mee a uniformly high cusomer service requiremen for all ypes of produc and opimizing a queuing nework model in 10

28 which orders pull compleed produc from he sysem. Under assumpions of heavy demand here is a hroughpu arge for each produc ype. A simple heurisic has been provided for finding minimum oal WIP and WIP (mix) which will achieve hroughpu hrough operaing close o he hroughpu arge. WIP (mix) is mixed WIP for all ypes of produc. They focused on he proporion of orders which wai o be fulfilled by he producion sysem. They worked on he problem o make a card coun for each ype of produc so ha he probabiliy of waiing for an order o be fulfilled can be lower according o he producion capaciy. As a resul, a higher oal hroughpu could have been achieved wihou produc mix consrain, bu resuling sysem design would grealy favor orders for some producs a he expense of ohers. We know ha a producion sysem can produce single or muliple produc also kanban sysems can be single or muli produc kanban sysems. Schonberger Richard J. (1983) handled his subjec bu he didn analyze a sample model, he only defined he characerisics of he sysem and made criicism abou he sysems. C. Duri e al. (1995) concerned a kanban sysem analysis which produces several ypes of producs. Also, hey presen an analyical mehod for analyzing he performance of a muli-produc kanban sysem wih using a closed muli-class queuing nework model which each class represens one ype of kanban. The sysem produces wo ypes of producs on he same machine so his creaes ordering problem, which produc would be he firs. The seup imes can be disinguishing characerisic for defining processing orders. There are wo cases: he firs one is; if seup imes are no zero, we have o ry o limi he number of seup, bu if seup imes are zero, we can choose and define processing orders. Therefore, here is no need o worry abou seups number limiaion. They focused on he second case for analyzing he sysem performance. They aimed a approximaing an analyical mehod for analyzing he speed and accuracy for designing of a muli-produc kanban sysem where hey need o es numerous configuraions of sysems for selecing he bes one o use in real-life producion sysems. In a kanban sysem, one of he mos imporan performance measure is Kanban sizes. Seing up kanban sizes in a producion sysem is very imporan bu we have o know he facors which influence he number of kanbans in a kanban conrolled producion sysem. Philpoom e al. (1987) made an invesigaion o idenify hese facors. The facors which hey ried o idenify include hroughpu velociy, coefficien of variaions 11

29 in procesing imes, he machine uilizaion and auocorrelaion of processing imes. All hese facors are analyzed by using a simulaion model. In a pull sysem, he sysem s efficiency is measured wih number of conainers which include finished goods produced and sored, i means ha more invenory is equal lower efficiency. When he auhors analyzed he facors ha influence he number of kanbans, hey assume ha one workcenre encompasses only one machine, and he sysem produces only one produc in each processing ime. Conveyance ime and kanban collecing ime are all zero or relaive o processing ime, also seup imes are all zero and all processing imes are equal. Analysis of sample simulaion models show ha if variabiliy in processing imes increase, he number of kanbans increase oo. If he machine uilizaion increases, he number of kanbans increase and correlaion of processing imes have he same effec as oher parameers. While we design a producion sysem, we have o choose sysem opions carefully. To ake he bes performance resuls from he designed sysem, he sysem opions have o be close o he real life producion sysem opions. In his respec, Deleersnyder (1989) noiced ha here are hree problems in designing and implemening a kanban conrolled JIT sysem such as followings; 1. he idenificaion of flow lines which is imporan for achieving he flow lines operaing around he producion families wih a good level of uilizaion bu wih a minimal exra invesmen, 2. loading flow lines, which is imporan for avoiding bolenecks developing in work saions, 3. conrolling he operaions, which is imporan for conrolling he ineracion beween producion and invenory levels and for deermining he expeced number of kanbans in sysems under sochasic condiions. They developed a 3 sage serial producion model based on N-sage serial producion sysems. Also, he sysem is developed as a discree ime Markov model. They described heir models wih 4 levels which are variabiliy in number of kanbans, he impac of machine reliabiliy, he impac of demand variabiliy and he impac of safey sock in he sysem. The sysem performance analyses are based on hree sources which are; uncerainies of machine reliabiliies, capaciy consrains and uncerainy of 12

30 demand. They aimed a analyzing a sample kanban based producion sysem under hese sources. They esed he effec of he number of kanbans variaions on he sysem s performance parameers such as average oal invenory, average backlog, variance backlog, % los demand, average job flow ime. As a resul, unil he number of kanbans become 15, here is a small increase in all he performance parameers excep average oal invenory, bu when he number of kanbans become more han 15, he effec on performance parameers will be dramaically bigger. When he producion sysem becomes more reliable he average backlog decreases, his is he resul of changing producion reliabiliy and he overall sysem performance. Also, he impac of demand variabiliy makes he sysem more sensiive. Anoher resul of he sysem is ha while number of kanbans and safey sock increases, he average backlog decreases and average oal invenory increases. For analyzing he kanban producion sysems performance, here are a lo of models developed, bu he analysis mehods are differen from each oher. For sample as we noiced before, Deleersnyder e al. (1989) analyzed his model in a whole perspecive. Also, Di Mascolo e al. (1996) used a differen mehod o analyze he performance of a kanban sysem. They developed a general purpose of analyical mehod for analyzing he performance of muli-sage kanban conrolled producion sysem by using decomposiion mehod. They considered single ype producion sysem and decomposed his sysem ino sages in series. The basic principle of decomposing a sysem is o decompose main sysem ino subsyems. They used a produc form approximaion echnique for each subsysem s analysis in isolaion, afer ha an ieraive procedure is used for deermining he unknown parameers such as he percenage of demands ha are backordered (no immediaely saisfied), average waiing ime of backordered demands and average work-in-process. The auhors modeled he sysem as a queuing nework. They analyzed he sysem for presening he problems of single-sage and muli-sage sysems. Afer he analysis of models, hey firs focused on he producion capaciy of he sysem because i s he sysem s maximum hroughpu. In he sysem developed in his work demand always comes o sysem for finished pars. As a resul of he analysis, hey noiced ha producion capaciy increases wih he number of kanbans a each sage. 13

31 On he conrary o Di Mascolo e al. (1996), Krieg Georg N.&K. Heinrich(2004) developed a decomposiion based mehod ha analyzes and generaes accurae esimaes for seady-sae performance measures of a kanban producion sysem. A sample model of kanban sysem which can produce muli-produc is developed. In he sample sysem, he seup and processing imes are assumed o be exponenially disribued. According o muually independen Poisson process, cusomers arrive o sysem. There is a arge invenory level given by he number of kanbans. When he number of producions reach he arge invenory level, he faciliy sops and seup for he nex produc according o a fixed seup sequence if he nex produc invenory is below arge. Oherwise, his produc is shipped. Also, when all producs are a heir arge level, he faciliy idles. Anoher poin in he sysem when a cusomer arrives o sysem, if here is no produc in he invenory of he produc which cusomer orders, cusomer saisfies his demand elsewhere, i is named as Los Sales. According o Coninuous Time Markov Chain algorihms; for a sysem which has five differen producs and five kanbans, sysem has saes. For anoher sysem wih 10 differen producs and 10 kanbans for each of hem, he number of sae is greaer han 471 billion, as a resul of his sae space explosion, exac analysis of a model is mahemaically no possible even for smaller sysems. In his work as an alernaive mehod he auhors used decomposion mehod. Krieg &Kuhn (2004) decomposing figure can be seen in Figure

32 Figure 2.2: Decomposiion of he original sysem ino r single-produc subsysems. Soruce: Georg N. Krıeg, Heınrıch Kuhn, (2004), Analysis Of Muli-Produc Kanban Sysems Wih Sae- Dependen Seups And Los Sales, Annals Of Operaions Research 125, 145. The number of kanbans play a major role in he performance of kanban conrolled producion sysems. Bard & Golany (1991) has developed a single-card producion sysem which he empy conainers funcion as kanbans and are used o rigger orders. They developed he sysem which is designed for a given demand and planning horizon. This sample model is very general, i is commiing he problem in a wide range. The firs and mos imporan ineres of he auhors is he number of kanbans in he sysem. They made some assumpions ha here is precisely one kanban of each produc ype for each conainer, he number of kanbans are equal o he number of conainers and for each par i should be minimized, he conainers which are used in he sysem have o be sandard and mus always be filled wih he prescribed quaniy. They also assumed ha he producion and wihdrawal can be sared only by appropriae kanbans. They aimed a minimizing holding and shorage coss wihou ignoring he basic kanban principles and balancing hese coss over he planning horizon. They hink ha his sysem and algorihm may help assis line managers in deermining opimal kanban numbers a each worksaion. As a resul, hey noiced ha his sysem is he mos appropriae when demand is seady and lead imes are shor. Making careful analysis can yield immediae 15

33 benefis by reducing invenories and provide managers wih a more deailed picure of curren acivies. Deermining he number of kanbans and analyzing is effecs can be done by differen mehodologies for differen sysems. Markham e al. (1998) made a rule inducion for using he number of kanbans in a JIT sysem by using a classificaion and regression ree (CART) echnique which is developed by Briemen e al. (1984). If he producion sysem is under ideal condiions such as sable demand, low process imes, wellrained workers, here is no need for an adjusmen on he number of kanbans in he sysem. However, in real life condiions i is impossible o make every condiion ideal. From his poin, hey presened a mehodology which allows he shop floor manager o idenify he relaionships beween shop facors which need o be moniored if he firm s aim is o operae is shop a leas cos producion kanban level in he near fuure. The mehodology has been used on a sample in hree seps which are daa collecing, formaion of desicion ree and inerpreaion of decision ree. There is a simple heurisic hey noiced as a resul, if here exis a high demand variabiliy in he curren period and if he lead ime in he previous period is shor and vendor supply variabiliy is high in he previous period, hen only a few kanbans are required for he sysem. Also using CART in a rule inducion provides us wih a viable soluion o he knowledge acquisiion boleneck. As a case sudy abou Kanban conrolled producion sysems Orbak & Bilgin (2005) conduced a research abou Kanban sysems. They ried o apply Kanban sysem in a small auomoive raw par manufacuring company. Before saring o implemen Kanban sysem, in a firm here are imporan hings o do. These hings are processing imes sandardizaion, reducing seup imes, seing up he uiliies according o JIT philosophy, oal qualiy managemen applicaions for JIT sysems such as he arges which are zero invenory and zero wase produc. According o he firm s producion procedure hey ge daa o analyze he sysem. The daa includes average mean of demand inerval, seup imes, lead imes, he number of los produced, having invenory cos and no having an invenory cos. Considering he daa colleced by using Monden s formula which has been described by Monden (1993), he auhors ried o deermine he opimal number of kanbans which ge he sysem o minimize he coss. As a resul of hese calculaions, hey noiced ha implemening kanban sysem in his 16

34 firm causes a decrease in couns of invenory wih a rae of 50%, a decrease in couns of los and his causes o an increase of lead imes. Kanban sysem implemenaion gives company a lo of advanages o firm such as conrolling defecs quickly, decrease in wase producs. Also, he producion process becomes easier o undersand and implemen, and over-producion is prevened. In a phrase, his sudy showed us implemening kanban sysem o a producion sysem make producion sysem easier and provide producion managers wih an easier conrol of sysem. Generally researchers make assumpions abou analyzing and designing a producion syem on he given kanban sizes. Chan (2001) ried o invesigae he effec of kanban size variaions on he performance of JIT manufacuring sysems. There exis wo ypes of JIT producion sysems; one is pull-ype, he oher one is hybrid ype. These are analyzed by using compuer simulaion models. Auhor considered some performance measures such as fill rae, inprocess invenory and manufacuring lead ime. Also, some oher parameers such as demand rae, processing imes are aken ino consideraion. He developed wo simulaion models for esing he effec of kanban sizes on differen JIT sysems. He aimed a deermining opimal kanban size for opimizing he performance of sysem in erms of lead ime and fill rae. As a resul of single produc sysem performance analysis while kanban size increases, fill rae decreases bu inprocess invenory and manufacuring lead ime increase. For muli produc sysem analysis, he noiced while kanban size increases fill rae increases, oo. However, manufacuring lead ime decreases in he sysem when kanban size of sysem increases. As a case sudy abou Kanban conrolled producion sysems Orbak & Bilgin (2005) conduced a research abou Kanban sysems. They ried o apply Kanban sysem in a small auomoive raw par manufacuring company. Before saring o implemen Kanban sysem, in a firm here are imporan hings o do. These hings are processing imes sandardizaion, reducing seup imes, seing up he uiliies according o JIT philosophy, oal qualiy managemen applicaions for JIT sysems such as he arges which are zero invenory and zero wase produc. According o he firm s producion procedure hey ge daa o analyze he sysem. The daa includes average mean of demand inerval, seup imes, lead imes, he number of los produced, having invenory cos and no having an invenory cos. Considering he daa colleced by using Monden s formula which has been described by Monden (1993), he auhors ried o 17

35 deermine he opimal number of kanbans which ge he sysem o minimize he coss. As a resul of hese calculaions, hey noiced ha implemening kanban sysem in his firm causes a decrease in couns of invenory wih a rae of 50%, a decrease in couns of los and his causes o an increase of lead imes. Kanban sysem implemenaion gives company a lo of advanages o firm such as conrolling defecs quickly, decrease in wase producs. Also, he producion process becomes easier o undersand and implemen, and over-producion is prevened. In a phrase, his sudy showed us implemening kanban sysem o a producion sysem make producion sysem easier and provide producion managers wih an easier conrol of sysem. As we menioned before, changing he number of kanbans in a kanban sysem is a big problem. Considering his problem Toyoa Moor Corporaion developed a new kanban sysem called e-kanban which uilizes compuers and a communicaion nework esablished beween Toyoa and is suppliers. Koani (2007) makes a descripion of e- Kanban sysem which we can see in Figure 2.3. Figure 2.3: Descripion Scheme of an e-kanban sysem Source: Koani, S.(2007) 'Opimal Mehod For Changing The Number Of Kanbans İn The <i>e</i>- Kanban Sysem And İs Applicaions', Inernaional Journal Of Producion Research, 45: 24, 5792 From he poin ha one goal of e-kanban sysem is improving he mehod which is used o change he number of kanbans, he auhor invesigaed a means of achieving his and proposed an opimal mehod for changing he number of kanbans. There are some improvemens which are he resul of implemening e-kanban sysem. These are greaer efficiency in he conrol of kanbans, reduced flucuaion in order quaniy and appropriae changes in he number of kanbans, reduced pars invenories and quick 18

36 response o changes in demand. Also, applying his mehod o e-kanban sysem showed us e-kanban sysem can manage pars ordering and delivery aciviies more efficienly and effecively han kanban sysem. We know ha here is a missing link beween heory and pracical applicaions of JIT sysems. Theoreically implemening JIT sysem can be he bes choice for a manufacurer bu pracically i s hard o implemen because in real life cases he manufacuring environmen is very dynamic. Also, adaping he JIT sysems o dynamic manufacuring environmen is very imporan for he implemenaion of JIT sysems. According o he idea of adaping JIT sysems o dynamic environmen Gupa & Al Turki (1997) developed a new sysem which uses an algorihm for manipulaing he number of kanbans. They called he new sysem as Flexible Kanban Sysem (FKS). Gupa e al. (1995) noiced ha FKS is a sysem which is quie robus and is performance is superior o TKS even for high processing imes. In FKS, he idea is o increase he flow of producion by reducing he blocking and sarvaion caused by he variabiliy in processing imes. This is achieved by increasing he number of kanbans in he sysem. FKS can increase or decrease he number of kanbans according o a base level number of kanbans, he sysem can reduce he number of kanbans below he base level. They developed a simulaion model and analyzed i under he condiions ha manufacuring sysem is composed by 8 sages, here is a demand for finished goods beween 140 and 260 unis for a planning horizon which is formed from 10 days, a every saion processing imes are independen and normally disribued, base level of number of producion kanbans and wihdrawal kanbans are se a wo for each saion, and ransiion imes are 30 seconds for all kanbans. Also, o work on he sample model simulaion hey assumed ha a saion 1 here are always raw maerial available, each conainer includes one par, for producing one uni of demand raw maerial have o be processed a each saion and firs come firs serve queuing discipline is used for processing he pars. For four performance measures which are ime in sysem (TIS), work-in-process (WIP), average order compleion ime (OCT), and oal number of unis backlog 20 replicaions are made. They made his analysis for TKS and FKS o make a comparison beween hem. As he resuls of analysis, hey noiced ha average ime in he sysem for FKS is longer han TKS, he average work-in-process in FKS is higher han TKS, he average order compleion ime in TKS is longer han FKS and he 19

37 oal number of unis which are backlogged during 50 days are zero for FKS. They aimed a developing a flexible kanban sysem which reduces he backlogs in he sysem by manipulaing he number of kanbans in he sysem under hese assumpions. We can see ha hey succeeded ha aim. Anoher work abou adaping kanban sysems o dynamic manufacuring environmen was done by Takahashi & Nakamura (1999). They propose a sysem ha can deec unsable changes in demand by using Exponenially Weighed Moving Average (EWMA) chars, and deermine he revision of buffer size for he deeced unsable changes based on radeoff beween performance measures under sable condiions. They used simulaion experimens for analyzing and comparing performance of proposed JIT ordering sysems. JIT sysems being used in his work are kanban sysem and he concurren ordering sysem which has been modified by Takahashi e al. (1996). In he modified concurren ordering sysem when a demand arrives from succeeding sage an order is released immediaely a he producion sysem. Concurren ordering sysem includes only one kind of informaion which is abou he demand arrival a he producion sysem. Besides ha informaion, minimum releasing orders are considered. Also base sock sysem which is invesigaed by Buzaco & Shanikumar (1993) is he same sysem as concurren ordering sysem. They developed wo muli-sage JIT producion sysems. One is Kanban based, he oher one is concurren ordering sysem based. They simulaed he sysems and hey compared he resuls of simulaions. The producion sysems have same assumpions ha a sandard produc is produced, he demand has sable and unsable changes, inerarrival ime of demand is disribued sochasically wih unsable changes in he mean bu variance is consan, producion ime a each sage is disribued sochasically, ransporaion process beween (n-1)s and nh sages is called nh ransporaion sage, each sage has wo invenory poins named before and afer invenory poins, backorder is allowed and buffer sizes and he number of kanbans are conrolled dynamically for reacing unsable changes in demand. As a resul of simulaion analysis boh sysem can reac o he unsable changes. Under igh requiremen for waiing ime of demand, kanban sysem is more efficien. Also, oal mean of WIP invenories in kanban sysem is less han he concurren ordering sysem. 20