8/8/2019 p616-dyck JIT

1/8

Proceedings of the 1988 Winter Simulation ConfereweM. .4brams, k. Haigh, and J. Comfort (eds.)

Transforming a traditional manufactuuing system intoa JUST-IN-TIME system with K.ANBAN

rsrc1 a DVCkDepartment ot Computer Intormatlon SystemsColleqe of BusinessSouthwest Illssour- State UniversitySpringfield, MO 65804

Richard A. JohnsonPresl dent

Business bolutlons Unllmlted . . .724. South We1 ler AvenueS,zrlngfleld. MO 65802-3345Jay VarrandcmhDepartment of Information & Declslo Sciences

Cdllfornla State UnlverSitL--San BernardinoSan Bernardino, CCI 92407

ABSTRACTTrade ti onal manufacturing svstems.characterized bv many U.S. +lrms. are knownfor malntalnlng relatively high .levels of rawmater1 al. work-1 n-process, and f lnl shed goodsinventories as a hedge against uncertainty Isuppl 1 et- de1 1 very and quality. prOdUCtlOrates and qua1 i tv, and customer demand.Just-i n-Time (JIT) manuf acturlng svstems,characterized by many J ap anese f lr-ills. areoutperfarmlng their U.S. counterparts bv usrngkanba productlo control I concert wl thmany other more common0 1 ace operat 1 on5management tech1 ques. IMarly U.S. firms haveattempted to ltnl tate Japanese metrods 1"piecemeal f ash1 on w1 th 1 i mi ted success perhapsdue to a failure to understand JIT as acomprehens l ve system and philosophy. tinexcel lent means of devel opi ng a betterunder standi ng Of J I 1. manuf acturl nq and tooegin lmplementl ng JIT within an existingtraditional system 1s through the use ofcomputer slmulat1on . This paper presents arationale c3f JJT and an example of usingI;PSS/PCtm simulation to study the eftects ofadopting JIT with kanban production control inan actual U.S. manufacturing environment. Thismethodology should be applicable to othertradltlonal manufactur ing svstems deslrrng tabetter understand and implement JIT.

1. INTRODUCTION1.1 U.S. Imitation of Japanese Success-rhe 11 terature abounds with account5 of howJapanese manufacturing has become the Standardof quality and productivity against which lJ.5.iirms compare very unt avorabl v(Chase and Aquilano 1985, Byrd and Larter198B. Lubben 1588). Americans often preferJapanese products on the basis of price andquality. The JJ,paese have even demonstratedthe ah1 llty to successfully produce goods 1the U.S. with Amet- can workers. SO~nfluentlal are the Japanese that maly U.S.i-irm5 have recently begun to imitate JaPae5eman agemen t techniques (JlT I particular) I1:he hope of becomi nq more competitive.However, It may be deoated that such lmltatla 1s o+ten IneffectIve because It 1s not coupledwith a more complete understanding of thei nteractlon of all elements ot JIT ( Mver s1988).

1.2 Slmulatlon and JITSlmuLatlo ot a tradlsystem with the abllltv

Implementationt1ona1 manufacturlnqto easl ly convert themodel into a JIT manufacturing 5ystem can

serve two important purposes: the firmsmanagement can (1) obtain a betterunderstandlnq ot the broad nature ot the 3 I .raporoach to manufacturing 1 contrast WI ththe1 .- exlstl"~ tradltronal manufacturingsvstem and (2) use the slmulatlon model todeveloo strategv and implement declslons 1 anattenot to graduallv transform the1 rtradltlonal system Into a more oroductlve 31Tsystem.1.3 AerviewThis paper first presents a rationale forunderstanding Jll as an interdependent Set ofelements which must be properly meshed into acoherent system 1 VOl1 Q both human andtechnologlcai aspects. This rationale 1sessential to understanding the deveiooment ofthe slmulatlon model of an actualmanu-cacturlng system presented later.Al though the human asoects of the JIT systemare not directly Included I the model a fairnumber of the technologica l aspects are. Themodel 1s tested and discussed under a varletvof manufacturing condltlons relevant to JIT.Flna-ly. the model 15 run I" an etfort todeter-mine an acceotable strategy which aexisting tradl t Ional svstem could u5e to1 rnDt-f? qua1 1 ty and productlvx ty through thegradual adoptlon of JIT. Conclusions ~111 thenbe drawn for the svstem under lnvestigatlonand tar the apDllcablllty of this method01 ogyto ot.her tradltional manufacturi ng systems.

2. F!AT IQNALE FOR JIT MANUFACTURlNG2. I Reasons for Japanese SuccessMany have argued that Jaoanese 5ucce5s I*manufacturlnq is due to cultural differences,to the proxlmltv of Jaoanese supollers to themanutactur1nq plant. or to lifetlmeemolovment. Some be1 1 eve that the J aoanese1 abor rates and/or untalr Japanese importlnaand exporting practices are primarilyresponsible for the Iower prices ot Japanesegoods. However. 1 t appears that Japanese5ucce55 ln maU+aCtUrlg 15 due in large partto their dejlcatlan in uslnq a 31T 5votem

616

8/8/2019 p616-dyck JIT

2/8

(Chase and Uqullano 1985). U.S. firms are tarthe most part sti 11 committed to thetraditional auproach characterized by hi qherdefect rates, larger inventory levels. andlower throughput.2.2 Interplant and Intrapl ant J 1TThe term "just-In-time" is usually taken to

aDplY to el ther (1) ordering from out 51 desuppliers to xippor t production II-! such a

way as to minimize raw materials inventorv. or(2) driving production schedules bv customerdemand I such a wa as to minimize finishedqoods inventory. These xnterolant facets ofJIT represent Only a part of a total JITmanufacturing picture. The bulk of JIT whichimoacts quality and productivity is concernedwx th ~ntraplant applications involved Inma1ntain1nq an ootimum work--l n-Droce ss t&IF,1 nventoi-y throughout the Droduction Dt-oce55.This 1ntraolant 517 system 15 usual 1 Yaccompl i shed by a kanban production controlsystem (JIT-Kl. .This paper tocuses onlv onthe intraplant JIl-K application althouqh avery strong set of uaral lels exist between theinterplant and lntraplant mechanics of JIT(Lubben 1988).

2.3 Benefits of WIP Inventory Reduction

The most apparent goal of a JIT-K system is tomlnlmi ze WIP i nventorv. HOWeVW-. the purposeOf reducing WIP inventorv is two-fold: (1)reduce cart-v~nq costs and (2) zmprove qualityand aroductivitv. While most see thereduction ot WIP simoly as a means to reducecarrel nq costs such as interest and storageexpense. perhaps the greatest beneti t ofminimizing WIP 15 the vastlv i morovedvieibilitv of oroblems in the manut acturi nqprocess. problems which contrl bute toconsiste ntly low oualitv. hiqh rework, 1 arqeinventories. and low tnroughput. Progresstoward auallty and productivity can only beaccom~ll shed when process flaws are exposedand effectivelv acted upon. Bv uslnq the JIT-K svstem the JaDanese have the best of bothworlds: they exDo5e and correct flaws whl chfirst 1 mproves qua1 1ty: improvements 1nsual1tv result in increased productivity.Reduct 1 on ln carrving costs can be viewed a5an important frinqe benefit. Thus. theJapanese produce hiqher aualltv at lower costwith the end re5uJ t being a superi orcompetitive Dositlon in world markets.

3. ELEMENTS OF JIT -- APPROACHING THE IDEPL3.1 The Ideal Manufacturing SvstemThe elements of a JIT-lc manufacturing svstemare quite numerous and must function In acoordinated fashion to be effective as asystem (Ebrahimpour and Lee 1987). Tounderstand how JIT-K is designed to operateOe can begin bv cons~derlnq the 1 dealmanutacturing system. A multistage, sinqleline process can be viewed as a simpleassembl Y line. If there are no Unnecessary

ae1 als and no uncertainties present. the5ystem i5 Ideal and orodUCtl"ltv will be

optimized. It is the presence of UnCertalntVin each of the mat-IV manuf acturinq svstemcomponents whl ch amollfles Drool ems andnecessitates a JIT approach tot- 5olutlo"s(Chaoman and Schminke lY881.

3.2 Simplicity. Automation and MethodsSuppose in this Ideal factory that the cvc1etimes for each production stage are Derfectlvbalanced and that wlthin each stage there isno variation .I" cycle time from part toindividual oart. This goal is approachedthrough a JIT system in a number of differentWdY5. First. improved DrOdUCt design helDs to1 nsure manufacturabilitv and provide s qreateropportuni tv for automation. thus mlnlmlzlngvariation in cvcle time from part to partwithin a productio n stage. The average cycletimes from stage to staqe are balanced throughstandard industrial engineering pracclces. Itmay be 5UrDri r;lnq t0 some that DrOdUCtredesl gn, automation. and cl asslcaJ industrialenqlneerinq are consldered necessarv functionsfor JIT success. The rel at i ve absence ofthese and other efforts may account for thereason whv JIT is not working In 5Oetraditional environments.3.3 Jidoka--Oual i tv at the Source

The ideal f actorv has virtually perfectauality and reJect rates of 0%. The moste+fective means of approaching this goal isthrougn 1 mproved employee i nvol vement.Workers and supervi sors are given a muchhi qher degree of re5DOnslbilltv ln a JlTsystem but not without a commensura te amountof training ln JlT evstems, qroUP problemsolv1nq, and a varletv of job skills. Withsuch training wet- ker5 and *u pet-v1 sot-5 =amonitor their own production Pr-ocesse5 andcontlnuallv check their own qualm ty withoutthe aid of a qualitv control inspector. Whenprocesses do run out o-4 COA trol . operatorshave the responsibility to shut the processdown until the oroblems are identified al-30corrected. Formal group proolem solvinqDrOoramS enhance both qua1 1 y andDrOduCtlVlty. In fact. such partlclpativemanagement techni sues can lead to oremotivated workers. The end result ot employeeinvolvement in the JlT manufacturing system ishi qher qua11tv. less rework, and tasterthroughput. all of which are extreme1 Yessentl al for JIT to ooerate successfullv(Lubben 1YElEl).3.4 Group TechnoloqyIn our Ideal JIT svstem. the process stagesare located so close to each other that zerotrans1 t time 15 required when a Dart 1s mOVeabetween stages. lh1S group technoioqv 1 sapDroaC bed through 1 proved plant 1 ayout andproduct flow. another industrial engineering

tunction. lf transit times are not minimizedthey simply reDresent another significant5taae in the production sequence decreasi nqorder throughput. Excessive hand1 i nq and

617

8/8/2019 p616-dyck JIT

3/8

warehousl nq between production stages in atradltlonal system call al 50 contribute tooual~ tv problems.3.5 Schedul inq., Setup Times. and Lot Sizes

For the idea.1 .I11 system tne master schedui e1s frozen to e;. 1 ml nate unexpectea chanqes 1. nDrOdUCt Ml x and lost time due to excess1 vesetups. Uf course. one way to m~n~rnlze thedetrlmental ettect of setuo times reqardlessoi their frequencv 1s to make a ser~cus ettortto reduce them (the Japanese havebeen success+ul 1 reduclnq setup times fromhours to oolv minutes). &not her Val uab I e by-product Of reduced setup time II that 1 ot51 zes may be slgnlflcantlv reduced wlthout anadverse effect on cost tas demonstrated bv theEOQ formula) _ hs lot 512es are reduced thecontrol over quality 15 enhanced andlnventorles can be further mlnlmlzed.

3.6 Preventive Plaintenance and SPCIn the Ideal system machines vlrtuallv neverbreak down. This is because of an excellentprevent] ve maintenance orogram. StatlStlca!process control (SPC) ic used to monrtor themost crltlcal aspects of machine periormanceso that out-o+.-tolerance condltlons can becorrected lmmecll ate1 y. The elimination ofvarlatlon and uncertalntv In machineoperations vastlv Improves system performance.

3.7 Kanban Production Control

Even with al 1 the above 1 mprovements andsaf equards built Into the ideal manufacturlnqsvstem, a kanban productlon control sv5temshould be malnt.alned to effectivelv restrictthe amount of WlP lnventorv allowed bet weenstages. Kanban production control 1s the13011 ceman whlc:h enforces comol 1 ante wl thstated objectives to identltv and solvemanufacturl nq system UrObiemS.

In the JIT-K system. units of production areconf lled to well-defined lots or containerswhich can be moved from a orecedlnq stage to asucceed1 ng staple only when the succeedlnqstaqe is ready to process the lot or when thebuffer inventory falls be1 ow 1ts allowednumber of 1 ots. Eaual 1 v important In JIT-K15 the fact that the orecedlng stage cannotbeg1 n production on a lot until the last lotproduced has been removed for processing Ovthe succeed1 no staqe. fhe total number o+I ots allowed lr a production stage at any onetime IS called the number of kanbans and ISu5uall Y kept to a ml nlmum of one or two I n a

J IT-K system. It 1s when the buffer Inventorypreceding a staqe 1s allowed to grow vlrtuallvWI thout llmlt that a system can be descrl beda5 trade tlonal . The orlglnal Toyota kanbansystem (kanban is Japanese for card orvlslble record) used a flxed number of cat-asto accompany lots throughout production. Thekanbans prove de the onlv authority to move1 ots fro" stage to staqe and thus limit WIP~nventorv. Thlc tyoe of hand-to-moutn teedlngfrom stage to staqe prevents excess.1 ve WIPlnventorles from bulldlnq whl ch tortesproduction to keep qua1 xtv under control

wt-lie t-eduClng carrying costs.4. TIiE TRADITIONA L SYSTEM UNDER INVESTIGATI 0l\l

H par-tlcular factor-v was selected to serve an example of uslnq simulation to investlqatethe apPllcatlon of Jr.-r orlnclples toexlstr np traaitional svstem. This factor-vproduc:es mblded plastic parts in a multistage,'~1 nqle 11.e orocess which cons1 sts oi fourSt ages: (1) llol d 1 ng , (22 Palntlng, (i-01 1 i nq. and (4) Packaging. Large inventor 1of Part5 are malntalned aiter each productionurocess In order to keep each productlon stagerunning at maxi mu utillzatlon. Ruall i::o7trol illSpeCtOrS are used to spot check thIYoldlng and Palntlnq operations while 1 Cinspection occurs after the Foilinq operation.

Rework OperatOrs are utilized following St a5 to repair the detective unjts. The process

tleS ln each stage are not balanced requlrlng1 arge buffer inventories to maintainoroductlon. U high degree of vat-lability oroduct de!;iqn fro Dart number to Dart nUDerotten results 1 ooor manutacturabll i tv ahi ,gh defect rates. The greatest problem oroduction is seen a5 the malfunction breakdown of equl pment. Production schedulesare prone to change very frequently due changing marketing requirements and eaui pmentbreakoown. Setup times usual 1 y run severalhours. The production departments are locatedVel-y close to each other but the warehoual oc buffer stock (and raw materials> r-e.. ati vel y remote. Methods work and time studyto improve setup and orocess times IS 1 I"I tesince most of the engineers time is spentso:. v1ng pro cess problems.

In order for such a tradltlonal system begin implementing J 1 T several basic stepsshould be consldered. Duality inspectIonsho,ulU be pertormed wlthln eacn staqe machl ne operators. Process cycle times fromstage to stage shouid be better balanced acycle time varlatlon within a stage should mlnimired through methods study, automation.etc. Product redesl gn. setup trme reduction,nreventlve maintenance. emp 1 oyee particxpa-tlon. and SPC can be developed wlthln a kanbanproduction controi svstem to restrict CInventory. Sl"UlatlOn of the system can helpguide management along the proper JIT course.

5. THE SIMLJLF)TION MODEL

5.1 tieneral Aspects of the Model

K_aban ProductIon Control-* The slmulatlonmodel represents a slmollfled version ofmanutacturl nq system described above. Orderst or olastlc cabinets arrive with f reauencvano 51 ze control led by the analvst. Since thisuatler 1s not concerned with Interplant Jsuup 13 er networks. the arrl val Of orders ad.lusted so that the plant never runs out work. Raw materials are assumed to be al waavailable. Wnen a new order art-Ives at stagetMoldl"q~ it LS orocessed in a series of lot

the? 51 re of which can be controlled by tanal vst. lhe number of lots whl ch can acc:umul ated prior to stages 2. 3 . and

618

8/8/2019 p616-dyck JIT

4/8

(Paint. For1 1 and Package, resDectively> canalso be controlled by the analyst. If thenumber of lots allowed at each stage 1s onlyone. then a completed lot cannot move to asucceeding stage until the succeeding stage 1sready for productlon. If the numoer ot lotsallowed at each sta ge II two then two lots canexist In a stage s1mul taneous1 y tone be1 ngDroceSsed and one as a buffer). This numberOf lots al lowed thus becomes the number ofkanbans of a Japanese JIf-K system. If thenumber Of lots (kanbans) allowed at-ei ncreased. the system eventual 1 y becomes atrade tlonal Ot-lF with each stage malntalnlngI arge buffers as required deDendlng on 11nebal ante, variation In orocess~ng time. SetuDtime. transit time. etc.Cycle Tme. Orders for cabinets enter thesystem and lots from these orders move fromstage to stage. The amount of orocesslng timerequl red for each lot at each stage 1sdetermined by the analyst. The varlatlon inprocessing time can be control led most sxmD1 yW a uniform dlstt-lbutlon about the mean.The mean Drocesslng times for each stage canbe changed to reflect balanced or 1 mbal ancedcondl tlons.Transit Time. The time required to move acompleted lot of parts from one stage to tnenext can be controlled by the analyst.

Set0 Tome. The frequency of new ordersentering the tactorv and movxng through thevarious stages o f Droductlon can be controlleddirectly in the model by adjusting the rate otarrival and/or the s1 ze of the order. El thert-41 II affect the freouencv of setups t-eauj redIn each stage. The setuD time II-I each staqecan be changed to deter-ml ne relationshipsbetween lot 51ze-s and number of kanbans for

optimum operating condltlons.Defect Rates. The defect rates encountereddurlnq Stage 3 (Foillnq> in the model can headjusted to determlne the lmoact on oDeratInga JIT-K svstem. In this system. the deiectlveunits must be reoalred bv t-ewor k ODeratorsand TeDrOCessd by the same machlnes. This isaCComDllshed In the model bY rerouting thereworked unit back through the same assembly11ne.Model Output. The performance of the modeledsystem can be evaluated v observl ng theoutput in three areas: (1) the total number0+ cabinets produced over a lven period ottime, ta? the total NIP inventory required tomaintain that level of DrOdUCtlon and (3) theaverage makespan (time required to complete1 yproduce an order) Over a ge DerlQd Of time.

Al though the m0lYel simulates the logicalDt-OCl?SS of the real system and Includes themecnanlsm to control DroduCtlOn WI th anvnumber of kanbans, it provides for onlv Onetotal Droduction line (one moldlnq machlne.one palntlng assemb 1 y line. one foilingassemb 1 y line. and one Package ng assembly11ne). In realrty, there are as many a s tent0 twenty such machines/lines I" each stageoperating slmul taneousl y. Thus tact lmplles

that the model output I the areas of WIPinventorv and auantlty produced should betactored accordingly to be representative ofthe real system. For examole. a WIP f3gureof 1000 units resulting from a model -runco1 d reoresen an actual WIP Of perhaos10.000 to 20.000 units).

5.2 Specif 1c hspects of the ModelModelino Kanbans. The model was devel oDedSing GPSS/PC on an IBM FIT comDatlblemicrocomputer. Some of the key elements indeveloping the model are the use of theSTORAGE command and the accompanvl ngENTER/LEAVE model blocks to control the numberof kanbans in the JIT-K svstem. Throuoh theapm-our3 ate use 0f ENTER and LEAVE blocks, alot transactIon is not allowed to leave aproduction stage until the succeeding stage 1sable to accept It. QUEUE and DEPART block areused to gather data on the amount of WlPlnventorv accumulated prior to each Droductlonstage. The SPLIT block IS used to generate lottransactions from the parent order transactionuntil the order quantltv II de01 eted. Thenthe ASSEMBLE block 1s used to co11 ect theseoarate lot transactlons so that ordermakespan can be determlned. The value of thistyDe of model 1s that It can be used toreither JIT-K or tradrtlonal svstems by s1mol Yaltering the number of kanbans (STORAGE unl ts)

used at each stage of production. See Schriber(1974) for a detailed explanation of the GPSS

simulation language. A Dartial program listingfor this model 1s provided In the apoendlx.rlethodoloo~. The methodology employed is toverify and valldate the model accordlnq to theway in which the factory is presently runningwhile including the kanban mechanism so thatthe present system (with relatively unlimitedWIP storage caDacltv) can be transformed 1 ntoa JIT-K svstem. The Impact which reduclnq thenumber of kanbans has on the system can thusbe stud3 ed under the present environment ofdefect rates, setup times, transit tzmes, 11neaal ante. cycle time vari aton, schedulestablllty, etc. Hn optimum number of kanbansto be used 1n the production control systemcan be determlned based on the Dresent state0t the system. As Improvements are made Inthe various productlo parameters the modelparameters can be changed and the model rerunto determlne a revised strategy for kanbanproduction control and ootimum performance. It1s through this orocess that a tradi tlonalsystem can be gradually transformed Into a JITsystem The cost effectiveness of implementingchanges in the manufacturing system can al sobe studled sing the slmulatlon model. Forexample the Impact which sclending 8500,iriX onautomat1 on WI11 have on cvcle times, cvcletime varlatlon. WlP Inventory, output. qualityand makespan can easily be estimated sing thesimulation model wlthin the context of a JIT-ksystem BEFORE the money 1s spent.

619

8/8/2019 p616-dyck JIT

5/8

b. EXPERIMENTAT~DN6. I ExperimenTal Des.1 gnThe tollowing parameters are considered inputvariables to the model:

la) mean cvcle time Pet- It Per stage(b) CVClE! time variation per unit asa +/-- percentage of the mean

tunlform dlstrlbutlan).lc) reject percentage of a lot (stage 3onlvr.(d) downt.lme rate and times of machlnes~fl each stage.le) setup time reoulred at each stage.(f) transit time required at eacn stage.(q) the umber o+ kanbans (buffer lotslocludrnq the lot in process>allowed at each staqe

Model output for the following variables arerecorded for each model run:(ai the total nllmhn r I ,ll, 1 )I, .,,,, I jdurrnq the time oerlod specifleci in

the model,

lb) the total WIP inventory carriedtexcl,Jdlng the lot which 1s beingactivelv processed),

ICJ the order makespan (averase time oDrodUCe a lot tram beqlnninq toend).

Id) the average machine utllizatlan forall four staqes oC uroductlon.

For each model -run the svstem is al I owed todchl eve steady state before statistics areqathered oh system performance. TlllS 19accompl~ shed t1 clearing the svst em andrunnlnq the model for 40 shifts. Thl s amountof time 15 determl ned by observl nq tneutlllzation of the last machine in the oroce~swith the aid of the PLOT command I GPSS/PC(Figure 1). The model is then reset beforethe actual model -run. A 5el- 1 es of +lvereolications of 1ocJ shifts each are thenPet-f armed for each lndlvidual experiment withthe results averaged and summarized in thetables which follow.

For- the first set of experiments, each of the1 put varI abl e!; L s examined lndlvlduallv as dependent var:.ab.le with the number of kanbansas tne Indeenden t Carl abJ e to determj ne tneefi ect ot JIT--K. on cvstem oerf or-mance. Forthe second set 04 exoerlments. al 1 thepar ameters of the rrlanuf acturl nq system arelnclud'ed 51 mu1 taneausl v I n the mode t tdetermine the eftect JIT-K has on a completesystem. These input parameters are chanqed toslfmulate the effect of improvements made Itns real svstem (reJ!Z'CtS. dQwntlme. etc. wlthin the context of JIT-K.6.i Experiment Results--Unlvarlate HoproachThe Ideal System. A mode 1 -run IS firstDerf armed for the 1 deal system. Varl ousnumbers of kanbans are tested when there 1no lmbalanze I cycle times from stage tostage, no cycle time va rlatlon. no rejects, ncl0wnt1me. no setup time. and no transx t t lme.The result5 are shown lfl Table 1. Aexpected,

8/8/2019 p616-dyck JIT

6/8

staqe Cycle lmbal ante. Table f shows the.results of using lmbalanced cycle times (theaverage of which 1s St111 1.0 mi utes uerIt). There 1s of course d marked reduction1 averal 1 outpu t. Notlce however that1 Ct-eaSlq the umber of kanbans QOF5 Otincrease outDut but does Increase makespan andWIP Inventorv.

Reiec t Rates. For this particularmanufacturing svstem, def ectl veo arerdentifled OlV after staqe 3 tfollinq) 1scompleted. Unique to this system 15 the factthat repalt-5 to these detect1 ves usual 1 vrequl t-e the same series of machines which arealready Set up for the regular Dt-oduct 1 onproce55. This means that the defectlves areusual 1 rerouted xmmedl ate1 Y down the sameproductlo line used for Vlt-Ql productresult 1 nq rn what can at times be a ser IO5bottleneck.Table 4 shows that for a reject rate of 10%.outout 1s maxlmlzed first for a total of threekanbans ta maxImum ot two lot5 waiting PlUSone lot in oroce65). However. the WIP andmdke5Da are higher for the three-kanbansystem than the two-kanban svstem. Therewoul cl be a obvious disadvantage of utilizlnqa traditional system (more than three kanbansjat this reject rate.

Setup T1 me. Table 5 gives the results ofrequlrlng a setup time of 30 ml nutes ior eacnof the four producti on stages whenever a lothaving d new Dart number arrives. The two-kanba svstem yields higher output withsllghtlv higher WIP and ldentlcal makespanwhen compared WI th the one-kanba svstem.There would be no advantage I using more thantwo kanbans per stage.

Down t 1 me.. The effects of 10% downtime on thesvstem are somewhat devsstatlng. However,TabI e 6 demonstrates that varying the numberOf kanbans has virtually no effect on Output.makesaan or NIP Inventory.

6.3 Experiment Resul ts--Mu1 tivariate Approachworst CZISF. The model 1s run w1 th al 1 inputparameters coming Into play 5~multaneouslv todeterml ne the effect which JIl-k has as thenumoer of kanDans 1s Increased. In this worstcase. the stages di-e severe1 Y 1 mbal anced.cycle time variation 15 +/- 20%. reject ratesare 20%. downtlme is 10X, setup is 60 minutes+or each stage, and transit time 1s 10 minutesfor each stage. 63s Table 7 indicates. theoutuut remal ns ta1r1v constant from twokanbans to ten kanbans but WIP and makesoan1 creases dramat3callv . The total output 15on1 v about half of the output from the I dealsystem. Uslnq two kanbans should be a betteralternative 51nce output 1s constant while WIPi5 mlnimlzed. The total 1 y traditiona lapproach (unllmlted kanbans, seems out of thequestion.

621

8/8/2019 p616-dyck JIT

7/8

I mpr oved CJSe. Table 8 qlves the results ofmak1 ag SW. 1 mprovemet 1 n al 1 1 nclutoarameters. output 15 UP sllghtlv comparedwith Table 7 while makespan and WIP are fairlveaulvalent. ClQdl", "51 nq a two- orthree-kanba" product 1 on control system wouldappear advantageous tPmPared wl th atradl tlonal svstem.

Much-Improved CC=. rable 9 shows the resul t50t makl ng another 50% 1mprOVeRWnt ln 1 nputoarameters. Overal 1. there aopears to bea sllqh t improvement in output while WIP andmakespan remaininq about the same. Agal",operat1nq with two or tnree kanbans JPpe-Slt-5advantageous to the traditional approach.

4.4 SuQgeStlons for Further StudyWhl le the model used tot- this studv wasreDrcsentatle of an actual manufacturlnqSvStem l t dlo not Include all the machines andlines found I the real system. H model couldbe develooea for this factorv (or any other !to Include all relevant facllltles. Then themodel could m13!-e accurate1 v predict outputin an absolute s(?ns.e. The model could also oeenhanced bv using a qamma dlstrloutlon(Instead of the uniform dlstrlbutlon, to modelProcesslnq Cycle times (Law and Keltan 1982).

klhl I e lnterorant J1-l functions Wet-e not1 nc 1 uded in this model, the add1 tion of suchWOL.~ d give a more comDlete oicture of theclveral I process ot JlT implementation. JlTap~ll& to ourc:hasinq. rece1v1 ng. and de1 IveryCar have a pronouncea Impact on raw materialsant finIshed goods inventories as well as NIP.As more detal led models are devel ooed oactual nlque manutacturlnq environments. more rigorous statistical treatment o+ theresults Should be apolled in order to Increasecontidence in the decision-maklng processrelative to actual JIT-K 1molementatlon.

IhlS simulation model does i 1 lustrate thede1 1 cate 1~lterrelatlonShlps between the largenumber o+ elements involved in either tradl tlonal or JIT-k manufacturing svstem.The development ot aetailed models for unlouemanufacturlnq systems should ass1 st managers1 understandlnq their exlstinq svstem and ldetermlnlnq strateqles tar lmorovlnqperformance even if JIT-K 1s not beingconsldered.The effect which a JIT-K produttl on Controlsvstem Cd have on several manufacturlnqsvstem element5 tatien i ndl vi dual Iv can beasl 1 y demonstrated slnq a srmulatlon modelot the un i que factory. The process aslmulatlng the effect of JIT-h on an eXlStlnqsystem one variable at a time should ass1 SmaJqers and enqineers In developlnq a KlOt-=comDrehenslve understandlnq ot thereoul rements iOr a sccessfi Jl I-k system.lJJsl"Q slm"lation. management can decide whichel ernents ot their exlstlnq system (reletts.downtime, set up. cycle 1 mbal ante. cvcieVal-lJtlO) current I y have tne qreatesrneqatlve 1npact on oroductlvlty in either tradltlonal or JIT-K envxronment. Lorrect 1 vact:.on can then be better tocused.FInally. al1 cr1t1c.31 parameters ot aex1st1nq system can be included 1slmulatlon model and tested under a J 1.FKoi-odct 10 control svstem. The results tarthis set of experiments aqal" show that a JlT-n system yields about the same output a5 thetrade tl anal svstem but WI th significantreductions in WIP and makespan. With the aldOf slmlatlon managers can e5t I mate hopertormance tdn be atfected as dlf+erentstrateqles are emp 1 oved for 1 mprovementswlthl" the context of JIT-h. The costef fectlveness of these strateqles can thus bascertal ned tot- each unr que mantactrlnqsystem.

fiPPEi\IDI X : PARTIAL PROGR17M LISTINGFollowlnq 1s a oartial llstln q of the GPSS/PCmodel oroqram show1 q the LOQlC cl+ lottransactlon flow ln staqe 2 of oroductlon:

622

8/8/2019 p616-dyck JIT

8/8

DISCLAIMERIBM QT 1s a product of the IGM Corporation.GPSS/PC 1s a trademark of Minuteman Software.

REFERENCES.Aqudi,lac~o~ -9.. -..J... and _Cha3el. -8. B.Hi-eductlon and UperatLons Ilanaqement. 1 985 ).HI chardD. Irwin. Inc.. Homewood. Illnols.

Byrd, J., Jr. (198~). b J,,St-In-tl,T,E'implementation strategy at L-dot-k. &.dustri alManaqement 30, b-lo.Chapman, 5. and Schmrnke. M. (19813). Orqan-1zat1on theory and implementing JIT:understanding why as well as what and how.Unpubl i shed rroceedlng5 Paper. i 988 UnnualMeetings of the academy of Manaqement.Clnahel m, Cal LSOt-nl a.Ebrahi mpour. PI. and Lee. S. M. (19&7), Just-In-Time. Manaqement Decisl on ZS. 50-54.Kel ton. w. D. and Law. A. Il. (1982).Slmul at1 on Nogel nq and CInal ys1 s_. McGraw-HlIIGook Company. INew York.Lubben. R. 1. ci988). Just-In-Txme Manuf ac-.--turins: An Aqq ressive Manutacturl nq Strateqy.IkGraw-Hi 11 Book Companv, New York.Myers. M. S. (1988). Let just-In-time mendyour split culture. Industrl l Manaqement JC!.1 l-18.Schrlber, T. J. (1974). Simulation Usino GPSS.John-Wiley & Sons. New pork.

AUTHORS' BIOGRAPHIESHAROLD DYCK is an associate professor In theDeoartment of Computer Information Systemsat Southwest Missouri State Unlverslty. Hereceived his Ph.D. from Purdue Universl ty andhas tauqht in the qua1 1ty control, productionoueratrons management and management SC1 enceareas. He 1s the au thor of articles in thearea5 of simulation. transportation researchana econometrics and has consul ted in theapplication of e1muiat1on to local busznessand xndustry. Contlnuinq memberships includeFISH and TlMb.Harold DvckDepartment of Computer lnformatlon SystemsCo1 leqe of ElusinessSouthwest Missourz State Uni vers1 tYSprlnqfleld. MO 65804417 836-4837

RICHARD @. JOHNSON 1 s president of Bus1 nessSolutions Unlimited . . . of SprIngfield,Il1ssour1, a bus.1 nes5 consultlnq firmspeclallz inq in microcomputer aoplications ofsimulation and database manaqement systems, aswell a5 in classical I.E. functions andemployee effectiveness tcalninq. tie recel vedhis B.S.Ed. and M.S.Ed. in mathematics/physicsfrom Southwest M1ssouri State university in1974 and 1979, respectively, and is currentlya candidate ior an M.B.A. from tne sameinstitution. He has a combined ten years ofexperience as an Industrial eng 1 new andenglneerlng manager for several Fortune 500manufscturlng comoanl es. He has authoredarticles and conducted seminars on almulatlonand taught unlverslty courses in mathematics.physics, statlstlcs. war k measurement .management, and computer science. Continuingmemberships include 11E and SME.

Business Solutions Unlimited . . .724 South Welle r AvenueSprlngfreld. MO 65802-3345417 862-3689

JHY vMi2FINDEi-l ia a PI-Of essot- of decisionscience at Calrfornla state u-71 ver53 t v--SanBernardino. He recel ved his Ph.D. fromOk 1 ahoma state Lhl vers.1 tv and has taughtstatistIca qua1 1 ty control. productionmanagement and dec 1 e.1 on 5c1 ence. He hasconducted numerous eemlnars on Droductlonmanaqement, master schedul lnq. quality

control. and JIT production nationwide. He isthe author ot a variety of articles onmanagement topics. In add1 tion he hasamlnistered tralnlnq programs at a number ofcompanies.

Jay Var2andehDepartment of Information & Decision SciencesCalifornia State University--San BernardinoSan Bernardino, CA 92407714 807-7729

623