The effect of sudden material handling system breakdown on the performance of a JIT system

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The effect of suddenmaterial handling systembreakdown on theperformance of a JITsystemS. M. Gupta & Y.A.Y. Al-TurkiPublished online: 15 Nov 2010.

To cite this article: S. M. Gupta & Y.A.Y. Al-Turki (1998) The effect ofsudden material handling system breakdown on the performance of a JITsystem, International Journal of Production Research, 36:7, 1935-1960, DOI:10.1080/002075498193048

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int. j. prod. res., 1998, vol. 36, no. 7, 1935± 1960

The e� ect of sudden material handling system breakdown on the

performance of a JIT system

S. M. GUPTA ² * and Y. A. Y. AL-TURKI³

In this paper we explore the impact of sudden breakdown of the material handlingsystem on the performance of a traditional kanban system (TKS). TKS, which isan element of the just-in-time system, is designed to operate in an ideal environ-ment such as constant processing times and uninterrupted processing. However,in a real life environment, the TKS could be subjected to various unpredictablefactors including stochastic processing times and process interruption due toequipment failure. These factors would seriously strain the performance ofTKS. We consider a TKS in which some stations are dependent on a materialhandling system to move parts between them. We study the e� ect of a suddenbreakdown of such a material handling system on the performance of the TKS. Inaddition, we also study a newly developed kanban system (which dynamically andsystematically manipulates the number of kanbans in order to o� set the blockingand starvation caused by these factors during a production cycle) under the sameconditions. We refer to the new system as the `̄ exible kanban system’ (FKS). Wecompare the overall performances of the TKS and FKS by considering a varietyof cases. We present the solution procedure, results and discussion for these cases.

1. Introduction

The just-in-time (JIT) philosophy evolved from a number of principles such asthe elimination of waste, reduction of production cost, total quality control andrecognition of employees abilities (see for example Hallihan et al. 1997, Keller andKazazi 1993 and Sohal et al. 1993). The kanban system is an element of JIT that hascaptured the most attention of researchers. Kanban is a Japanese word that meansv̀isible sign’ or card. An advantage of the kanban system is its ability to controlproduction. Other advantages include its simplicity in production scheduling,reduced burden on operators, ease in identi® cation of parts by the kanban attachedto the container and substantial reduction in paper work. The kanban system is alsoviewed as an information system. The information on the kanban depends on itstype. The kanban contains information such as the kanban type, component nameand number, the station location and the destination station. Monden (1993) andSuzaki (1987) discuss the di� erent types of kanbans and their functions. Theseinclude withdrawal kanbans, production kanbans, supplier kanbans, signal kanbans,common kanbans, tunnel kanbans, express kanbans and emergency kanbans.

JIT is designed for a perfect environment (e.g. smooth and stable demand, con-stant and balanced processing times among stations and no breakdowns). However,

0020± 7543/98 $12.00 Ñ 1998 Taylor & Francis Ltd.

Revision received June 1997.² 334 Snell Engineering Center, Department of Mechanical, Industrial and Manufacturing

Engineering, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA.³ King Abdulaziz City for Science and Technology, Technology Department, P.O. Box

6086, Riyadh 11442, Saudi Arabia.* To whom correspondence should be addressed.

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in most realistic cases this is very rarely achieved. JIT is fraught with numerous typesof problems such as processing time variation, line imbalance and material handlingsystem related problems.

The e� ect of processing time variation has been addressed by many researcherssuch as Chaturvedi and Golhar (1992), Huang et al. (1983) and Philipoom et al.(1987). Philipoom et al. (1987), for example, identi® ed four factors that in¯ uence thenumber of kanbans required at a machining centre, viz. (1) throughput velocity; (2)the coe� cient of variation of processing time; (3) the machine utilization; and (4)autocorrelation of processing times. They concluded that as the throughput velocityincreases, the probability of backorders decreases, and the number of kanbans canbe kept low. On the other hand, as the coe� cient of variation increases, the prob-ability of backorders increases, which requires a larger number of kanbans.Similarly, as the machine utilization increases, the probability of backordersincreases, and the number of kanbans should be larger. Finally, when the autocor-relation increases, the probability of backorders increases which also requires alarger number of kanbans.

Line imbalance can be caused by di� erences in set-up times and/or processingtimes at di� erent stations. Philipoom et al. (1990) suggested the use of signal kan-bans when the set-up time at a station is high. Signal kanbans work as reorder pointsto produce large batches instead of only a few units as are done by ordinary kanbans.The idea is to minimize the number of set-ups. To this end, the authors developedtwo integer programming models. The ® rst model minimized inventory while thesecond model minimized the total cost.

Meral and Erkip (1991) found that the mean production rate decreases as thecoe� cient of variation of processing time increases. This decrease due to the coe� -cient of variation is more severe in a balanced system than an unbalanced system.Furthermore, the mean production rate decreases as the degree of imbalance amongstations increases.

Sarker and Harris (1988) studied di� erent forms of processing times imbalanceamong stations, viz see-saw, bowl phenomenon and cantilever action. The resultsshowed that the bottleneck stations have large queue length, long waiting times andhigh utilization. The results also showed that the production rate is maximized whenthe stations are perfectly balanced. Huang et al. (1983) and Monden (1984) suggestedthat the production capacity should be set based on the station that has the longestprocessing time.

The only studies involving material handling system in a JIT environment werereported by Egbelu (1987) and Occena and Yokota (1991). Egbelu (1987) simulateda JIT system where the stations were served by an AGV operating as a materialhandling system. However, the stations output queue sizes were assumed to beunlimited. Occena and Yokota (1991) claimed that the study conducted by Egbelu(1987) was impractical (because of the unlimited queue assumption) since it violatesthe basic principles of JIT. They conducted a study involving an AGV which wasconsistent with the JIT principles.

Thus far, no researcher has addressed the e� ect of material handling systembreakdown in a JIT environment. This paper focuses on the e� ect of sudden materialhandling system breakdown when the processing times are stochastic.

In the past, although several researchers have reported analytical techniques tomodel the JIT systemÐ see for example Bard and Golany (1991), Bitran and Chang(1987), Deleersnyder et al. (1989), Hillier and So (1991), Moeeni and Chang (1990)

1936 S. M. Gupta and Y. A. Y. Al-Turki

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and Wang and Wang (1991) Ð they make unrealistic assumptions or assume constantvariables to avoid intractability. Any attempt to expand the model to a realisticsituation leads to state space problems. It is for this reason that simulation hasbeen by far the methodology of choice in the majority of studies reported in theliterature. Simulation is capable of handling just about all the dynamics that occur inmanufacturing systems and provide the appropriate experimental statistics.

In most of the studies reported in the literature, the number of kanbans arealways held ® xed during the production cycle. Although, it is well known that super-visors from time to time on an ad hoc basis increase or decrease the number ofkanbans depending on whether the system is experiencing shortages or inventorybuild-up (Stevenson 1993), no techniques have been reported to systematicallymanipulate the number of kanbans. In this paper, we investigate the possible bene® tof a system that is capable of manipulating the number of kanbans in a materialhandling system breakdown environment. We refer to such a system as the `̄ exiblekanban system’ (FKS). In the sections to follow, we ® rst state the objectives of thispaper. After giving a general description of the model, we present the speci® c modelstudied in this paper. The solution methodology to conduct the study is presentednext followed by the description of several cases experimented and the discussion oftheir results. Finally, we give some conclusions.

2. Objectives

The general objective of this paper is to explore the impact of sudden materialhandling system breakdown on the performance of the traditional kanban system(TKS) and the FKS. To this end we do the following.

(1) Present and discuss a procedure to manipulate the number of kanbans in theFKS once a material handling system breakdown occurs.

(2) Present the performance of the JIT system when the stations dependent onthe material handling system are unbalanced compared to the rest of thestations. To this end, we look at the TKS with no material handling systembreakdown, and various cases of TKS and FKS with material handlingsystem breakdown.

(3) Present the performance of the JIT system when the stations dependent onthe material handling system are balanced compared to the rest of the sta-tions. In this case, we look at the TKS with no material handling systembreakdown, and TKS and FKS with material handling system breakdown.

(4) Compare the overall performances of TKS and FKS.

3. General model description

The JIT system considered in this paper is composed of 7 stations in series. Eachstation has one processing machine, an input bu� er and an output bu� er. The raw-material is always available at station 1. One unit of raw-material must be sequen-tially processed by all 7 stations in order to ful® ll one unit of demand. No materialhandling system is needed to move the containers through station 5. However, amaterial handling system (forklif t) is needed to move containers from the outputbu� er of station 5 to the input bu� er of station 6 and from the output bu� er ofstation 6 to the input bu� er of station 7. There is only one forklif t available in theJIT system. No material handling system is needed to move containers from the

Sudden material handling system breakdown under JIT 1937

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input bu� er to the processing machine or from the processing machine to the outputbu� er at any station.

3.1. The traditional kanban systemThe TKS for any pair of stations i and i + 1 works as follows (Schmenner 1993).

When machine i + 1 is ready to perform work, the PK (production kanban) box (orpost) of station i + 1 is checked for a production kanban (see ® gure 1). A productionkanban of a station is an authorization to process a container of parts on itsmachine. If a production kanban is available, it is taken to the input bu� er of stationi + 1. The withdrawal kanban is removed from a container and placed in the WK(withdrawal kanban) box (or post) of station i + 1, and the production kanban isattached to the container. The container is now processed at machine i + 1.

A withdrawal kanban is picked up from the WK box of station i + 1 and taken tothe output bu� er of station i where a container of parts is located. The productionkanban attached to the container is removed and placed in the PK box of station iand the withdrawal kanban is attached to the container. The container is thentransported to the input bu� er of station i + 1.

When a demand for ® nished good occurs, a container is retrieved (the forklift isnot needed here) from the output bu� er of station 7 (® nished goods bu� er). Theitems in the container are used to ful® ll the demand and the production kanban(which is attached to the container) is detached from the container and placed in thePK box of station 7 (see ® gure 2). This triggers the process described above.

The number of kanbans in TKS is ® xed and never increases or decreases regard-less of the status of the material handling system. There are times when the inter-mediate stations can either be blocked or starved. The blocking of a station occurswhen all its production kanbans are attached to full containers in its output bu� er.Similarly, the starving of a station occurs when at least one production kanban is atits input bu� er waiting for a container while the machine at that station is idle. Thereare many reasons for blocking and starving of stations. For example, the stochasticbehaviour (and hence inequality created thereof ) of processing times can lead toblocking and starving of stations. Similarly, a breakdown of the material handlingsystem can also lead to blocking and starving.

Since the demand is known at the beginning of the production day, the produc-tion starts as soon as the day begins in order to ful® ll that demand. As the dayprogresses and the units are produced one at a time, the balance of the demandedunits starts to decrease. Hence, if there is no production interruption, the balance ofthe demanded units continues to decrease steadily eventually ful® lling the demandfor that day. However, if the material handling system suddently breaks down, the¯ ow of parts from station 5 to station 6 and from station 6 to station 7 stops, whichstarves stations 6 and 7 and eventually blocks stations 1 through 5. All this can havean adverse e� ect on performance measures such as the order completion time andthroughput.

3.2. The ¯ exible kanban systemSome of the di� culties experienced by the TKS (because of the material handling

system breakdown) can be alleviated by the FKS. (FKS has proven to be quiterobust and its performance is superior to TKS when processing time variances arehigh (Gupta et al. 1995) as well as when there is demand variability (Gupta and Al-Turki 1997). FKS maintains a minimal number of base level kanbans and increases


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Fig

ure

1.T

heop

erat

ion

ofT

KS.

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Fig

ure

2.T

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Tsy

stem

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atio

ns.

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the number of production kanbans at an appropriate station according to the needarising from the unexpected material handling system breakdown. After the repair ofthe material handling system, the number of production kanbans are reduced andbrought down to the base level (the number of kanbans can never be reduced belowthe base level). The purpose of increasing the number of kanbans is to compensatefor the loss of production time which would be incurred due to such interruptionsand the blocking and starvation caused by them as well as the stochastic processingtimes. Since the ® rst ® ve stations do not depend on the material handling system, it ispossible to reduce the e� ect of blocking at stations 1± 5 by increasing the number ofproduction kanbans at station 5. The increase in the number of production kanbansat station 5 triggers the production at that station and releases its withdrawalkanban. This action propagates production at stations 4± 1.

The increase in the number of production kanbans at station 5 should not exceedwhat is necessary to ful® ll the demand. The number of production kanbans to beincreased at station 5 is determined as follows:

PK(m) =

MinDC - NCm , RT

(¹m) (C){ } if the material handling system

repair time is deterministic

DC

- NCm if the material handling systemrepair time is probabilistic

ìïïïïïïïíïïïïïïïî

where

C container size (i.e. the number of parts in each container).D demand per day.m station number such that station m is the last station that does not

depend on the material handling system and station m + 1 is the ® rststation that depends on the material handling system (in our model,m = 5).

Min{x,y} minimum of x or y.¹m is the mean processing time per part at station m.NCm is the number of full containers processed by station m up to the time of

the material handling system breakdown.PK(m) is the number of production kanbans to be increased at station m.RT is the repair time of the material handling system (if deterministic).

Equation (1a) gives the value of PK(m) when the material handling system repairtime is deterministic. Here, we select the minimum of either the number of kanbansto meet the residual demand or the number of kanbans needed to process partscontinuously at stations 5± 1 while the material handling system is being repaired.However, when the repair time is probabilistic (stochastic), since it is impossible toestimate the exact duration of repair, we cannot determine the value of the termRT /(¹mC) . Consequently, we determine PK(m) by just using D /C - NCm , asdepicted in equation (1b).

The number of production kanbans should be increased as soon as the materialhandling system breaks down. The increase in production kanbans at station 5eliminates blocking all stations 5± 1. The production kanban of each processed con-


(1a)

(1b)

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tainer is removed from the system (except for the base kanbans) once the withdrawalkanban of station 6 arrives at station 5 to replace it.

4. Speci® c model description

In this section, we present the speci® c model (of the type shown in ® gure 2)studied in this paper. The relevant data is as follows:

� Both TKS and FKS stations were allocated two production and two with-drawal base kanbans.

� The demand for ® nished units is 80 units per day.

� A production day is composed of one shift (480 min). However, if the demandis not satis® ed during that shift (due to the material handling system break-down), and overtime shift is ordered to satisfy the demand before the next daystarts.

� The processing times at stations 1± 5 are independently and normally distrib-uted with a mean of 5min and a standard deviation of 0.5 min per part.

� It takes 0.5min to transfer the withdrawal kanbans from a station to its pre-ceding station. Similarly, it takes 0.5 min to transfer the parts and the with-drawal kanban from a station to its succeeding station.

� The forklift speed is 160 ft per min.

� The distance from station 5 output bu� er to station 6 input bu� er is 40 ft.Similarly, the distance between station 6 output bu� er and station 7 inputbu� er is also 40 ft. Finally, the distance from station 7 input bu� er to station5 output bu� er is 100 ft.

� The forklift has a capacity of one container.

� It takes 0.25 min to load a container on the forklift and 0.25 min to unload thecontainer from the forklift.

� The station with the minimum number of parts receives the highest transporta-tion priority from the forklift. The ® rst-come-® rst-serve priority rule is used tobreak the tie.

5. Solution methodology

A simulation model using the PC version of SIMAN (Pegden et al. 1995) incor-porating several modules was developed to study the model of the type shown in® gure 2. A schematic representation of the simulation model and its modules isshown in ® gure 3. The following is a brief description of the modules:

Raw material module. This module ensures an unlimited supply of raw material andprovides raw material units to the ® rst station by interacting with the kanban moduleand the production module.

Kanban module. This module keeps track of both the withdrawal and the productionkanbans at every station. It interacts with the ¯ exible kanban module (in the case ofFKS), the demand module, the production module and the raw material module. Byinteracting with the ¯ exible kanban module and the demand module, it controls theprocessing and ¯ ow of parts through the manufacturing system. The kanban moduleforms the nerve centre of the entire system.

Flexible kanban module. This module controls and keeps track of increases anddecreases in the production kanbans. Thus, it indirectly controls the ¯ ow of partsat various stations.


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Production module. This module controls the processing of the parts at di� erentstations. It interacts with the raw material and demand modules in conjunctionwith the kanban module to achieve this control.

Demand module. It creates an order for ® nished units on a daily basis and keeps trackof the time needed to complete them. It interacts directly with the kanban module byfreeing the production kanbans that are attached to the ® nished units which triggersthe production and parts moving throughout the manufacturing system.

Material handling system breakdown module. This module is responsible for inducingmaterial handlng system breakdown and its repair time. It communicates with the¯ exible kanban module by sending a signal. This signal allows the ¯ exible kanbansystem to increase the production kanbans at station 5.

The following assumptions have been incorporated in the simulation model:

� The raw-material is always available at station 1.

� There is only one part in each container.

� Neither any scrap is created nor any machine breakdown occurs.

� One unit of raw-material must be sequentially processed by all 7 stations inorder to ful® l one unit of demand.

� First-come-® rst-serve discipline is used to process the parts.

� To maintain equivalence in processing times at each station, material handlingbreakdown and material handling repair times, TKS and FKS were assignedidentical seed numbers.


Figure 3. Schematic representation of the simulation model.

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6. Experimentation

In order to understand the e� ect of sudden material handling system breakdownon JIT system performance, several di� erent experiments were conducted. It isobvious that, after the repair of the material handling system, if enough time isgiven, the system would eventually recover from the e� ect of the material handlingsystem breakdown. Consequently, it was essential to isolate the system performanceon the day of the material handling system breakdown to understand its e� ect on thesystem’s behaviour. Thus, the experiments were conducted by inducing the materialhandling system breakdown at a random time during the day and then repairing thematerial handling system during that same day. The demand for that day was metbefore the next day started.

However, before inducing the breakdown, the system must be in steady state. Forthis reason we assumed that no material handling system breakdown occurredduring the warm-up period. The Welch procedure (Law and Kelton 1991) wasused to con® rm the existence of a steady state. Although the steady state was noticedafter 11 days, a warm-up run of 50 days was used. After the warm-up, the arrayswere cleared and statistics were collected for the next day. The material handlingsystem breakdown occurred during that day. Overtime was used when the demandwas not satis® ed during the regular production shift. 30 independent replicationswere performed. The experiments were grouped in ® ve di� erent cases. Each casefollowed by its results and discussion is described next.

6.1. Case 1The purpose of this case was to study the TKS with no material handling system

breakdown. In this case, the processing times at stations 6 and 7 were normallydistributed with a mean of 1min and a standard deviation of 0.1 min per part (i.e.stations 6 and 7 were unbalanced compared to stations 1± 5).

6.1.1. Results and discussion of case 1The performance measures of the TKS for the 30 replications are summarized in

table 1. They are respectively, the average time in the system (TIS), the average ordercompletion time (OCT), the number of units that must be produced in overtime tosatisfy demand (NUO), the work-in-process at the end of the regular productionshift (WIP) and the resource utilization in the regular production shift. The resourceutilization measure consisted of: (a) forklif t utilization (FU), (b) machine 1 utiliza-tion (M1U), (c) machine 5 utilization (M5U) and (d) machine 7 utilization (M7U).

As can be seen from table 1, the system was extremely stable. This is in spite ofthe fact that the processing times were not constant. The average time in system was116.85 min and the average order completion time was 354.49 min. The demand wasalways satis® ed during the regular shift and hence there was no need for overtime.

6.2. Case 2The purpose of this case was to study the TKS and FKS with sudden material

handling system breakdown. Just as in case 1, the processing times at stations 6 and 7were normally distributed with a mean of 1 min and a standard deviation of 0.1 minper part (i.e. stations 6 and 7 were unbalanced compared to stations 1± 5). Thematerial handling system breakdown was exponentially distributed with a mean of120min from the start of the regular production shift. (Exponential distribution waschosen because of its high variance. A mean of two hours was chosen because we


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wanted to induce the breakdown randomly during the day yet early enough in theday so that the material handling system breakdown would cause maximum disrup-tion.) The repair time of the material handling system was normally distributed witha mean of 120 min and standard deviation of 30 min.

6.2.1. Results and discussion of case 2The performance measures collected for case 1 were also collected in this case.

The performances of TKS and FKS for the 30 replications are presented in tables 2and 3 respectively. The ® rst three columns in these tables present respectively, (1) thereplication number (no.), (2) the time of the material handling system breakdown


Performance measures

Resource utilization

No.(1)

TIS(2)

OCT(3)

NUO(4)

WIP(5)

FU(6)

M1U(7)

M5U(8)

M7U(9)

1 117.76 350.99 0 28 0.353 0.832 0.830 0.1672 114.62 349.59 0 28 0.353 0.831 0.826 0.1653 114.36 354.92 0 28 0.353 0.838 0.830 0.1664 117.30 353.10 0 28 0.353 0.824 0.834 0.1665 115.17 355.08 0 28 0.353 0.824 0.834 0.1706 121.15 365.77 0 28 0.353 0.838 0.856 0.1687 115.86 348.01 0 28 0.353 0.827 0.823 0.1698 119.46 360.81 0 28 0.353 0.835 0.846 0.1709 117.04 353.77 0 28 0.353 0.830 0.827 0.166

10 116.13 354.93 0 28 0.353 0.829 0.831 0.16711 114.71 353.96 0 28 0.353 0.837 0.829 0.16912 120.87 361.52 0 28 0.353 0.825 0.848 0.16913 118.79 354.08 0 28 0.353 0.830 0.834 0.16614 115.05 348.95 0 28 0.353 0.823 0.818 0.16915 117.52 352.63 0 28 0.353 0.842 0.826 0.16316 117.58 358.20 0 28 0.353 0.817 0.838 0.16917 112.81 349.03 0 28 0.353 0.832 0.822 0.16618 117.08 352.46 0 28 0.353 0.820 0.835 0.16619 116.73 355.46 0 28 0.353 0.835 0.835 0.16520 116.31 353.68 0 28 0.353 0.830 0.831 0.16521 116.80 350.38 0 28 0.353 0.821 0.823 0.16822 117.96 354.48 0 28 0.353 0.816 0.827 0.16823 118.26 360.82 0 28 0.353 0.824 0.843 0.16824 116.59 360.90 0 28 0.353 0.838 0.847 0.17025 116.70 351.34 0 28 0.353 0.825 0.827 0.16526 117.00 354.10 0 28 0.353 0.821 0.833 0.16827 113.58 348.28 0 28 0.353 0.834 0.819 0.16628 114.72 353.16 0 28 0.353 0.838 0.829 0.16529 119.56 356.48 0 28 0.353 0.854 0.832 0.16530 118.01 357.88 0 28 0.353 0.850 0.843 0.163

AV 116.85 354.49 0.0 28.0 0.353 0.831 0.832 0.167SD 1.99 4.34 0.0 0.0 0.000 0.009 0.009 0.002

Table 1. The performance of the TKS with no material handling system breakdown(unbalanced system).

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(BT) and (3) the repair time of the material handling system (RT). Note that the datain tables 2 and 3 was sorted on the time of the day the material handling systembreakdown occurred.

Comparing tables 1 (TKS with no breakdown) and 2 (TKS with breakdown)reveal that sudden material handling system breakdown causes a signi® cant e� ect onthe average time in system and the average order completion time. The TIS hasincreased from 116.85 to 140.75 and the OCT has increased from 354.49 to447.04. In some cases overtime was also required. In many cases, even though thedemand was ful® lled in regular time, there was not enough time to ® ll all the bu� ersas is evident from WIP values of less than 28. This also has an e� ect on machineutilization during the regular shift which tend to be less than the values in case 1.



Input Resource utilization

No.(1)

BT (sorted)(2)

RT(3)

TIS(4)

OCT(5)

NUO(6)

WIP(7)

FU(8)

M1U(9)

M5U(10)

M7U(11)

1 0.35 159.03 145.97 450.68 0 23 0.348 0.779 0.796 0.1662 8.21 124.34 145.11 467.45 0 21 0.595 0.760 0.763 0.1663 13.12 80.65 135.49 427.59 0 28 0.521 0.835 0.846 0.1704 19.75 133.89 152.03 477.90 0 18 0.329 0.735 0.755 0.1655 20.71 171.72 158.01 515.28 7 17 0.295 0.667 0.670 0.1436 23.21 54.63 128.70 396.42 0 28 0.353 0.842 0.838 0.1697 24.03 105.01 141.01 445.16 0 27 0.351 0.803 0.808 0.1678 27.58 94.32 136.72 435.92 0 28 0.548 0.824 0.830 0.1659 27.95 48.03 125.04 380.12 0 28 0.453 0.827 0.823 0.169

10 28.39 135.47 146.42 475.02 0 17 0.330 0.735 0.744 0.16211 31.50 62.79 133.29 413.78 0 28 0.353 0.838 0.856 0.16812 35.22 105.52 139.81 441.73 0 25 0.572 0.804 0.805 0.16913 39.23 126.13 141.15 459.65 0 21 0.343 0.755 0.763 0.16614 53.40 109.54 144.26 456.83 0 24 0.575 0.792 0.798 0.17015 67.51 77.94 133.63 414.74 0 28 0.353 0.823 0.834 0.16616 80.29 108.04 140.25 448.85 0 24 0.351 0.791 0.802 0.16717 94.11 103.57 140.40 443.64 0 23 0.566 0.790 0.808 0.16618 105.11 115.03 141.86 454.92 0 22 0.348 0.778 0.788 0.16819 116.99 171.25 156.35 513.87 7 16 0.655 0.653 0.667 0.15120 136.10 64.86 130.88 403.40 0 28 0.487 0.825 0.827 0.16521 141.87 123.16 144.01 457.38 0 21 0.347 0.773 0.773 0.16522 146.62 131.46 147.75 468.61 0 20 0.335 0.756 0.759 0.16523 148.57 111.83 144.38 451.74 0 25 0.581 0.779 0.795 0.16824 177.45 60.02 132.01 400.61 0 28 0.478 0.854 0.832 0.16525 188.67 167.90 157.04 508.19 6 18 0.651 0.660 0.674 0.14726 230.19 83.54 135.82 417.67 0 28 0.353 0.832 0.830 0.16727 230.32 101.47 141.25 445.74 0 27 0.351 0.796 0.821 0.16828 250.20 106.10 140.04 441.57 0 25 0.353 0.809 0.808 0.16829 256.25 124.32 139.26 457.68 0 23 0.346 0.765 0.772 0.16630 328.52 98.86 124.51 438.97 0 27 0.353 0.816 0.823 0.166

AV 101.71 108.68 140.75 447.04 0.7 23.9 0.429 0.783 0.790 0.165SD 90.38 33.54 8.52 32.59 2.0 4.0 0.114 0.052 0.050 0.006

Table 2. The performance of the traditional kanban system (unbalanced system).

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However, at ® rst glance, it appears that FU has increased. This is because of thematerial handling system breakdown. During the time the material handling systemis being repaired, the material handling system is unavailable. Hence, at other times,it has to work harder to catch up on the pending backlog giving an impression ofhigher utilization.

The performance measures of TKS are sensitive to both the time of the materialhandling system breakdown (BT) as well as the duration of the repair time (RT). Ingeneral, for an identical repair time duration, a breakdown that occurs at the begin-ning of the day results in larger values for the average time in the system and theorder completion time compared to a breakdown that occurs towards the end of theproduction day. As an example, rows 2 and 29 (table 2) show almost identical repair



Input Resource utilization

No.(1)

BT (sorted)(2)

RT(3)

TIS(4)

OCT(5)

NUO(6)

WIP(7)

FU(8)

M1U(9)

M5U(10)

M7U(11)

1 0.35 159.03 149.57 353.13 0 28 0.353 0.830 0.827 0.1662 8.21 124.34 151.55 354.92 0 28 0.612 0.838 0.830 0.1663 13.12 80.65 134.33 360.81 0 28 0.521 0.835 0.846 0.1704 19.75 133.89 162.17 361.52 0 28 0.353 0.827 0.848 0.1695 20.71 171.72 181.68 357.88 0 28 0.353 0.850 0.843 0.1636 23.21 54.63 124.46 358.20 0 28 0.353 0.842 0.838 0.1697 24.03 105.01 140.80 354.10 0 28 0.353 0.821 0.832 0.1678 27.58 94.32 136.53 355.46 0 28 0.547 0.835 0.825 0.1659 27.95 48.03 121.25 348.01 0 28 0.453 0.827 0.823 0.168

10 28.39 135.47 155.65 353.16 0 28 0.353 0.838 0.829 0.16511 31.50 62.79 130.01 365.77 0 28 0.353 0.838 0.856 0.16812 35.22 105.52 140.25 348.95 0 28 0.572 0.830 0.818 0.16913 39.23 126.13 148.80 348.28 0 28 0.352 0.834 0.819 0.16614 53.40 109.54 144.34 360.90 0 28 0.581 0.838 0.847 0.17015 67.51 77.94 131.66 353.10 0 28 0.353 0.823 0.833 0.16616 80.29 108.04 142.09 354.93 0 28 0.353 0.829 0.831 0.16717 94.11 103.57 141.55 352.46 0 28 0.568 0.820 0.835 0.16618 105.11 115.03 143.57 353.96 0 28 0.352 0.837 0.829 0.16819 116.99 171.25 178.21 391.31 0 28 0.709 0.824 0.834 0.17020 136.10 64.86 126.21 351.34 0 28 0.487 0.825 0.827 0.16521 141.87 123.16 148.29 354.86 0 28 0.353 0.831 0.826 0.16522 146.62 131.46 153.91 367.76 0 28 0.353 0.830 0.831 0.16523 148.57 111.83 146.15 354.48 0 28 0.585 0.816 0.827 0.16824 177.45 60.02 127.79 356.48 0 28 0.478 0.854 0.832 0.16525 188.67 167.90 170.08 430.06 0 28 0.702 0.823 0.826 0.16326 230.19 85.54 133.08 367.25 0 28 0.353 0.832 0.830 0.16727 230.32 101.47 139.05 387.48 0 28 0.353 0.824 0.843 0.16828 250.20 106.10 135.88 400.60 0 28 0.353 0.821 0.822 0.16829 256.25 124.32 136.06 421.24 0 28 0.352 0.817 0.822 0.16630 328.52 98.86 124.52 439.85 0 28 0.353 0.825 0.834 0.166

AV 101.71 108.68 143.32 367.27 0.0 28.0 0.439 0.830 0.832 0.167SD 90.38 33.54 15.11 24.83 0.0 0.0 0.118 0.009 0.009 0.002

Table 3. The performance of the ¯ exible kanban system (unbalanced system).

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times but the material handling system breakdowns occur at di� erent times. Row 2shows that the material handling system breakdown occurred after 8.21 min from thestart of the regular production shift and the repair duration is 124.34min. Theaverage time in the system is 145.11 and the order completion time is 467.45 min.On the other hand, row 29 shows that the material handling system breakdownoccurred after 256.25 min from the start of the regular production shift and therepair duration is 124.32 min. The time in the system is 139.26 min and the ordercompletion time is 457.68 min. Another example is shown in rows 5 and 19. In bothexamples, the average time in the system and the order completion time are worsewhen the material handling system breakdown occurs towards the beginning of theregular production shift. The increase of the average time in the system is caused byholding more needed parts in the system while the material handling system is beingrepaired. The increase of order completion time is caused by blocked stations whenmore parts are needed.

TKS is also a� ected by the length of the material handling system repair dura-tion. Long material handling system repair times cause an increase in the averagetime in the system, increase in order completion time, increase in production in theovertime shift, underutilized bu� ers and lower station utilization during the regularproduction shift. As an example, row 2 in table 2 shows that the material handlingsystem breakdown occurred after 8.21 min from the start of production and therepair duration was 124.34 min. Row 3 shows that the breakdown occurred after13.12 min from the start of production and the repair duration was 80.65 min. Theaverage time in the system and order completion time are higher in row 2 than in row3. Furthermore, due to the longer repair duration, the machines have lower utiliza-tion and the WIP is lower.

Overtime was needed in some cases to satisfy the demand. It is caused by lengthyrepair duration. The WIP in these cases indicate that the bu� ers were underutilizedduring the regular production shift. This is caused by the stations getting blockeddue to the limited number of kanbans.

Even though fewer parts are needed towards the end of the production day, thematerial handling system breakdown causes a delay of delivery to stations 6 and 7.FKS order completion time is largely a� ected by the time of the material handlingsystem breakdown and not as much by its repair duration. As an example, row 29and row 2 in table 3 both have almost identical material handling system repairduration but the material handling system breakdown occurred at di� erent times.The order completion time is higher when the breakdown occurs toward the end ofthe production day than when it occurs at the beginning.

FKS average time in the system is a� ected by the time of the material handlingsystem breakdown and the repair duration. The breakdown of the material handlingsystem at the beginning of the regular production shift leads to an increase in thenumber of production kanbans which authorizes the production. However, when thebreakdown occurs towards the end of the production day the opposite is true. Thetime that these parts spend in the system depends on the repair duration.

The comparison of the two systems based on the performance measures is givenbelow.

� Average time in the system. The average time in the system depends on thenumber of parts produced before and after the material handling systembreakdown, the length of repair time and the number of parts produced


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while the material handling system is undergoing repair. Figure 4 shows thatthe average time in the system for TKS and FKS are almost identical.

� Order completion time. Figure 5 shows the order completion time of TKS andFKS. The order completion time of FKS is always lower than that of the TKS.FKS performs better because it processes some parts through station 5 whilethe material handling system is being repaired. Note that in one case, TKS andFKS have identical order completion times. This will generally happen if thematerial handling system breakdown occurs towards the end of the productionday, the repair time is low and the residual demand is either zero or lowenough to satisfy the demand while the material handling system is beingrepaired.


Figure 4. TKS and FKS average time in the system.

Figure 5. TKS and FKS order completion time.

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� Number of units produced in the overtime shift. FKS always produced all neededunits to satisfy the demand during the regular production shift (table 3). On theother hand, overtime was needed to produce some units in TKS to satisfy thedemand (table 2). Again, this is caused by the blocking of station 5 when thereis residual demand and the material handling system repair time is long.

� Resource utilization. Figure 6 shows the TKS and FKS forklif t utilizationduring the regular production shift. FKS has slightly higher forklift utilizationthan TKS. Since all production, in the cases FKS, takes place in regularproduction shift, the forklif t transports more parts to stations 6 and 7 thanin the case of TKS (where some units are transported in overtime).

Figure 7 shows the utilization of machine 1 during the regular productionshift for TKS and FKS. TKS and FKS would have identical machine 1 utiliza-tion if machine 1 in either system would process an identical number of parts.


Figure 6. TKS and FKS forklift utilization during the regular production shift.

Figure 7. TKS and FKS machine 1 utilization during the regular production shift.

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However, due to blocking of machine 1 caused by the material handling systembreakdown, machine 1 in the case of TKS processes less number of parts incomparison to the case of FKS. Hence, the utilization of machine 1 in TKS (inmost cases) is lower than that of FKS.

Figure 8 shows machine 5 utilization during the regular production shift forTKS and FKS. The behaviour (and reasoning) of machine 5 is almost identicalto that of machine 1 in both systems.

Figure 9 shows machine 7 utilization during the regular production shift forTKS and FKS. Even if the demand is satis® ed, in some cases machine 7 (inTKS) has lower utilization than machine 7 in FKS. This is because, in the caseof FKS, after satisfying the demand, it also ® lls its output bu� ers. However,because of blocking and starvation in the system, TKS is not always able to




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achieve this. Hence, it ends up processing fewer parts in regular productionshifts, which leads to lower utilization.

6.3. Case 3In order to further investigate the e� ect of the time of the material handling

system breakdown and the duration of repair time, more experiments were carriedout. Thus, just like the previous two cases, the processing times at stations 6 and 7were normally distributed with a mean of 1 min and a standard deviation of 0.1 minper part (i.e. stations 6 and 7 were unbalanced compared to stations 1± 5). Thematerial handling system breakdown was uniformly distributed between the startand end of the regular shift (i.e. between 0 and 480min). The repair time of thematerial handling system was constant. Four di� erent scenarios for the repair timeswere considered, viz. 80 min, 100 min, 120 min and 140 min.

6.3.1. Results and discussion of case 3Figure 10 shows the time in system performance for TKS when the material

handling system repair times are 80 (TIS80), 100 (TIS100), 120 (TIS120) and 140(TIS140) minutes at identical breakdown times. When a material handling systembreakdown occurs towards the beginning of the production day, part time in thesystem is a� ected by the repair time duration. The longer the repair time duration thehigher the time in system. On the other hand, when a breakdown occurs towards theend of the production day, part time in the system is not a� ected by the repair timeduration. This is because the demand is already satis® ed or the parts that are alreadyin the system are su� cient to satisfy it.

Similarly the order completion time of TKS is a� ected by the material handlingsystem repair time duration (note that OCT80, OCT100, OCT120 and OCT140represent the order completion times when repair times are 80, 100, 120 and 140respectively). If the breakdown occurs at the beginning of the production day, thehigher the repair time the longer the order completion time. On the other hand, whenthe breakdown occurs towards the end of the production day, the order completiontimes become identical regardless of the repair time duration (see ® gure 11).


Figure 10. Time in system of TKS at di� erent material handling system repair times.

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Figure 12 shows the impact on the time in system for the FKS when the materialhandling system breakdown occurs at various times and repair times are 80(FTIS80), 100 (FTIS100), 120 (FTIS120) and 140 (FTIS140) minutes. Parts spendmore time in the system with high material handling system repair time when thebreakdown occurs at the beginning of the production day. However, the repair timeshave no e� ect when the material handling system breakdown occurs towards the endof the production day. That is because the demand is already satis® ed or the partsthat are already in the system are su� cient to satisfy it.

Figure 13 shows the order completion times for the FKS at various times ofmaterial handling system breakdown and the repair times are 80 (FOCT80), 100(FOCT100), 120 (FOCT120) and 140 (FOCT140) min. The order completion times


Figure 11. Order completion time of TKS at di� erent material handling system repair times.

Figure 12. Time in system of FKS at di� erent material handling system repair times.

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are identical for the four repair duration if breakdown occurs either earlier in the dayor late in the day. The reason for this behaviour is that at the beginning of theproduction day only partial demand is ful® lled. While the material system is beingrepaired, the ® rst ® ve stations are continuously operating. After the repair and dueto the unbalanced stations, there is enough time to catch up for lost production atstations 6 and 7. A breakdown towards the end of the day is not detrimental becauseeither the demand has not been satis® ed or the units already in the system aresu� cient to satisfy it.

Figure 14 combines the results of ® gures 10 and 12 while ® gure 15 combines theresults of ® gures 11 and 13.


Figure 14. Time in system of TKS and FKS at di� erent material handling system repairtimes.

Figure 13. Order completion time of FKS at di� erent material handling system repair times.

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6.4. Case 4The purpose of this case was to study the TKS with no material handling system

breakdown when the stations were balanced. In this case, the processing times atstations 6 and 7 were normally distributed with a mean of 5min and a standarddeviation of 0.5min per part (i.e. all stations were balanced).

6.4.1. Results and discussion of case 4The performance measures of the TKS for the 30 replications are summarized in

table 4. They are respectively, the average time in the system (TIS), the average ordercompletion time (OCT), the number of units that must be produced in overtime tosatisfy the demand (NUO), station 1 order completion time (S1OCT), station 5 ordercompletion time (S5OCT) and station 7 order completion time (S7OCT). The newperformance measures (viz. the various station order completion times) give the timeit takes an operator and/or a machine to ® nish processing an order so that theoperator or the machine is able to switch to perform other tasks such as performingpreventive maintenance, participating in quality circle meetings and supporting otheremployees in other production lines etc. A lower value of this measure means thatthe station was more e� ciently utilized in a shorter period of time.

As can be seen from table 4, the system was very stable. This is in spite of the factthat the processing times were not constant. The average time in system was148.92 min and the average order completion time was 390.55 min. The demandwas always satis® ed during the regular shift and hence there was no need for over-time.

6.5. Case 5The purpose of this case was to study the TKS and FKS with sudden material

handling system breakdown when the stations were balanced. Similarly to case 4, theprocessing times at stations 6 and 7 were normally distributed with a mean of 5 minand a standard deviation of 0.5 min per part (i.e. all stations were balanced). Thematerial handling system breakdown was exponentially distributed with a mean of


Figure 15. Order completion time of TKS and FKS at di� erent material handling systemrepair times.

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120min from the start of the regular production shift. (The reason for choosingexponential distribution is similar to the one given in the description of case 2).The repair time of the material handling system was normally distributed with amean of 120 min and standard deviation of 30 min.

6.5.1. Results and discussion of case 5The performances of TKS and FKS for the 30 replications are presented in tables

5 and 6 respectively. As before, the data in tables 5 and 6 were sorted on the time ofthe day the material handling system breakdown occurred.

Both TKS and FKS have identical order completion times, identical number ofunits produced in overtime shift and identical station 7 order completion times.However, TKS has lower average time in the system while FKS has lower station1 and station 5 order completion times.



No.(1)

TIS(2)

OCT(3)

NUO(4)

S1OCT(5)

S5OCT(6)

S7OCT(7)

1 146.96 390.51 0 403.16 401.20 401.212 146.06 387.13 0 404.08 402.24 396.963 145.60 388.50 0 405.15 401.97 398.494 150.02 388.39 0 404.82 401.39 399.005 152.43 397.05 0 408.88 406.55 407.616 151.91 393.86 0 409.17 411.98 403.807 152.79 396.88 0 407.35 406.77 405.308 152.87 397.81 0 410.18 407.94 407.389 149.41 388.54 0 406.60 401.92 398.61

10 148.59 390.53 0 405.61 399.93 399.8911 148.62 392.85 0 409.41 400.06 404.3312 149.43 395.30 0 408.07 408.15 405.3713 148.80 386.94 0 403.94 401.09 397.4614 152.08 394.97 0 403.89 401.21 404.6615 145.67 381.96 0 403.75 397.49 392.2116 152.58 394.87 0 407.04 404.11 404.9617 148.63 389.06 0 406.85 401.46 399.1018 148.55 388.55 0 405.18 404.64 397.9719 144.94 386.65 0 403.75 401.72 396.6420 148.53 386.72 0 404.56 399.79 396.8321 150.92 392.09 0 401.57 398.98 402.1722 151.08 394.22 0 404.21 399.09 403.6523 150.15 394.55 0 407.05 405.85 403.1024 151.60 397.75 0 409.61 407.96 408.0925 145.95 386.14 0 406.39 398.33 396.3326 151.52 391.46 0 399.37 402.38 401.9527 146.82 389.16 0 405.45 402.55 399.2128 147.60 385.80 0 408.36 399.59 396.1029 146.78 388.31 0 414.43 400.16 396.4130 140.83 379.92 0 411.08 405.49 390.61

AV 148.92 390.55 0.0 406.30 402.73 400.51SD 2.85 4.57 0.0 3.07 3.48 4.46

Table 4. The performance of the TKS with no material handling system breakdown(balanced system).

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FKS has higher average time in the system because of the increase in the numberof production kanbans at station 5 (while the material handling system is undergoingrepairs) and balanced processing times. The increase in the number of productionkanbans at station 5 stimulates production at that station and the release of thewithdrawal kanbans. The withdrawal kanbans trigger production at station 4 andeventually at all preceding stations. The raw-material and parts start to advance tosubsequent stations and then wait at the output bu� er of station 5 until the materialhandling system starts operating. Even when the material handling system starts tooperate, because of the balanced processing times, parts in FKS cannot be removedfrom the system any faster than in TKS. This also explains why the order completiontime and the number of units produced in the overtime shift are identical in the twosystems.

However, the increase in the number of production kanbans at station 5 has its


Input Performance measures

No.(1)

BT (sorted)(2)

RT(3)

TIS(4)

OCT(5)

NUO(6)

S1OCT(7)

S5OCT(8)

S7OCT(9)

1 0.35 159.03 173.91 480.56 1 503.46 494.44 490.622 8.21 124.34 176.71 496.43 4 516.32 512.66 506.423 13.12 80.65 169.84 462.66 0 477.06 473.97 472.224 19.75 133.89 185.10 513.96 7 526.45 526.54 524.045 20.71 171.72 189.70 540.62 13 570.48 565.40 551.316 23.21 54.63 160.18 431.25 0 448.06 445.95 441.347 24.03 105.01 175.80 479.62 0 491.08 494.39 490.118 27.58 94.32 166.27 465.71 0 485.17 482.18 475.709 27.95 48.03 160.98 428.28 0 441.53 438.17 436.70

10 28.39 135.47 182.61 506.74 5 531.95 523.41 517.0411 31.50 62.79 163.97 440.21 0 457.18 457.18 450.1512 35.22 105.52 176.28 484.04 1 495.97 490.28 493.7313 39.23 126.13 177.93 499.33 4 520.97 515.56 509.3914 53.40 109.54 179.90 492.09 3 503.98 505.70 502.4215 67.51 77.94 169.64 453.29 0 468.76 465.84 463.9016 80.29 108.04 175.79 483.95 1 500.87 497.28 493.3117 94.11 103.57 171.90 476.38 0 498.43 497.89 485.8118 105.11 115.03 178.00 491.84 3 510.96 502.53 503.3219 116.99 171.25 195.17 552.07 15 570.16 565.41 562.6320 136.10 64.86 160.63 436.52 0 455.32 450.92 446.7121 141.87 123.16 178.34 498.58 4 516.00 513.69 508.4022 146.62 131.46 181.13 502.31 5 522.47 519.97 512.4223 148.57 111.83 180.22 493.61 3 505.41 502.23 503.0424 177.45 60.02 161.67 436.44 0 460.30 446.83 444.5325 188.67 167.90 191.02 538.52 12 558.22 553.05 548.7826 230.19 83.54 176.18 481.24 1 495.10 492.78 489.7927 230.32 101.47 167.70 460.42 0 477.53 473.78 471.1228 250.20 106.10 177.87 482.15 1 494.07 494.30 492.2429 256.25 124.32 181.65 499.12 4 516.92 516.29 509.1530 328.52 98.86 158.56 473.74 0 487.17 487.85 484.26

AV 101.71 108.68 174.82 482.72 2.9 500.24 496.88 492.69SD 90.38 33.54 9.48 31.28 4.1 32.70 32.53 31.57

Table 5. The performance of the traditional kanban system (balanced system).

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advantages. It reduces blocking at stations 5Ð 1 which allows these stations toprocess the order in much less time. This means that stations and operators areutilized even while the material handling system is being repaired.

7. Conclusions

In this paper we examined the e� ect of a sudden breakdown of the materialhandling system on the performance of TKS and FKS. When the processing timesat the stations that depend on the material system are lower than other stations’processing times, FKS is very promising. For the case considered, it had lower ordercompletion time, no overtime and higher resource utilization than TKS. When thestations have balanced processing times, both FKS and TKS have identical ordercompletion times and the number of units produced in the overtime shift. However,for the stations that are not dependent on the material handling system, FKS always


Input Performance measures

No.(1)

BT (sorted)(2)

RT(3)

TIS(4)

OCT(5)

NUO(6)

S1OCT(7)

S5OCT(8)

S7OCT(9)

1 0.35 159.03 273.33 480.56 1 405.96 397.31 490.622 8.21 124.34 244.13 496.43 4 405.15 399.25 506.423 13.12 80.65 212.56 462.66 0 406.27 407.19 472.224 19.75 133.89 258.59 513.96 7 401.08 408.15 524.045 20.71 171.72 283.45 540.62 13 411.08 405.49 551.316 23.21 54.63 184.54 431.25 0 407.04 403.22 441.347 24.03 105.01 228.92 479.62 0 379.28 400.58 490.118 27.58 94.32 213.24 465.71 0 403.75 401.72 475.709 27.95 48.03 180.21 428.28 0 407.35 396.08 436.70

10 28.39 135.47 251.92 506.74 5 408.36 398.81 517.0411 31.50 62.79 191.82 440.21 0 406.35 411.98 450.1512 35.22 105.52 228.20 484.04 1 402.55 493.73 493.7313 39.23 126.13 240.15 499.33 4 405.40 394.77 509.3914 53.40 109.54 227.07 492.09 3 409.48 407.52 502.4215 67.51 77.94 199.59 453.29 0 404.82 401.09 463.9016 80.29 108.04 218.60 483.95 1 404.01 399.68 493.3117 94.11 103.57 210.59 476.38 0 402.44 401.59 485.8118 105.11 115.03 215.44 491.84 3 409.41 398.81 503.3219 116.99 171.25 253.65 552.07 15 408.89 401.35 562.6320 136.10 64.86 175.55 436.52 0 406.39 397.96 446.7121 141.87 123.16 211.15 498.58 4 402.07 398.16 508.4022 146.62 131.46 213.55 502.31 5 404.56 399.79 512.4223 148.57 111.83 208.75 493.61 3 403.73 398.15 503.0424 177.45 60.02 170.25 436.44 0 414.43 400.16 444.5325 188.67 167.90 218.08 538.52 12 403.75 397.49 548.7826 230.19 83.54 181.58 481.24 1 407.05 405.85 489.7927 230.32 101.47 172.45 460.42 0 402.42 399.43 471.1228 250.20 106.10 179.07 482.15 1 401.57 396.23 492.2429 256.25 124.32 181.65 499.12 4 406.85 401.11 509.1530 328.52 98.86 158.57 473.74 0 403.94 401.09 484.26

AV 101.71 108.68 212.89 482.72 2.9 404.85 404.12 492.69SD 90.38 33.54 32.06 31.26 4.1 17.38 17.36 31.57

Table 6. The performance of the ¯ exible kanban system (balanced system).

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® nished processing the order during the regular production shift. This was notalways the case in the TKS.

Further studies are needed to explore the e� ects of the number of base kanbans,the material handling system speed, loading and unloading times, distance it travelsand the number of stations it serves on the performance of the system. Additionalstudies are also needed to explore other hostile environments.

References

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1960 Sudden material handling system breakdown under JIT

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The effect of sudden material handling system breakdown on the performance of a JIT system

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